SURF - Research - College of Engineering

3D Forming of Advanced Composites for Automotive and Sports Applications

Description:

The Manufacturing Design Laboratory (MDLab) at Purdue University is driven by today's fast growing demands for cost-effectiveness and more sustainable solutions in the aerospace, automotive, and sports industries. Our research focuses on integrating next-generation composite manufacturing approaches with a full-scale Industry 4.0 Digital Manufacturing Testbed. As the utilization of advanced composites expands from the aerospace industry to high volume applications such as automotive and sports industries, increased complex forming, and cost-effective manufacturing has been increasingly demanded. The MDLab has integrated advanced robotics to automate the fiber performing process which has led to a significant reduction of cycle times for complex shaped structures.
One of the equipment in the lab is the FREESTYLETM machine which is used to form M-TOW® (overbraided composite tow) into any desired shape and is synonymous to metal roll forming methods. The method is a free-forming method, no mold required, and raises the issues of dimensional and forming accuracy, which highlights our research focus in this area.
The student’s project will focus on mastering the forming of thermoplastic composites into 3D shapes. The student should have a desire to work with novel manufacturing equipment which may require modifying equipment for better performance. The results from this research will contribute to a deeper understanding of the dimensional stability of thermoplastic composites and will serve as a preform for over-molded components to be used in the automotive industry.

Research categories:

Composite Materials and Alloys, Material Processing and Characterization

Preferred major(s):

Materials Science, Aerospace, Mechanical Engineering

Desired experience:

Enthusiasm for hands-on manufacturing and an interest in materials research. Prior experience with thermoplastic composites is preferred, but not required

School/Dept.:

Material Science and Engineering

Professor:

Jan-Anders Mansson

4D Materials Science - X-ray Microtomography, Image Analysis, and Machine Learning

Description:

The student will be working on state-of-the-art characterization techniques, such as x-ray microtomography and correlative microscopy of high performance materials. The project will involve image analysis and machine learning algorithms for efficiently and accurately analyzing the x-ray tomography datasets.

Research categories:

Big Data/Machine Learning, Composite Materials and Alloys, Material Modeling and Simulation, Material Processing and Characterization

Preferred major(s):

Materials science and engineering, mechanical engineering, and/or computer engineering

Desired experience:

Microstructural Characterization Computer programming/coding Image analysis Junior or Seniors are particularly encouraged to apply.

School/Dept.:

Description:

The student will work on microstructural characterization and mechanical properties of new aluminum-based additive manufactured alloys. Corrosion testing and analysis are also part of the existing project.

Research categories:

Composite Materials and Alloys, Material Processing and Characterization

Preferred major(s):

Materials science and engineering, mechanical engineering, aerospace engineering

Desired experience:

Independent, driven, and hard-working. Experience with sample preparation and optical microscopy would be a plus.

School/Dept.:

MSE

Professor:

Nik Chawla

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. Characterization efforts include experiments with live animals, extracted proteins, and peptide models. 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. This project will be more in the realm of materials engineering. B) Developing gel-based adhesives for wound closure. Work here will involve some aspects of biomedical engineering.

Research categories:

Composite Materials and Alloys, Ecology and Sustainability, Material Processing and Characterization, Medical Science and Technology

Preferred major(s):

Chemistry or Materials Engineering or Biomedical Engineering or Chemical Engineering

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 and Materials Engineering

Professor:

Jonathan Wilker

More information: http://www.chem.purdue.edu/wilker/

Advanced Textile based Wearable Devices

Description:

We are developing advanced textile materials towards next generation comfortable and wearable devices. The student will be involved in the design, fabrication and demonstration of the wearable devices including sensors, circuit components, power generators, etc.

Research categories:

Energy and Environment, Material Processing and Characterization, Nanotechnology

School/Dept.:

School of Mechanical Engineering

Professor:

Tian Li

More information: https://www.tianliresearch.com/

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.

Research categories:

Big Data/Machine Learning, Learning and Evaluation, Other

Preferred major(s):

Industrial Engineering, Mechanical Engineering, and/or Computer Science Engineering

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

Description:

Agricultural and Biological Engineering Department has contributed several tools for environmental modeling community. This project involves building Graphical User Interface (GUI) to connect with various components of a recently updated environmental model. The SURF student will be required to understand the current application, create a list of changes to be made in collaboration with the project supervisor, get a head start on developing new GUIs and document the process. The SURF student will work with a staff programmer.

Research categories:

Other

Preferred major(s):

Agricultural Engineering , Civil Engineering, Computer Science or related disciplines

Desired experience:

Programming skills in any language with some experience in frontend and backend web development is desired.

School/Dept.:

Agricultural and Biological Engineering

Professor:

Dharmendra Saraswat

Description:

This project will use public and custom datasets of images/sounds/videos for training deep learning models and explore methods to improve generalization abilities of the trained models.

Research categories:

Deep Learning

Preferred major(s):

Computer Engineering, Computer Science, Electrical Engineering, Computer and Information Technology

Desired experience:

Python programming and exposure to ML/deep learning libraries is preferred

School/Dept.:

Agricultural and Biological Engineering

Professor:

Dharmendra Saraswat

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):

Biochemistry, Biology, Biological Engineering or similar

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

Describing the collective motion of dislocations in metals

Description:

The collective behavior of dislocations (line defects) in crystals is not well understood. This is somewhat strange considering that this collective behavior is the physical origin of deformation in many crystalline materials. The only tool that we currently have to study this involves simulating how individual dislocations move in a crystal. However, we are creating a theory that treats these dislocations like a fluid, as a density field.

We have two projects available, please apply for this position if you are interested in either one.

• One project will involve simulating dislocations in face centered cubic metals to extract statistical information about how they form junctions. This junctions are the physical basis of work-hardening, and this statistical information will allow us to incorporate junctions into the density-based, fluid-like model.

• Another project will involve simulating x-ray diffraction patterns in face-centered cubic metals containing dislocations in order to identify signals relevant to the fluid-like properties of the dislocations. Basic machine learning techniques will be used to identify these signals. No experience with x-ray diffraction or machine learning is needed. These results will allow experimentalists at our national labs to measure the fluid-like properties of dislocations in a lab rather than through simulations.

Research categories:

Big Data/Machine Learning, Material Modeling and Simulation, Material Processing and Characterization, Nanotechnology

Preferred major(s):

Physics, Mathematics, Materials Science

Desired experience:

Calculus 3 (vector calculus), familiarity with basic statistical concepts

School/Dept.:

Materials Engineering

Professor:

Anter EL-AZAB

More information: Not yet

Design, construction and simulation of scaled test facility for gas cooled reactor cavity building blowdown

Description:

The main goal of the research is to develop a scaled experimental facility to study a High Temperature Gas-cooled Reactor (HTGR) building response in the event of a depressurization accident caused by a break in the primary coolant boundary and obtain first-of-a-kind data on the oxygen concentration distribution for validation of reactor safety codes and Computational Fluid Dynamics (CFD) models. It is proposed to conduct experiments in a well-scaled test facility representing reference GA-MHGTR reactor building cavities and obtain oxygen concentration as function of time and space for range of reactor building vent locations, flow paths, and break sizes, locations and orientations. To support the experimental program, it is proposed to perform analysis of the reactor building response with a system level reactor safety code complimented by a CFD analysis for detailed localized predictions. The task under this project include study of the HTGR reactor components, where actual dimensions of the systems components are collected data, using scaling design scaled facility, and perform CFD analysis. Students interested on hands on experience in the laboratory, willing to build test facility, perform experiment, and analyze data are welcome. Great opportunity to develop thermal hydraulics laboratory skills.

Research categories:

Energy and Environment, Thermal Technology, Other

Preferred major(s):

Nuclear Engineering or Mechanical Engineering or Technology

Desired experience:

Desired course work: Courses on Thermal and fluids, Skills: Willing to work on hardware, construction of test facility, experimental work, CFD modeling, Data analysis Desirable experience : Experience in AUTOCAD or similar tool , machining, CFD FLUENT or CFX

School/Dept.:

Nuclear Engineering

Professor:

Shripad Revankar

Design, fabrication, and testing of an environmental chamber for X-ray characterization

Description:

High energy X-rays produced by synchrotron sources can be used to characterize the 3D microstructure and evolution of the lattice strains (and thereby stresses) in each grain during thermo-mechanical loading. For this project, we would use high energy X-rays to characterize the evolution of a fatigue crack in a corrosive environment. This project would entail the design, fabrication, and testing of an environmental chamber. The chamber would enclose the specimen in a corrosive environment, and at the same time, applying loading to the specimen. The design would need to limit the impedance of the incoming/outgoing X-ray sources during characterization.

Research categories:

Composite Materials and Alloys, Energy and Environment, Material Modeling and Simulation, Thermal Technology, Other

Preferred major(s):

AAE or ME

Desired experience:

Experience inCAD tools, structural and thermal finite element analysis. Background in Matlab or Python coding.

School/Dept.:

School of Aeronautics and Astronautics

Professor:

Michael Sangid

More information: https://engineering.purdue.edu/~msangid/

Designing Epidemic Mitigation Methods with Limited Resources

Description:

By the end of 2020, the COVID-19 infection has caused more than one million deaths and a large amount of financial loss globally. To reduce the losses, social planners are implementing appropriate methods to mitigate the spread of the epidemic, such as developing vaccines, maintaining social distancing, quarantine, investing in effective medicines, etc. Meanwhile, each type of mitigation method has different costs and the decision-makers often have to carry out the policies under limited budgets. Our plan is to design epidemic mitigation methods with limited resources.

In the project, the students will participate in designing a dynamic epidemic model for COVID-19 spreading in a community. Further, the students will fill in the role of a decision-maker of the community. Given a restricted budget, the students will try to alternate the system parameters which correspond to actions such as allocating medical equipment, imposing lock-down, and distributing vaccines, so that the virus will be eradicated quickly. Once the virus is eradicated, we will study how to prevent the occurrence of subsequent waves with relatively moderate policies. Furthermore, we will extend the problem to study how to mitigate the spreading of the virus with the lowest budget possible. The students will learn to apply geometric programming ideas to solve these problems.

Research categories:

Learning and Evaluation, Other

Desired experience:

Preferred: Mathematical background, programming skills, data processing experience

School/Dept.:

ECE

Professor:

Philip Pare

Description:

Engineering programs provide a unique pathway for learners to reassert control over their environment, demonstrate agency and decision making, build strong social connections, and take on crucial roles in their communities, all while developing complementary professional (“21st century”) skills. We have demonstrated through our Localized Engineering in Displacement (LED101) course that authentic engineering learning opportunities can serve as a vehicle for community development while simultaneously expanding the representation of engineers to explicitly include marginalized, displaced learners. The course has run multiple times, each cohort with a central “authentic” (real-world) challenge that is the context for all learning activities. The class for which our undergraduate researcher would develop activities, assess the implementation process, and study the impact will offer as its “authentic problem” the need for students to design, build, optimize, and implement a solar-powered lighting solution for girls, mothers, and the community studying at home during COVID in Senegal.

Research categories:

Energy and Environment, Learning and Evaluation

Preferred major(s):

EEE

Desired experience:

interest in engineering education, fluency in French, experience with sustainable/renewable energy solutions

School/Dept.:

ENE

Description:

While intelligent systems promise to extend human capabilities within occupational settings, workers must increasingly collaborate with artificial intelligence (AI) to achieve desired outcomes. This research aims at enhancing bi-directional interaction between workers and robots at the construction jobsites by obtaining continuous neurophysiological and psychophysiological data from workers. The developed personalized AI will measure, adapt, and enhance the skill performance of the next generation of the workforce to work safely and communicate effectively in the future automated jobsites.

Research categories:

Big Data/Machine Learning, Deep Learning, Engineering the Built Environment

Preferred major(s):

All Engineering Fields or Neuroscience

Desired experience:

Programming (Python, Matlab, C++), Data Analytics (Machine learning, Deep-learning) Seeking applicants who are creative and passionate to explore new areas

School/Dept.:

School of Civil engineering and CEM

Professor:

Sogand Hasanzadeh

Epidemic Analysis Via Social Networks

Description:

Social media has significantly increased the rate at which news spreads through the population, enabling shifts in people’s beliefs towards the news. One such example is the disagreement on the severity of the disease over different communities during the COVID-19 pandemic. The contention over COVID-19 affects people’s attitudes and behaviors towards the policies and suggestions from the government and scientific institutions, respectively. Our question is if it is possible to mitigate the spreading of the epidemic by impacting the opinions over the social networks. Our proposed solution is to capture the opinions of the COVID-19 pandemic through dynamical social networks with both cooperative and antagonistic interactions. We will validate the network model with social network data. Through the data-based model, we will explore the role of opinion dissemination on epidemic spreading in reality. The undergraduate researchers will learn to model signed social networks via the opinions on COVID-19. The students will gain fundamental knowledge in systems and control, social network modeling and analysis, and hands-on experience in data collection, analysis, and model validation.

Research categories:

Big Data/Machine Learning, Learning and Evaluation, Other

Desired experience:

Preferred: Mathematical background, programming skills, data processing experience

School/Dept.:

ECE

Professor:

Philip Paré

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.

Research categories:

Energy and Environment, Material Processing and Characterization, Nanotechnology

School/Dept.:

Chemical Engineering

Professor:

Letian Dou

More information: https://letiandougroup.com/

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.

Research categories:

Big Data/Machine Learning, Learning and Evaluation, Medical Science and Technology, Other

Preferred major(s):

Industrial Engineering, Computer Science, Biomedical Engineering

Desired experience:

Human Factors, Machine Learning, Sensors, Programming

School/Dept.:

Industrial Engineering

Professor:

Denny Yu

Description:

The understanding of chemical reactivity plays a key role in the design of pharmaceutical facilities. This project will entail taking calorimetric measurements using an Advanced Reactive System Screening Tool (ARSST). The systems studied will include various reagents commonly used in the pharmaceutical industry. Work will begin with lab safety training and familiarization with the use of the ARRST, while conducting a literature search of existing heat of reaction data for the chemical systems to be studied. Overall, the work will entail making a series of measurements of various systems at a variety of conditions and then analyzing the data using computer models.

This project is well-suited for chemical engineers interested in the pharmaceutical industry and process safety. Very few students have the opportunity to use such a calorimeter, which will stand out on resumes.

Research categories:

Chemical Unit Operations, Other

Preferred major(s):

Chemical Engineering

Desired experience:

No prior training is required. Safety will be of the utmost importance in conducting this laboratory study. The researcher will take department safety training and then rigorous training on the use of the ARSST with close supervision.

School/Dept.:

Chemical Engineering

Professor:

Ray Mentzer

Lake Michigan Shoreline Erosion - Measurements and Modeling

Description:

In the Great Lakes, water levels have been at record highs in the last few years , and the damage to the shorelines has been immense and costly (just google "Lake Michigan erosion" to see newspaper articles and videos). As engineers, we need to be better able to predict this erosion and design resilient shorelines that can withstand the huge variations in water levels that may be a consequence of climate change. The aims of this research are two-fold: (1) Quantify recent erosion along Lake Michigan's shoreline, using both direct measurements and remote sensing; (2) Develop a computational model that can predict this erosion.

With these aims in mind, this summer research project aims to leverage students' strengths to contribute to the best of their abilities. Research activities can include boat work on Lake Michigan, beach surveys with LiDAR-equipped drones, data analysis using Matlab and/or Arc-GIS, laboratory experiments involving water flumes and acoustic instrumentation, and setting up/running sophisticated computer models that aim to simulate how waves and currents move sand along the shoreline. This project is best suited for a student really interested in water, potentially setting you down a path to become a hydraulic (water) or coastal engineer, working to create more sustainable and resilient coastlines and waterways.

Research categories:

Ecology and Sustainability, Energy and Environment, Engineering the Built Environment, Other

Preferred major(s):

Civil or Environmental Engineering

Desired experience:

Must love water. Must not hate Matlab. Must love to be outside. (no guarantees w/Covid!!) Must be a great team member and communicate well. Must be willing to work hard, get frustrated, and persevere.

School/Dept.:

Civil Engineering

Professor:

Cary Troy

More information: https://engineering.purdue.edu/CE/People/view_person?resource_id=24098

Laser diagnostics for studying shock-heated gases

Description:

The student will learn how to use mid-infrared laser diagnostics to measure the temperature of gases that are heated to 1000s of degrees by high-Mach shock waves in our shock tube. This will be used to improve our understanding of non-equilibrium processes that occur behind shock waves and play an important role in governing heat transfer to space vehicles entering the atmosphere.

Research categories:

Energy and Environment

Preferred major(s):

ME or AAE

Desired experience:

Previous experience working in Prof. Goldenstein's research lab.

School/Dept.:

Mechanical Engineering

Professor:

Christopher Goldenstein

More information: www.GoldensteinGroup.com

Lithium-ion Battery Analytics

Description:

Lithium-ion (Li-ion) batteries are ubiquitous. Thermo-electrochemical characteristics and porous electrode structures of these systems are critical toward safer and high-performance batteries for electric vehicles. As part of this research, physics-based modeling and experimental data-driven analytics will be performed over a wide range a normal and anomalous operating conditions of Li-ion cells.

Research categories:

Big Data/Machine Learning, Energy and Environment, Material Modeling and Simulation

Preferred major(s):

Mechanical, Chemical, Materials Engineering

Desired experience:

The student will work closely with a senior graduate student researcher on the modeling and experimental data analysis in the form of weekly reports. The final deliverable will be one end-of-summer research report (based on the weekly progress) and a presentation at the research group meeting. Experience with modeling and analysis tools and methods is desirable.

School/Dept.:

Mechanical Engineering

Professor:

Partha Mukherjee

More information: https://engineering.purdue.edu/ETSL/

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 diseases lead 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 associated with bovine respiratory diseases, ii) build a paper-based device for conducting loop-mediated isothermal amplification, and iii) build a heating/imaging device for conducting the paper-based assay in the field.
The SURF student will work on the third objective to build a heater coupled to an imager for detecting colorimetric/fluorometric output from the biosensor.

Research categories:

Biological Simulation and Technology

Preferred major(s):

Biochemistry, Agricultural and Biological Engineering, Biomedical Engineering, Mechanical Engineering, Electrical Engineering

Desired experience:

Relevant skills for the project: • 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) • Printed Circuit Board Design • Circuit Analysis/Design To be successful at this position, you should have a GPA>3.5, prior experience working in a lab, experience building electro-mechanical devices, and the ability to work in a team.

School/Dept.:

Agricultural and Biological Engineering

Professor:

Mohit Verma

More information: https://www.vermalab.com/

Magnetic RAM for space applications

Description:

Radiation in outer space 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 in this environment. Transient effects include single-event effects like memory bit flips; permanent effects include single-event latchups that prevent individual devices from operating.

In this project, the student will develop a model to predict failures for new and emerging types of memories and logic. It will consist of models for radiation in the space environment, as well as the susceptibility of devices to various types of ionizing radiation. The end goal will be to predict failures for certain classes of devices for validation in a beam-line, which may ultimately be used to adapt off-the-shelf electronics to space applications.

Research categories:

Other

Preferred major(s):

ECE, NE, ME, MSE

Desired experience:

Experience with programming in Python, C/C++, and/or MATLAB Enthusiasm for scientific programming Understanding of radiation transport and electromagnetism

School/Dept.:

Electrical & Computer Engineering

Professor:

Peter Bermel

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, Medical Science and Technology, 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/

Measurement and Modeling of Mass Transfer Characteristics During Pharmaceutical Lyophilization

Description:

Freeze-drying, also called lyophilization, is widely used in manufacturing of injectable pharmaceuticals, vaccines, biotech products, chemical reagents, food and probiotic cultures. The SURF undergraduate researchers will have an opportunity to be involved in one of the ongoing projects in LyoHUB technology demonstration facility in Discovery Park in collaboration with one or more of 20+ LyoHUB industry members.

Lyophilization is a desiccation method whereby a solvent is removed from a frozen system via sublimation. In industry, the process is typically performed at a slow rate due to large uncertainties associated with key mass transfer mechanisms. Current data is highly scattered and valid for a tightly constrained set of operating points. Frequently, these points are not optimal and the data provides little benefit to the user. Students will be responsible for computationally and experimentally characterizing the mass transfer properties of various representative pharmaceutical formulations over a range of process conditions. The goal of the project is to generalize and consolidate key results into a standardized database which will be directly integrated into LyoHUB’s LyoPronto simulation tool (http://lyopronto.rcac.purdue.edu/). The software is freely available and widely used among major pharmaceutical companies. The student will also perform benchmarking studies against current published mass transfer models.

The student will learn the basics of the freeze drying process and will get the skills of experimental work in the lab with different lyophilizers.

This project will include online meetings and hands-on lab work.

Research categories:

Material Modeling and Simulation, Other

Preferred major(s):

AAE, ABE, CHE, CS, ECE, ME

School/Dept.:

CHE/AAE

Professor:

Alina Alexeenko

More information: www.lyohub.org

Measurement and Modeling of Vial Heat Transfer Characteristics During Pharmaceutical Lyophilization

Description:

Freeze-drying, also called lyophilization, is widely used in manufacturing of injectable pharmaceuticals, vaccines, biotech products, chemical reagents, food and probiotic cultures. The SURF undergraduate researchers will have an opportunity to be involved in one of the ongoing projects in LyoHUB technology demonstration facility in Discovery Park in collaboration with one or more of 20+ LyoHUB industry members.

Lyophilization is a desiccation method whereby a solvent is removed from a frozen system via sublimation. In industry, the process is typically performed over the course of days for weeks due to large uncertainties associated with key heat transfer mechanisms. Accurate determination of these heat transfer characteristics is therefore critical to fully understanding and optimizing the process. Students will be responsible for computationally and experimentally characterizing the heat transfer properties of various vial geometries under a range of process conditions. The goal of the project is to consolidate key results into a standardized database which will be directly integrated into LyoHUB’s LyoPronto simulation tool (http://lyopronto.rcac.purdue.edu/). The software is freely available and widely used among major pharmaceutical companies. The student will also perform benchmarking studies against current published heat transfer models.

The student will learn the basics of the freeze drying process and will get the skills of experimental work in the lab with different lyophilizers.

This project will include online meetings and hands-on lab work

Research categories:

Material Modeling and Simulation, Other

School/Dept.:

CHE/AAE

Professor:

Alina Alexeenko

More information: www.lyohub.org

Measuring glutamate release in real time following traumatic brain injury with flexible printed biosensors

Description:

Following traumatic brain and spinal cord injury, damaged cells release toxic levels of excitatory neurotransmitter glutamate, which further damages cells through a secondary injury mechanism. This pathology is called glutamate excitotoxicity. The mechanism for sustained high levels of extracellular glutamate remains unclear, and a better understanding of glutamate excitotoxicity may lead to novel therapeutic interventions to minimize secondary injury following traumatic brain injury. Our lab has developed printed glutamate biosensors that we have used to measure glutamate release following simulated traumatic spinal cord injury with explanted rat spinal cord segments.

The student will work on integrating glutamate biosensors with anti-biofouling coating and wireless electronics, so the biosensors can be implanted in the brain and measure glutamate release following traumatic brain injury in anesthetized rats. Specific research tasks include printing biosensor devices by direct ink writing, electrochemical characterization, applying anti-biofouling coatings, and operating implanted biosensors. The student will collect, analyze, and interpret data, and write the results for a journal publication.

More information: https://engineering.purdue.edu/LIMR/research/

Research categories:

Biological Characterization and Imaging

Preferred major(s):

Any

School/Dept.:

BME

Professor:

Hugh Lee

More information: https://engineering.purdue.edu/LIMR/research/

Measuring wetland greenhouse gas emissions with environmental Internet of Things sensors.

Description:

Wetlands in agricultural landscapes are important sites for maintaining water quality in streams, rivers, and reservoirs that are downstream of farmland. Despite these benefits, such wetlands can be a large source of potent greenhouse gasses—primarily methane (CH4) and nitrous oxide (N2O). Yet, data on the amount of greenhouse gasses produced by agricultural wetlands and the environmental factors that cause these differences are not widely available. For this project, we will leverage environmental internet of things (IoT) technology to deploy networks of gas sensors in agricultural wetlands. We will use these gas sensors to determine what local environmental factors (e.g., water inundation length, elevation, soil organic matter content) cause CH4 and N2O emissions to increase and decrease from wetland soils.

The student working on this project would be responsible for deploying gas sensors, which will involve fieldwork at wetlands located near Purdue. This student will also have the opportunity to analyze the data collected from these sensors with the assistance of faculty and graduate student mentors.

Research categories:

Big Data/Machine Learning, Ecology and Sustainability, Environmental Characterization, Internet of Things

Preferred major(s):

Biology, Natural Resources, Computer Science, and Environmental Engineering majors (interpreted broadly).

Desired experience:

Students with an interest in working with IoT technology, including sensors powered by Arduino processors, are encouraged to apply. Experience with environmental sensors and/or wetland field work is beneficial, but not required.

School/Dept.:

Forestry & Natural Resources

Professor:

Jacob Hosen

More information: http://www.ecosystemscience.io

Microbiological Dynamics of Drinking Water during Stagnation

Description:

The pipes that deliver drinking water to individual taps develop into complex ecosystems. Most of the bacteria that live on these pipes and in the water are harmless, but several are capable of causing disease. For example, Legionella pneumophila is a bacterium that causes a potentially fatal pneumonia in immunocompromised individuals. It is thus critical to understand and ultimately control the ecosystem within these pipes. This work will contribute to policies (e.g., the minimum required temperature in a water heater) and technologies (e.g., auto-flushing sinks) that will limit needless disease.

In this project, the student will utilize bench scale experiments, a pilot-scale piping rig, and full-scale plumbing systems to test hypotheses regarding establishment of biofilm and relationships between biofilm and water over time. The student will collect and analyze water samples, using a variety of tools to fully characterize the physiochemical and biological dynamics within the system. They will also learn how to write a scientific report and will present it at the SURF symposium.

Research categories:

Biological Characterization and Imaging, Cellular Biology, Ecology and Sustainability, Engineering the Built Environment, Environmental Characterization

Preferred major(s):

Biology, Environmental and Ecological Engineering, Civil Engineering, Environmental Science

Desired experience:

Experience in a biological lab is desired but not required. All hands-on lab skills can be taught.

School/Dept.:

Agricultural and Biological Engineering

Professor:

Caitlin Proctor

Mobile Air Quality Sensors and the Internet of Things

Description:

The project goal is to design and develop a hardware, software and cloud computing system for the acquisition of air quality data from mobile platforms such as taxis, backpacks, and drones. The sensors will be deployed around Purdue and eventually in the city of Arequipa, Peru. Data will be used to assess the spatial and temporal changes in air pollutions in Peru's 2nd largest city. The research is a collaboration between Purdue and the University of San Augustin (UNSA) as part of the NEXUS project.

Research categories:

Big Data/Machine Learning, Ecology and Sustainability, Energy and Environment

Preferred major(s):

Data science, computer science, electrical engineering, computer engineering, chemistry

Desired experience:

One or more of the following Java, python, html, raspberriPI, aurdino, circuits, statistics, machine learning, internet of things, cloud computing

School/Dept.:

EAPS

Professor:

Greg Michalski

More information: https://www.purdue.edu/discoverypark/arequipa-nexus/en/index.php

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.

Research categories:

Nanotechnology

Preferred major(s):

ECE, ME, MSE

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

Modeling of Wound Mechanobiology Following Lumpectomy

Description:

The goal of this project is to model the coupled mechanics and mechanobiology of lumpectomy wounds. Lumpectomy, or breast conserving surgery, is becoming the first choice of treatment for breast cancer due to the advances in imaging and diagnosis which allow detection of early tumors. However, this surgical treatment creates a wound void in the breast upon resection of the tumor and a surrounding margin of healthy tissue. The wound heals in a process resembling the healing of other connective tissue organs like the skin. In particular, healing of lumpectomy wounds can lead to permanent contraction of the tissue and change in mechanical properties as the wound gets filled with scar tissue instead of the native breast tissue. Mechanics and mechanobiology of this process are key to understand how these wounds heal. To address this need, our groups (PI Buganza-Tepole from ME and PI VoytikHarbin from BME) are using a combination of experiments and mathematical modeling to improve scaffold design for lumpectomy wounds. The undergrad sought for this project will work in this interdisciplinary group, with a focus on the computational model. PI Buganza-Tepole has proposed a computational model of wound healing that combines large deformation tissue mechanics, reaction-diffusion for cells and cytokine dynamics, and permanent remodeling and growth processes that link the mechanics and mechanobiology. The undergraduate working in this project will learn about C++, finite elements, mechanics of soft materials, mechanobiology modeling, growth and remodeling.

Research categories:

Biological Simulation and Technology

Preferred major(s):

Mechanical Engineering

Desired experience:

Some programming experience desirable, knowledge of finite elements and mechanics of materials is also desirable

School/Dept.:

Mechanical Engineering

Professor:

Adrian Buganza-Tepole

More information: https://engineering.purdue.edu/tepolelab/

Nanostructural Evaluation of Human Bone Under Applied Loading

Description:

Student will design a test method for collecting small angle x-ray scattering data for bone specimen under in-situ loading conditions. Test parameters will be optimized for human bone and other associated materials. Data will be analyzed to determine extent of internal damage related to applied stress/strain conditions.

Research categories:

Material Processing and Characterization, Medical Science and Technology, Nanotechnology

Preferred major(s):

Materials Engineering, Mechanical Engineering, Physics

Desired experience:

no experience required. ready to learn. completion of MSE 335 (Characterization Methods) or similar course is considered a plus

School/Dept.:

Materials Engineergin

Professor:

John Howarter

Neural recording and stimulation using a wireless single-chip system

Description:

In this project, we aim to implement an implant that can record and stimulate neural activities of a live mouse brain. We will take advantage of wireless powering and wireless data transfer to miniaturize the neural implant, such that it does not require battery or wires. Students will help develop the Reader for testing and collecting data from in-vitro and in-vivo experiments.

Research categories:

Biological Characterization and Imaging, Biological Simulation and Technology, Internet of Things, Medical Science and Technology

Preferred major(s):

BME, ECE

Desired experience:

Some knowledge of pub design, circuits and biology

School/Dept.:

ECE

Professor:

Saeed Mohammadi

On-Line Programming Assessment

Description:

Computer programs are difficult to evaluate due to the large number of possibilities. Existing evaluation systems are restricted to simple programs or impose restrictions to limit possibilities. This project aims to build an online assessment system that can evaluate non-trivial programs and assist students learning computer programming.

Research categories:

Big Data/Machine Learning, Cybersecurity, Deep Learning, Learning and Evaluation

Preferred major(s):

computer engineering, computer science, electrical engineering

Desired experience:

at least two courses on computer programming

School/Dept.:

Electrical and Computer Engineering

Professor:

Yung-Hsiang Lu

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. The technology has been originally developed in Soviet Union and got adopted in the U.S. in 1990s. 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.

Research categories:

Other

School/Dept.:

AAE

Professor:

Alexey Shashurin

More information: https://engineering.purdue.edu/EPPL

Printable functional filaments and sensor for biomedical devices

Description:

Current surgical mesh implants require manual size adjustment from pre-fabricated sheets that can lead to improper fitting and thus post-surgical complications. 3D printing surgical mesh would avoid manual errors in addition to providing surgeons and hospitals with an increased number of choices for mesh design. This allows for greater personalization of treatment for patients suffering from hernias.
Design and characterize a novel 3D printable filament imbued with an antibacterial and piezoelectric electrical stimulating agent. This mesh should be biocompatible, flexible, and biodegradable over a period of years.
A second part of project will be focused on printing low-cost biosensors for detecting COVID-19 virus.

Research categories:

Nanotechnology

School/Dept.:

MSE

Professor:

Rahim Rahimi

Process Synthesis and intensification of Shale Gas Valorization

Description:

The assignment focuses on the creation of transformative process systems to convert light hydrocarbons from shale resources to liquid chemicals and transportation fuels in smaller, modular, local, and highly networked processing plants. The students will have the opportunities to learn cutting-edge technologies in process synthesis, intensification and optimization, as well as widely-used simulation tools such as Aspen Plus, Matlab, Chemkin, etc.

Research categories:

Chemical Unit Operations, Energy and Environment

Preferred major(s):

Chemical Engineering

Desired experience:

Undergraduate level thermodynamics

School/Dept.:

Davidson School of Chemical Engineering

Professor:

Rakesh Agrawal

Real time analysis of viral particles for continuous processing approach

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 the continuous manufacturing processes 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 alternatives for viral detection.

Research categories:

Big Data/Machine Learning, Biological Characterization and Imaging, Biological Simulation and Technology, Biotechnology Data Insights, Cellular Biology

Preferred major(s):

Chemical Eng, Biological Eng, Biomedical Eng, Physics, Mechanical Eng

Desired experience:

This project requires lab work and presence on campus, however, an online version can be offered to focus on coarse-grained modeling of proteins/cells.

School/Dept.:

Mechanical Engineering

Professor:

Arezoo Ardekani

Description:

Water is centrally important to environmental sustainability: it supports human societal needs and the natural environment, and powers the growth of economic sectors, geographic regions, and cities. Data science should be harnessed to better understand how much and where water is consumed. The undergraduate researcher will be apply methods to quantify and model industrial water consumption at fine spatial and industry-sector resolution, visualize the results with geographic information systems, and interpret the impacts of water consumption on the urban environment.

Research categories:

Big Data/Machine Learning, Ecology and Sustainability, Energy and Environment, Engineering the Built Environment, Environmental Characterization, Other

Preferred major(s):

EEE, CE, or IE

Desired experience:

Minimum GPA: 3.0. Preferred majors: Environmental and Ecological Engineering, Civil Engineering, or Industrial Engineering. Preferred coursework: CE/EEE 350 or CE/EEE 355 or EEE 250 Preferred skills: Proficiency with programming in R or Python Python, experience with ArcGIS or similar programs.

School/Dept.:

CE and EEE

Professor:

Inez Hua

SoCET: System on Chip Extension Technologies

Description:

The processors inside your cell-phone, automobile, television, etc. are some of the most complex and smallest devices created in human history, but with access to the right tools, design techniques, and fabrication facilities you can create new capabilities to be fabricated on silicon. Such processors are implemented in the form of a System-on-Chip (SoC). Design of SoC's and access to fabrication facilities are ordinarily extremely expensive and very restricted. However, thanks to industry and governmental support, interested undergraduates are able to join in the design, fabrication, and test of custom SoC's. The primary reason for the existence of the SoC team is to give students an integrated circuit design experience that as close as possible to what they would encounter in industry.

The technical objective of the SoC Team is to create and keep improving on an SoC design that we can then customize for special application and research needs. The team's major project is that of creating an SoC that is optimized for very small scale and low power machine learning applications, but there are numerous problems one can work on including modelling of a secure SoC architecture, design of chiplets, FPGA prototyping, extending a RISCV open source processor design, testing of recent chips designed by SoCET, analog circuit design, and using industry grade design verification techniques.

Research categories:

Cybersecurity, Internet of Things, Mobile Computing, Nanotechnology, Other

Preferred major(s):

Electrical Engineering, Computer Engineering, Computer Science

Desired experience:

A wide range of circuit and software design skills are needed on this project. No one team member is expected to have this full set of skills, but to be able to contribute to some aspect of SoCET over the course of a summer, you will need skills or course work in at least the following areas. *Verilog/System Verilog coding skills for logic synthesis and test bench design, *Analog and digital integrated circuit design background including circuit simulation and layout, *testing of digital and analog circuits, *Microcontroller programming in C and assembly language, *Managing code repositories in git, *Compiler design, *Operating systems and especially Real Time Operating Systems.

School/Dept.:

ECE

Professor:

Mark Johnson

More information: https://engineering.purdue.edu/SoC-Team

Study of Betavoltaic characteristics

Description:

The main goal of the research is to study betavoltaic cell characteristics using a facility for its voltage and current responses with load. A betavoltaic cell creates electricity similar to a photovoltaic or solar cell. Betavoltaic devices are self-contained power sources that convert high energy beta (β) particles emitted from the decay of radioactive isotopes into electrical current. In the cell the electrons are produced indirectly via the kinetic energy of the beta particles interacting within the semiconductor. The project also will involve testing on a hydrogen loading facility to simulate the tritium loading in betavoltaic cell film. The betavoltaic used are commercial cells that are tested in a radiation approved facility. Experimental facility preparation, testing and data acquisition is needed. Students interested on hands on experience in the laboratory, willing to build test facility, perform experiment, and analyze data are welcome. Great opportunity to develop radiation laboratory skills.

Research categories:

Energy and Environment, Material Modeling and Simulation, Other

Preferred major(s):

Nuclear Engineering

Desired experience:

Desired course work: Courses on basic nuclear engineering, radiation shielding, : Willing to work on hardware, experimental test facility modeling, Data analysis Desirable experience : Experience in MATLAB and data analysis. Required: Radiation training in handling sealed radiation material.

School/Dept.:

Nuclear Engineering

Professor:

Shripad Revankar

Study on the effects of non-traditional supplementary cementitious materials (SCMs) on transport properties and durability of concrete

Description:

The global increase in emissions of the carbon dioxide and rapid decrease of natural resources create a great demand for study and development of new materials, modified approaches to old technologies or new vision for already known materials. Usage of supplementary cementitious materials (SCMs) has been already proven as one of the efficient ways of reducing the CO2 emissions contributed by the cement industry. However, diminishing supply of traditional SCMs leads to the need to evaluate applicability of some of the alternative (non-traditional) pozzolanic materials for use in concrete. Some of the most promising materials in this category include potentially promising research direction on non-traditional SCMs which are known clays, natural pozzolans and bottom ashes.
One of the goals of this project is to develop a better understanding of the effects of the non-traditional SCMs on microstructure and transport properties of the concrete. In order to accomplish this goal, an experimental work on microstructural analysis of concrete, chemical analysis of the pore solution, water absorption and electrical resistivity of concrete needs to be performed. Some of the planned experiments involve concrete mixing and casting of the specimens, scanning electron microscopy (SEM) evaluation of microstructure, pore fluid extraction, chemical analysis of the pore fluid, evaluation of water sorption and electrical resistivity of concrete.
In addition, the scope of this project also involves evaluation of the impact of the non-traditional SCMs on durability performance of the concrete. Specifically, the chemical interaction of the concrete blended with SCMs with de-icing salts will be studied. The testing will involve use of Low-Temperature Differential Scanning Calorimeter (LT-DSC) to evaluate the durability of hydrated cement pastes with various amounts of non-traditional SCMs in the presence of de-icing salt solution. Also, DSC analysis will be used for so-called “low-temperature porosimetry” test to study the fluid amount in gel pores of the cementitious matrix. This part of the project will involve such tasks as preparation of paste specimens, preparation of de-icing salt solutions, setting up of the LT-DSC, performing of the measurements and analysis of data.
The student will assist the graduate student already working on the project with conducting the above-mentioned experiments, data analysis, reporting, and presentation of the results. The student will learn how operate certain equipment together with data analysis software, how to write a research report and will present a poster at the SURF research symposium

Research categories:

Engineering the Built Environment, Material Processing and Characterization

Preferred major(s):

Civil Engineering, Materials Science

Desired experience:

Seeking student passionate about materials research and having general interest in the instrumentation and hands-on, laboratory work. 2nd semester Sophomores, Juniors and first semester Seniors are preferred

School/Dept.:

Civil Engineering

Professor:

Jan Olek

Synthesis of energetic materials

Description:

Synthesis of energetic materials and related precursors.

Research categories:

Other

Preferred major(s):

Chemistry, Materials Engineering, Mechanical Engineering, Chemical Engineering.

Desired experience:

Organic chemistry lab

School/Dept.:

MSE

Professor:

Davin Piercey

More information: www.davinpiercey.com

Synthetic neuron

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, Biotechnology Data Insights, Cellular Biology

Preferred major(s):

CHE/BME/ABE

School/Dept.:

Davidson School of Chemical Engineering

Professor:

Chongli Yuan

Temperature-Dependent Ion Channel Conductance in Neurons

Description:

Degradation or loss of the fatty, myelin sheath about a neurons axon has been observed to cause severe loss of function and cognition in human patients with demyelinating disease or traumatic nervous system injuries. It has further been observed that these symptoms tend to increase in severity when the afflicted patient is suffering from a fever due to infection. In this project we are studying the biophysical mechanisms that underlie this symptomatic variation. Our hypothesis is that the severity of demyelination complications are strongly influenced by the distribution and temperature-dependent kinetics of the ion-channels responsible for action potential propagation. To investigate this, we will develop a computational model of a myelinated axon and simulate myelin degradation, changes in ion channel localization, and temperature fluctuations as might be observed in a patient. We expect to quantify the relationship between degree of demyelination and change in the neuron conductivity in response to changes in temperature. We expect these results will help inform targeted therapeutic treatment for patients with demyelination disease.

Research categories:

Biological Simulation and Technology

Preferred major(s):

Biomedical, Electrical, or Chemical Engineering

Desired experience:

A strong interest in using computational models to solve biomedical problems is required. Some programing experience and familiarity with solving systems of differential equations is strongly desired. Familiarity with the cell biology of neurons is recommended, but not required.

School/Dept.:

Biomedical Engineering

Professor:

Tamara Kinzer-Ursem

The impact of COVID-19 on user perceptions of public transit, shared mobility/micro-mobility services, and emerging vehicle types.

Description:

The objective of this project is to investigate the impact of COVID-19 on user perceptions of public transit, shared mobility services, and emerging vehicle types (electric, connected, and autonomous vehicles). As transportation systems remain at the forefront of the COVID-19 pandemic, it is critical to examine the transportation trends and behaviors of shared modes’ and emerging vehicle types’ users to best plan for transportation policies in the long-run. This study aims to provide a well-documented and easy-to-use framework that can support both planning and policy decisions in order to enhance urban shared mobility by better understanding the attributes which are affected and providing alternative options. The developed research framework will be applied in three urban areas with different transportation systems and densities, and corresponding policy and planning implications will be compared and contrasted.

The student will assist with literature review efforts to establish a baseline of user perceptions for public transit, shared mobility/micro-mobility services, and emerging vehicle types before the pandemic. The student will also assist the research team with analyzing the data from surveys that will include questions about travel behavior, such as change in travel habits because of new technologies, trip purpose and patterns, use of emerging and shared mobility services as well as questions related to how COVID-19 has affected these travel activities. The student will also interact with other undergraduate and graduate students at the Sustainable Transportation Systems Research (STSR) group as well as the project sponsors.

Research categories:

Energy and Environment, Engineering the Built Environment, Other

Preferred major(s):

Engineering or Statistics

Desired experience:

survey design, data analytics, statistics; good oral and written communication skills; experience working with diverse teams

School/Dept.:

CE/ABE

Professor:

Konstantina (Nadia) Gkritza

Description:

In 2020, the COVID-19 pandemic prompted building shutdowns across the globe to promote physical distancing. This however prompted worldwide concerns that the water, left in the plumbing, would become unsafe with high levels of lead, copper, and legionella, posing a health risk to building occupants who returned. Many of the shutdowns or low occupancy conditions still exist. Over the past 11 months, the PI and research teams have been working with public health officials and other researchers to understand the public safety risks and remediation measures needed for building reopening and preventing health risks to increase.

Separately, when disasters strike and drinking water becomes chemically contaminated, sometimes this water enters residential and commercial buildings. This results in do not use orders for the population and potentially contaminated plumbing. This SURF project focusses on better understanding drinking water safety under various plumbing use and contamination scenarios through laboratory testing.

This project will involve a student learning and applying water quality measurement techniques to determine the chemical safety of water in building plumbing systems. Theories that will be tested pertain to the impact of water stagnation time (no use) on the safety of the water inside plumbing systems of various configurations. Pilot- and bench-scale systems will be setup in the laboratory (Hampton Hall) to test specific theories identified by the team. Chemical drinking water characterization would include standard drinking water safety parameters, as well as heavy metals, organic carbon, etc. The student may also work with collaborating faculty and students on microbiology topics. If time permits, the student would conduct chemical contamination and decontamination experiments of different building water treatment devices or assist a graduate student already working on this effort. The purpose of this secondary experiment is to understand the vulnerability of these devices to damage and ability of them to be restored to safe use. For both of these efforts the student would learn and conduct testing, analyze, report, and present the results at the end of the SURF summer.

Research categories:

Energy and Environment, Engineering the Built Environment, Environmental Characterization

Preferred major(s):

Chemistry, Environmental and Ecological Engineering, Civil Engineering, Chemical Engineering, Environmental Science, Health Sciences

Desired experience:

Skills: Self-motivated, desire to learn, works well with others Coursework: Interests in chemistry, environmental science, environmental engineering, public health

School/Dept.:

CE & EEE

Professor:

Andrew Whelton

More information: www.PlumbingSafety.org

Virtual Reality animations of blood flow in a vessel network

Description:

The recently developed Paraview Immersive toolkit provides a simple way to produce virtual reality animations compatible with the Oculus Rift application using data from 3D simulations. This is a unique opportunity to better analyze the data by literally walking around inside them. In this project, the undergraduate students will produce a virtual reality animation using our 3D simulations of blood flow in capillaries.

Research categories:

Big Data/Machine Learning, Biological Simulation and Technology

Desired experience:

Knowledge about computer graphics and programming would be a plus.

School/Dept.:

Mechanical Engineering

Professor:

Hector Gomez

More information: https://engineering.purdue.edu/gomez/

Partners

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Programs

2021 Research Projects

All Research Projects (89)

3D Forming of Advanced Composites for Automotive and Sports Applications

4D Materials Science - X-ray Microtomography, Image Analysis, and Machine Learning

4D Printer Project

Accelerator Architecture Lab at Purdue (AALP): Optimizing Simulators for Advanced Processor Development

Accelerator Architecture Lab at Purdue (AALP): Modeling Diverse GPU Architectures in C++ Simulation

Additive Manufacturing of Lightweight Metallic Alloys

Adhesives at the Beach

Advanced Textile based Wearable Devices

Advanced Vehicle Automation and Human-Subject Experimentation

Advancing Pharmaceutical Manufacturing through Process Modeling and Novel Sensor Development

Analyzing educational teamwork dataset using quantitative and NLP techniques

Automatically Detecting and Fixing Software Bugs and Vulnerabilities

Bio-inspired Radiative Cooling Nanocomposites

Building Software for Environmental Modeling

Bursting of Leading Edge Vortices on Swept Wings

Catalytic Conversion of Methane to Chemicals and Fuels

Computational investigation of mechanosensitive behaviors of motile cells

Crawling the Internet for Denial of Service Vulnerabilities

Deep Learning applications in agriculture

Defining Chemical Modifications on Histones that Control Chromosome Integrity

Describing the collective motion of dislocations in metals

Design, construction and simulation of scaled test facility for gas cooled reactor cavity building blowdown

Design, fabrication, and testing of an environmental chamber for X-ray characterization

Designing Epidemic Mitigation Methods with Limited Resources

Developing Computational Methods to Classify Unlabeled Reactions Using Large Data Sets

Developing IoT sensors for real-time concrete strength monitoring

Developing Simple Mathematical Models to Track the Mass and Energy Flows in a Natural Gas Processing System

Developing and Studying Activities for Localized Engineering Curricula

Development of an anti-deterrent formulation against opioid abuse

Dynamic contractile behaviors of active cytoskeletal networks

Efficient and renewable water treatment

Engineering human stem cells for targeted cancer therapy

Enhancing Human-Robot Interaction Using Wearable Technologies

Epidemic Analysis Via Social Networks

Epidemic Modeling and Prediction with COVID-19 Dataset

Evaluation of a Prototype Membrane Heat Exchanger for Efficient Buildings

Evaluation of early changes in a non-surgical post-traumatic osteoarthritis model

High Performance Concrete from Recycled Hydrogel-Based Superabsorbent Materials

High Performance Halide Perovskite Solar Cells

Human Factors: Enhancing Performance of Nurses and Surgeons

Identification, Verification and Validation of a Surfactant Formulation for Chemical Enhanced Oil Recovery in the Illinois Basin

Image-based computational modeling of tissue interface mechanics

Immunohistochemical characterization of mouse secondary visual areas

In vitro tissue engineering scaffold maturation and integration for longitudinal MRI

Indoor Air Chemistry & Physics

IoT4Ag P1: Autonomous recharging of ground and aerial mobile agricultural robot platforms

IoT4Ag P2: IsoBlue integration to UGV/UAV platforms

IoT4Ag P3: Biophysical modeling and integration with in-situ and remotely sensed data

IoT4Ag P4: Frontiers in Thermal Stress Sensing

Laboratory study of key thermal characteristics of common pharmaceutical reagents

Lake Michigan Shoreline Erosion - Measurements and Modeling

Laser diagnostics for studying shock-heated gases

Lithium-ion Battery Analytics

Low-cost user-friendly biosensors for animal health

Magnetic RAM for space applications

Mass spectrometry of biomolecules and nanoclusters

Measurement and Modeling of Mass Transfer Characteristics During Pharmaceutical Lyophilization

Measurement and Modeling of Vial Heat Transfer Characteristics During Pharmaceutical Lyophilization

Measuring glutamate release in real time following traumatic brain injury with flexible printed biosensors

Measuring wetland greenhouse gas emissions with environmental Internet of Things sensors.

Microbiological Dynamics of Drinking Water during Stagnation

Mobile Air Quality Sensors and the Internet of Things

Modeling High Efficiency Thermophotovoltaic Systems

Modeling of Wound Mechanobiology Following Lumpectomy

Nanostructural Evaluation of Human Bone Under Applied Loading

Neural recording and stimulation using a wireless single-chip system

On-Line Programming Assessment

Operation and characterization of SPT-100 Hall thruster

Printable functional filaments and sensor for biomedical devices

Process Synthesis and intensification of Shale Gas Valorization

Real time analysis of viral particles for continuous processing approach

Reliable Deep Learning Software

Remote sensing of soil moisture using Signals of Opportunity: Field Experiments and Validation Studies

Removal of Nitrogen Oxide (NOx) Pollutants from Automotive Exhaust

Resilient Extraterrestrial Habitat Engineering