2022 Research Projects
Projects are posted below; new projects will continue to be posted. To learn more about the type of research conducted by undergraduates, view the archived symposium booklets and search the past SURF projects.
This is a list of research projects that may have opportunities for undergraduate students. Please note that it is not a complete list of every SURF project. Undergraduates will discover other projects when talking directly to Purdue faculty.
You can browse all the projects on the list or view only projects in the following categories:
All Research Projects (149)
3D printing of DNA origami laden hydrogels
Description:
The goal of this research is to develop distributed manufacturing strategies for robust mRNA-containing biomaterials. Our approach is to use yeast such as S. cerevisiae to produce large volumes of mRNA and oligo DNA sequences with unprecedented accuracy and scalability. The DNA strands will form the custom-designed nanocage via self-assembly which will encapsulate mRNA. The DNA architectures will be programmed for on-demand mRNA release and 3D printed into a hydrogel formulation for stable storage and administration. This specific project will focus on the printing of DNA origami ladened hydrogels and study the impact of printing parameters on the resulting geometry and functionality of the overall material system.
Research categories:
Biological Characterization and Imaging, Material Processing and Characterization, Other
Preferred major(s):
- Mechanical Engineering
- Biomedical Engineering
- Mechatronics Engineering
Desired experience:
Fluid mechanics
System dynamics and control
Familiarity with Labview
School/Dept.:
Mechanical Engineering
Professor:
George
Chiu
3D-Printing of concrete: Design of extrusion components and 3D-printing of large-scale structural elements
Description:
Objective: To assist in the design of components for 3D extrusion systems and in 3D-printing of structural concrete elements.
Motivation: 3D-printing of concrete represents an alternative for the construction of infrastructure at different scales using automated techniques to reduce the manufacturing costs, reduce waste and allow for formwork-free construction. One of the current research project performed at Lyles School of Civil Engineering by the Purdue Concrete 3D-Printing team (in collaboration with an industrial partner) is exploring the viability of 3D-printing structures designed for marine environments that will contribute to the generation of renewable energy. This state-of-the-art project looks to manufacture components that can withstand the extreme conditions associated with marine environments. Still, the 3D-printing process is a complex system that requires a careful integration of equipment, materials, and processes to produce high-quality structures. Therefore, the exploration and implementation of alternatives for parts and components that facilitate the control of material extrusion as well as the characteristics of the material during this process is required.
Activities and responsibilities of the student:
· To become intimately familiar with various components of a 3D-printing system and the printing process of cementitious materials.
· To design parts, components and mechanisms required for the control of the geometry of 3D-printed filaments.
· To produce technical drawings and manufacturing recommendations for the parts needed.
· To assist with the 3D-printing activities during fabrication of large-scale structural elements
· To present the results of the work performed during SURF program to the research group during the weekly project meetings.
· To prepare a report summarizing the design and printing activities performed during the SURF program.
· To disseminate the results of the research experience as required by the SURF program.
Motivation: 3D-printing of concrete represents an alternative for the construction of infrastructure at different scales using automated techniques to reduce the manufacturing costs, reduce waste and allow for formwork-free construction. One of the current research project performed at Lyles School of Civil Engineering by the Purdue Concrete 3D-Printing team (in collaboration with an industrial partner) is exploring the viability of 3D-printing structures designed for marine environments that will contribute to the generation of renewable energy. This state-of-the-art project looks to manufacture components that can withstand the extreme conditions associated with marine environments. Still, the 3D-printing process is a complex system that requires a careful integration of equipment, materials, and processes to produce high-quality structures. Therefore, the exploration and implementation of alternatives for parts and components that facilitate the control of material extrusion as well as the characteristics of the material during this process is required.
Activities and responsibilities of the student:
· To become intimately familiar with various components of a 3D-printing system and the printing process of cementitious materials.
· To design parts, components and mechanisms required for the control of the geometry of 3D-printed filaments.
· To produce technical drawings and manufacturing recommendations for the parts needed.
· To assist with the 3D-printing activities during fabrication of large-scale structural elements
· To present the results of the work performed during SURF program to the research group during the weekly project meetings.
· To prepare a report summarizing the design and printing activities performed during the SURF program.
· To disseminate the results of the research experience as required by the SURF program.
Research categories:
Composite Materials and Alloys, Material Modeling and Simulation
Preferred major(s):
- No Major Restriction
School/Dept.:
CE
Professor:
Jan
Olek
A Hyperspectral imager for Propulsion Testing
Description:
The SURF student will work with a PhD student and Professor Gore to contribute to the following project.
The feasibility of utilizing a mid-infrared hyperspectral imager as a general-purpose ground testing diagnostic for rocket propulsion systems will be demonstrated. Purdue University compared temperatures deconvoluted from the hyperspectral images of a hydrogen air premixed flame with our measurements of temperature using Rayleigh scattering at identical operating conditions. This comparison has helped sponsor address a key issue involving the establishment of feasibility of obtaining spatially and temporally resolved information from mid-infrared hyperspectral imager measurements.
Purdue University work in Phase II is focused on evaluating the imager for different flame configurations to help deliver the system to NASA. Purdue University will demonstrate the use of the hyperspectral imager using: (i) two previously studied turbulent premixed hydrogen air jet flames, (ii) two previously studied turbulent premixed and partially premixed methane air jet flames, and (iii) a plume emerging from an existing rocket propellant combustion test apparatus.
The feasibility of utilizing a mid-infrared hyperspectral imager as a general-purpose ground testing diagnostic for rocket propulsion systems will be demonstrated. Purdue University compared temperatures deconvoluted from the hyperspectral images of a hydrogen air premixed flame with our measurements of temperature using Rayleigh scattering at identical operating conditions. This comparison has helped sponsor address a key issue involving the establishment of feasibility of obtaining spatially and temporally resolved information from mid-infrared hyperspectral imager measurements.
Purdue University work in Phase II is focused on evaluating the imager for different flame configurations to help deliver the system to NASA. Purdue University will demonstrate the use of the hyperspectral imager using: (i) two previously studied turbulent premixed hydrogen air jet flames, (ii) two previously studied turbulent premixed and partially premixed methane air jet flames, and (iii) a plume emerging from an existing rocket propellant combustion test apparatus.
Research categories:
Big Data/Machine Learning, Energy and Environment, Thermal Technology
Preferred major(s):
- Aeronautical and Astronautical Engineering
- Mechanical Engineering
School/Dept.:
Mechanical Engineering
Professor:
Jay
Gore
More information: https://engineering.purdue.edu/GRG
A Rheometry Investigation of Microstructure-Property-Processing Relationships in Concentrated Surfactant Solutions
Description:
Aqueous surfactant solutions are widely used to formulate detergent-based products for cleaning, laundry, and other personal care activities (e.g., shampoo, body wash). The goal of this SURF project is to determine how the microstructure and properties of surfactant-based solutions are affected by the removal of water and the addition of processing aids. The project's hypothesis is that certain chemical additives, including salt and perfumes, will change how the surfactants self-assemble in water which will in turn lead to changes in the surfactant solution's viscosity and flow behavior (its "rheology"). Through this project, the SURF student will: (1) learn about conventional commercial surfactants like sodium laureth sulfate as well as environmentally friendly biosurfactants like rhamnolipids; (2) perform rheometry measurements on solutions with different amounts of water and additives and analyze the resulting data with mathematical models; and (3) observe how the application of shear forces will change the self-assembled surfactant structures by a combination of light microscopy and X-ray scattering. The main outcome of this project will be a better understanding of the surfactant solution’s microstructure-property-processing relationships which will enable companies to more efficiently manufacture concentrated solutions to achieve desired properties and performance while also meeting sustainability goals such as reducing water from commercial products.
Research categories:
Composite Materials and Alloys, Material Processing and Characterization
Preferred major(s):
- No Major Restriction
Desired experience:
Enthusiasm for chemistry and an interest in materials research. Past experiences with surfactants and/or rheometry is awesome but not required.
School/Dept.:
Materials Engineering
Professor:
Kendra
Erk
More information: https://soft-material-mechanics.squarespace.com/home/
AAMP UP- Adhesion of Printed Energetic Materials
Description:
This project is part of the AAMP-UP '22 program, which focuses on energetic material research.
AAMP-UP is separate but highly partnered with SURF.
The project is run by Dr. Stephen Beaudoin and his team. Additively manufactured energetic materials do not adhere to themselves and casings with sufficient strength to survive gun launch. This project is focused on assessing the properties of the energetic composites that dictate how strongly the composites adhere to themselves and to their casings. The measurements will be made by cutting the composites and measuring the force required to initiate and propagate a crack, and also by using atomic force microscopy to measure directly the adhesion between energetic particles and binders and casings.
AAMP-UP is separate but highly partnered with SURF.
The project is run by Dr. Stephen Beaudoin and his team. Additively manufactured energetic materials do not adhere to themselves and casings with sufficient strength to survive gun launch. This project is focused on assessing the properties of the energetic composites that dictate how strongly the composites adhere to themselves and to their casings. The measurements will be made by cutting the composites and measuring the force required to initiate and propagate a crack, and also by using atomic force microscopy to measure directly the adhesion between energetic particles and binders and casings.
Research categories:
Chemical Unit Operations, Chemical Catalysis and Synthesis, Composite Materials and Alloys, Fabrication and Robotics, Material Modeling and Simulation, Material Processing and Characterization, Other
Preferred major(s):
- No Major Restriction
Desired experience:
Must be a U.S. citizen, national, or permanent resident of the United States. Must have completed at least one academic semester of full-time study at associate's or bachelor's degree level from an accredited college or university.
School/Dept.:
Chemical Engineering
Professor:
Stephen
Beaudoin
More information: https://engineering.purdue.edu/ChE/people/ptProfile?resource_id=11574
AAMP UP- Computational Study of Energetic Materials
Description:
This project is part of the AAMP-UP '22 program, which focuses on energetic material research.
AAMP-UP is separate but highly partnered with SURF.
The project is run by Dr. Marcial Gonzalez and his team. The goal is to enable discovery of the physical mechanisms driving impact-induced reactions in explosives (such as interparticle relative velocities and contact pressures) and their correlation with material properties, formulation, and processing. Description: Energetic materials used in the propulsion industry, and particle-binder composites in general, consist of (explosive) crystals in a binder matrix. The mechanical properties and performance of these materials are strongly correlated to their microstructure. Specifically, the mechanical response under weak impact loads is characterized by long-range microstructural correlations (typical of granular materials) that need to be resolved with high statistical significance in order to understand how complex phenomena (such as detonation) emerges from local simpler interactions. The interplay between these long-range interactions and local mechanisms (such as deformation, failure and, ultimately, hot spot formation) is responsible for the sensitivity of the material. A mesoscale modeling methodology, capable of modeling the dynamic response of large energetic composites (>50,000 crystals) by analytically upscaling complex crystal-binder deformation and failure mechanics into closed-form contact laws, will be used to build time-dependent multivariate probability distribution functions of microstructural features.
AAMP-UP is separate but highly partnered with SURF.
The project is run by Dr. Marcial Gonzalez and his team. The goal is to enable discovery of the physical mechanisms driving impact-induced reactions in explosives (such as interparticle relative velocities and contact pressures) and their correlation with material properties, formulation, and processing. Description: Energetic materials used in the propulsion industry, and particle-binder composites in general, consist of (explosive) crystals in a binder matrix. The mechanical properties and performance of these materials are strongly correlated to their microstructure. Specifically, the mechanical response under weak impact loads is characterized by long-range microstructural correlations (typical of granular materials) that need to be resolved with high statistical significance in order to understand how complex phenomena (such as detonation) emerges from local simpler interactions. The interplay between these long-range interactions and local mechanisms (such as deformation, failure and, ultimately, hot spot formation) is responsible for the sensitivity of the material. A mesoscale modeling methodology, capable of modeling the dynamic response of large energetic composites (>50,000 crystals) by analytically upscaling complex crystal-binder deformation and failure mechanics into closed-form contact laws, will be used to build time-dependent multivariate probability distribution functions of microstructural features.
Research categories:
Big Data/Machine Learning, Deep Learning
Preferred major(s):
- No Major Restriction
Desired experience:
MATLAB knowledge.
Must be a U.S. citizen, national, or permanent resident of the United States. Must have completed at least one academic semester of full-time study at associate's or bachelor's degree level from an accredited college or university.
School/Dept.:
Mechanical Engineering
Professor:
Marcial
Gonzalez
More information: https://engineering.purdue.edu/ME/People/ptProfile?resource_id=106137
AAMP UP- Conducting Polymer Energetic Binders
Description:
This project is part of the AAMP-UP '22 program, which focuses on energetic material research.
AAMP-UP is separate but highly partnered with SURF.
The project is run by Dr. Bryan Boudouris and his team. The overarching objective of this project is to create polymeric binders that have robust electrical and mechanical properties. This will be achieved by modifying commercially-available materials as well as synthesizing next-generation conducting polymers. By developing the appropriate structure-property-processing relationships, we will develop, and eventually deploy, binders with electronically-triggerable properties. Specifically, the student associated with this project will focus on the design and mechanical testing of polymers and polymer-based binders for energetic materials applications.
There are no specific prerequisites in coursework or associated knowledge for this project. However, a chemistry or chemical engineering major would be the most relevant degree plan.
AAMP-UP is separate but highly partnered with SURF.
The project is run by Dr. Bryan Boudouris and his team. The overarching objective of this project is to create polymeric binders that have robust electrical and mechanical properties. This will be achieved by modifying commercially-available materials as well as synthesizing next-generation conducting polymers. By developing the appropriate structure-property-processing relationships, we will develop, and eventually deploy, binders with electronically-triggerable properties. Specifically, the student associated with this project will focus on the design and mechanical testing of polymers and polymer-based binders for energetic materials applications.
There are no specific prerequisites in coursework or associated knowledge for this project. However, a chemistry or chemical engineering major would be the most relevant degree plan.
Research categories:
Chemical Unit Operations, Chemical Catalysis and Synthesis, Composite Materials and Alloys, Material Processing and Characterization
Preferred major(s):
- No Major Restriction
Desired experience:
Must be a U.S. citizen, national, or permanent resident of the United States. Must have completed at least one academic semester of full-time study at associate's or bachelor's degree level from an accredited college or university.
School/Dept.:
Chemical Engineering
Professor:
Bryan
Boudouris
More information: https://engineering.purdue.edu/ChE/people/ptProfile?resource_id=71151
AAMP UP- Effect of the Microstructure on the Response of Energetic Materials
Description:
This project is part of the AAMP-UP '22 program, which focuses on energetic material research.
AAMP-UP is separate but highly partnered with SURF.
The project is run by Dr. Marisol Koslowski and her team. In this project we will quantify the effect of microstructure on the detonation of HMX and RDX. The student will collect experimental data from literature and will work in collaboration with a PhD student to generate geometries that will be used in detonation simulations.
Students must be familiar with Python.
AAMP-UP is separate but highly partnered with SURF.
The project is run by Dr. Marisol Koslowski and her team. In this project we will quantify the effect of microstructure on the detonation of HMX and RDX. The student will collect experimental data from literature and will work in collaboration with a PhD student to generate geometries that will be used in detonation simulations.
Students must be familiar with Python.
Research categories:
Material Modeling and Simulation, Material Processing and Characterization, Other
Preferred major(s):
- No Major Restriction
Desired experience:
Python knowledge.
Must be a U.S. citizen, national, or permanent resident of the United States. Must have completed at least one academic semester of full-time study at associate's or bachelor's degree level from an accredited college or university.
School/Dept.:
Mechanical Engineering
Professor:
Marisol
Koslowski
More information: https://engineering.purdue.edu/ME/People/ptProfile?resource_id=29264
AAMP UP- Explosives Fabrication and Experiments
Description:
This project is part of the AAMP-UP '22 program, which focuses on energetic material research.
AAMP-UP is separate but highly partnered with SURF.
The project is run by Dr. Steven Son and his team. The research topic seeks to explore the high-rate mechanics of energetic materials under impact or shock or detonation. It will involve advanced sample preparation, including microscale machining of energetic materials, as well as high rate experiments. The student would work closely with Research Scientists and graduate students to design experiments, perform experiments, analyze data, and report/share these results.
AAMP-UP is separate but highly partnered with SURF.
The project is run by Dr. Steven Son and his team. The research topic seeks to explore the high-rate mechanics of energetic materials under impact or shock or detonation. It will involve advanced sample preparation, including microscale machining of energetic materials, as well as high rate experiments. The student would work closely with Research Scientists and graduate students to design experiments, perform experiments, analyze data, and report/share these results.
Research categories:
Chemical Catalysis and Synthesis, Composite Materials and Alloys, Material Modeling and Simulation, Material Processing and Characterization
Preferred major(s):
- No Major Restriction
Desired experience:
Must be a U.S. citizen, national, or permanent resident of the United States. Must have completed at least one academic semester of full-time study at associate's or bachelor's degree level from an accredited college or university.
School/Dept.:
Mechanical Engineering
Professor:
Steven
Son
More information: https://engineering.purdue.edu/ME/People/ptProfile?resource_id=29385
AAMP UP- Extrusion Studies to Understand 3D Printing Parameters
Description:
This project is part of the AAMP-UP '22 program, which focuses on energetic material research.
AAMP-UP is separate but highly partnered with SURF.
The project is run by Dr. Steve Son and his team. The objective of this project would be to determine the similarity of mass flow rate for a variety of inert materials and ammonium perchlorate (AP) for multi-modal size distributions. The undergraduate student would gain experience researching relevant literature, mixing samples, designing experiments, and analyzing the data for the mock materials as well as assisting with the same tests using energetic materials.
AAMP-UP is separate but highly partnered with SURF.
The project is run by Dr. Steve Son and his team. The objective of this project would be to determine the similarity of mass flow rate for a variety of inert materials and ammonium perchlorate (AP) for multi-modal size distributions. The undergraduate student would gain experience researching relevant literature, mixing samples, designing experiments, and analyzing the data for the mock materials as well as assisting with the same tests using energetic materials.
Research categories:
Chemical Unit Operations, Chemical Catalysis and Synthesis, Composite Materials and Alloys, Material Modeling and Simulation, Material Processing and Characterization
Preferred major(s):
- No Major Restriction
Desired experience:
Must be a U.S. citizen, national, or permanent resident of the United States. Must have completed at least one academic semester of full-time study at associate's or bachelor's degree level from an accredited college or university.
School/Dept.:
Mechanical Engineering
Professor:
Steve
Son
More information: https://engineering.purdue.edu/ME/People/ptProfile?resource_id=29385
AAMP UP- In-situ Diagnosis of Additive Manufacturing with 3D Vision Sensor
Description:
This project is part of the AAMP-UP '22 program, which focuses on energetic material research.
AAMP-UP is separate but highly partnered with SURF.
The project is run by Dr. Song Zhang and his team. The research aims at designing a 3D printer that can incorporate a high-end customized 3D vision sensor for close-loop controls. Undergrads will closely work with graduate students on both software and/or hardware development depending upon interest.
AAMP-UP is separate but highly partnered with SURF.
The project is run by Dr. Song Zhang and his team. The research aims at designing a 3D printer that can incorporate a high-end customized 3D vision sensor for close-loop controls. Undergrads will closely work with graduate students on both software and/or hardware development depending upon interest.
Research categories:
Computer Architecture, Deep Learning, Mobile Computing, Other
Preferred major(s):
- No Major Restriction
Desired experience:
Must be a U.S. citizen, national, or permanent resident of the United States. Must have completed at least one academic semester of full-time study at associate's or bachelor's degree level from an accredited college or university.
School/Dept.:
Mechanical Engineering
Professor:
Song
Zhang
More information: https://engineering.purdue.edu/ME/People/ptProfile?resource_id=117610
AAMP UP- Machine Learning Applied to Explosives
Description:
This project is part of the AAMP-UP '22 program, which focuses on energetic material research.
AAMP-UP is separate but highly partnered with SURF.
The project is run by Dr. Steven Son and his team. Machine learning (ML) tools are playing an increasingly important role in science and engineering, revealing patterns and providing predictive capabilities not achievable otherwise. This research area explores the utility of machine learning algorithms in the design, development, and characterization of various energetic material systems. Particular emphasis is placed on bringing a data science formalism to the field, with an eye toward both future capability development and more intelligent (and appreciably faster) material formulation and system design. The REU student would work closely with a Research Scientist and graduate student to gather data, analyze it using ML tools, and share these results.
Some experience w/ coding, AI, or ML recommended.
AAMP-UP is separate but highly partnered with SURF.
The project is run by Dr. Steven Son and his team. Machine learning (ML) tools are playing an increasingly important role in science and engineering, revealing patterns and providing predictive capabilities not achievable otherwise. This research area explores the utility of machine learning algorithms in the design, development, and characterization of various energetic material systems. Particular emphasis is placed on bringing a data science formalism to the field, with an eye toward both future capability development and more intelligent (and appreciably faster) material formulation and system design. The REU student would work closely with a Research Scientist and graduate student to gather data, analyze it using ML tools, and share these results.
Some experience w/ coding, AI, or ML recommended.
Research categories:
Big Data/Machine Learning, Deep Learning, Material Modeling and Simulation
Preferred major(s):
- No Major Restriction
Desired experience:
Some experience w/ coding, AI, or ML recommended.
Must be a U.S. citizen, national, or permanent resident of the United States. Must have completed at least one academic semester of full-time study at associate's or bachelor's degree level from an accredited college or university.
School/Dept.:
Mechanical Engineering
Professor:
Steven
Son
More information: https://engineering.purdue.edu/ME/People/ptProfile?resource_id=29385
AAMP UP- Machine Learning Approaches to Energetic Materials
Description:
This project is part of the AAMP-UP '22 program, which focuses on energetic material research.
AAMP-UP is separate but highly partnered with SURF.
The project is run by Dr. Alejandro Strachan and his team. The project will use tools from data science and machine learning to develop predictive models for the performance of energetic materials. Students will learn about neural networks, deep learning, and the chemistry and physics of energetic materials.
Basic python knowledge is desirable for the project.
AAMP-UP is separate but highly partnered with SURF.
The project is run by Dr. Alejandro Strachan and his team. The project will use tools from data science and machine learning to develop predictive models for the performance of energetic materials. Students will learn about neural networks, deep learning, and the chemistry and physics of energetic materials.
Basic python knowledge is desirable for the project.
Research categories:
Big Data/Machine Learning, Deep Learning
Preferred major(s):
- No Major Restriction
Desired experience:
Basic python knowledge.
Must be a U.S. citizen, national, or permanent resident of the United States. Must have completed at least one academic semester of full-time study at associate's or bachelor's degree level from an accredited college or university.
School/Dept.:
Materials Engineering
Professor:
Alejandro
Strachan
More information: https://engineering.purdue.edu/MSE/people/ptProfile?id=33239
AAMP UP- Multifunctional Energetic Materials
Description:
This project is part of the AAMP-UP '22 program, which focuses on energetic material research.
AAMP-UP is separate but highly partnered with SURF.
The project is run by Dr. Steve Son and his team. Piezoelectric energetic materials (piezoenergetics or PEMs) offer the potential for a new generation of smart propellants and pyrotechnics with multifunctional capabilities that can be actively controlled via external stimuli. However, the fundamental physics and chemistry governing energy transfer, energy repartitioning, and chemical reactions/kinetics resulting from external stimulation of PEMs are not well understood. It is envisioned that, by coupling piezoelectric behavior and nanoenergetics, truly smart and switchable materials can result. Specifically, we envision reactive piezoelectric materials with multifunctional properties with reactivity and microstructure that can be controlled and altered by external stimuli including stress, temperature, or electromagnetic fields; while enabling integrated in situ sensing. The REU student would be mentored by two graduate students and would design experiments, perform those experiments, collect data and present/share those results.
AAMP-UP is separate but highly partnered with SURF.
The project is run by Dr. Steve Son and his team. Piezoelectric energetic materials (piezoenergetics or PEMs) offer the potential for a new generation of smart propellants and pyrotechnics with multifunctional capabilities that can be actively controlled via external stimuli. However, the fundamental physics and chemistry governing energy transfer, energy repartitioning, and chemical reactions/kinetics resulting from external stimulation of PEMs are not well understood. It is envisioned that, by coupling piezoelectric behavior and nanoenergetics, truly smart and switchable materials can result. Specifically, we envision reactive piezoelectric materials with multifunctional properties with reactivity and microstructure that can be controlled and altered by external stimuli including stress, temperature, or electromagnetic fields; while enabling integrated in situ sensing. The REU student would be mentored by two graduate students and would design experiments, perform those experiments, collect data and present/share those results.
Research categories:
Chemical Catalysis and Synthesis, Composite Materials and Alloys, Material Modeling and Simulation, Material Processing and Characterization
Preferred major(s):
- No Major Restriction
Desired experience:
Must be a U.S. citizen, national, or permanent resident of the United States. Must have completed at least one academic semester of full-time study at associate's or bachelor's degree level from an accredited college or university.
School/Dept.:
Mechanical Engineering
Professor:
Steven
Son
More information: https://engineering.purdue.edu/ME/People/ptProfile?resource_id=29385
AAMP UP- Novel Fuels in Energetic Materials
Description:
This project is part of the AAMP-UP '22 program, which focuses on energetic material research.
AAMP-UP is separate but highly partnered with SURF.
The project is run by Dr. Steven Son and his team. High density fuels, typically metals, are commonly added to propellants and explosives to improve their performance, as well as other factors such as sensitivity and toxicity. Other novel fuels could include solvated electrons (dissolved metals in ammonia, for example). This research topic explores the development, small-scale manufacturing, and characterization of high-density fuels in energetic materials. Particular emphasis is placed on emergent material systems, such as aluminum-lithium alloys, oxide-free coated nano-aluminum, and mechanically activated (MA) fuels. The REU student would work closely with Research Scientists and graduate students to design experiments, perform experiments, analyze data, and report/share these results.
AAMP-UP is separate but highly partnered with SURF.
The project is run by Dr. Steven Son and his team. High density fuels, typically metals, are commonly added to propellants and explosives to improve their performance, as well as other factors such as sensitivity and toxicity. Other novel fuels could include solvated electrons (dissolved metals in ammonia, for example). This research topic explores the development, small-scale manufacturing, and characterization of high-density fuels in energetic materials. Particular emphasis is placed on emergent material systems, such as aluminum-lithium alloys, oxide-free coated nano-aluminum, and mechanically activated (MA) fuels. The REU student would work closely with Research Scientists and graduate students to design experiments, perform experiments, analyze data, and report/share these results.
Research categories:
Chemical Unit Operations, Chemical Catalysis and Synthesis, Composite Materials and Alloys, Material Modeling and Simulation, Material Processing and Characterization
Preferred major(s):
- No Major Restriction
Desired experience:
Must be a U.S. citizen, national, or permanent resident of the United States. Must have completed at least one academic semester of full-time study at associate's or bachelor's degree level from an accredited college or university.
School/Dept.:
Mechanical Engineering
Professor:
Steven
Son
More information: https://engineering.purdue.edu/ME/People/ptProfile?resource_id=29385
AAMP UP- Reactive Wires to Tailor Propellant Burning Rate
Description:
This project is part of the AAMP-UP '22 program, which focuses on energetic material research.
AAMP-UP is separate but highly partnered with SURF.
The project is run by Dr. Steven Son and his team. Of the many techniques that have been employed to increase burning rates, embedding thermally-conductive and/or reactive wires appears to be the approach to do so without increasing sensitivity. We are utilizing our additive manufacturing capabilities, including vibration assisted printing (VAP), to produce both the wires and the propellant. These “wires” may not actually be metals, but include thermally conductive materials such as graphene. The objective of this project is to use both fused deposition modeling (FDM) and direct writing 3D printing techniques to tailor the surface area of propellants dynamically using conductive and reactive wire deposition. The REU student would work closely with Research Scientists and graduate students to design experiments, perform experiments, analyze data, and report/share these results.
AAMP-UP is separate but highly partnered with SURF.
The project is run by Dr. Steven Son and his team. Of the many techniques that have been employed to increase burning rates, embedding thermally-conductive and/or reactive wires appears to be the approach to do so without increasing sensitivity. We are utilizing our additive manufacturing capabilities, including vibration assisted printing (VAP), to produce both the wires and the propellant. These “wires” may not actually be metals, but include thermally conductive materials such as graphene. The objective of this project is to use both fused deposition modeling (FDM) and direct writing 3D printing techniques to tailor the surface area of propellants dynamically using conductive and reactive wire deposition. The REU student would work closely with Research Scientists and graduate students to design experiments, perform experiments, analyze data, and report/share these results.
Research categories:
Chemical Catalysis and Synthesis, Composite Materials and Alloys, Material Modeling and Simulation, Material Processing and Characterization
Preferred major(s):
- No Major Restriction
Desired experience:
Must be a U.S. citizen, national, or permanent resident of the United States. Must have completed at least one academic semester of full-time study at associate's or bachelor's degree level from an accredited college or university.
School/Dept.:
Mechanical Engineering
Professor:
Steven
Son
More information: https://engineering.purdue.edu/ME/People/ptProfile?resource_id=29385
AAMP UP- Sample Heating using Infrared Laser and Optics
Description:
This project is part of the AAMP-UP '22 program, which focuses on energetic material research.
AAMP-UP is separate but highly partnered with SURF.
The project is run by Dr. Wayne Chen and his team. Mechanical properties are important metrics that provide insight for different engineering applications ranging from chemical bonding type on an atomic scale to macroscale design applications. However, research shows that mechanical properties can change as a function of strain rate (impact velocity) and temperature. Therefore, it is necessary to test materials and gather properties while replicating the environment they will endure in application to best inform researchers and engineers in the material design process. A Kolsky bar apparatus is used to perform mechanical testing on materials at high strain rates. This experimental technique has been used for the last ~50 years and has resulted in many materials characterization papers. Missing from the literature is temperature dependence of mechanical properties at high strain rates. We would like a student interested in lasers and optics to design and build an infrared laser device that will evenly heat a polymer composite sample to a specified temperature. The device must attach to the Kolsky bar apparatus and be both safe and efficient. This will allow for coupled temperature and strain rate mechanical experiments and extrapolation of the temperature effects of different materials.
An understanding of laser and optics would be beneficial but is not required.
AAMP-UP is separate but highly partnered with SURF.
The project is run by Dr. Wayne Chen and his team. Mechanical properties are important metrics that provide insight for different engineering applications ranging from chemical bonding type on an atomic scale to macroscale design applications. However, research shows that mechanical properties can change as a function of strain rate (impact velocity) and temperature. Therefore, it is necessary to test materials and gather properties while replicating the environment they will endure in application to best inform researchers and engineers in the material design process. A Kolsky bar apparatus is used to perform mechanical testing on materials at high strain rates. This experimental technique has been used for the last ~50 years and has resulted in many materials characterization papers. Missing from the literature is temperature dependence of mechanical properties at high strain rates. We would like a student interested in lasers and optics to design and build an infrared laser device that will evenly heat a polymer composite sample to a specified temperature. The device must attach to the Kolsky bar apparatus and be both safe and efficient. This will allow for coupled temperature and strain rate mechanical experiments and extrapolation of the temperature effects of different materials.
An understanding of laser and optics would be beneficial but is not required.
Research categories:
Composite Materials and Alloys, Engineering the Built Environment, Fabrication and Robotics, Material Modeling and Simulation, Material Processing and Characterization, Other
Preferred major(s):
- No Major Restriction
Desired experience:
Must be a U.S. citizen, national, or permanent resident of the United States. Must have completed at least one academic semester of full-time study at associate's or bachelor's degree level from an accredited college or university.
School/Dept.:
Aeronautics and Astronautics & Materials Engineering
Professor:
Wayne
Chen
More information: https://engineering.purdue.edu/AAE/people/ptProfile?resource_id=1261
AAMP UP- Synthesis of New Materials
Description:
This project is part of the AAMP-UP '22 program, which focuses on energetic material research.
AAMP-UP is separate but highly partnered with SURF.
The project is run by Dr. Davin Piercey and his team. It is centered around chemical synthesis of new materials for use in propellants, explosives, and pyrotechnics.
Completion of both Organic Chemistry classes and labs is a requirement for the students who fill this position. There is not a specific major requirement, but Chemistry and Chemical Engineering degree plans would be the most relevant.
AAMP-UP is separate but highly partnered with SURF.
The project is run by Dr. Davin Piercey and his team. It is centered around chemical synthesis of new materials for use in propellants, explosives, and pyrotechnics.
Completion of both Organic Chemistry classes and labs is a requirement for the students who fill this position. There is not a specific major requirement, but Chemistry and Chemical Engineering degree plans would be the most relevant.
Research categories:
Chemical Unit Operations, Chemical Catalysis and Synthesis, Composite Materials and Alloys, Material Processing and Characterization
Preferred major(s):
- No Major Restriction
Desired experience:
Organic Chemistry classes & labs.
Must be a U.S. citizen, national, or permanent resident of the United States. Must have completed at least one academic semester of full-time study at associate's or bachelor's degree level from an accredited college or university.
School/Dept.:
Materials and Mechanical Engineering
Professor:
Davin
Piercey
More information: https://engineering.purdue.edu/MSE/people/ptProfile?resource_id=184725
AAMP UP- Ultrasonically Additive Manufactured Multifunctional Material Systems for SHM
Description:
This project is part of the AAMP-UP '22 program, which focuses on energetic material research.
AAMP-UP is separate but highly partnered with SURF.
The project is run by Dr. James Gibert and his team. Ultrasonic Additive Manufacturing (UAM) machine consists of an ultrasonic horn, also known as the sonotrode, transducers, a heater, and a movable base. The process begins with the placement of a thin metal foil, on a sacrificial base plate bolted on a heated anvil. The foil is compressed under pressure by the rolling sonotrode, which is also excited by the piezoelectric transducers at a constant frequency with amplitudes ranging on the order of microns in a direction transversal to the rolling motion. Once the first layer is bonded, additional layers are added and can be machined as needed until the desired geometry and dimensions of a feature are realized.
The ADAMs lab is currently exploring techniques to create multi-functional material systems utilizing UAM. Candidate projects include embedded piezoelectric actuator for sensing applications and shape memory alloy sheets to create localized structural changes in a metal skin. Other potential projects are the creation of metal structures beam with magno-elastic properties. One embodiment is the creation of composite aluminum beams elastomer core filled with magnetic materials. Different configurations of magnetic materials will be explored to create structures that buckle or stiffen in the presence of magnetic fields.
Preferably, students would have MATLAB, Data Acquisition, and some machining knowledge.
AAMP-UP is separate but highly partnered with SURF.
The project is run by Dr. James Gibert and his team. Ultrasonic Additive Manufacturing (UAM) machine consists of an ultrasonic horn, also known as the sonotrode, transducers, a heater, and a movable base. The process begins with the placement of a thin metal foil, on a sacrificial base plate bolted on a heated anvil. The foil is compressed under pressure by the rolling sonotrode, which is also excited by the piezoelectric transducers at a constant frequency with amplitudes ranging on the order of microns in a direction transversal to the rolling motion. Once the first layer is bonded, additional layers are added and can be machined as needed until the desired geometry and dimensions of a feature are realized.
The ADAMs lab is currently exploring techniques to create multi-functional material systems utilizing UAM. Candidate projects include embedded piezoelectric actuator for sensing applications and shape memory alloy sheets to create localized structural changes in a metal skin. Other potential projects are the creation of metal structures beam with magno-elastic properties. One embodiment is the creation of composite aluminum beams elastomer core filled with magnetic materials. Different configurations of magnetic materials will be explored to create structures that buckle or stiffen in the presence of magnetic fields.
Preferably, students would have MATLAB, Data Acquisition, and some machining knowledge.
Research categories:
Composite Materials and Alloys, Material Modeling and Simulation, Material Processing and Characterization
Preferred major(s):
- No Major Restriction
Desired experience:
MATLAB, Data Acquisition, some machining knowledge.
Must be a U.S. citizen, national, or permanent resident of the United States. Must have completed at least one academic semester of full-time study at associate's or bachelor's degree level from an accredited college or university.
School/Dept.:
Mechanical Engineering
Professor:
James
Gibert
More information: https://engineering.purdue.edu/ME/People/ptProfile?resource_id=127242
AAMP UP-Machine Learning Based Development of Multiscale Reactive Model of High Explosives
Description:
This project is part of the AAMP-UP '22 program, which focuses on energetic material research.
AAMP-UP is separate but highly partnered with SURF.
The project is run by Dr. Alejandro Strachan and his team. Computationally efficient models for accurately predicting the thermal and chemical properties of high explosive (HE) materials are of great interest for both military and civilian applications. In this project, students will work on developing a multiscale model for HE materials, namely HMX and TATB, via combination of dimensionality reduction through machine learning techniques and atomistic molecular dynamics (MD) simulations. Specifically, the decomposition behavior of HE will be investigated through reactive, atomistic MD simulations. The dimensionality of the thermal and chemical data collected from the MD will then be reduced through non-negative matrix factorization technique to develop reduced-order chemistry models for HMX and TATB. Utilizing these, continuum-level models that capture the decomposition behavior of these materials will be developed.
Students with programming experience would be a good fit for this project.
AAMP-UP is separate but highly partnered with SURF.
The project is run by Dr. Alejandro Strachan and his team. Computationally efficient models for accurately predicting the thermal and chemical properties of high explosive (HE) materials are of great interest for both military and civilian applications. In this project, students will work on developing a multiscale model for HE materials, namely HMX and TATB, via combination of dimensionality reduction through machine learning techniques and atomistic molecular dynamics (MD) simulations. Specifically, the decomposition behavior of HE will be investigated through reactive, atomistic MD simulations. The dimensionality of the thermal and chemical data collected from the MD will then be reduced through non-negative matrix factorization technique to develop reduced-order chemistry models for HMX and TATB. Utilizing these, continuum-level models that capture the decomposition behavior of these materials will be developed.
Students with programming experience would be a good fit for this project.
Research categories:
Big Data/Machine Learning, Deep Learning
Preferred major(s):
- No Major Restriction
Desired experience:
Programming experience a plus
Must be a U.S. citizen, national, or permanent resident of the United States. Must have completed at least one academic semester of full-time study at associate's or bachelor's degree level from an accredited college or university.
School/Dept.:
Materials Engineering
Professor:
Alejandro
Strachan
More information: https://engineering.purdue.edu/MSE/people/ptProfile?id=33239
AAMP-UP: Additive Manufacturing
Description:
This research project seeks to additively manufacture (3D print) highly viscous materials using a novel 3D-printing method: Vibration Assisted Printing (VAP). This technique uses high frequency vibrations concentrated at the tip of the printing nozzle to enable flow of viscous materials at low pressures and temperatures. VAP has the potential to create next-generation munitions with more precision, customizability, and safety than traditional additive manufacturing methods. The objective of this project is to design formulations which are capable of being vibration-assisted printed, maintain energetic performance, and retain desirable mechanical properties after printing. The REU student would be mentored by graduate students and work within a team to design experiments, perform experiments, analyze data, and disseminate the results. The REU student will have the opportunity to present the findings in regular meetings, poster sessions, formal presentations, and papers.
Research categories:
Chemical Unit Operations, Composite Materials and Alloys, Energy and Environment, Engineering the Built Environment, Fabrication and Robotics, Material Modeling and Simulation, Other
Preferred major(s):
- No Major Restriction
Desired experience:
U.S. Citizenship Required
Must have completed 1 semester of undergraduate courses
School/Dept.:
Mechanical Engineering
Professor:
Jeff
Rhoads
More information: https://engineering.purdue.edu/ME/People/ptProfile?resource_id=34218
AI in manufacturing and deployment of TinyML devices.
Description:
Private project for Eugene Thomson
Research categories:
Big Data/Machine Learning
Preferred major(s):
- No Major Restriction
School/Dept.:
ECE
Professor:
Ali
Shakouri
Additive manufacturing to enable hypersonic flight
Description:
The overall project will develop and mature high temperature materials, new additive manufacturing processes, and joining technologies to provide structural solutions to hypersonics components and sub-systems. While the overall projects will be interdisciplinary in nature, students are invited to work on specific aspects of this project, including (i) materials modeling of metals, ceramics, and composites, in order to support a digital twin of the aircraft, (ii) the digital flow of information through the product lifecycle, and (iii) the design and development of high temperature, controlled environmental testing facilities.
Research categories:
Composite Materials and Alloys, Material Modeling and Simulation, Material Processing and Characterization
Preferred major(s):
- Aeronautical and Astronautical Engineering
- Mechanical Engineering
- Materials Engineering
Desired experience:
Background includes:
1.) Required background in (a) CAD software and (b) either Python (preferred) or Matlab programming familiarity.
2.) Preferred background in finite element analysis.
3.) Due to work with controlled information, US Citizenship or Legal US Permanent Resident status is required.
School/Dept.:
AAE
Professor:
Michael
Sangid
More information: https://engineering.purdue.edu/hypersonics
Adhesives at the Beach
Description:
The oceans are home to a diverse collection of animals producing intriguing materials. Mussels, barnacles, oysters, starfish, and kelp are examples of the organisms generating adhesive matrices for affixing themselves to the sea floor. Our laboratory is characterizing these biological materials, designing synthetic polymer mimics, and developing applications. Synthetic mimics of these bioadhesives begin with the chemistry learned from characterization studies and incorporate the findings into bulk polymers. For example, we are mimicking the cross-linking of DOPA-containing adhesive proteins by placing monomers with pendant catechols into various polymer backbones. Adhesion strengths of these new polymers can rival that of the cyanoacrylate “super glues.” Underwater bonding is also appreciable. Future efforts are planned in two different areas: A) Using biobased and biomimetic adhesives as the basis for making new plastic materials, such as systems like carbon fiber reinforced polymers, but with all components sourced sustainably. B) Developing new adhesive systems that function completely underwater.
Research categories:
Composite Materials and Alloys, Ecology and Sustainability, Material Processing and Characterization
Preferred major(s):
- No Major Restriction
Desired experience:
Students in our lab are not required to arrive with any particular expertise. Marine biology (e.g., working with live mussels), materials engineering (e.g., measuring mechanical properties of adhesives), and chemistry (e.g., making new polymers) are all involved in this work. Few people at any level will come in with knowledge about all aspects here. Consequently, we are looking for adventurous students who are wanting to roll up their sleeves, get wet (literally), and learn several new things.
School/Dept.:
Chemistry
Professor:
Jonathan
Wilker
More information: https://www.chem.purdue.edu/wilker/
Admixture Compatibility of Eleven Nontraditional and Natural Pozzolans in Cementitious Composites
Description:
Objective: To assist in evaluating admixture compatibility of eleven nontraditional and natural pozzolans in cementitious composites.
Motivation: It is expected that in the near future, the demand for traditional supplementary cementitious materials (SCMs) will surpass its supply. These traditional SCMs can increase sustainability in addition to ensuring high performance and durability in cementitious composites. Finding alternative SCMs that can fulfill the supply gap while also adequately performing in cementitious composites is therefore critical. One of the current research projects performed at Lyles School of Civil Engineering by Purdue University (in collaboration with Penn State and Clarkson University) is exploring the effect of eleven nontraditional and natural pozzolans (NNPs) on cementitious systems. Currently, there is limited knowledge of whether these NNPs are capable of satisfactory performance in cementitious composites. More specifically, the response of these NNPs to commercially available chemical admixtures such as superplasticizers (SP) and air-entraining agents (AEA) is not well known. The usage of SP and AEA admixtures is fairly common as they decrease the water demand and increase durability respectively. Therefore, the exploration of the potential issues of incompatibilities between admixtures and NNPs is required.
Activities and responsibilities of the student:
· To become familiar with cementitious composites and different experiments that will be performed.
· To perform a literature review on the effect of admixtures in cementitious composites and present the findings.
· To evaluate rheological properties at room and elevated temperatures, set time of pastes, strength gain of mortar, and foam index test.
· To assist with different measurements of experiments.
· To present the results of the work performed during SURF program to the research group during the weekly project meetings.
· To prepare a report summarizing the admixture compatibilities of the eleven NNPs performed during the SURF program.
· To disseminate the results of the research experience as required by the SURF program.
Motivation: It is expected that in the near future, the demand for traditional supplementary cementitious materials (SCMs) will surpass its supply. These traditional SCMs can increase sustainability in addition to ensuring high performance and durability in cementitious composites. Finding alternative SCMs that can fulfill the supply gap while also adequately performing in cementitious composites is therefore critical. One of the current research projects performed at Lyles School of Civil Engineering by Purdue University (in collaboration with Penn State and Clarkson University) is exploring the effect of eleven nontraditional and natural pozzolans (NNPs) on cementitious systems. Currently, there is limited knowledge of whether these NNPs are capable of satisfactory performance in cementitious composites. More specifically, the response of these NNPs to commercially available chemical admixtures such as superplasticizers (SP) and air-entraining agents (AEA) is not well known. The usage of SP and AEA admixtures is fairly common as they decrease the water demand and increase durability respectively. Therefore, the exploration of the potential issues of incompatibilities between admixtures and NNPs is required.
Activities and responsibilities of the student:
· To become familiar with cementitious composites and different experiments that will be performed.
· To perform a literature review on the effect of admixtures in cementitious composites and present the findings.
· To evaluate rheological properties at room and elevated temperatures, set time of pastes, strength gain of mortar, and foam index test.
· To assist with different measurements of experiments.
· To present the results of the work performed during SURF program to the research group during the weekly project meetings.
· To prepare a report summarizing the admixture compatibilities of the eleven NNPs performed during the SURF program.
· To disseminate the results of the research experience as required by the SURF program.
Research categories:
Composite Materials and Alloys, Material Modeling and Simulation, Material Processing and Characterization
Preferred major(s):
- No Major Restriction
School/Dept.:
CE
Professor:
Jan
Olek
Advanced Vehicle Automation and Human-Subject Experimentation
Description:
Vehicle automation is developing at a rapid rate worldwide. While fully autonomous vehicles will be pervasive on the roadway for the next several years, many research initiatives are currently underway to understand and design approaches that will make this technology a future reality. This work ranges from the development of sensors and controls algorithms, to schemes for networks and connectivity, to the creation of in-vehicle driver interfaces. Here, one component that is key to the effective design of next-generation autonomous driving systems is the human driver and, thus studying human-vehicle interactions and defining driver’s roles/tasks will be important.
The goal of this project is to describe and measure the ways in which a person interacts with advanced vehicle automation. Students will assist with multiple activities and will learn a combination of the following: how to a) develop/code advanced driving simulation scenarios, b) collect driving performance data, c) analyze driver and performance data (using methods via software packages), and d) write technical reports and/or publications. Students may also gain experience collecting and analyzing complementary physiological measures, such as eye movement data, brain activity, skin conductance, and heart rate. The students will work closely with graduate student mentors to enhance learning.
The goal of this project is to describe and measure the ways in which a person interacts with advanced vehicle automation. Students will assist with multiple activities and will learn a combination of the following: how to a) develop/code advanced driving simulation scenarios, b) collect driving performance data, c) analyze driver and performance data (using methods via software packages), and d) write technical reports and/or publications. Students may also gain experience collecting and analyzing complementary physiological measures, such as eye movement data, brain activity, skin conductance, and heart rate. The students will work closely with graduate student mentors to enhance learning.
Research categories:
Big Data/Machine Learning, Learning and Evaluation, Other
Preferred major(s):
- No Major Restriction
Desired experience:
Human Factors, Matlab, transportation, some experience in statistics, some computer programming and machine learning experience (in any language)
School/Dept.:
Industrial Engineering
Professor:
Brandon
Pitts
More information: https://engineering.purdue.edu/NHanCE
Advancing Pharmaceutical Manufacturing through Process Modeling and Novel Sensor Development
Description:
The limitations of batch processes to manufacture pharmaceutical products such as tablets, coupled with advances in process analytical technology (PAT) tools have led to a shift towards continuous manufacturing (CM), which represents the future of the pharmaceutical industry.
The flexibility of continuous processes can reduce wasted materials and facilitate scale-up more easily with active plant-wide control strategies. Ultimately, this results in cheaper and safer drugs, as well as a more reliable drug supply chain.
To fully realize the benefits of continuous manufacturing, it is important to capture the dynamics of the particulate process, which can be more complex than common liquid-based or gas-based chemical processes. In addition, effective fault detection and diagnostic systems need to be in place, so intervention strategies can be implemented in case the system goes awry.
All of these require the development of process models that leverages knowledge of the process and big data. Students in this part of the research would have a chance to gain experience in industry-leading software for process modeling (e.g. Simulink, gProms, OSI PI) and machine learning (e.g. Matlab, Python, .NET).
Most importantly, they would be able to test the models in Purdue's Newly Installed Tablet Manufacturing Pilot Plant at the FLEX Lab in Discovery Park.
Another important aspect of the research are sensors. In this project, we will be investigating the feasibility of two novel sensors: a capacitance-based sensor to measure mass flow, and a particle imaging sensor that directly captures images of the powder particles to give you a particle size distribution. We will be testing these sensors together with NIR and Raman sensors, and use data analytics to determine their feasibility of application in a drug product manufacturing process.
The flexibility of continuous processes can reduce wasted materials and facilitate scale-up more easily with active plant-wide control strategies. Ultimately, this results in cheaper and safer drugs, as well as a more reliable drug supply chain.
To fully realize the benefits of continuous manufacturing, it is important to capture the dynamics of the particulate process, which can be more complex than common liquid-based or gas-based chemical processes. In addition, effective fault detection and diagnostic systems need to be in place, so intervention strategies can be implemented in case the system goes awry.
All of these require the development of process models that leverages knowledge of the process and big data. Students in this part of the research would have a chance to gain experience in industry-leading software for process modeling (e.g. Simulink, gProms, OSI PI) and machine learning (e.g. Matlab, Python, .NET).
Most importantly, they would be able to test the models in Purdue's Newly Installed Tablet Manufacturing Pilot Plant at the FLEX Lab in Discovery Park.
Another important aspect of the research are sensors. In this project, we will be investigating the feasibility of two novel sensors: a capacitance-based sensor to measure mass flow, and a particle imaging sensor that directly captures images of the powder particles to give you a particle size distribution. We will be testing these sensors together with NIR and Raman sensors, and use data analytics to determine their feasibility of application in a drug product manufacturing process.
Research categories:
Big Data/Machine Learning, Chemical Unit Operations, Material Processing and Characterization
Preferred major(s):
- No Major Restriction
Desired experience:
Basic skills for MATLAB and powder characterization would be a plus, but they are not necessary. The student should be safety conscious, self-motivated, and can work with minimal supervision. Aptitude for mastering the use of gadgets is desired, as well as the ability to understand research papers, documents, and manuals.
Any student who prefers a combination of simulation/modeling and hands-on pilot plant work is welcome. Moreover, this project is ideal for a student who is interested in a career in pharma or in powder manufacturing.
School/Dept.:
Davidson School of Chemical Engineering
Professor:
Gintaras
Reklaitis
Advancing Pharmaceutical Manufacturing through Process Modeling and Novel Sensor Development
Description:
The limitations of batch processes to manufacture pharmaceutical products such as tablets, coupled with advances in process analytical technology (PAT) tools have led to a shift towards continuous manufacturing (CM), which represents the future of the pharmaceutical industry.
The flexibility of continuous processes can reduce wasted materials and facilitate scale-up more easily with active plant-wide control strategies. Ultimately, this results in cheaper and safer drugs, as well as a more reliable drug supply chain.
To fully realize the benefits of continuous manufacturing, it is important to capture the dynamics of the particulate process, which can be more complex than common liquid-based or gas-based chemical processes. In addition, effective fault detection and diagnostic systems need to be in place, so intervention strategies can be implemented in case the system goes awry.
All of these require the development of process models that leverages knowledge of the process and big data. Students in this part of the research would have a chance to gain experience in industry-leading software for process modeling (e.g. Simulink, gProms, OSI PI) and machine learning (e.g. Matlab, Python, .NET).
Most importantly, they would be able to test the models in Purdue's Newly Installed Tablet Manufacturing Pilot Plant at the FLEX Lab in Discovery Park.
Another important aspect of the research are sensors. In this project, we will be investigating the feasibility of two novel sensors: a capacitance-based sensor to measure mass flow, and a particle imaging sensor that directly captures images of the powder particles to give you a particle size distribution. We will be testing these sensors together with NIR and Raman sensors, and use data analytics to determine their feasibility of application in a drug product manufacturing process.
The flexibility of continuous processes can reduce wasted materials and facilitate scale-up more easily with active plant-wide control strategies. Ultimately, this results in cheaper and safer drugs, as well as a more reliable drug supply chain.
To fully realize the benefits of continuous manufacturing, it is important to capture the dynamics of the particulate process, which can be more complex than common liquid-based or gas-based chemical processes. In addition, effective fault detection and diagnostic systems need to be in place, so intervention strategies can be implemented in case the system goes awry.
All of these require the development of process models that leverages knowledge of the process and big data. Students in this part of the research would have a chance to gain experience in industry-leading software for process modeling (e.g. Simulink, gProms, OSI PI) and machine learning (e.g. Matlab, Python, .NET).
Most importantly, they would be able to test the models in Purdue's Newly Installed Tablet Manufacturing Pilot Plant at the FLEX Lab in Discovery Park.
Another important aspect of the research are sensors. In this project, we will be investigating the feasibility of two novel sensors: a capacitance-based sensor to measure mass flow, and a particle imaging sensor that directly captures images of the powder particles to give you a particle size distribution. We will be testing these sensors together with NIR and Raman sensors, and use data analytics to determine their feasibility of application in a drug product manufacturing process.
Research categories:
Big Data/Machine Learning, Chemical Unit Operations, Material Processing and Characterization
Preferred major(s):
- No Major Restriction
Desired experience:
Basic skills for MATLAB and powder characterization would be a plus, but they are not necessary. The student should be safety conscious, self-motivated, and can work with minimal supervision. Aptitude for mastering the use of gadgets is desired, as well as the ability to understand research papers, documents, and manuals.
Any student who prefers a combination of simulation/modeling and hands-on pilot plant work is welcome. Moreover, this project is ideal for a student who is interested in a career in pharma or in powder manufacturing.
School/Dept.:
Davidson School of Chemical Engineering
Professor:
Gintaras
Reklaitis
Advancing Pharmaceutical Manufacturing through Process Modeling and Novel Sensor Development
Description:
The limitations of batch processes to manufacture pharmaceutical products such as tablets, coupled with advances in process analytical technology (PAT) tools have led to a shift towards continuous manufacturing (CM), which represents the future of the pharmaceutical industry.
The flexibility of continuous processes can reduce wasted materials and facilitate scale-up more easily with active plant-wide control strategies. Ultimately, this results in cheaper and safer drugs, as well as a more reliable drug supply chain.
To fully realize the benefits of continuous manufacturing, it is important to capture the dynamics of the particulate process, which can be more complex than common liquid-based or gas-based chemical processes. In addition, effective fault detection and diagnostic systems need to be in place, so intervention strategies can be implemented in case the system goes awry.
All of these require the development of process models that leverages knowledge of the process and big data. Students in this part of the research would have a chance to gain experience in industry-leading software for process modeling (e.g. Simulink, gProms, OSI PI) and machine learning (e.g. Matlab, Python, .NET).
Most importantly, they would be able to test the models in Purdue's Newly Installed Tablet Manufacturing Pilot Plant at the FLEX Lab in Discovery Park.
Another important aspect of the research are sensors. In this project, we will be investigating the feasibility of two novel sensors: a capacitance-based sensor to measure mass flow, and a particle imaging sensor that directly captures images of the powder particles to give you a particle size distribution. We will be testing these sensors together with NIR and Raman sensors, and use data analytics to determine their feasibility of application in a drug product manufacturing process.
The flexibility of continuous processes can reduce wasted materials and facilitate scale-up more easily with active plant-wide control strategies. Ultimately, this results in cheaper and safer drugs, as well as a more reliable drug supply chain.
To fully realize the benefits of continuous manufacturing, it is important to capture the dynamics of the particulate process, which can be more complex than common liquid-based or gas-based chemical processes. In addition, effective fault detection and diagnostic systems need to be in place, so intervention strategies can be implemented in case the system goes awry.
All of these require the development of process models that leverages knowledge of the process and big data. Students in this part of the research would have a chance to gain experience in industry-leading software for process modeling (e.g. Simulink, gProms, OSI PI) and machine learning (e.g. Matlab, Python, .NET).
Most importantly, they would be able to test the models in Purdue's Newly Installed Tablet Manufacturing Pilot Plant at the FLEX Lab in Discovery Park.
Another important aspect of the research are sensors. In this project, we will be investigating the feasibility of two novel sensors: a capacitance-based sensor to measure mass flow, and a particle imaging sensor that directly captures images of the powder particles to give you a particle size distribution. We will be testing these sensors together with NIR and Raman sensors, and use data analytics to determine their feasibility of application in a drug product manufacturing process.
Research categories:
Big Data/Machine Learning, Chemical Unit Operations, Material Processing and Characterization
Desired experience:
Basic skills for MATLAB and powder characterization would be a plus, but they are not necessary. The student should be safety conscious, self-motivated, and can work with minimal supervision. Aptitude for mastering the use of gadgets is desired, as well as the ability to understand research papers, documents, and manuals.
Any student who prefers a combination of simulation/modeling and hands-on pilot plant work is welcome. Moreover, this project is ideal for a student who is interested in a career in pharma or in powder manufacturing.
School/Dept.:
Davidson School of Chemical Engineering
Professor:
Gintaras
Reklaitis
Agricultural Data Pipeline and Integration with Models
Description:
Digital Agriculture, at its best, builds upon decades of discipline research with some integration of new IoT sensors and communication pathways as well as public resource data such as weather, soil, and topography. One challenge to be addressed is to more fully document the backstory or fuller context of situations so that artificial intelligence and machine learning can be more complete and robust. Another is the integration of mechanistic (descriptive of the fundamental science) models that might be biological, physical, chemical, logistical, economic, etc. in origin. The better parameterization of these models and even auto-population of initial conditions can stem from data sets and data streams. In this project, the student will extract biophysical model(s) from literature and other simulations to meld model + data. It will require interoperability focus and that involves wise choices of data architecture and an integration with data pipelines (often based on open source tools). The end game is to provide better insight (including probabilities, when applicable) for tactical and strategic cropping decisions while preserving security and privacy.
Research categories:
Internet of Things, IoT for Precision Agriculture
Preferred major(s):
- No Major Restriction
Desired experience:
Knowledge of cropping systems and coding desired.
School/Dept.:
Agricultural and Biological Engineering
Professor:
Dennis
Buckmaster
More information: iot4ag.us
Altered pathways and microRNAs in vascular tumors
Description:
Angiosarcomas are aggressive cancers with a poor prognosis for patients. We utilize genetically engineered cell lines and in vivo models to study the molecular drivers of angiosarcoma. In recent work, we found that DICER1 and microRNAs may function as critical tumor suppressors. We have gone on to generate additional tumor models investigating other genes known to be altered in patients. In this project we will study a novel oncogene to determine its role in angiosarcoma and potential as a therapeutic target.
Research categories:
Biological Characterization and Imaging, Cellular Biology, Genetics
Preferred major(s):
- No Major Restriction
School/Dept.:
Biological Sciences
Professor:
Jason
Hanna
More information: https://www.bio.purdue.edu/People/profile/hannaja.html
Anomaly Detection in Extrusion Based Additive Manufacturing
Description:
This project is a part of Prof. Monique McClain's team. Cheap commercial Fused Filament Fabrication 3D printers do not currently have the ability to sense when a print error is occurring (e.g. delimination, overextrusion, etc.). If an operator is not monitoring the print or has limited experience, then this will lead to the creation of defective parts and wasted material, time, and effort. In order to move towards automatic on-the-fly correction during a print, it is important to be able to measure and classify critical 3D printing defects. By using an existing 3D printer with integrated sensors, the research assistant will have to design and conduct printing experiments that can allow us to distinguish normal (good) operation from abnormal (bad) operation. Then, machine learning/statistical process control algorithms can be applied to the carefully collected data in order to test the accuracy of the algorithms in detecting such errors. Ultimately, the research assistant will gain experience in additive manufacturing, design of experiments, and data analysis from this project.
Research categories:
Big Data/Machine Learning, Other
Preferred major(s):
- No Major Restriction
Desired experience:
Python, 3D printing, linear algebra, sensor measurement, statistics
School/Dept.:
Mechanical Engineering
Professor:
Monique
McClain
Antibiotic Induction of Streptomyces Natural Products
Description:
Novel natural products from Streptomyces are challenging to discover, often because they are not produced under standard laboratory conditions. We are exploring methods of activating production of novel natural products using antibiotics.
Research categories:
Biological Characterization and Imaging, Cellular Biology
Preferred major(s):
- No Major Restriction
School/Dept.:
Chemistry
Professor:
Elizabeth
Parkinson
More information: https://www.parkinsonlaboratory.com/
Autonomous 3D printing platform for manufacturing pharmaceuticals
Description:
The Covid-19 pandemic has caused major disruptions in supply chains for nearly all consumer goods leading to shortages and delays across the product spectrum. This has highlighted the need for robust logistics networks to ensure reliable product supply insulated from market fluctuations. In pharmaceuticals, one approach to strengthen supply chains is to use continuous, automated and agile production sites to make the drug products.
At present there is a dearth of such advanced manufacturing systems. Previous studies by our group have sought to bridge this gap by developing a novel 3D printing platform that possesses the desired features. It is an inkjet style printer that processes the active ingredient and excipient as a solution, melt or suspension formulation and prints it onto capsules or placebo tablets. The goal of this project is to further development of the 3D printing platform in two broad directions: 1) Sensing and real time process monitoring of critical quality attributes (experimental), 2) Investigating the operating regime for different drug excipient systems (experimental).
At present there is a dearth of such advanced manufacturing systems. Previous studies by our group have sought to bridge this gap by developing a novel 3D printing platform that possesses the desired features. It is an inkjet style printer that processes the active ingredient and excipient as a solution, melt or suspension formulation and prints it onto capsules or placebo tablets. The goal of this project is to further development of the 3D printing platform in two broad directions: 1) Sensing and real time process monitoring of critical quality attributes (experimental), 2) Investigating the operating regime for different drug excipient systems (experimental).
Research categories:
Chemical Unit Operations
Preferred major(s):
- Chemical Engineering
- Mechanical Engineering
- Materials Engineering
Desired experience:
No prior experience required.
School/Dept.:
Chemical Engineering
Professor:
Gintaras
Reklaitis
Blood sample preparation for HIV diagnostics in a smartphone-based microfluidic device
Description:
HIV/AIDS effects millions of people all over the world. The antiretroviral therapy used to treat HIV is effective, but HIV first must be diagnosed and then monitored to measure the treatment effectiveness to eliminate transmission to others and increase a patient’s quality of life. The Linnes Lab uses state of the art microfluidic technologies to prevent, detect, and understand the pathogenesis of diseases, such as HIV. This undergraduate summer research project will focus on developing new technology for HIV diagnostics that will also aid in diagnostics research of other bloodborne illnesses. The student will learn about biological sample preparation, nucleic acid amplification methods, microfluidic device design, fabrication, and testing, and rapid prototyping tools such as 3D printing and laser cutting. The researcher will develop a new tool for sample preparation of the blood that minimizes the number of user steps to integrate into an easy-to-use point-of-care diagnostic tool for people living with HIV to monitor their viral load within the convenience and privacy of their homes. The new tool design specifications include that it must be compatible with the smartphone imaging platform, microfluidic chip, and the HIV assay to diagnose the disease with high sensitivity and specificity.
Research categories:
Biological Characterization and Imaging, Fabrication and Robotics, Human Factors, Medical Science and Technology, Nanotechnology
Preferred major(s):
- Biomedical Engineering
- Biochemistry
- Biological Engineering - multiple concentrations
- Microbiology
Desired experience:
3d printing and prototyping, medical technology
School/Dept.:
Weldon School of Biomedical Engineering
Professor:
Jacqueline
Linnes
More information: https://engineering.purdue.edu/LinnesLab
Bone Fracture and Microscale Deformation Processes
Description:
We seek to modify the deformation characteristics of bone through a pharmacological treatment. This project would demonstrate such a concept using animal bone. Treated and untreated bone will be made available for the interrogation of bone by x-rays. Students will be engaged in the data interpretation of x-ray scattering experiments on bone, not subjected to mechanical loads or subjected to mechanical loads.
Research categories:
Biological Characterization and Imaging, Biological Simulation and Technology, Material Modeling and Simulation, Material Processing and Characterization, Other
Preferred major(s):
- Materials Engineering
- Mechanical Engineering
- Biomedical Engineering
Desired experience:
Materials Characterization, X-ray techniques; Experience in lab work
School/Dept.:
School of Mechanical Engineering
Professor:
Thomas
Siegmund
More information: https://engineering.purdue.edu/MYMECH
Bone Fracture and Toughness Modification
Description:
This SURF research project seems to engage a student in the study of fracture of bone. In particular we seek to change the strength and toughness of bone through a pharmacological treatment. A project participant would use pig or cow bone, modifiy such bone with the pharmacological treatment and conduct mechanical property measurements on said bone.
Research categories:
Biological Characterization and Imaging, Material Modeling and Simulation, Other
Preferred major(s):
- Mechanical Engineering
- Biomedical Engineering
- Materials Engineering
Desired experience:
Knowledge in strength of materials desired; Some experience with lab work
School/Dept.:
School of Mechanical Engineering
Professor:
Thomas
Siegmund
More information: https://engineering.purdue.edu/MYMECH
CISTAR - Design of metal-functionalized porous carbons for electrochemical reactions
Description:
This project is supported by CISTAR, an NSF Engineering Research Center headquartered at Purdue.
Electrochemical reactions are a critical component of technologies to generate energy and fuels with lower carbon footprint. Metal-functionalized carbon materials are promising electrocatalysts, and their catalytic performance is influenced by the porous and surface properties of the carbon support. This project will focus on developing novel methods to synthesize carbon supports with tailored pore structures and surface properties. The student will learn about catalyst synthesis techniques, characterization methods for bulk and atomic structure (X-ray diffraction, spectroscopy, microscopy), and catalyst evaluation. This project will be co-advised by Professor Brian Tackett .
Students working on this project will also have the opportunity to participate in information sessions, tours and informal mentoring with CISTAR's partner companies.
Purdue students are not eligible for this project. Students must be from outside institutions. Participants must be US Citizens. Students with disabilities, veterans, and those from traditionally underrepresented groups in STEM are encouraged to apply.
Electrochemical reactions are a critical component of technologies to generate energy and fuels with lower carbon footprint. Metal-functionalized carbon materials are promising electrocatalysts, and their catalytic performance is influenced by the porous and surface properties of the carbon support. This project will focus on developing novel methods to synthesize carbon supports with tailored pore structures and surface properties. The student will learn about catalyst synthesis techniques, characterization methods for bulk and atomic structure (X-ray diffraction, spectroscopy, microscopy), and catalyst evaluation. This project will be co-advised by Professor Brian Tackett .
Students working on this project will also have the opportunity to participate in information sessions, tours and informal mentoring with CISTAR's partner companies.
Purdue students are not eligible for this project. Students must be from outside institutions. Participants must be US Citizens. Students with disabilities, veterans, and those from traditionally underrepresented groups in STEM are encouraged to apply.
Research categories:
Chemical Catalysis and Synthesis
Preferred major(s):
- Chemical Engineering
School/Dept.:
School of Chemical Engineering
Professor:
Rajamani
Gounder
More information: https://cistar.us/
CISTAR - High temperature catalysts for conversion of ethylene and propylene to gasoline and diesel fuel
Description:
This project is supported by CISTAR, an NSF Engineering Research Center headquartered at Purdue.
CISTAR's vision is to convert natural gas liquids, for example, ethane and propane, to fuels and chemicals by two catalytic steps. The first requires dehydrogenation of alkanes to olefins, which are subsequently converted to final products. This project investigates a new class of catalyst for conversion of ethylene and propylene to higher molecular weight hydrocarbons suitable for blending into gasoline or diesel fuels. These reactions occur at high temperature and pressure in a fixed bed reactor. The research plan is to synthesize catalysts and test these to determine the rates, selectivity and stability.
Students working on this project will also have the opportunity to participate in information sessions, tours and informal mentoring with CISTAR's partner companies.
Purdue students are not eligible for this project. Students must be from outside institutions. Participants must be US Citizens. Students with disabilities, veterans, and those from traditionally underrepresented groups in STEM are encouraged to apply.
CISTAR's vision is to convert natural gas liquids, for example, ethane and propane, to fuels and chemicals by two catalytic steps. The first requires dehydrogenation of alkanes to olefins, which are subsequently converted to final products. This project investigates a new class of catalyst for conversion of ethylene and propylene to higher molecular weight hydrocarbons suitable for blending into gasoline or diesel fuels. These reactions occur at high temperature and pressure in a fixed bed reactor. The research plan is to synthesize catalysts and test these to determine the rates, selectivity and stability.
Students working on this project will also have the opportunity to participate in information sessions, tours and informal mentoring with CISTAR's partner companies.
Purdue students are not eligible for this project. Students must be from outside institutions. Participants must be US Citizens. Students with disabilities, veterans, and those from traditionally underrepresented groups in STEM are encouraged to apply.
Research categories:
Chemical Catalysis and Synthesis
Preferred major(s):
- Chemical Engineering
- Chemistry
Desired experience:
None, but reaction engineering is desirable.
School/Dept.:
Chemical Engineering
Professor:
Jeff
Miller
More information: https://cistar.us/
CISTAR - Synthesis of Alloy Nanoparticles for Selective Catalysis
Description:
This project is supported by CISTAR, an NSF Engineering Research Center headquartered at Purdue.
In this project, students will develop precise colloidal and impregnation-based syntheses for supported metal alloy nanoparticles. These materials will then be utilized as heterogeneous catalysts in thermal and solution-phase hydrogenation and dehydrogenation reactions. A particular focus will be placed on controlling the ensemble geometry and electronic properties of the alloy surface in order achieve highly selective catalysis.
Students working on this project will also have the opportunity to participate in information sessions, tours and informal mentoring with CISTAR's partner companies.
Purdue students are not eligible for this project. Students must be from outside institutions. Participants must be US Citizens. Students with disabilities, veterans, and those from traditionally underrepresented groups in STEM are encouraged to apply.
In this project, students will develop precise colloidal and impregnation-based syntheses for supported metal alloy nanoparticles. These materials will then be utilized as heterogeneous catalysts in thermal and solution-phase hydrogenation and dehydrogenation reactions. A particular focus will be placed on controlling the ensemble geometry and electronic properties of the alloy surface in order achieve highly selective catalysis.
Students working on this project will also have the opportunity to participate in information sessions, tours and informal mentoring with CISTAR's partner companies.
Purdue students are not eligible for this project. Students must be from outside institutions. Participants must be US Citizens. Students with disabilities, veterans, and those from traditionally underrepresented groups in STEM are encouraged to apply.
Research categories:
Chemical Catalysis and Synthesis
Preferred major(s):
- Chemistry
- Chemical Engineering
- Materials Engineering
Desired experience:
General chemistry, organic chemistry
School/Dept.:
Chemistry Department
Professor:
Christina
Li
More information: https://cistar.us/
CISTAR - Zeolite catalyst design for light hydrocarbon conversion
Description:
This project is supported by CISTAR, an NSF Engineering Research Center headquartered at Purdue.
Zeolites are crystalline materials that are used as catalysts for upgrading light hydrocarbons from shale gas into transportation fuels and chemicals. However, improved catalyst materials that are more active, selective and stable are needed. These aspects of catalytic performance are linked to their atomic-scale properties, specifically the distribution of Al atoms in the crystalline material. This project will focus on developing novel methods to synthesize zeolite materials with different Al site distributions. The student will learn about catalyst synthesis techniques, characterization methods for bulk and atomic structure (X-ray diffraction, spectroscopy, microscopy), and catalyst evaluation.
Students working on this project will also have the opportunity to participate in information sessions, tours and informal mentoring with CISTAR's partner companies.
Purdue students are not eligible for this project. Students must be from outside institutions. Participants must be US Citizens. Students with disabilities, veterans, and those from traditionally underrepresented groups in STEM are encouraged to apply.
Zeolites are crystalline materials that are used as catalysts for upgrading light hydrocarbons from shale gas into transportation fuels and chemicals. However, improved catalyst materials that are more active, selective and stable are needed. These aspects of catalytic performance are linked to their atomic-scale properties, specifically the distribution of Al atoms in the crystalline material. This project will focus on developing novel methods to synthesize zeolite materials with different Al site distributions. The student will learn about catalyst synthesis techniques, characterization methods for bulk and atomic structure (X-ray diffraction, spectroscopy, microscopy), and catalyst evaluation.
Students working on this project will also have the opportunity to participate in information sessions, tours and informal mentoring with CISTAR's partner companies.
Purdue students are not eligible for this project. Students must be from outside institutions. Participants must be US Citizens. Students with disabilities, veterans, and those from traditionally underrepresented groups in STEM are encouraged to apply.
Research categories:
Chemical Catalysis and Synthesis
Preferred major(s):
- Chemical Engineering
School/Dept.:
School of Chemical Engineering
Professor:
Rajamani
Gounder
More information: https://cistar.us/
CISTAR - Zero Carbon Dioxide Emission Ethylene Production Process
Description:
This project is supported by CISTAR, an NSF Engineering Research Center headquartered at Purdue.
Ethylene and propylene are the largest volume organic intermediates. Almost all ethylene is produced by steam cracking of natural gas condensates (mostly ethane and propane) or of refinery light naphtha (also mostly ethane and propane), co-producing hydrogen. Because of natural gas combustion in the cracking furnaces, and the gasification of coke deposits, and all the electricity required for the process and refrigeration systems compressors, ethylene production indirectly results large amounts of carbon dioxide emissions to the atmosphere, which is unsustainable.
One possible carbon dioxide mitigation strategy would be to fit carbon capture and sequestration technologies onto the cracking furnace flues, onto the CO2 absorption strippers (if used), and onto the fossil-fueled power plants producing electricity for the process and refrigeration compressors. As an alternative to fossil-fueled power plants with carbon capture and sequestration, there are other existing (near) zero-carbon electricity sources including for example nuclear, hydro, geothermal, wind, solar thermal, and solar photovoltaic.
The aim of this project is to design a world-scale condensate cracking plant to produce polymer-grade ethylene and propylene using only renewable electricity utilities.
Students working on this project will also have the opportunity to participate in information sessions, tours and informal mentoring with CISTAR's partner companies.
Purdue students are not eligible for this project. Students must be from outside institutions. Participants must be US Citizens. Students with disabilities, veterans, and those from traditionally underrepresented groups in STEM are encouraged to apply.
Ethylene and propylene are the largest volume organic intermediates. Almost all ethylene is produced by steam cracking of natural gas condensates (mostly ethane and propane) or of refinery light naphtha (also mostly ethane and propane), co-producing hydrogen. Because of natural gas combustion in the cracking furnaces, and the gasification of coke deposits, and all the electricity required for the process and refrigeration systems compressors, ethylene production indirectly results large amounts of carbon dioxide emissions to the atmosphere, which is unsustainable.
One possible carbon dioxide mitigation strategy would be to fit carbon capture and sequestration technologies onto the cracking furnace flues, onto the CO2 absorption strippers (if used), and onto the fossil-fueled power plants producing electricity for the process and refrigeration compressors. As an alternative to fossil-fueled power plants with carbon capture and sequestration, there are other existing (near) zero-carbon electricity sources including for example nuclear, hydro, geothermal, wind, solar thermal, and solar photovoltaic.
The aim of this project is to design a world-scale condensate cracking plant to produce polymer-grade ethylene and propylene using only renewable electricity utilities.
Students working on this project will also have the opportunity to participate in information sessions, tours and informal mentoring with CISTAR's partner companies.
Purdue students are not eligible for this project. Students must be from outside institutions. Participants must be US Citizens. Students with disabilities, veterans, and those from traditionally underrepresented groups in STEM are encouraged to apply.
Research categories:
Chemical Unit Operations, Chemical Catalysis and Synthesis, Material Modeling and Simulation
Preferred major(s):
- Chemical Engineering
- Mechanical Engineering
- Electrical Engineering
Desired experience:
Note - Chemical Engineering – Preferred; Mechanical and Electrical Engineering – Acceptable.
School/Dept.:
School of Chemical Engineering
Professor:
Cornelius
Masuku
More information: https://cistar.us/
CISTAR - Zero Emission Chemical Production from Shale Gas
Description:
This project is supported by CISTAR, an NSF Engineering Research Center headquartered at Purdue.
While chemical engineering evolved against the backdrop of an abundant supply of fossil resources, re-cent trend of carbon neutrality offers an unprecedented opportunity to imagine more sustainable chemical plants with net-zero carbon emission. In CISTAR, we are interested in converting shale gas into useful chemicals without any carbon emissions during the process, which requires careful selection of product combination and innovative design of chemical processes. In this project, the student will participate in synthesis, simulation and optimization of processes described above.
Students working on this project will also have the opportunity to participate in information sessions, tours and informal mentoring with CISTAR's partner companies.
Purdue students are not eligible for this project. Students must be from outside institutions. Participants must be US Citizens. Students with disabilities, veterans, and those from traditionally underrepresented groups in STEM are encouraged to apply.
While chemical engineering evolved against the backdrop of an abundant supply of fossil resources, re-cent trend of carbon neutrality offers an unprecedented opportunity to imagine more sustainable chemical plants with net-zero carbon emission. In CISTAR, we are interested in converting shale gas into useful chemicals without any carbon emissions during the process, which requires careful selection of product combination and innovative design of chemical processes. In this project, the student will participate in synthesis, simulation and optimization of processes described above.
Students working on this project will also have the opportunity to participate in information sessions, tours and informal mentoring with CISTAR's partner companies.
Purdue students are not eligible for this project. Students must be from outside institutions. Participants must be US Citizens. Students with disabilities, veterans, and those from traditionally underrepresented groups in STEM are encouraged to apply.
Research categories:
Chemical Unit Operations, Chemical Catalysis and Synthesis, Material Modeling and Simulation
Preferred major(s):
- Chemical Engineering
School/Dept.:
School of Chemical Engineering
Professor:
Rakesh
Agrawal
More information: https://cistar.us/
Characterizing novel pressure vessel steel welds
Description:
Nuclear reactor pressure vessels (RPVs) are forged in one piece, since previous welds demonstrated severe irradiation-induced embrittlement. However, new and promising welding technologies have surpassed the arc-welding technologies of the 1960s. Specifically, electron beam welding has shown great promise since it does not introduce differing materials to the weld, thus reducing the amount of embrittlement experienced. This project will use positron annihilation lifetime spectroscopy and metallography to characterize the integrity and microstructure of the weld seam and heat affected zone.
Research categories:
Material Processing and Characterization
Preferred major(s):
- No Major Restriction
Desired experience:
physics or materials engineering
School/Dept.:
Materials Engineering
Professor:
Maria
Okuniewski
Computational investigation of mechanosensitive behaviors of motile cells
Description:
Cell migration plays an important role in physiology and pathophysiology. Migrating cells are able to sense surrounding mechanical environments. For example, a number of experiments have demonstrated that nano- and micro-patterns can guide migration of cells. This migratory behavior is called the contact guidance and is of great importance in various physiological processes, such as cancer metastasis. In this research project, we aim to use a rigorous computational model and collaborate with experimentalists in order to investigate intrinsic mechanisms of the contact guidance. A participating student will run computer simulations and analyze data from the simulations to perform the research. If necessary, everything for this project can be done remotely.
Research categories:
Biological Simulation and Technology, Cellular Biology
Preferred major(s):
- Mechanical Engineering
- Biomedical Engineering
- Biological Engineering - multiple concentrations
- Computer Engineering
Desired experience:
Intermediate/Proficient C coding skills
Sufficient experiences in MATLAB coding
Basic knowledge of cell biology (optional)
School/Dept.:
Weldon School of Biomedical Engineering
Professor:
Taeyoon
Kim
More information: https://engineering.purdue.edu/mct
Data Driven Modeling of Electric Vehicle Impacts on Traffic Safety
Description:
As the Biden administration recently announced a national target for electric vehicle (EV) sales, more and more EVs will be on road in the future. Meanwhile, there will be an increasing possibility of traffic crashes between EVs and Internal Combustion Engine (ICE) vehicles or between EVs and pedestrians/cyclists. However, we have a limited understanding of how EVs will influence traffic safety, especially at road intersections. This study will leverage affluent historical traffic crash data (including driver demographic information, driver behavior, and traffic conditions) in Indiana and conduct data-driven modeling to uncover what factors are associated with crashes involving EVs. In specific, this study will focus on crashes on all interstate and state highways in Indiana. The expected outcome will lead to policy recommendations on developing EV safety regulations, improving vehicle safety features and highway design in the future.
Research categories:
Big Data/Machine Learning, Energy and Environment, Other
Preferred major(s):
- Civil Engineering
- Computer Science
- Statistics - Applied Statistics
Desired experience:
This research will involve statistical modeling and spatial-temporal data analysis and require basic programming skills (e.g., Python or R). Other desired qualifications include ability to work independently, strong work ethic, ability to work in diverse teams, and tehnical writing skills.
School/Dept.:
Lyles School of Civil Engineering
Professor:
Konstantina (Nadia)
Gkritza
More information: https://engineering.purdue.edu/ASPIRE; https://engineering.purdue.edu/STSRG
Data-based Explanations for Fairness Debugging of Decision Trees
Description:
About the project: Machine learning (ML) models are increasingly being used in a wide range of domains such as loan applications, healthcare, crime prevention and justice management. However, there is mounting concern that the complexity and opacity of ML models perpetuates systemic biases and discrimination reflected in training data. This project centers at the (realistic) assumption that the bias in the predictions of an ML model is, in large part, due to the data used to train the model. We are interested in identifying coherent parts of the training data responsible for the bias. Such explanations are termed as data-based explanations. We have recently developed a system that generates interpretable data-based explanations for ML models with (twice-differentiable) loss functions. In the proposed SURF project, we are interested in generating data-based explanations for tree-based ML models such as decision trees and gradient boosted decision trees.
Importance of the project: This project is a part of our efforts to generate explanations for ML model outcomes and bias. Results from the project will be instrumental in advancing the state of the art in the area of explainable artificial intelligence.
Scope of the project, and the student's contribution: The project will focus at a specfic class of ML models (decision trees). Given an ML model and a dataset used to train the model, we will develop efficient algorithms to identify subsets of the training data that are the most responsible for the bias of the model. The student is expected to have basic knowledge of machine learning (in particular, supervised learning and decision trees), and will participate in developing and implementing the solutions in Python.
Importance of the project: This project is a part of our efforts to generate explanations for ML model outcomes and bias. Results from the project will be instrumental in advancing the state of the art in the area of explainable artificial intelligence.
Scope of the project, and the student's contribution: The project will focus at a specfic class of ML models (decision trees). Given an ML model and a dataset used to train the model, we will develop efficient algorithms to identify subsets of the training data that are the most responsible for the bias of the model. The student is expected to have basic knowledge of machine learning (in particular, supervised learning and decision trees), and will participate in developing and implementing the solutions in Python.
Research categories:
Big Data/Machine Learning
Preferred major(s):
- Computer Science
- Computer Engineering
- Computer and Information Technology
Desired experience:
Introductory course in Machine Learning or Data Science
School/Dept.:
Computer and Information Technology
Professor:
Romila
Pradhan
Decisions for handling contaminated personal effects and plumbing after drinking water contamination
Description:
Chemical spills and backflow incidents are common threats to drinking water distribution and plumbing systems. Sometimes free product and drinking water with dissolved contaminants can travel through this infrastructure and reach building faucets. When this occurs health officials, system owners, and infrastructure owners rapidly seek information about whether individual constituents became sequestered in certain parts of the systems and how best to remove them. Plastics are an important concern because many are easily permeated by organic compounds which prompts them to leach chemicals into clean water making it unsafe.
In response to drinking water contamination incidents over the past 20 years and requests from health departments and households affected, this project will examine the fate of fuel chemicals in contact with plumbing materials (i.e., pipes, gaskets) and plastic personal effect materials (i.e., baby bottles, plates, cups, etc.). Diesel, gasoline and crude oil are being considered. The student will conduct the contamination experiments, collect water samples and analyze them using state-of-the-art instrumentation. The student will analyze, interpret, and report the information with advisement of one graduate research assistant and two faculty who respond to these types of water contamination incidents.
Other questions that may be explored include the chlorination of the fuel components and formation of disinfectant byproducts, mechanical integrity impacts on the plastic materials, chemical transformations of the leached products. This work directly supports emergency response and recovery activities of the Center for Plumbing Safety.
In response to drinking water contamination incidents over the past 20 years and requests from health departments and households affected, this project will examine the fate of fuel chemicals in contact with plumbing materials (i.e., pipes, gaskets) and plastic personal effect materials (i.e., baby bottles, plates, cups, etc.). Diesel, gasoline and crude oil are being considered. The student will conduct the contamination experiments, collect water samples and analyze them using state-of-the-art instrumentation. The student will analyze, interpret, and report the information with advisement of one graduate research assistant and two faculty who respond to these types of water contamination incidents.
Other questions that may be explored include the chlorination of the fuel components and formation of disinfectant byproducts, mechanical integrity impacts on the plastic materials, chemical transformations of the leached products. This work directly supports emergency response and recovery activities of the Center for Plumbing Safety.
Research categories:
Chemical Unit Operations, Chemical Catalysis and Synthesis, Engineering the Built Environment, Environmental Characterization, Other
Preferred major(s):
- Environmental and Ecological Engineering
- Chemistry
- Chemical Engineering
- Civil Engineering
- Materials Engineering
- Materials Science
- Plastics Engineering
- Agricultural Engineering
- Pharmacy
- Military Science
- Public Health
- Environmental Health Sciences
- Food Science
Desired experience:
Strong internal motivation to learn
Basic understanding of chemistry
School/Dept.:
CE & EEE
Professor:
Andrew
Whelton
More information: www.PlumbingSafety.org
Defining Chemical Modifications on Histones that Control Chromosome Integrity
Description:
The student will join a multi-disciplinary team investigating epigenetic processes, chromatin structure and gene regulation. This project will involve learning and applying biochemical, genetic and molecular biology strategies to build and characterize customized budding yeast (Saccharomyces cerevisiae) strains or mammalian cell lines for the investigation of evolutionarily conserved protein-protein interactions and post-translational modifications using state-of-the-art detection and quantification strategies. Biological targets may include histone modifying enzymes, histone modifications, histone variants and chromatin assembly and DNA replication factors.
Research categories:
Biological Characterization and Imaging, Cellular Biology, Genetics
Preferred major(s):
- No Major Restriction
Desired experience:
General Chemistry required, introduction to molecular biology, biochemistry, genetics preferred.
School/Dept.:
Biochemistry
Professor:
Ann
Kirchmaier
More information: https://ag.purdue.edu/biochem/Pages/Profile.aspx?strAlias=akirchma&intDirDeptID=9
Deformation analysis in non-linear conformal contacts
Description:
Tribology is a discipline that studies friction, lubrication, and wear. Those topics affect almost all machines that have moving parts. For example, in fluid power application, which consumes 3% of the energy contributes 8% of the greenhouse gas, the lubricating interface tribological behavior of the positive displacement machines determines the system's total efficiency. The lubricating interface is formed by two solid boundaries a few microns apart. Therefore, the deformation of the solid bodies is crucial to the friction and wear of the sliding interface. The objective is to explore the nonlinear elastic deformation of the lubricating interface solid boundaries using commercial FEA software. The challenge is to generate enough simulation data to train a machine-learning algorithm. Your work will constitute a new modeling approach in the fluid power field. Therefore, it is also very possible to be published with you as a co-author. The main tasks are 1) familiarize yourself with the CAD and FEA software, 2) learn how to conduct batch FEA simulations, 3) generate code to pre- and post-process the simulation result, and compare simulation results with different simulation assumptions.
Research categories:
Fluid Modelling and Simulation, Material Modeling and Simulation
Preferred major(s):
- No Major Restriction
Desired experience:
->Some coursework in Solid Mechanics would help speed up the initial inertia.
->Proficient in MATLAB (pre- and post-process simulation data)
->Basic experience with stress analysis using Ansys.
School/Dept.:
Mechanical Engineering or Agricultural and Biological Engineering
Professor:
Lizhi
Shang
More information: https://engineering.purdue.edu/Maha/
Design and Control of Hybrid Thermal Management Systems
Description:
Thermal management systems are used in a wide range of systems primarily for electronics cooling, and are becoming increasingly critical for aircraft as air vehicles become increasingly electrified or even hybridized. However, designing these systems is becoming increasingly challenging because the heat loads that they need to manage vary frequently in duration and magnitude. A "hybrid" thermal management system (TMS) is one that also includes a thermal battery (thermal energy storage device) to improve the system's ability to respond quickly to unexpected heat loads. These systems are similar in nature to hybrid electric vehicles that balance the use of the engine and a battery to achieve a common objective.
Designing a thermal energy storage (TES) device that has a large enough capacity, can absorb heat quickly, and is lightweight is challenging because it needs to perform well under many different load conditions, including when the heat loads are random. Performance metrics need to be simple enough that they can be evaluated by iterative optimization algorithms while capturing the complexity of the design requirements. In this project, the student(s) will design a TES device using optimization algorithms to find the best dimensions and test it in simulation against previously-designed TES devices. They will also support experimental work related to ongoing research in the area of design and control of these complex thermal systems.
Designing a thermal energy storage (TES) device that has a large enough capacity, can absorb heat quickly, and is lightweight is challenging because it needs to perform well under many different load conditions, including when the heat loads are random. Performance metrics need to be simple enough that they can be evaluated by iterative optimization algorithms while capturing the complexity of the design requirements. In this project, the student(s) will design a TES device using optimization algorithms to find the best dimensions and test it in simulation against previously-designed TES devices. They will also support experimental work related to ongoing research in the area of design and control of these complex thermal systems.
Research categories:
Energy and Environment, Thermal Technology, Other
Preferred major(s):
- Mechanical Engineering
- Aeronautical and Astronautical Engineering
Desired experience:
Ideally the student will have completed Differential Equations, Thermodynamics I, as well as dynamics or controls courses in their major. Proficiency coding in MATLAB or Python is also desirable.
School/Dept.:
School of Mechanical Engineering
Professor:
Neera
Jain
More information: https://engineering.purdue.edu/JainResearchLab/
Design and scalable manufacturing of point-of-care diagnostics for infectious diseases
Description:
Infectious diseases are a major cause of death and disability throughout the world. Research in the Linnes Lab focuses on using state of the art microfluidic and paperfluidic technologies to prevent, detect, and understand the pathogenesis of these disease. This undergraduate summer research project will focus on the and other infectious disease detection. The student will learn about robotic automation, screen printing of thin film devices, and other scalable manufacturing techniques and will develop strategies to reduce the number of manual interventions and errors during the fabrication process of paper-based diagnostic devices. The participant will also test and characterize different materials and explore design choices that do not interfere with the sensitivity of the test and do not compromise usability.
Research categories:
Fabrication and Robotics
Preferred major(s):
- Engineering (First Year)
- Automation and Systems Integration Engineering Technology
- Biomedical Engineering
- Electrical Engineering
- Energy Engineering Technology
- Engineering (First Year)
- Industrial Engineering
- Industrial Engineering Technology
- Engineering / Technology Teacher Education
- Materials Engineering
- Mechanical Engineering
- Mechanical Engineering Technology
- Mechatronics Engineering Technology
- Multidisciplinary Engineering
- Robotics Engineering Technology
Desired experience:
knowledge or interest in programming, electronics design, manufacturing and automation techniques
highly motivated to work in a highly cooperative, interdisciplinary, and productive translational research environment
School/Dept.:
Weldon School of Biomedical Engineering
Professor:
Jacqueline
Linnes
More information: https://engineering.purdue.edu/LinnesLab
Design of an IoT4Ag Robotic Sensor Deployment System
Description:
The goal of this project is to design an IoT4Ag sensor deployment system for autonomous agricultural ground robot. Two types of IoT sensors must be deployed by the robotic platform. Chaff sensors need to be distributed on the surface of soil at locations with designated spacing to ensure appropriate spatial coverage for the field of interest. The second type of sensors similarly need to be spread about the field but require them to be inserted into the soil at a depth of approximately 3” deep. Thus, the developed sensor deployment system should be able to 1. Store the sensors that need to be deployed; 2. Distribute sensors at a designated spacing above the soil; and 3. Insert the sensors into the ground at a designated spacing in the soil; and 4. Log the type of sensor that has been distributed, its sensor ID, and its placement location. This project will require the mechanical design of the deployment systems, mechatronic system design for operating and controlling the systems, and integration and interfacing with the agricultural ground robot for execution and tracking of sensor deployment locations. Field tests will be conducted at the Purdue University Agronomy Center for Research and Education (ACRE) facility.
Research categories:
Fabrication and Robotics, IoT for Precision Agriculture
Preferred major(s):
- Mechanical Engineering
- Electrical Engineering
- Computer Engineering
Desired experience:
US citizens/permanent residents only
Mechanical design, mechatronics, 3D printing, electronics, robotics, programming experience preferred.
School/Dept.:
Mechanical Engineering
Professor:
David
Cappelleri
More information: https://iot4ag.us/
Development of Automated Load-Based Testing Apparatus for Air Conditioners & Heat Pumps Performance Evaluation
Description:
Project Description: The energy demands for space conditioning is continuously increasing with population growth, rising temperatures, and improving standards of living. To counteract the effect of growing air-conditioners and heat-pumps demand on overall energy consumption, improving the energy efficiency of systems sold in the market is crucial. One of the effective and tested approaches for this has been to set energy efficiency benchmarks based on the minimum energy performance standards (MEPS) which drive technological innovation. For air-conditioners and heat pumps, a testing and rating procedure forms the technical basis for these energy efficiency standards to estimate equipment seasonal performance. However, with current rating standards for residential heat pumps, significant dissimilarities have been observed between the equipment rated performance and the equipment's actual operational performance in field applications. Load-based testing is evolving as an alternative approach for obtaining equipment performance data that captures the effects of dynamic interactions between a heat pump or air conditioner, its integrated controls, and a prototypical building that it serves. Current load-based testing requires the use of psychometric chambers to vary ambient temperatures and building loads which is time-consuming and expensive, particularly for residential split systems when different combinations of indoor and outdoor units need to be tested. Thus, there is a need for a low-cost, automated, load-based method of test that doesn’t require psychrometric chambers and where multiple units could be tested in a single large test room similar to a life-test facility. In this project, we are working on the development of a low-cost and automated testing apparatus and methodology for direct expansion air conditioners and heat pumps. The student who joins this project will have the opportunity to contribute to important experimental work will learn about air-conditioners working and their testing approach, thermodynamics, and heat transfer applicable to thermal systems, and will also learn about the test facility development process.
Final Deliverables: The student will work closely with the graduate student mentor on test facility development and experiments related to the performance evaluation of heat-pumps and air-conditioners based on the load-based testing methodology. The student will also assist in analyzing the experimental data. Students will partake in weekly literature reading and discussion, small group meetings, and will keep a log of their weekly progress. They will present their updates at weekly meetings and will present a talk or poster at the end of the summer. Students will end the summer with a greater understanding of the energy challenges in space conditioning and will develop a broad range of technical skills pertinent to the experimentation and performance evaluation of residential air-conditioning and heat-pumping systems.
Final Deliverables: The student will work closely with the graduate student mentor on test facility development and experiments related to the performance evaluation of heat-pumps and air-conditioners based on the load-based testing methodology. The student will also assist in analyzing the experimental data. Students will partake in weekly literature reading and discussion, small group meetings, and will keep a log of their weekly progress. They will present their updates at weekly meetings and will present a talk or poster at the end of the summer. Students will end the summer with a greater understanding of the energy challenges in space conditioning and will develop a broad range of technical skills pertinent to the experimentation and performance evaluation of residential air-conditioning and heat-pumping systems.
Research categories:
Energy and Environment, Engineering the Built Environment, Thermal Technology
Preferred major(s):
- Mechanical Engineering
- Civil Engineering
Desired experience:
Applicants should have a general interest in energy and sustainability. Should also have a strong background/interest in thermodynamics and heat transfer. Applicants with experience in some (not all) of the following are preferred: LabVIEW, Python, Engineering Equation Solver, MATLAB, 3D-CAD Software. 2nd semester Sophomores, Juniors, and 1st semester Seniors are preferred.
School/Dept.:
Mechanical Engineering
Professor:
Travis
Horton
Development of Immersive Mixed-reality Environment for IoT-Human interaction
Description:
The emergence of the Internet of Things (IoT) has transformed our world with billions of interconnected smart devices. It has received ubiquitous adoption in numerous industries such as healthcare transportation, manufacturing, and agriculture. Most IoT systems nowadays are empowered by AI technology to automate a lot of tasks with little human intervention. However, designing and customising IoT devices for individual needs still remains challenging for lots of end users with limited technical background , which greatly hinders the IoT’s mass adoption. In order to bridge this gap and lower the entry barrier, we plan to take full advantage of the immersive technology(AR/VR), which allows common users to create, author and debug IoT behaviours effortlessly inside a simulated virtual environment. Specifically, the undergrad will participate in design and fabrication of unique IoT devices for a variety of tasks and integrating them to an overarching virtual environment. The undergrad will also get involved in the user evaluation process. This work will eventually lead to the submission of a paper to a top-tier ACM conference.
Research categories:
Deep Learning, Human Factors, Internet of Things, Learning and Evaluation
Preferred major(s):
Desired experience:
Applicants should have a general interest in developing mixed-reality applications and designing electronic hardware. Applicants with experience in some (not all) of the following are preferred: PCB design, embedded programming, C#, Unity,3D-CAD Software, deep learning.
School/Dept.:
Electrical and Computer Engineering
Professor:
Karthik
Ramani
More information: https://engineering.purdue.edu/cdesign/wp/
Development of Next-Generation Performance Evaluation Methodology for Air-Conditioners and Heat-Pumps
Description:
Project Description: Space conditioning accounts for a major portion of the energy consumption in buildings over the world and the energy demands for this is continuously increasing with population growth, rising temperatures, and improving standards of living. To counteract the effect of growing air-conditioners and heat-pumps demand on overall energy consumption, improving the energy efficiency of systems sold in the market is crucial. One of the effective and tested approaches for this has been to set energy efficiency benchmarks based on the minimum energy performance standards (MEPS) which drives the technological innovation and implementation in the market. For air-conditioners and heat pumps, a testing and rating procedure forms the technical basis for these energy efficiency standards to estimate equipment seasonal performance. However, with current rating standards for residential heat pumps, significant dissimilarities have been observed between the equipment rated performance or efficiency based on these standards in the lab and the equipment's actual operational performance in field applications. Thus, there is a great need for the development of a testing and rating methodology which captures the dynamic performance of an equipment representative of its actual field application. In this project, we are working on the development of a next-generation load-based testing methodology for residential air-conditioners and heat-pumps. The student who joins this project will have the opportunity to contribute to important experimental work, will learn about air-conditioners working and their testing approach, thermodynamics, and heat transfer applicable to thermal systems, and will also learn about the standard development process.
Final Deliverables: The student will work closely with the graduate student mentor on experiments related to the performance evaluation of heat-pumps and air-conditioners based on the load-based testing methodology. The student will also assist in analyzing the experimental data. Students will partake in weekly literature reading and discussion, small group meetings, and will keep a log of their weekly progress. They will present their updates at weekly meetings and will present a talk or poster at the end of the summer. Students will end the summer with a greater understanding of the energy challenges in space conditioning and will develop a broad range of technical skills pertinent to the experimentation and performance evaluation of residential air-conditioning and heat-pumping systems.
Final Deliverables: The student will work closely with the graduate student mentor on experiments related to the performance evaluation of heat-pumps and air-conditioners based on the load-based testing methodology. The student will also assist in analyzing the experimental data. Students will partake in weekly literature reading and discussion, small group meetings, and will keep a log of their weekly progress. They will present their updates at weekly meetings and will present a talk or poster at the end of the summer. Students will end the summer with a greater understanding of the energy challenges in space conditioning and will develop a broad range of technical skills pertinent to the experimentation and performance evaluation of residential air-conditioning and heat-pumping systems.
Research categories:
Thermal Technology
Preferred major(s):
- Mechanical Engineering
Desired experience:
Applicants should have a general interest in energy and sustainability. Should also have a strong background/interest in thermodynamics and heat transfer. Applicants with experience in some (not all) of the following are preferred: LabVIEW, Python, Engineering Equation Solver, MATLAB, 3D-CAD Software. 2nd semester Sophomores, Juniors, and 1st semester Seniors are preferred.
School/Dept.:
Civil Engineering
Professor:
Travis
Horton
Development of a 3D Model to Evaluate Reactivation from Dormancy
Description:
Breast cancer is the number one diagnosed cancer among women and affects over a quarter of a million people annually. The five-year survival rate is exceptional if the disease remains local, however, once breast cancer has metastasized, patient survival rates drop precipitously. There is a critical need to better understand the events required for breast cancer metastasis and how these events culminate in systemic tumor growth. During breast cancer metastasis, the composition and structure of the extracellular matrix (ECM) in the metastatic niche are dramatically altered before the arrival of colonizing cells. As such, the ECM is emerging as a potential therapeutic target for disrupting the metastatic process. Our goal is to determine how changes in the ECM are permissive to metastasis and to manipulate these events in order to inhibit metastatic disease. Our recent studies have demonstrated that Fibronectin (FN) is upregulated in the lungs before the arrival of metastatic cancer cells, and clinical evidence has shown that increased FN is predictive of decreased patient survival. Despite these findings, there remain fundamental gaps in determining how matrix remodeling events that occur during metastasis can dictate the cancer cell fate. In particular, the architecture of the FN matrix can induce phenotypic changes of invading cancer cells that can make the cells less sensitive to drug treatment. Additionally, changes in the local tissue architecture can direct a cell to enter a growth cycle or a dormant phenotype, which can diminish the clinical efficacy of ECM-targeted therapeutics.
Our group has recently observed that tumor-derived metastatic cancer cells express elevated levels of FN, but unlike fibroblasts and other stromal cells, the tumor cells do not deposit FN as a fibrillar matrix. Instead, tumor cells secrete FN in a soluble form which must be converted into insoluble fibrils through a cell-mediated event, exposing cryptic binding domains and transitioning the protein into a bioactive state. Our studies suggest that the assembly of fibrillar FN is dependent on a functional relationship between tumor cells and fibroblasts. Interestingly, we have demonstrated that the FN matrix produced and assembled by resident lung fibroblasts during pre-metastatic niche formation results in a highly aligned and organized FN matrix. However, the matrix formed by fibroblasts utilizing FN produced by tumor cells is less organized and more dispersed, which can significantly alter how forces are transmitted to local cells. To study the impact of FN architecture on the metastatic process independent of the confounding influence of other cell populations, our group has developed an advanced 3D cell culture platform that allows us to create a bioactive fibrillar FN network without the need for cell-mediated assembly. Utilizing this platform, we can tune the alignment of the resultant 3D fibrillar FN network to interrogate the role of the matrix on cell fate decisions. Based on our strong preliminary results, we hypothesize that dynamic changes in the FN network architecture will alter both biochemical and mechanical signaling within the niche, influencing the cell phenotype and dormancy and ultimately altering the cell sensitivity to drugs.
Through this project, we seek to evaluate the effect of FN architecture on dormancy. We will use genetic depletion strategies along with a rigorous panel of markers to determine the effect of matrix architecture on the entrance to or exit from dormancy.
Our group has recently observed that tumor-derived metastatic cancer cells express elevated levels of FN, but unlike fibroblasts and other stromal cells, the tumor cells do not deposit FN as a fibrillar matrix. Instead, tumor cells secrete FN in a soluble form which must be converted into insoluble fibrils through a cell-mediated event, exposing cryptic binding domains and transitioning the protein into a bioactive state. Our studies suggest that the assembly of fibrillar FN is dependent on a functional relationship between tumor cells and fibroblasts. Interestingly, we have demonstrated that the FN matrix produced and assembled by resident lung fibroblasts during pre-metastatic niche formation results in a highly aligned and organized FN matrix. However, the matrix formed by fibroblasts utilizing FN produced by tumor cells is less organized and more dispersed, which can significantly alter how forces are transmitted to local cells. To study the impact of FN architecture on the metastatic process independent of the confounding influence of other cell populations, our group has developed an advanced 3D cell culture platform that allows us to create a bioactive fibrillar FN network without the need for cell-mediated assembly. Utilizing this platform, we can tune the alignment of the resultant 3D fibrillar FN network to interrogate the role of the matrix on cell fate decisions. Based on our strong preliminary results, we hypothesize that dynamic changes in the FN network architecture will alter both biochemical and mechanical signaling within the niche, influencing the cell phenotype and dormancy and ultimately altering the cell sensitivity to drugs.
Through this project, we seek to evaluate the effect of FN architecture on dormancy. We will use genetic depletion strategies along with a rigorous panel of markers to determine the effect of matrix architecture on the entrance to or exit from dormancy.
Research categories:
Biological Characterization and Imaging, Cellular Biology, Medical Science and Technology
Preferred major(s):
- No Major Restriction
School/Dept.:
BME
Professor:
Luis
Solorio
More information: https://soloriolab.wixsite.com/tmet
Development of protein labeling techniques for analysis of protein synthesis in brain in a mouse model of autism spectrum disorder
Description:
Autism spectrum disorder (ASD) is estimated to affect 1 in 54 children and is often diagnosed in early childhood. More than 200 genes have been implicated in ASD, and recent studies have shed light on particular proteins ASD in adult brains. In contrast, little is known about the ASD-related changes in the protein composition in the critical early stages of brain development.
In the proposed research, we will use our recently developed protein labeling techniques and protein enrichment techniques together with tandem mass spectrometry analysis to identify proteomic changes that occur during early brain development in the Syngap1+/- mouse model of ASD.
Work on this project will include a variety of biochemical methods as well as animal handling and tissue collection. The specific techniques and training will be taught on site, but a familiarity with basic biochemistry and laboratory work as well as an interest in neuroscience is desired.
In the proposed research, we will use our recently developed protein labeling techniques and protein enrichment techniques together with tandem mass spectrometry analysis to identify proteomic changes that occur during early brain development in the Syngap1+/- mouse model of ASD.
Work on this project will include a variety of biochemical methods as well as animal handling and tissue collection. The specific techniques and training will be taught on site, but a familiarity with basic biochemistry and laboratory work as well as an interest in neuroscience is desired.
Research categories:
Biological Characterization and Imaging
Preferred major(s):
- Biochemistry (Biology)
- Biomedical Engineering
- Biology
- Cell Molecular and Developmental Biology
- Neurobiology and Physiology
Desired experience:
A basic understanding and interest in biochemistry is desired
School/Dept.:
Biomedical Engineering
Professor:
Tamara
Kinzer-Ursem
Development of synthetic communicating cells mimicking synaptic functions
Description:
Neurons convert biochemical information (through binding of a neurotransmitter) to electrical signal (via action potential) and back to biochemical signal (through the release of neurotransmitters). These distinct and separable processes can be reconstituted in a synthetic neuron by using natural and engineered proteins, and a synthetic neuron platform can be used to understand the rules governing the emergence of the present morphology of a neuron and the architecture of the neuronal system. This project thus aims to construct a synthetic neuron with a modular design and a programmable synthetic neuronal network capable of recapitulating basic functions of a natural neuronal system (e.g., action potential, synaptic communication, and basic computation) and with a long-term vision of incorporating more advanced computation and potentiation.
Research categories:
Biological Characterization and Imaging, Biological Simulation and Technology, Cellular Biology
Preferred major(s):
- Chemical Engineering
- Biological Engineering - multiple concentrations
- Biomedical Engineering
- Neurobiology and Physiology
School/Dept.:
Chemical Engineering
Professor:
Chongli
Yuan
More information: https://cyuangroup.com/
Developmental, Behavioral & Environmental Determinants of Infant Dust Ingestion
Description:
Our project is funded by the U.S. Environmental Protection Agency (EPA) and involves an interdisciplinary collaboration between engineers, chemists, and psychologists at Purdue University and New York University (NYU). We will elucidate determinants of indoor dust ingestion in 6- to 24-month-old infants (age range for major postural and locomotor milestones). Specific objectives are to test: (1) whether the frequency and characteristics of indoor dust and non-dust mouthing events change with age and motor development stage for different micro-environments; (2) how home characteristics and demographic factors affect indoor dust mass loading and dust toxicant concentration; (3) how dust transfer between surfaces is influenced by dust properties, surface features, and contact dynamics; and (4) contributions of developmental, behavioral, and socio-environmental factors to dust and toxicant-resolved dust ingestion rates. In addition, the project will (5) create a shared corpus of video, dust, toxicant, and ingestion rate data to increase scientific transparency and speed progress through data reuse by the broader exposure science community.
Our transdisciplinary work will involve: (1) parent report questionnaires and detailed video coding of home observations of infant mouthing and hand-to-floor/object behaviors; (2) physical and chemical analyses of indoor dust collected through home visits and a citizen-science campaign; (3) surface-to-surface dust transfer experiments with a robotic platform; (4) dust mass balance modeling to determine distributions in and determinants of dust and toxicant-resolved dust ingestion rates; and (5) open sharing of curated research videos and processed data in the Databrary digital library and a public website with geographic and behavioral information for participating families.
The project will provide improved estimates of indoor dust ingestion rates in pre-sitting to independently walking infants and characterize inter-individual variability based on infant age, developmental stage, home environment, and parent behaviors. Dust transport experiments and modeling will provide new mechanistic insights into the factors that affect the migration of dust from the floor to mouthed objects to an infant’s mouth. The shared corpus will enable data reuse to inform future research on how dust ingestion contributes to infants’ total exposure to environmental toxicants.
U.S. EPA project overview: https://cfpub.epa.gov/ncer_abstracts/index.cfm/fuseaction/display.abstractDetail/abstract_id/11194
Our transdisciplinary work will involve: (1) parent report questionnaires and detailed video coding of home observations of infant mouthing and hand-to-floor/object behaviors; (2) physical and chemical analyses of indoor dust collected through home visits and a citizen-science campaign; (3) surface-to-surface dust transfer experiments with a robotic platform; (4) dust mass balance modeling to determine distributions in and determinants of dust and toxicant-resolved dust ingestion rates; and (5) open sharing of curated research videos and processed data in the Databrary digital library and a public website with geographic and behavioral information for participating families.
The project will provide improved estimates of indoor dust ingestion rates in pre-sitting to independently walking infants and characterize inter-individual variability based on infant age, developmental stage, home environment, and parent behaviors. Dust transport experiments and modeling will provide new mechanistic insights into the factors that affect the migration of dust from the floor to mouthed objects to an infant’s mouth. The shared corpus will enable data reuse to inform future research on how dust ingestion contributes to infants’ total exposure to environmental toxicants.
U.S. EPA project overview: https://cfpub.epa.gov/ncer_abstracts/index.cfm/fuseaction/display.abstractDetail/abstract_id/11194
Research categories:
Biological Characterization and Imaging, Ecology and Sustainability, Energy and Environment, Engineering the Built Environment, Environmental Characterization, Human Factors
Preferred major(s):
- No Major Restriction
Desired experience:
We are seeking students passionate about studying environmental contaminants and infant exposure to chemicals in the indoor environment. Preferred skills: experience with MATLAB, Python, or R. Coursework: environmental science and chemistry, microbiology, physics, thermodynamics, heat/mass transfer, fluid mechanics, developmental psychology.
School/Dept.:
Lyles School of Civil Engineering
Professor:
Brandon
Boor
More information: www.brandonboor.com
Distributed Deep Learning for Multi-Robot Control
Description:
This project aims to design new deep learning/machine learning approaches to control two robots navigating through an environment. The robots aim to achieve a certain destination while avoiding obstacles and performing specific tasks, e.g., making a delivery along the way. The robots further perform information sharing and collaborate with each other to further increase their capability (the robot info:https://www.robotis.us/turtlebot-3-waffle-pi/). This setup aims to provide proof-of-concept for the efficacy of distributed deep learning models in a realistic multi-robot navigation task. The role of the undergraduate research would be to contribute to algorithm design, implementation, and benchmarking steps.
Research categories:
Big Data/Machine Learning, Deep Learning
Preferred major(s):
- No Major Restriction
School/Dept.:
ECE
Professor:
Abolfazl
Hashemi
More information: https://abolfazlh.github.io/
Drop-on-demand printing of soft biomaterials
Description:
This project aims to develop drop-on-demand (aka inkjet) printing technology of soft biomaterials including cell-laden hydrogel and RNA containing materials. Specifically, the undergraduate student will formulate and characterize the mechanical and rheological properties of polymeric inks to print and cure for advanced tissue constructs or drug delivery systems.
Research categories:
Cellular Biology, Material Processing and Characterization, Medical Science and Technology, Nanotechnology
Preferred major(s):
- Mechanical Engineering
- Chemical Engineering
- Biomedical Engineering
Desired experience:
Course work of solid or fluid mechanics are required. Experience in LabVIEW, CAD software and Matlab are preferred. Cell biology background is plus but not required.
School/Dept.:
Mechanical Engineering
Professor:
Bumsoo
Han
More information: http://biotransportgroup.org
Drug screening for improved functional recovery from zebrafish spinal cord injury
Description:
Spinal cord injury is a significant human health problem affecting about 300,000 people in the US. Better treatment options are needed to overcome the limited regeneration potential of the human spinal cord. Zebrafish larvae are an emerging model system for drug screening for several reasons including large number of embryos per breeding, genetics, and availability of behavioral assays for drug testing. Our lab is conducting a large scale drug screen with an FDA-approved library to identify novel compounds that enhance functional recovery following injury as assessed by a swimming assay. The student will be involved with fish breeding, spinal cord injury, drug treatment, and behavioral assay. We hope that this work will identify new compounds with translational potential.
Research categories:
Biological Characterization and Imaging, Biological Simulation and Technology, Cellular Biology, Medical Science and Technology
Preferred major(s):
- Biology
- Cell Molecular and Developmental Biology
- Biochemistry
- Neurobiology and Physiology
- Genetics
- Microbiology
Desired experience:
Cell Biology, Neurobiology, fine motor skills, working with zebrafish
School/Dept.:
Biological Sciences
Professor:
Daniel
Suter
More information: https://suterlab.bio.purdue.edu
EMBRIO Projects
Description:
Multiple projects hosted under EMBRIO
Research categories:
Other
Preferred major(s):
- No Major Restriction
School/Dept.:
Mechanical Emgneering
Professor:
Pranshul
Sardana
EMBRIO-Optimizing action imaging in mammalian oocytes
Description:
EMBRIO
Research categories:
Biological Characterization and Imaging, Biological Simulation and Technology
Preferred major(s):
- No Major Restriction
School/Dept.:
BIOL
Professor:
Janice
Evans
Electrical Dehydrogenation Reactor Optimization for The Production of Ethylene Using Renewable Energies
Description:
Ethylene is one of the most important building blocks of the chemical industry1. Its global market was estimated at ~160 million Tons in 2020 and it is forecast to reach ~210 million Tons by 20272. Between 1.0 and 1.6 tons of CO2 are emitted per ton of Ethylene produced. This means Ethylene production accounted for around 0.47-0.75% of the world’s total carbon emissions in 2020, estimated at 34 billion tons3. The U.S. has set a course to reach net-zero emissions economy-wide by no later than 20507,8. This makes it imperative decarbonizing Ethylene production.
Ethylene is mainly produced by Steam Cracking (SC), where hydrocarbons transform into ethylene in the presence of steam at high temperatures11. SC normally implements hydrocarbon combustion to produce the necessary energy for reaction. This is the main reason why SC emits so much CO21. The NSF Center for Innovative and Strategic Transformation of Alkane Resources (CISTAR)5 is currently researching the coupling of SC with renewable electricity. This would allow a significant reduction of CO2 emissions during SC4.
As part of its research, CISTAR carries out detailed Computational Fluid Dynamics (CFD) simulations. This allows evaluating the impact of fluid behavior during reactions. Several geometries are currently under evaluation. As part of the SURF Program, CISTAR is interested in recruiting one student to support the CFD simulations team. The goal is to evaluate the performance of the different reactor geometries considered, as well as propose potentially attractive new configurations. No previous experience with CFD simulations is necessary. However, it is advisable the student has a strong motivation for computer simulations. Experience working with Ansys Fluent and Aspen Plus could be beneficial.
Ethylene is mainly produced by Steam Cracking (SC), where hydrocarbons transform into ethylene in the presence of steam at high temperatures11. SC normally implements hydrocarbon combustion to produce the necessary energy for reaction. This is the main reason why SC emits so much CO21. The NSF Center for Innovative and Strategic Transformation of Alkane Resources (CISTAR)5 is currently researching the coupling of SC with renewable electricity. This would allow a significant reduction of CO2 emissions during SC4.
As part of its research, CISTAR carries out detailed Computational Fluid Dynamics (CFD) simulations. This allows evaluating the impact of fluid behavior during reactions. Several geometries are currently under evaluation. As part of the SURF Program, CISTAR is interested in recruiting one student to support the CFD simulations team. The goal is to evaluate the performance of the different reactor geometries considered, as well as propose potentially attractive new configurations. No previous experience with CFD simulations is necessary. However, it is advisable the student has a strong motivation for computer simulations. Experience working with Ansys Fluent and Aspen Plus could be beneficial.
Research categories:
Chemical Unit Operations, Energy and Environment, Fluid Modelling and Simulation, Material Modeling and Simulation, Thermal Technology
Preferred major(s):
- Chemical Engineering
- Mechanical Engineering
- Electrical Engineering
Desired experience:
• It is advisable the student has a strong motivation for computer simulations
• Experience working with Ansys Fluent and Aspen Plus could be beneficial
School/Dept.:
Davidson School of Chemical Engineering
Professor:
Rakesh
Agrawal
More information: https://engineering.purdue.edu/RARG/ and https://cistar.us/
Energetic Particle Adhesion via enhanced centrifuge method
Description:
This project is part of the AAMP-UP '22 program, which focuses on energetic material research.
AAMP-UP is separate but highly partnered with SURF.
The project is run by Dr. Stephen Beaudoin and his team. The research will use tools from data science and machine learning to develop predictive models for the performance of energetic materials. Students will learn about neural networks, deep learning, and the chemistry and physics of energetic materials.
AAMP-UP is separate but highly partnered with SURF.
The project is run by Dr. Stephen Beaudoin and his team. The research will use tools from data science and machine learning to develop predictive models for the performance of energetic materials. Students will learn about neural networks, deep learning, and the chemistry and physics of energetic materials.
Research categories:
Big Data/Machine Learning, Deep Learning, Other
Preferred major(s):
- No Major Restriction
Desired experience:
Must be a U.S. citizen, national, or permanent resident of the United States. Must have completed at least one academic semester of full-time study at associate's or bachelor's degree level from an accredited college or university.
School/Dept.:
Chemical Engineering
Professor:
Stephen
Beaudoin
More information: https://engineering.purdue.edu/ChE/people/ptProfile?resource_id=11574
Engineering Trust and Safety in Social Media Platforms
Description:
Social media platforms (SMPs) are a ubiquitous and powerful technology. Their influence on society cannot be understated. However, unlike many other classes of engineered computer systems, SMPs are a bit of a “Wild West” – we lack insight into effective engineering practices to ensure proper function and safety for users. The goal of this project is to investigate the mechanisms that underlie the software engineering process of this technology. The student will investigate the difficulties faced by software engineers when building SMPs. Tasks will include analysis of issue reports regarding open-source SMPs, assistance with interview design for SMP engineers, and reporting of public failures of major SMPs.
Research categories:
Human Factors
Preferred major(s):
- No Major Restriction
Desired experience:
Good programming background, e.g. knows multiple programming languages including scripting languages like Python
Strong English skills
Curiosity about the human side of software engineering
School/Dept.:
Electrical & Computer Engineering
Professor:
James
Davis
Evaluation of a Prototype Membrane Energy Exchanger for Efficient Buildings
Description:
Buildings are the largest source of energy consumption in the U.S., constituting roughly 48% of our primary energy consumption, and air conditioning is one of the largest uses of energy within buildings. As global temperatures rise from global warming, populations grow, and greater emphasis is put on indoor air quality and comfort, cooling energy demand will grow too. The long-standing conventional technologies we rely on for space cooling are inherently inefficient in warm, humid climates where a large portion of the cooling energy goes to the condensation dehumidification process instead of air cooling. Thus, there is a great need for innovative, disruptive technological development that can challenge the way we’ve provided space cooling for decades. In this project, we are developing a novel technology that mechanically separates water vapor out of air using water vapor selective membranes, which is much more efficient than condensing water out of air. Additionally, we are exploring innovative heat and mass transport phenomena using novel materials. The student who joins this project will have the opportunity to contribute to important experimental work, will learn about energy use and the thermodynamics and heat transfer in buildings, and will learn about material development, too.
The student will work closely with the graduate student mentor on experiments related to porous membrane fabrication and characterization along with the testing of the novel membrane energy exchanger’s performance (heat transfer and dehumidification properties). The student will also assist in validating thermodynamic models using the experimental data. Students will partake in weekly literature reading and discussion small group meetings and will keep a log of their weekly progress. They will present their updates at weekly meetings and will present a talk or poster at the end of the summer. Students will end the summer with a greater understanding of the energy challenges in the building sphere and will develop a broad range of scientific skills pertinent to the design and evaluation of new technologies.
The student will work closely with the graduate student mentor on experiments related to porous membrane fabrication and characterization along with the testing of the novel membrane energy exchanger’s performance (heat transfer and dehumidification properties). The student will also assist in validating thermodynamic models using the experimental data. Students will partake in weekly literature reading and discussion small group meetings and will keep a log of their weekly progress. They will present their updates at weekly meetings and will present a talk or poster at the end of the summer. Students will end the summer with a greater understanding of the energy challenges in the building sphere and will develop a broad range of scientific skills pertinent to the design and evaluation of new technologies.
Research categories:
Energy and Environment, Engineering the Built Environment, Thermal Technology
Preferred major(s):
- Mechanical Engineering
Desired experience:
Applicants should have a general interest in energy and sustainability. Should also have a strong background/interest in thermodynamics and heat transfer. Applicants with experience in some (not all) of the following are preferred: LabVIEW, Python (Jupyter, Google Colab, etc.) Engineering Equation Solver, MATLAB, 3D-CAD Software, prototype design/manufacturing, and Adobe Illustrator. 2nd semester Sophomores, Juniors, and 1st semester Seniors are preferred.
School/Dept.:
Mechanical Engineering
Professor:
James
Braun
More information: https://engineering.purdue.edu/CHPB
Experimental Methods for Aerothermal Environments
Description:
The student will help graduate students and faculty to design and develop experimental methods and instrumentation for research in high-enthalpy aerothermal flow systems relevant to advanced propulsion devices. They will integrate and operate flow hardware, install and evaluate instrumentation and data acquisition system, and help collect and analyze data acquired during testing. The student will gain valuable hands-on experience culminating in a final presentation that will be graded by the advisor.
Research categories:
Energy and Environment, Thermal Technology
Preferred major(s):
- Mechanical Engineering
- Aeronautical and Astronautical Engineering
Desired experience:
CAD, MATLAB, P&ID, fabrication
School/Dept.:
School of Mechanical Engineering
Professor:
Terrence
Meyer
More information: engineering.purdue.edu/trmeyer
Experimental Study of Heat Transfer in Nanomaterials
Description:
This project deals with the study of heat transfer in very thin film materials using Raman Spectroscopy and Ultrafast Laser Spectroscopy. Heat transfer in nanoscale materials including 2D materials (very thin layered materials bonded by van der Waal’s force) shows superior characteristics for applications in numerous advanced devices. Their thermal transport behaviors are also different compared with bulk materials, and an understanding of the transport process is important for applications of these materials. We use non-contact, optical method (i.e., lasers etc.) to investigate heat flow in these materials. The undergraduate student will work with graduate students to learn to use state-of-the-art experimental facilities, carry out experiments, and analyze experimental results.
Research categories:
Energy and Environment, Nanotechnology, Thermal Technology
Preferred major(s):
- Mechanical Engineering
- Physics
Desired experience:
Junior or Senior standing
School/Dept.:
Mechanical Engineering
Professor:
Xianfan
Xu
More information: https://engineering.purdue.edu/~xxu/; https://engineering.purdue.edu/NanoLab/
Field Engineering of Quantum Memories
Description:
The goal of this project is to develop a quantum memory using a crystal that can store quantum optical information. Such quantum memory will be essential for developing the future quantum networks where storage of optical entanglement is key to long-distance secure communication. The quantum memory operates below 4K temperature and it requires field engineering to control optical information. Students will be designing and implementing electronic circuit and electrodes around the crystal to achieve high frequency , high voltage control of the field around the crystal used as quantum memory. This is an experimental project in Prof Hosseini Lab in the Birck Nanotechnology Center at Purdue Discovery Park.
Research categories:
Material Processing and Characterization, Nanotechnology, Other
Preferred major(s):
- Electrical Engineering
- Electrical Engineering Technology
- Physics
Desired experience:
Junior or senior students with GPA>3.6
School/Dept.:
ECE
Professor:
Mahdi
Hosseini
Forecasting the 2022 U.S. Elections using Mathematical Modeling
Description:
Election forecasting involves polling likely voters, weighting polling data, combining it with other information (e.g., how the economy is doing), accounting for uncertainty, and communicating forecasts to the public. In this project, we will use mathematical modeling to produce forecasts of the upcoming 2022 U.S. midterm elections, and we will build a website to post our election forecasts.
Research categories:
Big Data/Machine Learning, Other
Preferred major(s):
- No Major Restriction
- Computer Science
- Mathematics
- Civil Engineering
- Biomedical Engineering
Desired experience:
Good team members with experience in linear algebra and differential equations, interest in interdisciplinary research, and strong programming skills (Matlab, Html/Css, preferred but not necessary).
School/Dept.:
Mathematics
Professor:
Alexandria
Volkening
More information: https://modelingelectiondynamics.gitlab.io/2020-forecasts/index.html
Friction and Wear Study of Carbon Carbon Disk Brakes
Description:
The objective of this investigation is to measure friction and wear of carbon carbon disk brakes under ambient and high temperature applications. The objectives will be achieved by learning to use a disk brake apparatus to measure friction and wear. The Carbon Carbon Disk Brake (CCDB) is turn key and easily operated using computer controls.
The candidate involved will learn how carbon disks are prepared and learn to use this state of the art rig to collect a set of data for publication.
The candidate involved will learn how carbon disks are prepared and learn to use this state of the art rig to collect a set of data for publication.
Research categories:
Other
Preferred major(s):
- No Major Restriction
Desired experience:
Undergraduate course work in ME.
School/Dept.:
School of Mechanical Engineering
Professor:
Farshid
Sadeghi
Functional Near-InfraRed Spectroscopy (fNIRS) Testbed Development for Studying Human Interaction with Autonomous Systems
Description:
Bio or physiological sensors have become ubiquitous, and this additional sensing capability is being integrated into clothing and other devices (e.g. smart watches) that we wear to constantly provide us with feedback about our health. Physiological sensors can also be used to infer mental processes occurring in the brain; this is called "psychophysiological" sensing. In our lab we are studying the use of various types of psychophysiological measurements to infer or predict human decision-making and other cognitive factors (such as their trust) during interactions with machines, robots, and other autonomous systems. This type of research is important for improving the safety and performance of autonomous systems designed to interact with or assist humans, such as autonomous vehicles, nurse robots, or surgical robots used by physicians. We are looking for a student to set up a new sensor, called functional Near-InfraRed Spectroscopy (fNIRS), for use in our lab and to test it against some prior work we've done with similar sensors to study human trust in automation.
Research categories:
Big Data/Machine Learning, Human Factors, Other
Preferred major(s):
- Mechanical Engineering
- Industrial Engineering
- Electrical Engineering
- Aeronautical and Astronautical Engineering
- Civil Engineering
Desired experience:
Prior experience or familiarity with sensors, hardware, and interfacing such devices with software is desirable.
School/Dept.:
School of Mechanical Engineering
Professor:
Neera
Jain
More information: https://engineering.purdue.edu/JainResearchLab/
Functional Skeletal Muscle Restoration for Large-Volume Muscle Loss
Description:
Loss of large volumes of skeletal muscle (volumetric muscle loss (VML)), as may occur with cancer resection or combat-related traumatic injury, represents an ongoing clinical challenge that affects both civilian and military populations. Because VML surpasses the body’s natural capacity for tissue repair and regeneration, affected individuals suffer long-term disabilities, with significant loss of musculoskeletal strength, mobility, and function. Present day standard of care for VML patients includes physical therapy and/or orthotics, both of which do not adequately address strength and tissue structural deficits. Surgical muscle transfers, where a working muscle from another location is placed in defect area, may also be performed; however, such procedures do not restore function owing to lack of graft “take” and reinnervation. While researchers continue to evaluate various potential skeletal muscle restoration options, only a few have progressed to large animal or human clinical studies, with only modest improvements being observed to date. As such, new therapeutic options are needed that support restoration and functional re-innervation of lost skeletal muscle, thereby improving functional strength, mobility, and overall quality of life for VML patients. The Harbin laboratory, which specializes in scaffold-forming (polymerizable) collagen, and Stacey Halum, a head and neck surgeon-scientist, who has developed a special population of nerve-attracting muscle stem cells, have enjoyed a long-standing collaboration focused, in part, on skeletal muscle regeneration, especially as it relates to head and neck surgery applications. By combining the collagen and muscle stem cells, the team has fashioned a skeletal muscle replacement that when used in preclinical studies for laryngeal muscle reconstruction restored skeletal muscle volume and associated muscle function. The engineered muscle replacement showed an exceptional bodily acceptance, characterized by noninflammatory cellularization, vascularization, reinnervation, and skeletal muscle generation. Based on these encouraging results to date, an ongoing goal is to further innovate and evaluate skeletal muscle restoration strategies for VML. An important next step for this translational research is to further development and evaluation of skeletal muscle replacements using electrophysiologic techniques and established small animal models of VML. Measured outcomes from these preclinical studies will include functional measures of muscle innervation and contraction, restoration of limb mobility and strength, as well as definition of the implant’s unique regenerative mechanism of action. This project team will be led by veterinary-scientist Sarah Brookes, operating under the co-mentorship of Prof. Harbin and Dr. Halum.
Research categories:
Medical Science and Technology
Preferred major(s):
- Biomedical Engineering
- Mechanical Engineering
Desired experience:
Coursework or skills related to biomaterials, biofabrication, biomechanical testing, protein and gene expression, preclinical animal models, in-vitro cell culture, other wet lab procedures.
School/Dept.:
Biomedical Engineering
Professor:
Sherry
Harbin
Geometry Optimization for Electrical Dehydrogenation Reactor
Description:
This project is supported by CISTAR, an NSF Engineering Research Center headquartered at Purdue.
Ethylene is one of the most important building blocks of the chemical industry. Its global market was estimated at ~160 million Tons in 2020 and it is forecast to reach ~210 million Tons by 20272. Between 1.0 and 1.6 tons of CO2 are emitted per ton of Ethylene produced. This means Ethylene production accounted for around 0.47-0.75% of the World’s Total Carbon Emissions in 2020, estimated at 34 billion tons3. The U.S. has set a course to reach net-zero emissions economy-wide by no later than 20507,8. This makes it imperative to decarbonize Ethylene production.
Ethylene is mainly produced by Dehydrogenation through Steam Cracking (SC), where hydrocarbons transform into ethylene in the presence of steam at high temperatures11. SC normally implements hydrocarbon combustion to produce the necessary energy for the reaction. This is the main reason why SC emits so much CO21. The NSF Center for Innovative and Strategic Transformation of Alkane Resources (CISTAR)5 is currently researching the redesign of SC to make it compatible with renewable electricity and eliminate the need for steam. This would allow a significant reduction of CO2 emissions during Ethylene production4. The new concept is called Electrical Dehydrogenation Reactor.
As part of its research, CISTAR is optimizing the reactor geometry of its Electrical Dehydrogenation Reactor through detailed Computational Fluid Dynamics (CFD). The goal is to reduce the reactor cost while maximizing its performance.
Students working on this project will also have the opportunity to participate in information sessions, tours and informal mentoring with CISTAR's partner companies.
Purdue students are not eligible for this project. Students must be from outside institutions. Participants must be US Citizens. Students with disabilities, veterans, and those from traditionally underrepresented groups in STEM are encouraged to apply.
Ethylene is one of the most important building blocks of the chemical industry. Its global market was estimated at ~160 million Tons in 2020 and it is forecast to reach ~210 million Tons by 20272. Between 1.0 and 1.6 tons of CO2 are emitted per ton of Ethylene produced. This means Ethylene production accounted for around 0.47-0.75% of the World’s Total Carbon Emissions in 2020, estimated at 34 billion tons3. The U.S. has set a course to reach net-zero emissions economy-wide by no later than 20507,8. This makes it imperative to decarbonize Ethylene production.
Ethylene is mainly produced by Dehydrogenation through Steam Cracking (SC), where hydrocarbons transform into ethylene in the presence of steam at high temperatures11. SC normally implements hydrocarbon combustion to produce the necessary energy for the reaction. This is the main reason why SC emits so much CO21. The NSF Center for Innovative and Strategic Transformation of Alkane Resources (CISTAR)5 is currently researching the redesign of SC to make it compatible with renewable electricity and eliminate the need for steam. This would allow a significant reduction of CO2 emissions during Ethylene production4. The new concept is called Electrical Dehydrogenation Reactor.
As part of its research, CISTAR is optimizing the reactor geometry of its Electrical Dehydrogenation Reactor through detailed Computational Fluid Dynamics (CFD). The goal is to reduce the reactor cost while maximizing its performance.
Students working on this project will also have the opportunity to participate in information sessions, tours and informal mentoring with CISTAR's partner companies.
Purdue students are not eligible for this project. Students must be from outside institutions. Participants must be US Citizens. Students with disabilities, veterans, and those from traditionally underrepresented groups in STEM are encouraged to apply.
Research categories:
Material Modeling and Simulation
Preferred major(s):
- Electrical Engineering
- Chemical Engineering
- Mechanical Engineering
Desired experience:
• No previous coursework is required. However, it is advisable the student has a strong motivation for computer simulations.
• Experience in Ansys Fluent could be beneficial.
School/Dept.:
School of Chemical Engineering
Professor:
Rakesh
Agrawal
More information: https://cistar.us/
Geospatially resolved model of heat pump operating costs & emissions
Description:
Commercial and residential buildings account for 13% of greenhouse gas emissions in the United States. Most of these emissions are driven by heating and cooling energy demand. Heat pumps, especially new technologies that make use of low global warming potential refrigerants, offer higher energy efficiency but can still be cost prohibitive. Working as part of a larger research project, this summer project would use performance testing data of a new heat pump technology to estimate the energy consumption for operating a new heat pump technology at different locations with different weather conditions in the United States. Using this energy consumption data, we can then estimate the greenhouse gas emissions associated with these new technologies using today’s electricity grid, and the cost of operating these new heat pumps. The results will be incorporated into a broader study of the geospatial environmental and energy burdens to residents.
Research categories:
Big Data/Machine Learning, Energy and Environment
Preferred major(s):
- Mechanical Engineering
- Industrial Engineering
- Chemical Engineering
- Environmental and Ecological Engineering
- Electrical Engineering
Desired experience:
Completed introductory thermodynamics and electrical engineering coursework (ME200, ECE20001 or similar). Working familiarity with Python and/or Matlab. Experience with data science and/or GitHub is also a plus.
School/Dept.:
Mechanical Engineering
Professor:
Rebecca
Ciez
Heterogeneous Integration/Advanced packaging
Description:
The rapid increase in chip performance associated with Moore’s law has also raised interest and expectations around creating packaging devices with improved size, weight, and power. To keep sizes manageable while improving functionality, complex packaged electronics like iPhones require similar components to be compressed together horizontally and vertically, and combined with dissimilar components providing complementary functions. Significant challenges in heterogeneous integration to be addressed in research include maintaining the reliability of connections such as solder bumps, managing thermal cycling, and limiting damage from mechanical stress that can cause failures.
Research categories:
Other
Preferred major(s):
- No Major Restriction
School/Dept.:
Mechanical Engineering
Professor:
Ganesh
Subbarayan
High Field Vector Magnetization Measurements in Quantum Materials
Description:
The goal of this project is to set up a novel method for measuring the magnetic properties of quantum materials. Quantum magnets hold a lot of promise in new devices for the future where the properties are determined by tenets of the Heisenberg Uncertainty principle. But how to get access to the weak quantum effects, especially in a challenging environment of a dilution refrigerator in millikelvin? Here we set up a Josephson Junction-based device that can sample small magnetic fields from quantum materials placed at a milliKelvin temperature at up to a 14 T magnetic field, and attempt to discern the magnetic properties, and assess their usefulness for future magnetic routes to solid-state quantum computation.
Research categories:
Material Modeling and Simulation, Material Processing and Characterization, Nanotechnology
Preferred major(s):
- No Major Restriction
- Physics
- Electrical Engineering
- Mechanical Engineering
- Computer Engineering
- Chemistry
Desired experience:
The candidate should have excellent in-lab etiquettes and desirably have some initial experience in working in a research laboratory project. The project involves working with cryogenic systems under high magnetic fields with delicate electronics. Excellent communication and interpersonal skills are also desired. The person should have some expertise in electrical engineering and circuits, and idea of fabrication.
School/Dept.:
Physics and Astronomy
Professor:
Arnab
Banerjee
High Performance Perovskite Solar Cells
Description:
Sunlight is the most abundant renewable energy resource available to human beings, and yet it remains one of the most poorly utilized sources of clean energy. Solar cell modules incorporating single crystalline silicon and gallium arsenide currently provide the highest efficiencies for solar energy conversion to electricity but remain limited due to their high costs.
In the past few years, perovskite solar cell technology has made significant progress, improving in efficiency to ~25%, while maintaining attractive economics due to the use of inexpensive soluble materials coupled with ultra low-cost deposition technologies. However, the real applications of these devices requires new breakthroughs in device performance, large-scale manufacturing, and improved stability. Among these, stability and degradation are among the most significant challenges for perovskite technologies. Perovskite absorber layer and organic charge transport materials can be sensitive to water, oxygen, high temperatures, ultraviolet light, and even electric field, all of which will be encountered during operation. To address these issues, significant efforts have been made, including mixed dimensionality and surface passivation; alternative absorber materials and formulations, new charge transport layers, and advanced encapsulation techniques, etc. Now, T80 lifetimes (i.e., the length of time in operation until measured output power is 80% of original output power) of over 1,000 hours have been demonstrated. However, it is still far below the industry required 20 years lifetime, indicating the ineffectiveness of current approaches. To make this advance, non-incremental and fundamentally new strategies are required to improve the intrinsic stability of perovskite active materials.
In this project, we propose a new paradigm to develop intrinsically robust perovskite active layers through the incorporation of multi-functional semiconducting conjugated ligands. In preliminary work, we have demonstrated that semiconducting ligands can spontaneously organize within the active layer to passivate defects and restrict halide diffusion, resulting in dramatic improvements in moisture and oxygen tolerance, reduced phase segregation, and increased thermal stability. Combining a team with expertise spanning the gamut of materials synthesis, computational materials design, and device engineering, we will develop a suite of multi-functional semiconducting ligands capable of improving the intrinsic stability perovskite materials while preserving and even enhancing their electronic properties. Through this strategy, we aim to achieve over 25% cell efficiency with operational stability over 20 years for future commercial use.
More information: https://letiandougroup.com/
In the past few years, perovskite solar cell technology has made significant progress, improving in efficiency to ~25%, while maintaining attractive economics due to the use of inexpensive soluble materials coupled with ultra low-cost deposition technologies. However, the real applications of these devices requires new breakthroughs in device performance, large-scale manufacturing, and improved stability. Among these, stability and degradation are among the most significant challenges for perovskite technologies. Perovskite absorber layer and organic charge transport materials can be sensitive to water, oxygen, high temperatures, ultraviolet light, and even electric field, all of which will be encountered during operation. To address these issues, significant efforts have been made, including mixed dimensionality and surface passivation; alternative absorber materials and formulations, new charge transport layers, and advanced encapsulation techniques, etc. Now, T80 lifetimes (i.e., the length of time in operation until measured output power is 80% of original output power) of over 1,000 hours have been demonstrated. However, it is still far below the industry required 20 years lifetime, indicating the ineffectiveness of current approaches. To make this advance, non-incremental and fundamentally new strategies are required to improve the intrinsic stability of perovskite active materials.
In this project, we propose a new paradigm to develop intrinsically robust perovskite active layers through the incorporation of multi-functional semiconducting conjugated ligands. In preliminary work, we have demonstrated that semiconducting ligands can spontaneously organize within the active layer to passivate defects and restrict halide diffusion, resulting in dramatic improvements in moisture and oxygen tolerance, reduced phase segregation, and increased thermal stability. Combining a team with expertise spanning the gamut of materials synthesis, computational materials design, and device engineering, we will develop a suite of multi-functional semiconducting ligands capable of improving the intrinsic stability perovskite materials while preserving and even enhancing their electronic properties. Through this strategy, we aim to achieve over 25% cell efficiency with operational stability over 20 years for future commercial use.
More information: https://letiandougroup.com/
Research categories:
Energy and Environment, Material Processing and Characterization, Nanotechnology
Preferred major(s):
- No Major Restriction
School/Dept.:
Chemical Engineering
Professor:
Letian
Dou
More information: https://letiandougroup.com/
High speed 3D microscopy imaging
Description:
This project is NSF REU project that aims to develop a high-speed 3D microscopy imaging system for robotics, biological applications. Undergraduate will work with a graduate student to develop novel image processing algorithms , data analytics methods, and instrumentation.
Research categories:
Big Data/Machine Learning, Biological Characterization and Imaging, Fabrication and Robotics
Preferred major(s):
- No Major Restriction
Desired experience:
Prior programming experiences.
School/Dept.:
Mechanical Engineering
Professor:
Song
Zhang
More information: http://www.xyztlab.com
High-efficiency solar-powered desalination
Description:
Water and energy are tightly linked resources that must both become renewable for a successful future. The United Nations predicts that 6 billion people will face water scarcity by 2050. This warrants the need to develop efficient and realizable engineering solutions for desalination using the vast availability of solar energy.
This project aims to design, prototype, and test novel configurations for membrane-based desalination (reverse osmosis), powered by solar-thermal engines. The student will be part of a team of graduate and undergraduate students responsible for process design, thermal-fluid modeling and simulation, hydraulic circuit prototyping and testing, and experimental data analysis.
All students will be required to read relevant, peer-reviewed literature and keep a notebook or log of weekly research progress. At the end of the semester or term, each student will present a talk or poster on their results.
This project aims to design, prototype, and test novel configurations for membrane-based desalination (reverse osmosis), powered by solar-thermal engines. The student will be part of a team of graduate and undergraduate students responsible for process design, thermal-fluid modeling and simulation, hydraulic circuit prototyping and testing, and experimental data analysis.
All students will be required to read relevant, peer-reviewed literature and keep a notebook or log of weekly research progress. At the end of the semester or term, each student will present a talk or poster on their results.
Research categories:
Ecology and Sustainability, Energy and Environment, Fluid Modelling and Simulation, Internet of Things, Nanotechnology, Thermal Technology
Preferred major(s):
- No Major Restriction
Desired experience:
Applicants should have an interest in thermodynamics, water treatment, and sustainability. Applicants with experience in some (not all) of the following are preferred: experimental design and prototyping, manufacturing, Python, LabView, EES, MATLAB, 3D CAD Software, & Adobe Illustrator. Rising Juniors and Seniors are preferred.
School/Dept.:
Mechanical Engineering
Professor:
David
Warsinger
More information: www.warsinger.com
High-performance Radiative Cooling Nanocomposites
Description:
Radiative cooling is a passive cooling technology without power consumption, via reflecting sunlight and radiating infrared heat, both into the deep space. Compared to conventional air conditioners, radiative cooling not only saves energy, but also combats climate crisis since all the heat goes to deep space instead of stays on the earth. Recently, our group has invented commercial-like particle-matrix paints (nanocomposites) that cool below the surrounding temperature under direct sunlight. The Purdue cooling paints attracted remarkable global attention and won a Guinness World Record. Read, for example, the BBC News coverage here: https://www.bbc.com/news/science-environment-56749105. Currently we are working to improve the performance and create new radiative cooling solutions.
In this SURF project, we are looking for self-motivated students to work with our PhD students. The student will first synthesize nanocomposites via some wet chemistry and/or 3D printing methods. The optical, mechanical, and other relevant properties will then be characterized with spectrometers and other specialized equipment. Field tests will be performed to measure the cooling performance of the materials and devices. The work is expected to results in journal paper(s) of high impact. Students who make substantial contributions to the work can expect to be co-authors of the paper(s).
In this SURF project, we are looking for self-motivated students to work with our PhD students. The student will first synthesize nanocomposites via some wet chemistry and/or 3D printing methods. The optical, mechanical, and other relevant properties will then be characterized with spectrometers and other specialized equipment. Field tests will be performed to measure the cooling performance of the materials and devices. The work is expected to results in journal paper(s) of high impact. Students who make substantial contributions to the work can expect to be co-authors of the paper(s).
Research categories:
Energy and Environment, Material Processing and Characterization, Nanotechnology, Thermal Technology
Preferred major(s):
- Mechanical Engineering
- Environmental and Ecological Engineering
Desired experience:
courses in heat transfer and thermodynamics are a plus but not required
School/Dept.:
Mechanical Engineering
Professor:
Xiulin
Ruan
More information: https://engineering.purdue.edu/NANOENERGY/
How do zebrafish get their stripes A mathematical and computational study
Description:
From leopards to fish, many animals sport patterns (like stripes or spots) on their bodies. My group takes a mathematical approach to understand how patterns form in the skin of zebrafish, which are small striped with important biomedical applications. Zebrafish development takes months, but simulating pattern formation takes minutes. In this project, I will mentor a student in building image-processing software to make simulated zebrafish patterns look more like real fish.
Research categories:
Big Data/Machine Learning, Biological Characterization and Imaging, Biological Simulation and Technology, Cellular Biology
Preferred major(s):
- Mathematics
- Computer Science
- Biomedical Engineering
- Biological Engineering - multiple concentrations
- Engineering (First Year)
- Agricultural Engineering
Desired experience:
Good team members who are excited about interdisciplinary research, have taken a course in linear algebra, and have strong programming skills.
School/Dept.:
Mathematics
Professor:
Alexandria
Volkening
More information: https://www.alexandriavolkening.com/agentBased.html
Human Factors: Enhancing Performance of Nurses and Surgeons
Description:
High physical and cognitive workload among surgeons and nurses are becoming more common. The purpose of this project is to examine the contributors to these and develop technology to understand and enhance their performance.
The SURF student will participate in data collection in the operating room at Indiana University School of Medicine, data analysis and interpretation, and write his/her results for a journal publication. The student will regularly communicate his/her progress and results with faculty, graduate mentors, and surgeon collaborators.
More information: https://engineering.purdue.edu/YuGroup
The SURF student will participate in data collection in the operating room at Indiana University School of Medicine, data analysis and interpretation, and write his/her results for a journal publication. The student will regularly communicate his/her progress and results with faculty, graduate mentors, and surgeon collaborators.
More information: https://engineering.purdue.edu/YuGroup
Research categories:
Big Data/Machine Learning, Human Factors, Learning and Evaluation, Medical Science and Technology
Preferred major(s):
- No Major Restriction
- Industrial Engineering
- Computer Science
- Biomedical Engineering
Desired experience:
Human Factors, Machine Learning, Sensors, Programming
School/Dept.:
Industrial Engineering
Professor:
Denny
Yu
More information: https://engineering.purdue.edu/YuGroup
Identification, Verification and Validation of a Surfactant Formulation for Chemical Enhanced Oil Recovery in the Illinois Basin
Description:
Challenge: The Enhanced Oil Recovery (EOR) Lab has an ardent interest in developing a practical and economical program for the Illinois Basin. The Illinois basin is characterized as a mature asset that is typified by its shallow depths and low temperatures. Many of the fields have been waterflooded for the last several decades to aid in the recovery of the stranded oil within the sandstone and carbonate reservoirs. Significant progress has been made in understanding the brine constituents, oil viscosity/API gravity and reservoir mineralogy of the Illinois Basin; however, suitable chemical formulations, primarily surfactant/polymer combinations are still elusive. Considerable chemical testing is necessary to complement the Illinois Basin reservoir characteristics in order to move a project to pilot scale implementation.
The most pressing technical challenge is the design of a surfactant formulation that provides technical confidence (performance) for the reservoir brine and the crude oil. Notwithstanding, the areas of low/ultralow IFT, phase behavior and core flood are all key areas that need to demonstrate performance before implementing a field pilot program. Once a suitable surfactant formulation is determined, its stability, compatibility and performance with respect to the addition of polymer must also be understood and evaluated.
Targeted Goal: This project will focus on using the library of commercial surfactant products available in the EOR lab to find a suitable formulation for a target reservoir in the Illinois Basin. Once a surfactant formulation is determined through satisfactory phase behavior testing, Interfacial tension testing followed by core flood validation experiments will be carried out. Students should expect to learn about chemical enhanced oil recovery while performing experiments with surfactants, various brine solutions and oils.
The most pressing technical challenge is the design of a surfactant formulation that provides technical confidence (performance) for the reservoir brine and the crude oil. Notwithstanding, the areas of low/ultralow IFT, phase behavior and core flood are all key areas that need to demonstrate performance before implementing a field pilot program. Once a suitable surfactant formulation is determined, its stability, compatibility and performance with respect to the addition of polymer must also be understood and evaluated.
Targeted Goal: This project will focus on using the library of commercial surfactant products available in the EOR lab to find a suitable formulation for a target reservoir in the Illinois Basin. Once a surfactant formulation is determined through satisfactory phase behavior testing, Interfacial tension testing followed by core flood validation experiments will be carried out. Students should expect to learn about chemical enhanced oil recovery while performing experiments with surfactants, various brine solutions and oils.
Research categories:
Energy and Environment, Material Processing and Characterization
Preferred major(s):
- No Major Restriction
School/Dept.:
Chemical Engineering
Professor:
Nathan
Schultheiss
More information: https://engineering.purdue.edu/cheeor/
Identifying and reducing health and environmental impacts of plastic used to repair buried pipes
Description:
Drinking water and sewer pipes are decaying across the nation, and inexpensive methods for repairing these assets are being increasingly embraced. One method called cured-in-place-pipe (CIPP) involves workers chemically manufacturing a new plastic pipe inside an existing damaged pipe. This is the least expensive pipe repair method and, as such, is preferred by utilities and municipalities. The practice is often conducted outdoors and industry ‘best’ practice involves discharging the plastic manufacturing waste into the environment and nearby pipelines. Under some conditions, this waste finds its way into public areas and buildings prompted illnesses and environmental damage. Another consequence can be direct leaching of unreacted chemicals into water or volatilization of chemicals from the new plastic into air.
This project will involve the student working with a graduate student as well as leading experts on plastics manufacturing, chemistry, public health, civil/environmental engineering, and communications. The student will learn plastic manufacturing methods, environmental sampling and analysis methods, and participate in the process of reducing human health and environmental risks of the practice. To complete this work, the student will learn and apply infrastructure, environmental, and public health principles.
This project will involve the student working with a graduate student as well as leading experts on plastics manufacturing, chemistry, public health, civil/environmental engineering, and communications. The student will learn plastic manufacturing methods, environmental sampling and analysis methods, and participate in the process of reducing human health and environmental risks of the practice. To complete this work, the student will learn and apply infrastructure, environmental, and public health principles.
Research categories:
Composite Materials and Alloys, Energy and Environment, Engineering the Built Environment, Environmental Characterization, Other
Preferred major(s):
- Chemical Engineering
- Environmental and Ecological Engineering
- Civil Engineering
- Public Health
- Chemistry
- Environmental Health Sciences
Desired experience:
Strong interest in learning and applying scientific methods and techniques to help solve a pressing day problem; Basic understand of chemistry; General lab experience desirable as the student will help manufacture plastics in the lab using chemical formulations
School/Dept.:
CE & EEE
Professor:
Andrew
Whelton
Illumination of Damage through X-ray analysis
Description:
Damage in structural materials is often difficult to quantify, instead we rely on large scale component level testing and curve fitting. With the advent of advanced high energy X-ray characterization tools, including diffraction and tomography, we have the ability to identify damage inside the bulk of the material, in which the samples are subjected to mechanical loading. Thus, in this project, X-ray data will be reconstructed and the damage will be characterized and quantified in several material systems (including carbon fiber reinforced composites and Ti-6Al-4V produced via additive manufacturing). The interaction of damage with microstructural features will be assessed, in order to achieve a physics-based understanding of material failure.
Research categories:
Big Data/Machine Learning, Composite Materials and Alloys, Material Modeling and Simulation, Material Processing and Characterization
Preferred major(s):
- Aeronautical and Astronautical Engineering
- Materials Engineering
- Mechanical Engineering
- Computer Science
- Computer Engineering
Desired experience:
Students are expected to work with Image Processing and Visualization tools, as well as Matlab or Python.
School/Dept.:
School of Aeronautics and Astronautics
Professor:
Michael
Sangid
More information: https://engineering.purdue.edu/~msangid/
Imaging and designs of the bio-inspired tissue-engineered matrix.
Description:
This project will be a nexus of the design, imaging, and manufacturing science and engineering of tissue matrix by cellular engineering. The project will engage students in high-resolution imaging, building a 2D and 3D digital design construct of the cellular matrix, and application of AR/VR for the human-matrix interface. Students will learn convergence of imaging, design, tissue engineering, and visualization. This project will be conducted in the College of Engineering and the College of Agriculture.
Research categories:
Biological Characterization and Imaging, Cellular Biology, Other
Preferred major(s):
- Agricultural & Biological Engineering
Desired experience:
Junior and Senior students are preferred.
Student's personality expectations-
Self-motivated, able to "figure out" solutions, persistent, and trustworthy to complete assigned projects.
School/Dept.:
School of Mechanical Engineering
Professor:
Ajay
Malshe
More information: https://engineering.purdue.edu/ME/People/ptProfile?resource_id=232598
In-Sensor Computing with Ferroelectric Resonators
Description:
Edge computing is a growing necessity for the Internet of Things (IoT) given the demand for sensor networks collecting and communicating information to central processing points. The power required to transmit data at high bandwidth is prohibitive, and solutions for efficient lower level computation at each sensor node are required. Ferroelectrics (FEs), with unique hysteresis properties, are currently under investigation for in-memory computing. In this project, we will leverage the combined benefits of nonlinear piezoelectricity and hysteresis of ferroelectrics in the context of MEMS resonators to explore oscillatory computation for resonant sensors. Goals of this project include analysis and simulation of computational schemes based on existing large-signal FE models recently developed in our group, as well as experimental prototyping using existing ferroelectric resonators also previously designed in the HybridMEMS Lab. This would be the first experimental demonstration of FE resonant computation.
Research categories:
Internet of Things, Nanotechnology
Preferred major(s):
- Electrical Engineering
- Computer Engineering
- Mechanical Engineering
School/Dept.:
ECE
Professor:
Dana
Weinstein
More information: https://engineering.purdue.edu/hybridmems/
Industrial IoT Implementation and Machine Learning for Smart Manufacturing
Description:
The student will work with PhD students on implementation of IoT technology on manufacturing machines and processes, database development, dashboard development, and machine learning for smart manufacturing.
Research categories:
Big Data/Machine Learning, Deep Learning, Fabrication and Robotics, Internet of Things
Preferred major(s):
- Mechanical Engineering
- Computer Engineering
- Computer Science
School/Dept.:
Mechanical Engineering
Professor:
Martin
Jun
More information: https://web.ics.purdue.edu/~jun25/
Infield study of long term virtual and augmented reality-based training for vocational skilling
Description:
Welding is a skill that requires manual dexterity, adept psychomotor skills, and attention to numerous details of the process. Virtual Reality (VR)-based simulators for welding have gained popularity in recent decades and have been integrated with in-person training methods to provide hands-on practice to learners for improving the necessary psychomotor skills. Previous research studies have shown that respondents agree with using VR-based welding simulators as a tool to develop basic welding skills in new trainees. Using a VR welding simulator, the trainee’s understanding was much clearer when doing the welding process, and welding skills also developed. In addition, the simulator helps the trainee redo the exercises without considering the wastage of workpiece material and access to other equipment. Considering these advantages offered by VR-based training methods, our research would focus on the systematic study to explore and evaluate the usage of the technology to facilitate user experience and the development of psychomotor skills required for welding. The student researcher would be needed to help conduct experiments with field subjects. He/she would collect data during the investigations and later help perform statistical analysis of the data. This work will eventually lead to submitting a paper to a top-tier ACM conference.
Research categories:
Human Factors, Internet of Things, Learning and Evaluation, Other
Preferred major(s):
- Mechanical Engineering
Desired experience:
Applicants with experience in the following are preferred (But not necessary) :
Unity, 3D-CAD Package, Conducting experiment and data collection, Statistical Analysis
School/Dept.:
Mechanical Engineering
Professor:
Karthik
Ramani
Interpreting paleoclimate data from Antarctica using numerical models
Description:
Scientists study the Earth's past climate in order to understand how the climate will respond to ongoing global change in the future. One of the best analogs for future climate might the period that occurred approximately 3 million years ago, during an interval known as the mid-Pliocene Warm Period. During this period, the concentration of carbon dioxide in the atmosphere was similar to today's and sea level was 15 or more meters higher, due primarily to warming and consequent ice sheet melting in polar regions. However, the temperatures in polar regions during the mid-Pliocene Warm Period are not well determined, in part because we do not have records like ice cores that extend this far back in time. We are studying surface temperatures in Antarctica during the mid-Pliocene Warm Period using a new type of climate proxy, known as cosmogenic noble gas (CNG) paleothermometry. In this project, the SURF student will be involved with improving numerical models that we use to interpret CNG data.
Research categories:
Energy and Environment, Other
Preferred major(s):
- No Major Restriction
Desired experience:
Applicants should have an interest in climate science. Applicants who are rising juniors or seniors and who have experience coding with Python, MATLAB, or Julia are preferred.
School/Dept.:
Earth, Atmospheric, and Planetary Sciences
Professor:
Marissa
Tremblay
More information: https://www.purdue.edu/science/geochronology/thermochron/
Leveraging the Power of Social Networks to Eradicate Epidemics
Description:
Since the popularization of handheld communication devices and social media applications, opinion dynamics and social networks have played a more critical role in politics, economics, and public health issues. In particular, opinion polarization on vaccination has tolled thousands of lives in the recent pandemic. Consider the following question: "If you could only convince three nodes in a social network to get vaccinated, which nodes should you choose?"
This project will guide students to answer this resource allocation problem through analyzing the spread transmission network and the dynamic opinion network. The project will be composed of four parts:
1. Constructing epidemic spread simulators.
2. Designing a control strategy for epidemic mitigation.
3. Developing mathematical proofs which guarantee the algorithm's performance.
4. Applying the strategy to real networks generated from online COVID data as a case study.
Students who participated in the project will learn the basics of the epidemic modeling paradigm, network science, control theory, and Python/MATLAB programming skills.
This project will guide students to answer this resource allocation problem through analyzing the spread transmission network and the dynamic opinion network. The project will be composed of four parts:
1. Constructing epidemic spread simulators.
2. Designing a control strategy for epidemic mitigation.
3. Developing mathematical proofs which guarantee the algorithm's performance.
4. Applying the strategy to real networks generated from online COVID data as a case study.
Students who participated in the project will learn the basics of the epidemic modeling paradigm, network science, control theory, and Python/MATLAB programming skills.
Research categories:
Big Data/Machine Learning, Engineering the Built Environment, Learning and Evaluation, Medical Science and Technology
Preferred major(s):
- No Major Restriction
School/Dept.:
Elmore Family School of Electrical Engineering
Professor:
Philip E.
Paré
Linking Flow Behavior to 3D-Printability in Highly Loaded Polymer-Ceramic Suspensions
Description:
Aqueous suspensions of ceramic particles are used in electronics manufacturing to improve heat transfer between components. Polymers are often added to ceramic suspensions to improve the flow behavior at high particle loadings (> 50 vol%). Through 3D-printing, custom and precise structures can be rapidly fabricated; however, one challenge encountered when 3D-printing these suspensions is deposition of excess material when the nozzle is lifted and moved to a new location (also called “tailing”), which results in material wastage and sample defects. The goal of this SURF project is to design ceramic suspensions that exhibit reduced tailing. Parameters including component volume fractions, particle size and roughness, and polymer molecular weight can all affect the flow behavior and in turn, the printability of these materials. In this project, the SURF student will: (1) prepare aqueous polymer-ceramic suspensions of varying composition; (2) characterize their flow behavior using rheometry; (3) conduct extrusion 3D-printing tests and qualitatively evaluate printability; (4) devise a method to quantify the tailing behavior, and (5) draw conclusions between the rheometry and 3D-printing data. By developing a better understanding of the relationships between suspension composition, flow behavior, and printability, this work will enable the design of 3D-printable composite materials for a variety of applications, such as flexible electronics, aircraft parts, or medical implants.
Research categories:
Composite Materials and Alloys, Material Processing and Characterization
Preferred major(s):
- No Major Restriction
Desired experience:
General lab experience and an interest in materials research. Some prior knowledge of polymer science and/or non-Newtonian fluid mechanics would be beneficial but is not required.
School/Dept.:
Materials Engineering
Professor:
Kendra
Erk
More information: https://soft-material-mechanics.squarespace.com/
Low-cost user-friendly biosensors for animal health
Description:
Infectious diseases are a leading cause of economic burden on food production from animals. For example, bovine respiratory disease leads to a loss of ~$1 billion annually. Current methods for tackling these diseases includes the administration of antibiotics by trial-and-error. This approach leads to failure of treatment in up to one-third of the cases. In addition, it also leads to a proliferation of antibiotic resistance in pathogens.
Our research project focuses on developing a low-cost user-friendly biosensor based on paper that can detect which pathogen is causing the disease and whether it exhibits antibiotic resistance. Such a biosensor would provide a readout to the farmer or the veterinary physician and suggest which antibiotics are likely to be successful.
Lab members working in the team have three objectives: i) design, test, and optimize primers for detecting pathogens and genes associated with bovine respiratory diseases, ii) build and field-test a paper-based device for conducting loop-mediated isothermal amplification, and iii) build and field-test a heating/imaging device for conducting the paper-based assay in the field.
The SURF student will work on one of the objectives depending on their background and experience.
Our research project focuses on developing a low-cost user-friendly biosensor based on paper that can detect which pathogen is causing the disease and whether it exhibits antibiotic resistance. Such a biosensor would provide a readout to the farmer or the veterinary physician and suggest which antibiotics are likely to be successful.
Lab members working in the team have three objectives: i) design, test, and optimize primers for detecting pathogens and genes associated with bovine respiratory diseases, ii) build and field-test a paper-based device for conducting loop-mediated isothermal amplification, and iii) build and field-test a heating/imaging device for conducting the paper-based assay in the field.
The SURF student will work on one of the objectives depending on their background and experience.
Research categories:
Biological Simulation and Technology, IoT for Precision Agriculture
Preferred major(s):
- Biochemistry
- Biological Engineering - multiple concentrations
- Biomedical Engineering
- Agricultural Engineering
- Mechanical Engineering
- Electrical Engineering
Desired experience:
Relevant skills for the project:
• Wet lab skills and experience with molecular biology
• Autodesk Fusion 360 for 3D Modeling/Printing and Laser Cutting
• Python Programming Language for image processing and graphical user-interface using Raspberry Pi (or any other single board computer)
To be successful at this position, you should have a GPA>3.5, prior experience working in a lab, and the ability to work in a team.
School/Dept.:
Agricultural and Biological Engineering
Professor:
Mohit
Verma
More information: www.vermalab.com
Machine learning-based modeling of linear and non-linear deformation in high-pressure hydrostatic machines
Description:
Machine learning, image processing, fluid-structure interaction, linear and nonlinear deformation, elasto-hydrodynamic lubrication... You may have learned or have heard some of those topics. But you may never see how those interdisciplinary techniques can be used together to solve a real-life engineering problem. This project offers you a unique experience to participate in my research group developing a first-in-kind machine learning-based simulation model for nonlinear contact problems in high-pressure hydrostatic machines. The objective of the SURF project is to create a machine learning algorithm capable of fast predicting the two-dimensional, nonlinear deformation distribution from the pressure distribution. The project will be rewarding and challenging, and your work will constitute a new modeling approach in the fluid power field. Therefore, it is also very possible to be published with you as a co-author.
You will be challenged to 1) learn to program a machine learning algorithm in TensorFlow, 2) generate a training dataset for machine learning models using a state-of-the-art numerical simulation tool, and 3) integrate the neural network into the existing modeling suite.
You will be supported by your graduate mentor, who specializes in these topic areas and will provide guidance throughout the project. You will also be supported by a group of 8 developers of the hydrostatic machine modeling toolset that are working on different aspects of the code.
You will be challenged to 1) learn to program a machine learning algorithm in TensorFlow, 2) generate a training dataset for machine learning models using a state-of-the-art numerical simulation tool, and 3) integrate the neural network into the existing modeling suite.
You will be supported by your graduate mentor, who specializes in these topic areas and will provide guidance throughout the project. You will also be supported by a group of 8 developers of the hydrostatic machine modeling toolset that are working on different aspects of the code.
Research categories:
Big Data/Machine Learning, Deep Learning, Fluid Modelling and Simulation, Material Modeling and Simulation
Preferred major(s):
- No Major Restriction
Desired experience:
• Intermediate knowledge of fluid mechanics
• Basic programming knowledge (Python, MATLAB, C++, or similar languages)
School/Dept.:
Mechanical Engineering
Professor:
Lizhi
Shang
More information: https://engineering.purdue.edu/Maha/
Making Blockchains/Cryptocurrencies Secure and Scalable for Billions
Description:
The cryptocurrency boom has seen millions of people adopting digital assets; the recent economic successes of Bitcoin and several other blockchains/cryptocurrencies have enthused a broad population to explore them. The diversity in the needs and objectives of these cryptocurrency users is vast, ranging from just being enthused by technology to trading, sometimes even using all of their savings. With increasing adoption and valuation, the attacks on the system have also seen a rise. Moreover, the current systems cannot scale beyond a few million users. There is a desperate need to improve the blockchain architectures towards combating these issues. In collaboration with the blockchain industry, this project considers the security and scalability aspects of real-world blockchain solutions for finance, supply-chain, precision agriculture, and beyond.
Research categories:
Cybersecurity, Internet of Things
Preferred major(s):
- Computer Science
- Computer Engineering
- Mathematics - Computer Science
- Computer Engineering Technology
- Cybersecurity
Desired experience:
Programming proficiency/interest in Golang and Rust will be necessary. Knowledge about distributed systems, blockchains, cryptocurrencies will be useful.
School/Dept.:
Computer Science
Professor:
Aniket
Kate
Mass spectrometry of biomolecules and nanoclusters
Description:
We are using mass spectrometry to study the localization of lipids, drugs, and proteins in biological tissues and to prepare novel functional interfaces using well-defined polyatomic ions. The student will work with a graduate student mentor to either perform nanocluster synthesis and characterization using mass spectrometry and electrochemical measurements or to develop new analytical approaches for quantitative analysis of biomolecules in biological samples. In both projects, the student will be trained to operate state-of-the-art mass spectrometers and perform independent data acquisition and analysis. The student will also work with the scientific literature to obtain a broader understanding of the field.
Research categories:
Biological Characterization and Imaging, Material Processing and Characterization, Nanotechnology
Preferred major(s):
- chemistry, biochemistry, computer science, engineering
Desired experience:
general chemistry, calculus, analytical or physical chemistry
School/Dept.:
Chemistry
Professor:
Julia
Laskin
More information: https://www.chem.purdue.edu/jlaskin/
Metal Polyselenide Chemistry for Photovoltaic Applications
Description:
Fabrication of metal chalcogenide semiconductors by solution-based methods is a promising route to inexpensive and high-throughput manufacturing of photovoltaic devices. However, these methods often rely on simple metal salts (such as metal halides, nitrates, or acetates) as precursors, and the anions in these salts can lead to impurities in the final product. To bypass this challenge, researchers have developed chemistries that allow for the dissolution of metal and metal chalcogenide precursors through a reactive dissolution that produces a soluble complex with metal-sulfur bonding. While this is suitable for the synthesis of metal sulfides, similar routes for metal selenides are lacking.
In this project, we investigate a new and facile route to directly produce soluble metal polyselenides and the application of these complexes as solution-phase precursors for metal selenide synthesis. Researchers will crystallize the metal polyselenides and utilize X-Ray Diffraction to determine the exact structure of the complexes. Additionally, researchers will utilize these precursors to make metal selenide thin films for application in solar cells. In this work, researchers will gain experience in chemical synthesis, thin-film fabrication, and materials characterization, while learning how these concepts can be applied to photovoltaics.
In this project, we investigate a new and facile route to directly produce soluble metal polyselenides and the application of these complexes as solution-phase precursors for metal selenide synthesis. Researchers will crystallize the metal polyselenides and utilize X-Ray Diffraction to determine the exact structure of the complexes. Additionally, researchers will utilize these precursors to make metal selenide thin films for application in solar cells. In this work, researchers will gain experience in chemical synthesis, thin-film fabrication, and materials characterization, while learning how these concepts can be applied to photovoltaics.
Research categories:
Energy and Environment, Material Processing and Characterization
Preferred major(s):
- Chemical Engineering
Desired experience:
General Chemistry-level lab experience
School/Dept.:
Davidson School of Chemical Engineering
Professor:
Rakesh
Agrawal
More information: https://engineering.purdue.edu/RARG/members/solar-energy/
Mixed-Reality Testbed for Human-Robot Interaction
Description:
In various emerging applications of autonomy, including autonomous driving, teleoperation, and assistive robotics, a human and an autonomous system closely interact with each other. This project’s goal is to develop a mixed-reality platform to facilitate research on trustworthy human-robot interaction. The platform will enable the creation of different environments and scenarios, in which human users/operators and robots interact, to facilitate the development and training of learning-based control algorithms for autonomous systems. The undergraduate researcher will contribute to setting up the mixed-reality platform as well as the design and implementation of high-level control algorithms.
Research categories:
Big Data/Machine Learning, Fabrication and Robotics, Human Factors
Preferred major(s):
- No Major Restriction
School/Dept.:
Electrical and Computer Engineering
Professor:
Mahsa
Ghasemi
More information: https://mahsaghasemi.github.io/
Modeling High Efficiency Thermophotovoltaic Systems
Description:
This project studies by numerical simulation the impact of optical multilayer structure on improving the efficiency of thermophotovoltaic (TPV) devices. TPV devices convert heat to electricity using thermal radiation to illuminate a photo-voltaic (PV) diode made from semiconductor materials. Typically, this radiation is generated by a blackbody-like emitter. Thermal radiation includes a broad range of wavelengths, but only high energy photons can be converted to heat by the PV diode, which severely limits efficiency. Thus, introducing a selective emitter and filter to recycle unwanted photons can greatly enhance performance.
In this project, the student will develop/upgrade a GUI-based tool to calculate the emittance spectrum and efficiency of a multilayer structure based TPV device. The tool is hosted and run through nanoHUB.org - an open-access science gateway for cloud-based simulation tools and resources in nanoscale science and technology. The student will also work with graduate students and use this tool to study how to improve the TPV efficiency based on physical models.
In this project, the student will develop/upgrade a GUI-based tool to calculate the emittance spectrum and efficiency of a multilayer structure based TPV device. The tool is hosted and run through nanoHUB.org - an open-access science gateway for cloud-based simulation tools and resources in nanoscale science and technology. The student will also work with graduate students and use this tool to study how to improve the TPV efficiency based on physical models.
Research categories:
Nanotechnology, Thermal Technology
Preferred major(s):
- Electrical Engineering
- Computer Engineering
- Mechanical Engineering
- Physics
Desired experience:
Programming experience in Python, C/C++, and/or MATLAB/Octave
Enthusiasm for scientific computing
Good understanding of electromagnetism and heat transfer
School/Dept.:
Electrical & Computer Engineering
Professor:
Peter
Bermel
Modular soft robots
Description:
Soft robots offer new capabilities compared to traditional rigid robots due to their ability to continuously deform into arbitrary shapes and allowing safe interaction with humans. One limitation of soft robots is that they are not easily re-purposed. Modular robotics is a recent development to enable a wider range of application for soft robots. My lab has developed a type of modular soft robot that works much like Lego. Additional work is needed to refine the control systems and the mechanics of individual modules. A main motivation is to use the modular robots to build biologically-inspired assemblies.
Research categories:
Fabrication and Robotics
Preferred major(s):
- No Major Restriction
Desired experience:
Required: Electronics, controls (e.g. arduino or other micro-processors)
Required: Programming
Desired: Finite-element analysis
School/Dept.:
Mechanical Engineering
Professor:
Adrian
Buganza Tepole
More information: https://engineering.purdue.edu/tepolelab/
Molecular microscopy to inform the design of medications
Description:
As illustrated with the COVID vaccines, storage and stability of medications can limit widespread availability. We are developing innovative chemical imaging tools with ultrafast pulsed lasers capable of mapping transformations within medical formulations to model and inform stability and bioavailability. Depending on the interests of the students, project scope can range from: i) bench-science in sample preparation and characterization, ii) instrument design and optical path alignment, iii) data acquisition and image analysis algorithm development, iv) partnership with collaborators in the pharmaceutical industry. We have a vibrant and diverse cohort of current researchers dedicated to fostering a supportive and collaborative research environment for all.
Research categories:
Big Data/Machine Learning, Biological Characterization and Imaging, Material Processing and Characterization, Medical Science and Technology
Preferred major(s):
- No Major Restriction
School/Dept.:
Chemistry
Professor:
Garth
Simpson
More information: http://www.chem.purdue.edu/simpson/
Multi-physics simulation software development for tribology experiment
Description:
Simulation software development has become an important skill for engineering researchers. However, unlike your coding class, you will not be the first person to contribute to the code in most software development scenarios. This SURF project allows you to experience simulation software development for a real-life application from an in-house developed API (roughly 75,000 lines already in place, current 8 active developers). The real-life application is a tribology test rig that will test friction and wear with pressurized fluid (up to 500 bar). Your 'customer' is a Ph.D. student who designed the test rig and is working with a manufacturing company to ensure the device will arrive in the lab by September. The 'product' will be a simulation software for this test rig that can predict friction, leakage, pressure, thermal behavior of the test rig, considering hydrodynamics, elastic deformation, macro and micro motions, fluid properties, and thermal condition. Your graduate mentor will guide you through the simulation API (you are not expected to understand the entire 75k lines of code) and a series of examples that will equip you with all the knowledge on the coding side. At the same time, you will need to actively communicate with your 'customer' to understand the dynamic and kinematic of the engineering problem. Your work will constitute world-leading tribological research in the fluid power field. Therefore, it is also very possible to be published with you as a co-author.
Research categories:
Fluid Modelling and Simulation, Material Modeling and Simulation
Preferred major(s):
- No Major Restriction
Desired experience:
Familiarity with C++, MATLAB, Reading CAD models
Some coursework related to Fluid Mechanics.
School/Dept.:
Mechanical Engineering
Professor:
Lizhi
Shang
More information: https://engineering.purdue.edu/Maha/
Multimaterial 3D Printing of Bioinspired Robotics
Description:
Technologies that integrate with biology enable new approaches to augmented reality as well as improved quality of life for people with medical conditions. To enable this integration, technology must take on some of the characteristic of biological systems, such as softness and 3D form factors. 3D printing can create soft electronic systems that mimic biological systems, including the ability sense their surroundings, process information, and actuate in response.
In this project, a student will work with a PhD student to prepare electronic materials, fabricate bio-inspired electronic devices and test their device operation.
There are different research scopes that are available depending on student interest/capabilities. Examples include:
-Materials development, consisting of preparing bio-inspired materials and optimizing their composition to achieve target electromechanical properties. Learned skills include elastomer chemistry, polymer physics, and electromechanical testing.
-Device fabrication, consisting of printing devices that include multiple electronic materials and testing their properties. Learned skills include device physics, printer operation and print path design, and circuit design for system measurement/controls.
-System modeling, consisting of modeling using COMSOL or ABAQUS to identify ideal device structures and materials properties that act as targets for experimental efforts. Learned skills include mechanical modeling software and application knowledge.
In this project, a student will work with a PhD student to prepare electronic materials, fabricate bio-inspired electronic devices and test their device operation.
There are different research scopes that are available depending on student interest/capabilities. Examples include:
-Materials development, consisting of preparing bio-inspired materials and optimizing their composition to achieve target electromechanical properties. Learned skills include elastomer chemistry, polymer physics, and electromechanical testing.
-Device fabrication, consisting of printing devices that include multiple electronic materials and testing their properties. Learned skills include device physics, printer operation and print path design, and circuit design for system measurement/controls.
-System modeling, consisting of modeling using COMSOL or ABAQUS to identify ideal device structures and materials properties that act as targets for experimental efforts. Learned skills include mechanical modeling software and application knowledge.
Research categories:
Fabrication and Robotics, Material Modeling and Simulation, Material Processing and Characterization
Preferred major(s):
- No Major Restriction
Desired experience:
No specific experience is necessary. Any previous lab experience is an asset.
School/Dept.:
Mechanical Engineering
Professor:
Alex
Chortos
More information: https://engineering.purdue.edu/ME/People/ptProfile?resource_id=243743
Nanoscale High-Speed 3D Printing
Description:
The ability to create 3D structures in the micro and nanoscale is important for many applications including electronics, microfluidics, and tissue engineering. This project deals with development and testing of a setup for building 3D structures using a femtosecond pulsed laser. A method known as two photon polymerization is typically used to fabricate such structures in which a polymer is exposed to a laser beam and at the point of the exposure the polymer changes its structure. Moving the laser in a predefined path helps in getting the desired shape, and the structures are then built in a layer by layer fashion. The setup incorporates all the steps from a designing a CAD model file to slicing the model in layers to generating the motion path of the laser needed for fabricating the structure. Like many other 3D printing processes, 3D printing at nanoscale is also slow. In order to make a 3D structure rapidly, many processes are currently being developed, including projecting 2D images and printing 3D structures in a rapid, layer-by-layer fashion. Other efforts include the use of machine learning to produce high quality 3D parts and printing materials other than polymers to achieve specific mechanical, electrical or optical properties. The undergraduate student will work with graduate student to learn the state-of-the-art 3D nanoprinting systems, help to develop rapid printing processes, and analyze printing results.
Research categories:
Big Data/Machine Learning, Deep Learning, Fabrication and Robotics, Material Processing and Characterization, Nanotechnology
Preferred major(s):
- Mechanical Engineering
- Physics
- Industrial Engineering
- Computer Engineering
Desired experience:
Junior or Senior standing, knowledge in CAD, knowledge in Python is a plus
School/Dept.:
Mechanical Engineering
Professor:
Xianfan
Xu
More information: https://engineering.purdue.edu/~xxu/; https://engineering.purdue.edu/NanoLab/
Nanotechnology-based advanced materials
Description:
This project aims to develop advanced materials with programmability and multifunctionality. Two positions are available. One is for 2D materials (such as graphene and TMDs) for energy and electronics; the other is for DNA engineering for nanomachines.
Research categories:
Nanotechnology
Preferred major(s):
- Mechanical Engineering
School/Dept.:
Mechanical Engineering
Professor:
Jong Hyun
Choi
Non-Invasive Physiological Signals that Indicate Severity of Parkinsons Disease
Description:
The goal is to collect data non-invasively from physiological signals that can be used as indicators of disorders in the motor cortex in subjects. Additionally, we hope to relate any abnormalities in these signals back to past behaviours and experiences such as demographics (e.g. age and gender), medication and head injuries (TBI). The aim for the summer is to create and test methods for obtaining a reliable, non-invasive measure of galvanic skin response from patients as well as to obtain patient test data from Parkinson's Disease at IU Health Physicians Neurology clinic in Indianapolis.
These methods will first replicate and then test conditions important for measuring motor function and size effects. Findings will be used as pilot data for possible future research.
These methods will first replicate and then test conditions important for measuring motor function and size effects. Findings will be used as pilot data for possible future research.
Research categories:
Big Data/Machine Learning, Medical Science and Technology
Preferred major(s):
- Biomedical Engineering
Desired experience:
Human Subject Data Collection Experience
Statistics
Signal Processing
School/Dept.:
Psychological Sciences
Professor:
Anne
Sereno
On a Microgrid for Renewable Energy Systems and Water Security
Description:
NA
Research categories:
Other
Preferred major(s):
- No Major Restriction
School/Dept.:
ME
Professor:
Luciano
Castillo
Operation and characterization of SPT-100 Hall thruster
Description:
Hall thrusters are widely utilized for spacecraft propulsion. Mars exploration missions currently planned by NASA utilize Deep Space Transport which is going to be propelled by Hall thruster technology. In Hall thruster neutral gas propellant is ionized and accelerated in ExB-field configuration to reach high propellant exhaust velocities in the range 10 - 50 km/s.
In this project student will work with Hall thruster SPT-100. The project will include operating the thruster and hollow cathode neutralizer, and measurements of electrical parameters of the thruster, exhaust plasma jet properties, and thrust. The student will use Langmuir probes for measurements of plasma parameters and hanging pendulum thrust stand for the thrust measurements. In addition, the student is going to prepare and update related documentation for AAE 521 Plasma Lab.
In this project student will work with Hall thruster SPT-100. The project will include operating the thruster and hollow cathode neutralizer, and measurements of electrical parameters of the thruster, exhaust plasma jet properties, and thrust. The student will use Langmuir probes for measurements of plasma parameters and hanging pendulum thrust stand for the thrust measurements. In addition, the student is going to prepare and update related documentation for AAE 521 Plasma Lab.
Research categories:
Other
Preferred major(s):
- No Major Restriction
School/Dept.:
AAE
Professor:
Alexey
Shashurin
More information: https://engineering.purdue.edu/EPPL
Physics and Analytics of Lithium Batteries
Description:
Lithium ion (Li-ion) batteries are ubiquitous. Thermal, electrochemical, and degradation characteristics of these systems are critical toward safer and high-performance batteries for electric vehicles. As part of this research, physics-based and data-driven analytics of experimental and simulated performance under normal and anomalous operating conditions of lithium-ion and lithium metal batteries will be performed.
The final deliverable will be one research report (based on weekly progress presentations and updates) and one final presentation.
The final deliverable will be one research report (based on weekly progress presentations and updates) and one final presentation.
Research categories:
Energy and Environment, Material Modeling and Simulation, Material Processing and Characterization, Thermal Technology
Preferred major(s):
- No Major Restriction
Desired experience:
Strong analytical skill and desire to learn new experimental and modeling & analysis tools.
School/Dept.:
Mechanical Engineering
Professor:
Partha
Mukherjee
More information: https://engineering.purdue.edu/ETSL/
Physics-Informed Machine Learning to Improve the Predictability of Extreme Weather Events
Description:
Atmospheric blocking events and 'Bomb Cyclones' are an important contributor to high impact extreme weather events. Both these weather extremes lead to heat waves, cold spells, droughts, and heavy precipitation episodes, which have dire consequences for the public health, economy, and ecosystem. For example, the blocking-induced heat waves of 2003 in Europe led to tens of thousands of human casualties and tens of billions of dollars of financial damage.
Traditionally, prediction of extreme weather events is based on direct numerical simulation of regional or global atmospheric models, which are expensive to conduct and involve a large number of tunable parameters. However, with the rapid rise of data science and machine learning in recent years, this proposed work will apply convolutional neural network to an idealized atmospheric model to conduct predictability analysis of extreme weather events within this model. With this proposed machine-learning algorithm, our project will provide a robust forecast of heat waves and atmospheric blocking with a lead-time of a few weeks. With more frequent record-breaking heat waves in the future, such a prediction will offer a crucial period of time (a few weeks) for our society to take proper preparedness steps to protect our vulnerable citizens.
This project is based on developing and verifying the machine learning algorithm for detecting extreme weather events in an idealized model. We will use Purdue’s supercomputer Bell to conduct the simulations. The undergraduate student will play an active and important role in running the idealized model, and participate in developing the algorithms. As an important component of climate preparedness, the proposed work aims to develop a physics-informed machine learning framework to improve predictability of extreme weather events.
Closely advised by Prof. Wang, the student will conduct numerical simulations of an idealized and very simple climate model, and use python-based machine learning tools to predict extreme weather events within the model. Prof. Wang will provide weekly tutorial sessions to teach key techniques along with interactive hands-on sessions. The students will get access to the big datasets on Purdue’s Data Depot, analyze and visualize data of an idealized atmospheric model. The student will use convolutional neural networks (CNNs) to train and assess a Machine-Learning model. The student will further use feature tracking algorithm to backward identify the physical structure in the atmosphere that is responsible for the onset of extreme weather events.
Traditionally, prediction of extreme weather events is based on direct numerical simulation of regional or global atmospheric models, which are expensive to conduct and involve a large number of tunable parameters. However, with the rapid rise of data science and machine learning in recent years, this proposed work will apply convolutional neural network to an idealized atmospheric model to conduct predictability analysis of extreme weather events within this model. With this proposed machine-learning algorithm, our project will provide a robust forecast of heat waves and atmospheric blocking with a lead-time of a few weeks. With more frequent record-breaking heat waves in the future, such a prediction will offer a crucial period of time (a few weeks) for our society to take proper preparedness steps to protect our vulnerable citizens.
This project is based on developing and verifying the machine learning algorithm for detecting extreme weather events in an idealized model. We will use Purdue’s supercomputer Bell to conduct the simulations. The undergraduate student will play an active and important role in running the idealized model, and participate in developing the algorithms. As an important component of climate preparedness, the proposed work aims to develop a physics-informed machine learning framework to improve predictability of extreme weather events.
Closely advised by Prof. Wang, the student will conduct numerical simulations of an idealized and very simple climate model, and use python-based machine learning tools to predict extreme weather events within the model. Prof. Wang will provide weekly tutorial sessions to teach key techniques along with interactive hands-on sessions. The students will get access to the big datasets on Purdue’s Data Depot, analyze and visualize data of an idealized atmospheric model. The student will use convolutional neural networks (CNNs) to train and assess a Machine-Learning model. The student will further use feature tracking algorithm to backward identify the physical structure in the atmosphere that is responsible for the onset of extreme weather events.
Research categories:
Big Data/Machine Learning, Deep Learning, Energy and Environment, Environmental Characterization, Fluid Modelling and Simulation
Preferred major(s):
- Physics
- Planetary Sciences
- Atmospheric Science/Meteorology
- Computer Science
- Mathematics - Computer Science
- Mathematics
- Environmental Geosciences
- Mechanical Engineering
- Civil Engineering
- Aeronautical and Astronautical Engineering
- Computer Engineering
- Engineering (First Year)
- Multidisciplinary Engineering
- Natural Resources and Environmental Science (multiple concentrations)
Desired experience:
Familiar with Machine Learning or prior knowledge of convolutional neural networks (CNNs);
Have basic level training on PHYS172 Modern Mechanics or PHYS 15200 Mechanics or equivalent courses from other institutions; Familiar with Python scripting and visualization
School/Dept.:
Earth, Atmospheric, and Planetary Sciences
Professor:
Lei
Wang
More information: https://www.eaps.purdue.edu/people/profile/wanglei.html
Polaritonic Energy Transport: Hybridizing Radiation and Conduction for Microelectronics Cooling
Description:
Who we are… Specere is a latin word that means “to look or behold.” That’s what we do. We look, explore, and examine different ways to: (1) move energy with light and (2) get information from light. More specifically, we are a light lab employing infrared physics to create spectroscopic, thermal, and sensing solutions.
Who we are seeking… We look for motivated and hard-working undergraduates having both strong aspirations for post-graduate studies as well as those that are just “grad school curious.” All applicants should be capable of working independently while effectively communicating within a team setting.
Research Topic, Polaritonic Energy Transport: We seek to design materials capable of more effectively moving heat at extremely small scales like those in modern microelectronics. Success will enable: more efficient data centers, power electronics like those in EV’s, and new computing architectures.
What’ You’ll Do: Team members will be responsible for designing novel metamaterial stacks capable of maximizing heat transfer using a combination of computational modeling and experimental measurements of optical properties. Direct mentoring from Dr. Beechem will build your skills up in each area such that you will gain proficiency in advanced simulation (COMSOL) and spectroscopic tools (Raman, IR-ellipsometry). In addition, you will have the chance to participate in writing journal articles and pursuing patents based on your work.
Who we are seeking… We look for motivated and hard-working undergraduates having both strong aspirations for post-graduate studies as well as those that are just “grad school curious.” All applicants should be capable of working independently while effectively communicating within a team setting.
Research Topic, Polaritonic Energy Transport: We seek to design materials capable of more effectively moving heat at extremely small scales like those in modern microelectronics. Success will enable: more efficient data centers, power electronics like those in EV’s, and new computing architectures.
What’ You’ll Do: Team members will be responsible for designing novel metamaterial stacks capable of maximizing heat transfer using a combination of computational modeling and experimental measurements of optical properties. Direct mentoring from Dr. Beechem will build your skills up in each area such that you will gain proficiency in advanced simulation (COMSOL) and spectroscopic tools (Raman, IR-ellipsometry). In addition, you will have the chance to participate in writing journal articles and pursuing patents based on your work.
Research categories:
Big Data/Machine Learning, Material Modeling and Simulation, Material Processing and Characterization, Nanotechnology, Thermal Technology
Preferred major(s):
- No Major Restriction
Desired experience:
Proficiency in Matlab, COMSOL or both is a plus.
School/Dept.:
School of Mechanical Engineering
Professor:
Thomas
Beechem
More information: www.specere.org
Processing of Sustainable Food Packaging and/or Fire Fighting Foams
Description:
Packaging is a critical feature of the food delivery supply chain. Food packaging is not just there to “hold” food but provides a critical function of preservation over long periods in a wide variety of temperature and humidity conditions. Plastics are the material of choice for many applications due to its low density and cost for the function and can actually be lower total carbon emissions that other materials such as glass and paper. Unfortunately, most are not inherently sustainable. However, as food waste is ~10% of greenhouse gas emissions, elimination of plastic in packaging could actually be worse for the environment. What is needed is an alternative material that can obtain the same stringent barrier requirements that is sustainable. Cellulose is one such material and is biodegradable as well. This project will investigate Cellulose Nanomaterials extracted from trees to investigate sustainable packaging. Due to its nanoscale size, it obtains properties more like plastic films than paper with regards to barrier, strength, etc. Alternatively, research will be conducted into finding replacements for aqueous fluorinated fire-fighting foams (AFFF). AFFF is currently the only qualified milspec Naval fire fighting foam surfactant, but the principal component, fluorosurfactants, are known toxic compounds, hence, replacement is necessary. This project will research non-toxic, non-fluorinated replacements as well as additives to improve performance. At this point, it is unknown which project will be pursued, but it is sure that fun with sustainable materials will be had!
Research categories:
Chemical Catalysis and Synthesis, Material Processing and Characterization
Preferred major(s):
- No Major Restriction
Desired experience:
Enthusiasm and interest for materials engineering, chemistry, and sustainability.
School/Dept.:
MSE
Professor:
Jeffrey
Youngblood
More information: https://scholar.google.com/citations?hl=en&user=qkkQBDsAAAAJ&view_op=list_works&alert_preview_top_rm=2&sortby=pubdate
Radiation-hardened technologies
Description:
Radiation in natural and manmade environments can greatly affect the operation and long-term performance of microelectronics. Radiation hardening is making electronic components and circuits resistant to damage or malfunction caused by high levels of ionizing radiation. Transient effects include single-event effects like memory bit flips; permanent effects include single-event latchups that prevent individual devices from operating. In these projects, students will explore the underlying failure mechanisms for electronics exposed to radiation, methods to predict failure rates, and a range of mitigation approaches for radiation damage, which include radiation-hardening by process and radiation-hardening by design.
Research categories:
Material Modeling and Simulation
Preferred major(s):
- No Major Restriction
School/Dept.:
Nuclear Engineering
Professor:
Allen
Garner
Reachability Analysis for Complex Dynamic Systems
Description:
Numerical simulations of complex dynamical systems are difficult to evaluate in realistic scenarios due to uncertainty in the environment. To understand properties of these systems, such as performance and safety, the dynamic models are simulated for sets of possible interactions – a method known as Reachability Analysis. We are looking for a student interested in numerical methods and learning about these tools so that they can apply them to existing examples and prepare for a friendly competition of related tools that will occur later in 2022. Students with an interest in dynamics or control systems are encouraged to apply.
Research categories:
Other
Preferred major(s):
- Mechanical Engineering
- Electrical Engineering
- Aeronautical and Astronautical Engineering
Desired experience:
Understanding of dynamic systems and proficiency with coding in MATLAB.
School/Dept.:
School of Mechanical Engineering
Professor:
Neera
Jain
More information: https://engineering.purdue.edu/JainResearchLab/
Real time analysis of viral particles
Description:
The increasing worldwide demand for vaccines along with the intensifying economic pressure on health care systems underlines the need for further improvement of vaccine manufacturing. In addition, regulatory authorities are encouraging investment in continuous manufacturing process to ensure robust production, avoid shortages, and ultimately lower the cost of medications for patients. The limitations of in-line process analytical tools are a serious drawback of the efforts taken in place. In line analysis of viral particles are very limited, due to the large time required for the current techniques for detection, qualitative and quantitative analysis. Therefore, there is a need for new process analytical technology. This project has both experimental and computation components and two students will be recruited for perform different tasks. The student focusing on experiments will fabricate devices and test them. The student focusing on computations will focus on developing machine learning codes.
Research categories:
Big Data/Machine Learning, Biological Characterization and Imaging, Biological Simulation and Technology, Biotechnology Data Insights, Cellular Biology, Computer Architecture, Deep Learning
Preferred major(s):
- No Major Restriction
Desired experience:
For the experimental portion of the project: fabrication, cell culture, microfluidics, microscopy
For the computational portion of the project: Coding, Python
School/Dept.:
Mechanical Engineering
Professor:
Arezoo
Ardekani
More information: https://engineering.purdue.edu/ComplexFlowLab/
Real-Time Measurements of Volatile Chemicals in Buildings with Proton Transfer Reaction Mass Spectrometry
Description:
The objective of this project is to utilize state-of-the-art proton transfer reaction mass spectrometry (PTR-MS) to evaluate emissions and exposures of volatile chemicals in buildings. My group is investigating volatile chemical emissions from consumer and personal care products, disinfectants and cleaning agents, and building and construction materials. You will assist graduate students with full-scale experiments with our PTR-MS in our new Purdue zEDGE Tiny House and process and analyze indoor air data in MATLAB.
Research categories:
Big Data/Machine Learning, Ecology and Sustainability, Energy and Environment, Engineering the Built Environment, Environmental Characterization, Human Factors, Internet of Things
Preferred major(s):
- No Major Restriction
Desired experience:
Preferred skills: experience with MATLAB, Python, or R. Coursework: environmental science and chemistry, physics, thermodynamics, heat/mass transfer, and fluid mechanics.
School/Dept.:
Lyles School of Civil Engineering
Professor:
Nusrat
Jung
More information: https://www.purdue.edu/newsroom/stories/2020/Stories%20at%20Purdue/new-purdue-lab-provides-tiny-home-for-sustainability-education.html
Renewable energy-powered water technologies
Description:
Water and energy are tightly linked resources that must both become renewable for a successful future. However, today, water and energy resources are often in conflict with one another, especially related to impacts on electric grids. Further, advances in nanotechnology, material science and artificial intelligence allow for new avenues to improve the widespread implementation of desalination and water purification technology. The team is pursuing multiple projects that aim to explore solar and wind-powered desalination, nanofabricated membranes, light-driven reactions, artificial intelligence control algorithms, and thermodynamic optimization of energy systems. The student will be responsible for fabricating membranes, building hydraulic systems, modeling thermal fluid phenomenon, analyzing data, or implementing control strategies in novel system configurations. More information here: www.warsinger.com
Research categories:
Big Data/Machine Learning, Chemical Catalysis and Synthesis, Ecology and Sustainability, Energy and Environment, Engineering the Built Environment, Environmental Characterization, Fluid Modelling and Simulation, Material Modeling and Simulation, Nanotechnology, Thermal Technology
Preferred major(s):
- Mechanical Engineering
- Civil Engineering
- Environmental and Ecological Engineering
- Chemistry
- Chemical Engineering
- Materials Engineering
Desired experience:
Applicants should have an interest in thermodynamics, water treatment, and sustainability. Applicants with experience in some (not all) of the following are preferred: experimental design and prototyping, manufacturing, Python, LabView, EES, MATLAB, 3D CAD Software, & Adobe Illustrator. Rising Juniors and Seniors are preferred.
School/Dept.:
Mechanical Engineering
Professor:
David
Warsinger
More information: www.warsinger.com
Resilient AI Network (RAIN) for next-generation manufacturing.
Description:
Private project for Devin Kelly
Research categories:
Big Data/Machine Learning
Preferred major(s):
- No Major Restriction
School/Dept.:
ECE
Professor:
Ali
Shakouri
Resilient Extraterrestrial Habitat Engineering: Design and Testing
Description:
There is growing interest from Space agencies such as NASA and the European Space Agency in establishing permanent human settlements outside Earth. To advance knowledge in the field, the Resilient Extra-Terrestrial Habitat Institute (RETHi) is taking steps to develop technologies that will enable resilient habitats in deep space, that will adapt, absorb and rapidly recover from expected and unexpected disruptions without fundamental changes in function or sacrifices in safety.
To study, demonstrate, and evaluate the technologies developed in pursuit of this mission, a multi-physics cyber-physical testbed is being founded at the Ray W. Herrick Laboratories at Purdue University with collaboration from partners at three universities and two industrial partners. It allows to examine emergent behaviors in habitat systems and the interactions among its virtual (computational) and physical components. The testbed will consider a habitat system and will aim to emulate the extreme temperature fluctuations that happen in deep space. To achieve this goal, a thermal transfer system is being developed, consisting of a chiller, an array of glycol lines, in-line heaters, actuated valves, and a series of sensors. Operated under a tuned controller, the thermal transfer system can cool or heat a certain surface area of the structure of the habitat to maintain a given temperature. However, to fully control the thermal transfer system is not straightforward. One of the critical challenges is its deep uncertainty, which results from inaccurate or long-delay sensors, variant test setup, complex controller design, etc. Therefore, a systematic study is needed to quantify the uncertainties to facilitate the thermal transfer system development. Emulation of a particular scenario considering a meteoroid impact will be performed, with random variations in the location and size of the impact and resulting consequences.
We also aim to consider design trade-offs aimed toward the goals of resilience. Thus, we have also established a modeling platform to support rapid, stochastic simulations of habitat systems to quantify the space architectures that enhance resilience. These might consider the important features of the robots, the sensors, and the structure itself that make the habitat resilient. Physics-Infused modeling is a gray-box method to model physical parameters using low-fidelity/computationally-efficient models in conjunction with high-fidelity/computationally-expensive samples. We combine samples from the high-fidelity model framework with low-fidelity dynamic models and create a better combination for state prediction to achieve this goal. One of the critical problems here is the difference in state space of the models and finding the optimal method to sample a high-fidelity model.
We are looking for undergraduate students to play key roles in this project, under the guidance of a graduate student and faculty members. The students are also expected to prepare a poster presentation on the results, and author a research paper if the desired results are achieved.
To study, demonstrate, and evaluate the technologies developed in pursuit of this mission, a multi-physics cyber-physical testbed is being founded at the Ray W. Herrick Laboratories at Purdue University with collaboration from partners at three universities and two industrial partners. It allows to examine emergent behaviors in habitat systems and the interactions among its virtual (computational) and physical components. The testbed will consider a habitat system and will aim to emulate the extreme temperature fluctuations that happen in deep space. To achieve this goal, a thermal transfer system is being developed, consisting of a chiller, an array of glycol lines, in-line heaters, actuated valves, and a series of sensors. Operated under a tuned controller, the thermal transfer system can cool or heat a certain surface area of the structure of the habitat to maintain a given temperature. However, to fully control the thermal transfer system is not straightforward. One of the critical challenges is its deep uncertainty, which results from inaccurate or long-delay sensors, variant test setup, complex controller design, etc. Therefore, a systematic study is needed to quantify the uncertainties to facilitate the thermal transfer system development. Emulation of a particular scenario considering a meteoroid impact will be performed, with random variations in the location and size of the impact and resulting consequences.
We also aim to consider design trade-offs aimed toward the goals of resilience. Thus, we have also established a modeling platform to support rapid, stochastic simulations of habitat systems to quantify the space architectures that enhance resilience. These might consider the important features of the robots, the sensors, and the structure itself that make the habitat resilient. Physics-Infused modeling is a gray-box method to model physical parameters using low-fidelity/computationally-efficient models in conjunction with high-fidelity/computationally-expensive samples. We combine samples from the high-fidelity model framework with low-fidelity dynamic models and create a better combination for state prediction to achieve this goal. One of the critical problems here is the difference in state space of the models and finding the optimal method to sample a high-fidelity model.
We are looking for undergraduate students to play key roles in this project, under the guidance of a graduate student and faculty members. The students are also expected to prepare a poster presentation on the results, and author a research paper if the desired results are achieved.
Research categories:
Deep Learning, Human Factors, Material Modeling and Simulation, Thermal Technology
Preferred major(s):
- No Major Restriction
- Mechanical Engineering
- Aeronautical and Astronautical Engineering
- Civil Engineering
- Computer Engineering
- Computer Science
Desired experience:
Students interested in this project should be critical thinkers, and have good experimental skills. Some projects will require programming skills (Python), CAD skills, and experience in MATLAB/Simulink.
School/Dept.:
Mechanical Engineering, Civil Engineering, Aerospace Engineering (we will have multiple faculty advising)
Professor:
Shirley
Dyke
More information: https://www.purdue.edu/rethi/
Robust Deep Learning in Complex Real-World Environment
Description:
Machine learning discovers statistical knowledge from data and has escaped from the cage of perception. A growing number of complex systems from walking robots, drones to the computer Go player rely on learning techniques to make decisions to achieve optimal control of complex systems. This change represents a truly fundamental departure from traditional classification and regression methods as such learning systems must deal with complex real-world environment. This team investigates how to utilize robust deep learning for predicting complex real-world environment, such as autonomous driving, and complex network systems.
Research categories:
Big Data/Machine Learning
Preferred major(s):
- No Major Restriction
Desired experience:
Familiar with Python programming and PyTorch or TensorFlow. Have some basic knowledge about machine learning and neural network.
School/Dept.:
School of Mechanical Engineering
Professor:
Guang
Lin
SURF 2022 Colombia Purdue Partnership Project 2
Description:
NA
Research categories:
Other
Preferred major(s):
- No Major Restriction
School/Dept.:
CE
Professor:
Julio
Ramirez
Sentiment analysis and performance monitoring of the air transport sector
Description:
The aim of this project is to build a pipeline to automate the data collection, analysis, and visualization process so that we can monitor the customer satisfaction of the air transport sector. The student researcher will be responsible for the design and implementation of the project with the supervision and guidance of the faculty advisor.
Research categories:
Big Data/Machine Learning
Preferred major(s):
- No Major Restriction
Desired experience:
The student should have good motivation for learning.
The student should have some skills and experience of coding.
The student need to have some general understanding of statistics.
The student is expected to communicate with the faculty advisor often during the project period.
School/Dept.:
School of Aviation and Transportation Technology
Professor:
Yi
Gao
Sister2Sister
Description:
Multiple projects hosted under Sister2Sister
Research categories:
Other
Preferred major(s):
- No Major Restriction
School/Dept.:
School of Materials Engineering
Professor:
John
Howarter
Software for deep learning and deep learning for software
Description:
Possible industry involvement: Some of these projects are funded by Facebook research awards and J.P.Morgan AI research awards.
We have three openings for different tasks including those listed below.
NOTE: We especially encourage applications from women, Aboriginal peoples, and other groups underrepresented in computing.
*** Subproject 1. Testing Deep Learning Systems
We will build cool and novel techniques to make deep learning code such as TensorFlow and PyTorch reliable and secure. We will build it on top of our award-winning paper (ACM SIGSOFT Distinguished Paper Award)!
Machine learning systems including deep learning (DL) systems demand reliability and security. DL systems consist of two key components: (1) models and algorithms that perform complex mathematical calculations, and (2) software that implements the algorithms and models. Here software includes DL infrastructure code (e.g., code that performs core neural network computations) and the application code (e.g., code that loads model weights). Thus, for the entire DL system to be reliable and secure, both the software implementation and models/algorithms must be reliable and secure. If software fails to faithfully implement a model (e.g., due to a bug in the software), the output from the software can be wrong even if the model is correct, and vice versa.
This project aims to use novel approaches including differential testing to detect and localize bugs in DL software (including code and data) to address the testing oracle challenge.
Early work and background can be found here:
EAGLE: Creating Equivalent Graphs to Test Deep Learning Libraries (our ICSE 2022 paper, forthcoming, check my homepage)
https://www.cs.purdue.edu/homes/lintan/publications/fairness-neurips21.pdf
https://www.cs.purdue.edu/homes/lintan/publications/variance-ase20.pdf
https://www.cs.purdue.edu/homes/lintan/publications/cradle-icse19.pdf
*** Subproject 2. Inferring Specifications from Software Text for Finding Bugs and Vulnerabilities
A fundamental challenge of detecting or preventing software bugs and vulnerabilities is to know programmers’ intentions, formally called specifications. If we know the specification of a program (e.g., where a lock is needed, what input a deep learning model expects, etc.), a bug detection tool can check if the code matches the specification.
Building upon our expertise on being the first to extract specifications from code comments to automatically detect software bugs and bad comments, in this project, we will analyze various new sources of software textual information (such as API documents and StackOverflow Posts) to extract specifications for bug detection. For example, the API documents of deep learning libraries such as TensorFlow and PyTorch contain a lot of input constraint information about tensors.
Early work and background can be found here:
https://www.cs.purdue.edu/homes/lintan/projects.html
*** Subproject 3. Leveraging Deep Learning to Detect and Fix Software Bugs and Vulnerabilities
In this project, we will develop cool machine learning approaches to automatically learn bug and vulnerability patterns and fix patterns from historical data to detect and fix software bugs and security vulnerabilities.
Early work and background can be found here:
https://www.cs.purdue.edu/homes/lintan/publications/cure-icse21.pdf
https://www.cs.purdue.edu/homes/lintan/publications/deeplearn-tse18.pdf
We have three openings for different tasks including those listed below.
NOTE: We especially encourage applications from women, Aboriginal peoples, and other groups underrepresented in computing.
*** Subproject 1. Testing Deep Learning Systems
We will build cool and novel techniques to make deep learning code such as TensorFlow and PyTorch reliable and secure. We will build it on top of our award-winning paper (ACM SIGSOFT Distinguished Paper Award)!
Machine learning systems including deep learning (DL) systems demand reliability and security. DL systems consist of two key components: (1) models and algorithms that perform complex mathematical calculations, and (2) software that implements the algorithms and models. Here software includes DL infrastructure code (e.g., code that performs core neural network computations) and the application code (e.g., code that loads model weights). Thus, for the entire DL system to be reliable and secure, both the software implementation and models/algorithms must be reliable and secure. If software fails to faithfully implement a model (e.g., due to a bug in the software), the output from the software can be wrong even if the model is correct, and vice versa.
This project aims to use novel approaches including differential testing to detect and localize bugs in DL software (including code and data) to address the testing oracle challenge.
Early work and background can be found here:
EAGLE: Creating Equivalent Graphs to Test Deep Learning Libraries (our ICSE 2022 paper, forthcoming, check my homepage)
https://www.cs.purdue.edu/homes/lintan/publications/fairness-neurips21.pdf
https://www.cs.purdue.edu/homes/lintan/publications/variance-ase20.pdf
https://www.cs.purdue.edu/homes/lintan/publications/cradle-icse19.pdf
*** Subproject 2. Inferring Specifications from Software Text for Finding Bugs and Vulnerabilities
A fundamental challenge of detecting or preventing software bugs and vulnerabilities is to know programmers’ intentions, formally called specifications. If we know the specification of a program (e.g., where a lock is needed, what input a deep learning model expects, etc.), a bug detection tool can check if the code matches the specification.
Building upon our expertise on being the first to extract specifications from code comments to automatically detect software bugs and bad comments, in this project, we will analyze various new sources of software textual information (such as API documents and StackOverflow Posts) to extract specifications for bug detection. For example, the API documents of deep learning libraries such as TensorFlow and PyTorch contain a lot of input constraint information about tensors.
Early work and background can be found here:
https://www.cs.purdue.edu/homes/lintan/projects.html
*** Subproject 3. Leveraging Deep Learning to Detect and Fix Software Bugs and Vulnerabilities
In this project, we will develop cool machine learning approaches to automatically learn bug and vulnerability patterns and fix patterns from historical data to detect and fix software bugs and security vulnerabilities.
Early work and background can be found here:
https://www.cs.purdue.edu/homes/lintan/publications/cure-icse21.pdf
https://www.cs.purdue.edu/homes/lintan/publications/deeplearn-tse18.pdf
Research categories:
Big Data/Machine Learning, Cybersecurity, Deep Learning, Other
Preferred major(s):
- Computer Science
- Computer Engineering
- Software Engineering
Desired experience:
Strong coding skills and motivation in research are required. Background in security or machine learning is not required but a plus.
School/Dept.:
Computer Science
Professor:
Lin
Tan
More information: https://www.cs.purdue.edu/homes/lintan/research.html
Solving Navigation Challenges for aerial and ground robots in agricultural fields
Description:
The primary aim of this project is to explore mechanisms to establish reliable communication between aerial and ground robots. The aerial robot helps navigate a team of ground robots to reach objects of interest by avoiding collision with nearby objects in an agricultural field. The research would involve experiments with various deep learning models to identify objects of interest and develop an optimal path planning strategy, preferably using deep reinforcement learning. A Remote Control (RC) car platform is being used to mount sensors, edge devices, and implement learning algorithms. The edge device mounted on the RC car offers increased computational power to run the deep learning and deep reinforcement learning models in real-time. With an easy-to-use ROS interface, the RC car is ready to run various deep learning models
The research will be performed in two stages. The first stage would require developing a simulation environment of the field. In the second stage, the strategies designed for the simulation will be translated to RC car (hereafter referred to as ground robot) available in Digital Agriculture Discovery (DAD) lab.
In this project, one student will work with a Ph.D. student to help with the tasks identified below:
Student Task List:
· Report weekly progress using PowerPoint.
· Survey current literature - aerial guided navigation of ground robots and develop a report as per guidance provided
· Simulate aerial robots using a simulation environment
· Train deep learning-based object detection models for identifying objects of interest through aerial robots
· Establish communication between the ground and aerial robots using appropriate communication protocols
· Test the communication between the aerial and ground robots available in the lab
· Deploy deep learning models on the aerial robot
· Test deep learning models on the aerial robot for identifying objects
· Prepare a final report as per the format provided
The research will be performed in two stages. The first stage would require developing a simulation environment of the field. In the second stage, the strategies designed for the simulation will be translated to RC car (hereafter referred to as ground robot) available in Digital Agriculture Discovery (DAD) lab.
In this project, one student will work with a Ph.D. student to help with the tasks identified below:
Student Task List:
· Report weekly progress using PowerPoint.
· Survey current literature - aerial guided navigation of ground robots and develop a report as per guidance provided
· Simulate aerial robots using a simulation environment
· Train deep learning-based object detection models for identifying objects of interest through aerial robots
· Establish communication between the ground and aerial robots using appropriate communication protocols
· Test the communication between the aerial and ground robots available in the lab
· Deploy deep learning models on the aerial robot
· Test deep learning models on the aerial robot for identifying objects
· Prepare a final report as per the format provided
Research categories:
Fabrication and Robotics
Preferred major(s):
- Agricultural Engineering
- Biological Engineering - multiple concentrations
- Agricultural and Biological Engineering
- Computer Science
- Computer Engineering
- Electrical Engineering
- Industrial Engineering
- Industrial Engineering Technology
- Mechanical Engineering
- Mechanical Engineering Technology
- Mechatronics Engineering Technology
- Robotics Engineering Technology
- Automation and Systems Integration Engineering Technology
- or related disciplines
Desired experience:
Desired Skills:
· Python Programming
· MATLAB Programming and Simulation
· Robot Operating Systems (ROS)
· TensorFlow / PyTorch / (equivalent machine learning framework)
· Experience with robotics
· Experience with circuit design
· Interest in Unmanned Aerial Systems (UAS)
· Highly motivated and ready to work in a team
School/Dept.:
Agricultural and Biological Engineering
Professor:
Dharmendra
Saraswat
Spatio-temporal modeling of inter-correlated arrivals: A case study on Indiana state-wide nursing homes.
Description:
Maintaining an appropriate staffing level is essential to providing a healthy workplace
environment at nursing homes and ensuring quality care among residents. To alleviate staffing level concerns and maintain the necessary care quantity, we use the Minimum Data Set (MDS) to calculate and forecast the resident arrivals and care needs (measured in staff-time). For the care transition from hospitals to nursing homes, the demand come from different hospitals and dispose to different nursing facilities. Thus, there is a need to analyze the spatial and temporal pattern of the inter-correlated residents arrivals, confounded by multi-level risk factors, including individual level (clinical and behavioral assessment variables), health systems level (access to acute care), and health policy level.
environment at nursing homes and ensuring quality care among residents. To alleviate staffing level concerns and maintain the necessary care quantity, we use the Minimum Data Set (MDS) to calculate and forecast the resident arrivals and care needs (measured in staff-time). For the care transition from hospitals to nursing homes, the demand come from different hospitals and dispose to different nursing facilities. Thus, there is a need to analyze the spatial and temporal pattern of the inter-correlated residents arrivals, confounded by multi-level risk factors, including individual level (clinical and behavioral assessment variables), health systems level (access to acute care), and health policy level.
Research categories:
Big Data/Machine Learning
Preferred major(s):
- Economics (School of Management)
- Computer Science
- Data Science
- Industrial Engineering
Desired experience:
basic data processing courses offered by the Data Mine or equivalent;
some experience in processing and visualizing large amount of data, preferably healthcare claims data.
basic knowledge in data modeling (e.g., regression), preferably time-series analysis and spatial analysis
School/Dept.:
Biomedical Engineering
Professor:
Nan
Kong
Stem cell immunoengineering for targeted cancer therapy
Description:
Cancer is a major threat for humans worldwide, with over 18 million new cases and 9.6 million cancer-related deaths in 2019. Although most common cancer treatments include surgery, chemotherapy, and radiotherapy, unsatisfactory cure rates require new therapeutic approaches. Recently, adoptive cellular immunotherapies with chimeric antigen receptor (CAR) engineered T and natural killer (NK) cells have shown impressive clinical responses in patients with various blood and solid cancers. However, current clinical practices are limited by the need of large numbers of healthy immune cells, resistance to gene editing, lack of in vivo persistence, and a burdensome manufacturing strategy that requires donor cell extraction, modulation, expansion, and re-introduction per each patient. The ability to generate universally histocompatible and
genetically-enhanced immune cells from continuously renewable human pluripotent stem cell (hPSC) lines offers the potential to develop a true off-the-shelf cellular immunotherapy. While functional CAR-T and NK cells have been successfully derived from hPSCs, a significant gap remains in the scalability, time-consuming (5 or more weeks), purity and robustness of the differentiation methods due to the cumbersome use of serum, and/or feeder cells, which will incur potential risk for contamination and may cause batch-dependency in the treatment. This project thus aims to develop a novel, chemically-defined platform for robust production of CAR-T and CAR-NK cells from hPSCs.
genetically-enhanced immune cells from continuously renewable human pluripotent stem cell (hPSC) lines offers the potential to develop a true off-the-shelf cellular immunotherapy. While functional CAR-T and NK cells have been successfully derived from hPSCs, a significant gap remains in the scalability, time-consuming (5 or more weeks), purity and robustness of the differentiation methods due to the cumbersome use of serum, and/or feeder cells, which will incur potential risk for contamination and may cause batch-dependency in the treatment. This project thus aims to develop a novel, chemically-defined platform for robust production of CAR-T and CAR-NK cells from hPSCs.
Research categories:
Biological Characterization and Imaging, Biological Simulation and Technology, Cellular Biology, Genetics, Medical Science and Technology
Preferred major(s):
- No Major Restriction
- Chemical Engineering
- Biological Engineering - multiple concentrations
- Biochemistry
- any related major
Desired experience:
Previous experience with cell culture and molecular biology is a bonus, but NOT required.
School/Dept.:
Davidson School of Chemical Engineering
Professor:
Xiaoping
Bao
More information: https://engineering.purdue.edu/ChE/people/ptProfile?resource_id=210038
Structural Engineering for Blast Resistant Design
Description:
Today’s structures are highly engineered buildings and bridges capable of carrying everyday and extreme loads. In this project, students will get to work on understanding blast engineering design with a special focus on building materials like concrete and steel. Undergraduate researchers will work day-to-day alongside graduate students and permanent sta! to create test plans, fabricate test specimens, and test large-scale structures to failure. Students will leave this summer with a greater understanding of engineering principles including structural dynamics, impact and blast loading, and composite behavior.
Research categories:
Composite Materials and Alloys, Engineering the Built Environment, Fabrication and Robotics, Material Modeling and Simulation
Preferred major(s):
- No Major Restriction
- Civil Engineering
- Mechanical Engineering
- Mechanical Engineering Technology
- Aeronautical and Astronautical Engineering
- Aeronautical Engineering Technology
- Construction Engineering
- Construction Management Technology
- Engineering (First Year)
- Materials Engineering
Desired experience:
Willing to work in a large-scale structural testing facility which may include some manual labor.
School/Dept.:
Civil Engineering
Professor:
Amit
Varma
More information: https://engineering.purdue.edu/~ahvarma/
Studying the role of infection in specific cancers
Description:
We are interested in identifying how certain viruses are causing specific cancers. To this end, we hope to extract host and pathogen specific gene expression patterns from single cell omics technologies from public data.
Research categories:
Big Data/Machine Learning, Cellular Biology
Preferred major(s):
- No Major Restriction
Desired experience:
familiarity with genomics technologies are welcome
School/Dept.:
Computer Science
Professor:
Majid
Kazemian
More information: kazemianlab.com
Studying the role of noncoding RNAs in immune system
Description:
We have identified a range of noncoding RNAs that are affected in cells of human adaptive immune system. We have identified potential biological pathways that are affected by these noncoding RNAs in silico. We now intend to modulate these noncoding RNAs in human and mice cells and measure their contribution to immune system function. Students will learn how to perform qPCR assays and knockdowns of these RNA species in human cells.
Research categories:
Cellular Biology
Preferred major(s):
- No Major Restriction
Desired experience:
basic molecular biology skills such as PCR, qPCR, WB are welcome
School/Dept.:
Biochemistry and computer science
Professor:
Majid
Kazemian
More information: kazemianlab.com
Super-Resolution Optical Imaging with Single Photon Counting and Optomechanics with Nanostructured Membranes
Description:
Two projects are available. One involves the investigation of enhancing optical imaging resolution using single photon counting techniques. Conventional optical imaging has a hard limit on its spatial resolution, to about one half of the wavelength, and many situations can benefit from higher resolution. In addition, it is challenging to image through scattering media. By way of example, being able to sense with light deeper in the brain would be of enormous benefit in neuroscience. The statistics of photons emitted by or transmitted through an object contain valuable information about the object which could be used to enhance image resolution and possibly see through substantial background scatter. Experiments will be conducted using laser light and with a set of single photon avalanche detectors (SPADs) to measure photon correlations in time, over wavevector (direction), and between detectors in various imaging configurations. Results from these experiments will be used to assess the effectiveness of various techniques for enhancing spatial resolution in imaging applications. This work has a diverse set of potential applications including biological imaging, sensing defects in semiconductors, and imaging through fog. The other project relates to optical forces on structured membranes induced by a laser. The modeling of the mechanical motion of a thin membrane deflected by laser light will be used to determine the membrane properties from experimental and simulated data. This will allow extraction of the mechanical material properties and more generally the validation of a theory for optomechanics that can then be used in design. The nascent field of optomechanics offers enormous impact scope, including remote actuation and propulsion, of importance in fields as diverse and molecular biology, communication, and transport. This project relates to attaining the underpinnings to move along such paths in engineering, as well as the basic physics of optical forces in material at small length scales.
Research categories:
Biological Characterization and Imaging, Biological Simulation and Technology, Deep Learning, Material Modeling and Simulation, Nanotechnology, Other
Preferred major(s):
- Electrical Engineering
- Physics
Desired experience:
Students with an interest in experimental work and a strong background in electromagnetics would be a good fit for this project. The undergraduate student will work with graduate students to perform experiments in an optics laboratory, perform modeling and data analysis using MATLAB or python, and review relevant literature to develop a working understanding of single photon measurement techniques and their applications to super-resolution imaging. This project would be suitable for students majoring in electrical engineering, physics, or a related discipline.
School/Dept.:
Electrical and Computer Engineering
Professor:
Kevin
Webb
Surface sound and AI based machine monitoring for smart manufacturing
Description:
A stethoscope-based surface sound sensor has been developed at Purdue and this sensor is being used to monitor manufacturing equipment. Deep learning will be used to classify machine and process conditions using the sound sensor. Student will work with PhD student to implement sound sensors on machines in local manufacturing companies, collect data, build database and dashboard, and develop AI models.
Research categories:
Big Data/Machine Learning, Deep Learning, Fabrication and Robotics, Internet of Things
Preferred major(s):
- Mechanical Engineering
- Computer Engineering
- Electrical Engineering
- Computer and Information Technology
- Computer Science
School/Dept.:
Mechanical Engineering
Professor:
Martin
Jun
More information: https://web.ics.purdue.edu/~jun25/
Sustainable Drinking Water Filtration Systems
Description:
Clean drinking water is a universal right, but on the global scale, we still struggle to provide water free of contaminants to everyone. By developing more efficient systems to purify water, we can expand the availability of clean drinking water and reduce the environmental impact of treatment operations. This project will explore the operation of reverse osmosis membranes as a means of efficiently purifying water.
Reverse osmosis membranes are traditionally an expensive and energy intensive drinking water treatment method, and the membranes can suffer from biofouling that reduce the life of the membrane. Operating reverse osmosis membranes intermittently has profound implications for energy savings, and is still an effective form of water treatment. It is unclear if these systems will also be subject to biofouling, or growth of organisms on and after the filter. In this project, the student will utilize real-time microbiology tools and community sequencing to measure and characterize the microbes able to survive fluctuating salinity levels. It is hypothesized that the fluctuations in salinity will prevent significant growth of any microorganisms, thus extending the life and optimizing the operation of reverse osmosis membranes.
Reverse osmosis membranes are traditionally an expensive and energy intensive drinking water treatment method, and the membranes can suffer from biofouling that reduce the life of the membrane. Operating reverse osmosis membranes intermittently has profound implications for energy savings, and is still an effective form of water treatment. It is unclear if these systems will also be subject to biofouling, or growth of organisms on and after the filter. In this project, the student will utilize real-time microbiology tools and community sequencing to measure and characterize the microbes able to survive fluctuating salinity levels. It is hypothesized that the fluctuations in salinity will prevent significant growth of any microorganisms, thus extending the life and optimizing the operation of reverse osmosis membranes.
Research categories:
Biological Characterization and Imaging, Ecology and Sustainability, Energy and Environment, Engineering the Built Environment
Preferred major(s):
- No Major Restriction
Desired experience:
Biology or engineering background. Lab skills in drinking water characterization, microbiology (e.g. culture plating), or mechanical engineering are desired but not required.
School/Dept.:
Environmental and Ecological Engineering
Professor:
Caitlin
Proctor
Synthetic data generation for flow phenomenon
Description:
Modern machine learning techniques, like deep learning, require a high amount of data for the training process. Acquiring a high amount of experimental data can be a resource-intensive task and can slow down deep learning workflows. For fluids mechanics problems, synthetic data can be used as a good substitute for experimental data as it can be generated in a way that follows the underlying physics of the problem by following the flow behavior. Additionally, the images can be generated to mimic the distribution of experimental data and hence minimize distribution shift. This project aims to develop a software module to generate synthetic particle image data that follows the physics of underlying flows and mimics experimental data for a low distribution shift.
Research categories:
Fluid Modelling and Simulation
Preferred major(s):
- Mechanical Engineering
Desired experience:
ME 308/ME 309: Fluid Mechanics
School/Dept.:
Mechanical Engineering
Professor:
Steven
Wereley
System on Chip design, verification and test
Description:
Engage in a blend of research and design in connection with the SoCET (System on Chip Extension Technologies) team. SoCET is continually designing and fabricating chips for use in a variety of research projects. Several projects are in progress related to electronics security, machine learning acceleration, MRAM, and SoC architecture. Chip design and fabrication is a complex process involving many different specialized skills including architectural modelling, logic design, extensive verification of hardware description code, physical layout of chips, and verification of physical layout. Based on your interests and needs of the team, you will start out in one of these specialties to learn the necessary tools and participate in the design and evaluation of a particular chip or architecture.
Research categories:
Computer Architecture
Preferred major(s):
- No Major Restriction
School/Dept.:
Electrical and Computer Engineering
Professor:
Mark
Johnson
Testing and analysis of simulated reactor cavity building depressurization experiments
Description:
The main goal of the research is to perform tests on an experimental facility that simulates nuclear reactor building response in the event of a depressurization accident caused by a break in the primary coolant boundary of a high temperature nuclear reactor and obtain first-of-a-kind data on the oxygen concentration distribution for validation of reactor safety codes and Computational Fluid Dynamics (CFD) models.
The SURF researcher will: (i) Participate in an experimental testing program along with team members- test preparation-that include checking loop, instruments, conduct of tests, and data acquisition. (ii) Suppot data analysis. (iii) Support CFD analysis using ANSYS-FLUENT
The SURF researcher will: (i) Participate in an experimental testing program along with team members- test preparation-that include checking loop, instruments, conduct of tests, and data acquisition. (ii) Suppot data analysis. (iii) Support CFD analysis using ANSYS-FLUENT
Research categories:
Energy and Environment, Fluid Modelling and Simulation
Preferred major(s):
- Nuclear Engineering
- Mechanical Engineering
Desired experience:
Fluid mechanics, thermodynamics, heat transfer, nuclear engineering courses. Prefer previous experience in Experimental work on thermal and fluid systems, CFD such as ANSYS - FLUENT. Willingness to learn and work with a team on a thermalhydraulics test facility
School/Dept.:
School of Nuclear Engineering
Professor:
Shripad
Revankar
Thermal management of electronic devices
Description:
The continued miniaturization of electronic devices, with expanded functionality at reduced cost, challenges the viability of products across a broad spectrum of industry applications. The electronics industry is driven by global trends in storage, transmission, and processing of extreme quantities of digital information (cloud computing, data centers), increasing electrification of the transportation sector (electric vehicles, hybrid aircraft, batteries), and the proliferation of interconnected computing devices (mobile computing, IoT, 5G). Proper thermal management of electronic devices is critical to avoid overheating failures and ensure energy efficient operation. In view of these rapidly evolving markets, most of the known electronics cooling technologies are approaching their limits and have a direct impact on system performance (e.g., computing power, driving range, device size, etc.).
Research projects in the Cooling Technologies Research Center (CTRC) are exploring new technologies and discovering ways to more effectively apply existing technologies to addresses the needs of companies and organizations in the area of high-performance heat removal from compact spaces. One of the distinctive features of working in this Center is training in practical applications relevant to industry. All of the projects involve close industrial support and collaboration in the research, often with direct transfer of the technologies to the participating industry members. Projects in the Center involve both experimental and computational aspects, are multi-disciplinary in nature, and are open to excellent students with various engineering and science backgrounds. Multiple different research project opportunities are available based on student interests and preferences.
Research projects in the Cooling Technologies Research Center (CTRC) are exploring new technologies and discovering ways to more effectively apply existing technologies to addresses the needs of companies and organizations in the area of high-performance heat removal from compact spaces. One of the distinctive features of working in this Center is training in practical applications relevant to industry. All of the projects involve close industrial support and collaboration in the research, often with direct transfer of the technologies to the participating industry members. Projects in the Center involve both experimental and computational aspects, are multi-disciplinary in nature, and are open to excellent students with various engineering and science backgrounds. Multiple different research project opportunities are available based on student interests and preferences.
Research categories:
Big Data/Machine Learning, Energy and Environment, Fluid Modelling and Simulation, Material Modeling and Simulation, Nanotechnology, Thermal Technology
Preferred major(s):
- No Major Restriction
School/Dept.:
School of Mechanical Engineering
Professor:
Justin
Weibel
More information: https://engineering.purdue.edu/CTRC/research/
Transport in Vanadium Oxides
Description:
Vanadium oxides undergo a metal-insulator transition, changing their resistivity by five orders of magnitude. However, they don't do it all at once! Rather, the material changes piece by piece, at the nanoscale. This project will be to model in detail the total resistance of a substance that has interleaved bits of metal and insulator.
Research categories:
Big Data/Machine Learning, Material Modeling and Simulation
Preferred major(s):
- Physics
Desired experience:
Experience programming and in data analysis is strongly preferred.
School/Dept.:
Physics and Astronomy
Professor:
Erica
Carlson
More information: http://www.physics.purdue.edu/~erica/
Uncovering Patterns in Innovator Behavior and Decision Making
Description:
The objective of the proposed work is to develop an in-depth understanding of the interrelationships between innovator challenges and the resources they require to succeed. This will be done by analyzing innovator stories from publicly available podcasts like ‘How I built this’, and ‘Y-combinator’ which present first-hand accounts of well-known innovator paths to success. Stories from the podcasts will be analyzed to highlight specific problems faced by innovators and the ways they addressed them. The project will involve working with a relational database and a graphical user interface to document and call out variables that are key to each studied story. These data will be analyzed to generate insights to help future innovators. The project offers the opportunity to work closely with a graduate mentor who will assist in conducting the analysis and synthesizing results to draw out insights.
Research categories:
Human Factors, Other
Preferred major(s):
- No Major Restriction
Desired experience:
There are no specific prerequisites in coursework or knowledge for the project.
All that is needed is curiosity and enthusiasm to learn about innovation.
School/Dept.:
School of Civil Engineering
Professor:
Joseph
Sinfield
More information: N/A
Understanding Nanosilica in Concrete Science for Low Carbon Materials
Description:
The project’s goal is to improve the properties of concrete by the incorporation of nano silica. The performance of the concrete such as strength and durability will be evaluated. In this project, the student will be trained to conduct the related experiments and learn how to analyze the data. Undergraduate student works will include the concrete preparation, scanning electron microscope (SEM), pore structure evaluation, and data analysis.
Research categories:
Energy and Environment, Engineering the Built Environment
Preferred major(s):
- No Major Restriction
School/Dept.:
civil engineering
Professor:
Luna
Lu
More information: https://engineering.purdue.edu/SMARTLab
Understanding Quantum Correlations of Light for Imaging
Description:
We aim to leverage the expertise in two fields of computational and quantum imaging to develop classical algorithms to optimize and process quantum correlated images. On one hand, we introduce quantum complexity to imaging algorithms which deserves the attention of AI-assisted signal/image processing to extract hidden information from measurements. On the other hand, we iteratively engineer quantum states of a light source to enhance imaging resolution. Our goal is to implement a room-temperature quantum light source and understand and optimize its quantum correlations in multiple dimensions. We plan to apply computational and machine learning methods to reconstruct images using model-based gradient ascent and Bayesian estimation techniques.
Research categories:
Big Data/Machine Learning, Material Processing and Characterization, Nanotechnology, Thermal Technology
Preferred major(s):
- Electrical Engineering
- Computer Science
- Physics
Desired experience:
Junior or Senior students with experience/knowledge of image processing, machine learning and optics. GPA>3.5
School/Dept.:
Electrical and computer Engineering
Professor:
Mahdi
Hosseini
Understanding Soft Robot Growth
Description:
Soft growing robots are a new type of robot that move similar to plants: growing into their environments (vinerobots.org). While the mechanism of growth has been tested on a wide range of systems, from less that 1 mm to 10 cm in diameter and up to 97m long, the kinematics and mechanics behind this movement are not completely understood as of yet. This project builds on previous work collecting and analyzing data of robot growth using different materials and dimensions in order to build a model of the system. The student will build soft growing robots, design and run experiments to measure different properties of growing, analyze data gathered, and help build potential kinematic models while interfacing with other students working on growing robot projects. This work can help develop the basic equations that allow us and other researchers to understand how these robots move and what they can achieve.
Research categories:
Fabrication and Robotics, Material Modeling and Simulation
Preferred major(s):
- No Major Restriction
Desired experience:
Basic physics
Proficiency with data structure coding (excel, Matlab, etc.)
Experience with optimization and fitting techniques is a plus but not necessary.
School/Dept.:
School of Mechanical Engineering
Professor:
Laura
Blumenschein
More information: http://engineering.purdue.edu/raad
Using plastic microspheres to increase freezing-thaw resistance of construction materials
Description:
Project description: Freezing thaw is one of the major damage sources of construction materials. Traditional methods use air-entrainment admixtures to increase the freeing thaw resistance but cause other issues such as strength decrease. This project will explore to use micrometer scale plastic microspheres in construction materials to increase the freezing thaw resistance while maintain the service properties. Student will help graduate students with various research and experimental work, including but not limited to, material preparation, polishing, and testing. The undergraduate student will also have an opportunity to learn the essential skillsets such experiments design, data analysis and project presentation etc.
Research categories:
Engineering the Built Environment, Material Processing and Characterization
Preferred major(s):
- No Major Restriction
School/Dept.:
civil engineering
Professor:
Luna
Lu
More information: https://engineering.purdue.edu/SMARTLab
Validation of Miniature Coreflood Equipment for Chemical Package Evaluation in the Petroleum Industry
Description:
Challenge: Over the lifespan of a typical oil well one can expect to recover only 20-40% of the total oil in a field, to access the remaining 60-80% advanced technological solutions need to be leveraged. One solution is chemical enhanced oil recovery (cEOR), this technique utilizes a field specific chemical package, consisting of surfactants, polymers, and brine solutions, to mobilize and sweep the remaining oil. In the laboratory we can recreate the subsurface environment and optimize the chemical package to maximize oil recovery through coreflood testing. However, these tests are expensive and it takes weeks to complete a single test. By decreasing the time and cost required to obtain an optimal chemical package, by means of higher throughput, it is expected that cEOR could become a more widely utilized recovery technique.
Target Goal: This project will have both a background review and a hands-on (in laboratory) component with the possibility of being an author in future publications. The initial literature review will introduce the student(s) to the cEOR methodology and industry standards. Once a thorough understanding of accepted processes is obtained the student(s) will begin working on the miniaturized coreflood apparatus designing experiments and performing tests. The student(s) will use the collected data to evaluate the miniature coreflood device performance, throughput, and finally compare results with data collected on a larger, lab-scale coreflood device. Ultimately, the time and cost savings will also be evaluated, and device usefulness will be assessed.
Target Goal: This project will have both a background review and a hands-on (in laboratory) component with the possibility of being an author in future publications. The initial literature review will introduce the student(s) to the cEOR methodology and industry standards. Once a thorough understanding of accepted processes is obtained the student(s) will begin working on the miniaturized coreflood apparatus designing experiments and performing tests. The student(s) will use the collected data to evaluate the miniature coreflood device performance, throughput, and finally compare results with data collected on a larger, lab-scale coreflood device. Ultimately, the time and cost savings will also be evaluated, and device usefulness will be assessed.
Research categories:
Energy and Environment
Preferred major(s):
- No Major Restriction
School/Dept.:
Chemical Engineering
Professor:
Nathan
Schultheiss
More information: https://engineering.purdue.edu/cheeor/
Vision Based Navigation for UAVs
Description:
Develop optical flow/depth estimation with dynamic vision sensor cameras as the only vision sensor.
Research categories:
Big Data/Machine Learning, Deep Learning
Preferred major(s):
- Electrical Engineering
Desired experience:
Deep learning
School/Dept.:
IN
Professor:
Kaushik
Roy