2020 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 2021 Research Symposium Abstracts (PDF) and search the 2021 SURF Research 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 (86)

 

00 REMOTE Research Project Example for the COVID-Summer 

Professor:
John Howarter
Preferred major(s):
Materials Engineering, Chemical Engineering

Here is the description of your REMOTE summer research project. The typical project should be 20 hours per week and 10 weeks of work and paired with a graduate student mentor. Students will formally start the research on June 1 and work through August 7. Students will participate in a remote/online professional development program organized by SURF and project outcomes will be disseminated in an online symposium on July 30.

More information: https://engineering.purdue.edu/MSE/news/2020/mse-sounds-like-the-future

 

2-photon calcium imaging of the dorsal visual stream areas of mouse visual cortex 

Professor:
Alexander Chubykin
Preferred major(s):
Biology or Neuroscience

In humans, we perceive motion and location in different areas of the visual cortex. This is called the “what” and “where” pathways in the human brain. The ‘ventral stream’ is used for object vision while the ‘dorsal stream’ is used for spatial vision (Ungerleider, 1994). While mice do have smaller, less complex brains than humans, they do have primary and secondary parts of their visual cortex, but the functional roles of their secondary visual cortices remain unclear.
My research project will be investigating the secondary visual cortices in mice to determine which areas are responsible for perceiving motion. To achieve this goal, in collaboration with the graduate student Mang Gao, we will use in vivo 2-photon calcium imagine to simultaneously record the visual response from secondary visual cortices. The mice will first go through a head-plate implantation surgery, so the head of the mice can be fixed during imaging. After the implantation, the mice will be given an injection of Adeno-associated virus that carries the sequence of a fluorescent calcium indicator, GCaMP6s, so that the GCaMP6s will be expressed in the neurons. The fluorescence signal will brighten up when these neurons fire action potentials. The day after injection, a glass window will be placed over the hole in their skull. After the mice recover from surgery and get habituated to head fixation setup, their visual cortical calcium activity will be imaged when visual stimulation is presented.
The visual stimulus that the mice will be presented with will be a random dot kinematogram, which is used to study to properties of low-level motion processes. This will be coded by myself using Python. The mice will be put in front of this visual stimulus and their brain will be imaged through calcium imaging to determine which parts of their secondary visual cortices process motion.

More information: https://chubykinlab.wixsite.com/chubykinlab

 

3D Printed Mobile Microrobots 

Professor:
David Cappelleri
Preferred major(s):
ME, ECE, ChE
Desired experience:
Junior standing or higher, 3D printing experience, electrical circuits experience, CAD design fluency, programming experience.

In this project, the student will be tasked with using a state-of-the-art two photon polymerization (TPP) 3D printer to fabricate mobile microrobots for biomedical and manufacturing applications. A new technique will be developed for printing with photoresist with embedded magnetic particles and aligning the particles during the 3D printing process. The student will explore various micro-3D printer settings and evaluate them for different microrobot designs. The mobile microrobots produced will be tested with an external magnetic field generating system.

More information: multiscalerobotics.org

 

3D printing at nanoscale  

Professor:
Xianfan Xu
Preferred major(s):
Mechanical Engineering, Physics, Electrical Engineering
Desired experience:
Junior or higher standing, GPA 3.6 or higher, an interest in graduate school

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 laser 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. In order to make a 3D structure rapidly, a process called stimulated emission depletion (STED) is incorporated. Possible improvements to the process include control algorithms as well as development of new chemicals.

 

Active Learning: Choosing the Right Data for Machine Learning 

Professor:
Yung-Hsiang Lu
Preferred major(s):
Computer Engineering, Computer Science, Electrical Engineering, Computer and Information Technology
Desired experience:
Computer Programming

Machine learning can be classified into different categories. One is called supervised learning: each piece of data is associated with a correct answer (also called label). Since machine learning is not perfect, labeling usually needs human efforts and can be very expensive. Another type of learning is unsupervised learning: there is no correct answer and this is frequently used in clustering data into groups. Active learning is somewhat in between. Unsupervised learning is used to cluster data and identify the data that is distinct and should be labeled.

This project will use public datasets of images (or videos) as the foundation for training machine models (supervised learning). Then, new data is clustered to discover which should be labeled. This is part of the CAM2 (Continuous Analysis of Many CAMeras). CAM2 discovers, retrieves, and analyzes vast amounts of real-time data from worldwide network cameras.

More information: https://www.cam2project.net/

 

Additive Manufacturing (3D Printing) of Solid Propellants 

Professor:
Steven Son
Preferred major(s):
ME, AAE, ChE or MSE
Desired experience:
Junior or senior level students are preferred. Aptitude and interest in graduate school also desirable. Good laboratory or hands on work experience desirable.

Significant advancements have been made in the fabrication of energetic materials with additive manufacturing (AM) processes. The geometric flexibility of AM has been touted, but little has been done to combine complex geometries with spatially-varying thermodynamically optimized materials in solid propellants. Investigation of the intersection of these areas is needed to fulfill the potential of tailorability of AM processes for propellant optimization. The propellant grains result in complex geometries. Recent development of an ultrasonic-vibration assisted direct write printing system at Purdue has opened a range of new materials for printing. Steps are being taken to combine AM techniques in a single, multi-nozzle printer to allow continuous fabrication of a propellant with two or more major components. This project will focus on printing thermodynamically optimized solid propellants in with a range of internal geometries and investigating their effects with classical and more recent diagnostic techniques.

More information: https://engineering.purdue.edu/ME/News/3d-printing-extremely-viscous-materials

 

Adhesive Proteins for Surgical Applications 

Professor:
Julie Liu
Preferred major(s):
BME or CHE

Sutures and staples are used to close tissue upon completion of the surgery, but these techniques frequently result in pain and post-surgical complications. Surgical sealants typically supplement to form a better seal, but there are currently no sealants available that can replace sutures and staples. We have designed an elastin-based protein that has been modified to incorporate adhesive residues to function as a surgical sealant. Students will be involved in recombinant protein design, adhesive testing, and material characterization techniques to better engineer these protein formulations.

 

Adhesives at the Beach 

Professor:
Jonathan Wilker
Preferred major(s):
Biology, Biomedical Engineering, Chemical Engineering, Chemistry, Materials Engineering
Desired experience:
This project will involve aspects of marine biology (e.g., working with live mussels), materials engineering (e.g., measuring mechanical properties of adhesives), and chemistry (e.g., making surfaces with varied functionalities). 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.

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. In order to design higher performing synthetic materials we must, first, learn all of the tricks used by nature when making adhesives. Future efforts for this coming summer will revolve around work with live mussels. Plans for experiments include changing the water, surfaces, and other environmental conditions around the animals. Mechanical performance of the resulting adhesives will be quantified and compared. Microscopy and other methods will be used to further understand the factors that dictate how these fascinating biological materials can function under such demanding conditions.

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

 

Advanced Processing of Functional Thin Film Materials and Devices 

Professor:
Sunghwan Lee
Preferred major(s):
Engineering Technology; Materials Engineering; Chemical Engineering; Electrical Engineering
Desired experience:
preferred skills with thin film processing; sputtering; CVD but not required

Lee’s research interests are in advanced processing and novel synthesis of functional thin film materials with a focus on high performance oxides, large area CVD polymers and low-temperature ionic conductors. These materials have potential applications in next generation flexible devices, energy conversion devices (e.g., solar cells and fuel cells) and bio-compatible devices (e.g., disposable medical devices and organic sensors).

1. Transparent and Flexible Electronics
Next generation high performance display devices require new materials that present high carrier mobility, excellent stability over time, and low cost-processability. We focus on the development of new types of materials including transition-metal oxides, conjugated polymers, and the fabrication of thin film transistors (TFTs) for the potential applications in high-performance active matrix displays.

2. Functional Materials for Energy Conversion Devices
There is a clear and urgent need for the development of new- and/or renewable energy technologies. Our research interests lie in unique materials processing using novel synthesis techniques (e.g., oxidative CVD, extremely low oxygen pressure annealing) and uncovering their physical properties for energy conversion device applications (e.g., Solar cells, Fuel cells).

3. Advanced Polymer Processing and Wearable Devices
Oxidative Chemical-Vapor-Deposition (oCVD) is a unique solvent-free polymer coating technique that offers a simple and easy approach to synthesize and deposit functional thin film polymers irrespective of polymer solubility or the properties of the substrate material, unlike solvent-involving processes or electrochemical polymerization. The oCVD method has the merits of excellent film uniformity over large areas, high electrical conductivity, conformal coating on non-planar substrates (e.g., textiles and trenches), and low process temperature (20-100 °C), along with scalability for roll-to-roll mass production. The uniform conformal polymeric coating provides a unique functionality to realize breathable wearable clothing devices.

More information: https://baylorme.wixsite.com/leethinfilm

 

Advanced Vehicle Automation and Human-Subject Experimentation  

Professor:
Brandon Pitts
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 experience (in any language)

Vehicle automation is developing at a rapid rate worldwide. While fully autonomous vehicles will not dominate 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. The field of Cyber-physical systems (CPS) helps to integrate all of these activities. 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 (using the National Advanced Driving Simulator), 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.

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

 

Applications of Deep Reinforcement Learning  

Professor:
Vaneet Aggarwal
Preferred major(s):
CS, EE, Math, Stats

Reinforcement learning refers to goal-oriented algorithms, which learn how to attain a complex objective (goal) or maximize along a particular dimension over many steps; for example, maximize the points won in a game over many moves. Our group is involved in coming up with fundamental algorithms for many aspects in reinforcement learning, and applying them to problems in social networks and transportation.

More information: https://engineering.purdue.edu/CLANLabs/publications/publi

 

Automatic Grading of Programming Assignments 

Professor:
Milind Kulkarni
Preferred major(s):
must be familiar with scripting (Python) and version control
Desired experience:
ECE 264 or ECE 20875 or CS 240 or ECE 368; CS 159 is NOT sufficient

This project will build a workflow using version control (github) and continuous integration (travis-ci) to automatically grade programming assignments used in several courses in Electrical and Computer Engineering. Continuous Integration (CI) automatically triggers testing after new versions of software have been pushed to a repository. This project will create the tools common to all participating courses, sample test scripts for some assignments, and most important, documentations for students, teaching assistants, and instructors.

More information: https://engineering.purdue.edu/~milind/ece20875/2019fall/

 

Battery state-of-health (SoH) monitoring and prediction 

Professor:
Byunghoo Jung
Preferred major(s):
ECE
Desired experience:
Analog and mixed-signal circuit design Micro-controller firmware coding and test Real-time sensing and feedback-based calibration Firmware implementation of neural network

Rechargeable batteries are everywhere -- EV, renewable energy systems, energy storage systems, flying vehicle, etc., and the number of applications utilizing rechargeable batteries is increasing exponentially. However, monitoring and predicting the health conditions of rechargeable batteries remains a big challenge. We are developing a non-intrusive sensing system for battery health condition monitoring and neural network-based battery failure prediction system. The scope of the work includes analog and mixed-signal circuit design, firmware implementation of in-situ sensing and calibration algorithms, and implementation of neural network-based failure algorithms.

 

Bio-inspired Nanocomposites for Radiative Cooling 

Professor:
Xiulin Ruan
Preferred major(s):
Mechanical Engineering, Materials Sciences, Physics, Electrical Engineering, Computational Engineering

In this project we will fabricate and characterize bio-inspired nanocomposites for efficient radiative cooling. Radiative cooling is a passive cooling technology that can cool outdoor surfaces to below ambient temperature without any power consumption, and hold the promise for saving energy for buildings and infrastructures and fighting global warming. The nanocomposites will be fabricated using coating or additive manufacturing methods. We need certain selective optical properties of the nanocomposites and these properties will be characterized using spectrometers.

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

 

Biofuels from methanol 

Professor:
John Morgan
Preferred major(s):
ABE or CHE
Desired experience:
bio 230, CHE 48

The student will perform experiments on converting methanol into longer chain fuel alcohols. The experience will involve growth of yeast in laboratory conditions, will eventually be performed in a bioreactor.

 

Biomimetic microfluidic tumor models for high throughput drug screening 

Professor:
Bumsoo Han
Preferred major(s):
Mechanical Engineering, Chemical Engineering or Biomedical Engineering
Desired experience:
Course work in solid/fluid mechanics and heat/mass transfer is preferred, but not required.

The overall objective of this project is to develop a new in-vitro tumor model for the rapid screening of drugs and nanoparticles compounds for treating drug-resistant cancers. This model, called as "tumor-microenvironment-on-chip," is based on tissue engineering and microfluidics technologies, to mimic highly dynamic and heterogeneous in-vivo human tumor microenvironment. In addition to its fabrication and development, its rapid screening capability for drugs and nanoparticle compounds will be tested on multidrug-resistant cancers.

In the context of this overall objective, the SURF students will perform research to develop and characterize the tumor-microenvironment-on-chip platform. This platform with heterogeneous cancer types will further tested for their drug response. Specific research tasks include cell culture, microfabrication, fluorescence microscopy, and image analysis for drug response quantification. He/she will also participate in collaboration through Purdue Center for Cancer Research, and IU School of Medicine.

More information: http://www.biotransportgroup.org/

 

Building Software for Environmental Modeling 

Professor:
Dharmendra Saraswat
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.

Agricultural and Biological Engineering Department has contributed several tools for environmental modeling community. It is a challenge to review and understand old codes with minimum documentation. This project involves modernizing an environmental modeling software written primarily in Perl. In this project, the SURF student will first assess the current application, create a plan for the new iteration in collaboration with the project supervisor, get a head start on developing the new application and document the process. The SURF student will work with a staff programmer.

 

Building computer systems software for AI and IoT 

Professor:
Felix Lin
Preferred major(s):
Computer engineering / science
Desired experience:
Passion in hacking system software

While the mobile computers are still flourishing, we are quickly embracing a variety of new computing paradigms -- wearables, IoT, and VR headsets, just to name a few. They challenge the way we design and build computer software today. In this project, you will involve in redefining systems software to make computers smarter, faster and cooler.

This project will give you a lot of fun in hacking Linux, frameworks, and various modern hardware.

Underrepresented minority students are particularly encouraged to apply.

More information: http://xsel.rocks

 

CISTAR REU Project - Synthesis of Pt Nano-particle Catalysts 

Professor:
Maeve Drummond Oakes
Preferred major(s):
Chemical Engineering

This summer project will be to synthesize Pt nano-particle catalysts of different size and test them for propane dehydrogenation. In addition to the effect of particle size on catalytic performance, the structure of the particles will be determined by X-ray absorption spectroscopy at Argonne National Laboratory. It is expected that larger particles are less selective, produce more carbon and are less stable than smaller particles. In addition, preliminary results suggest that surface atoms have a much shorter bond distance than interior atoms.

More information: https://cistar.us/CISTAR/education/reu

 

Cartilage Tissue Engineering 

Professor:
Julie Liu
Preferred major(s):
BME or CHE

Cartilage tissue engineering emerged nearly thirty years ago as having the potential to treat osteoarthritis. In our lab, we use natural biopolymers, such as collagen and hyaluronic acid, to make hydrogels that recapitulate the native tissue environment and regenerate damaged cartilage. To better model the ability of our hydrogels to regenerate cartilage, we analyze the hydrogels under conditions that simulate those of osteoarthritis. Students will learn about tissue engineering principles, including the major components of a tissue engineered construct, important factors to consider when designing a tissue, and how to probe different properties of a tissue engineered construct.

 

Characterization of Decomposition and Detonation of Cocrystal Explosives 

Professor:
Steven Son
Preferred major(s):
ME, AAE, ChE or MSE
Desired experience:
Junior or Senior UG students preferred. Good lab skills are highly desired.

Cocrystal explosives offer the possibility improved safety and performance over conventional materials. The SURF student would assist graduate students in the study of novel cocrystal explosives. Both slow heating and detonation experiments and detonation experiments will be designed and performed.

 

Characterization of mass transfer in dialysis 

Professor:
Vivek Narsimhan
Preferred major(s):
Chemical Engineering, Mechanical Engineering, Biomedical Engineering

We have an open position available in Narsimhan lab focusing on improvement of a medical technology. In this project, you will be working on dialysis, a medical treatment for people with kidney failure. You will be engaged in the experimental study of the efficiency of clinically used dialyzers and the ways to improve the treatment outcome by employing the chemical engineering principles. The project is in close collaboration with a post-doctoral researcher in the group as well as a company pioneering on the development of medical technologies. We are looking for an enthusiastic undergraduate student majoring chemical engineering who is eager to learn about the implementation of chemical engineering fundamentals in medical treatments. By the end of this project, you will have extensive knowledge on how to work with a continuous flow process with multiple components and also acquire the knowledge on the close connection between transport phenomena and a currently-used clinical treatment. Interested students can directly contact Prof Vivek Narsimhan (vnarsim@purdue.edu) or Farzad Mohajerani (fmohajer@purdue.edu) regarding this position

 

Cognitive effort-based decision making 

Professor:
Yu-Chin Chiu
Preferred major(s):
Computer Science, Psychology, Neuroscience
Desired experience:
- Programming skills (e.g., Python or MATLAB) - GPA > 3.0

Intuitively, cognitive control is effortful, and people have a bias away from exerting effortful control operations like task switching. However, providing rewards can motivate some people (but likely not all) to expend extra cognitive effort. We will explore how individual differences in reward-effort trade-off play a role in exerting cognitive control when facing cognitively-demanding situations. We will conduct experiments to assess individual's reward-effort trade-offs and use computational algorithms to model the decision making behavior.

The student will be involved in the process of literature search, task programming, data collection, and data analysis and modeling.

More information: https://www.purdue.edu/hhs/psy/directory/faculty/images/Chiu,%20Yu-Chin.html

 

Comparing structural and functional consequences of forest fragmentation in urban and rural contexts 

Professor:
Brady Hardiman
Preferred major(s):
EEE/FNR
Desired experience:
Experience conducting field research in forest ecosystems Successfully completed at least 1 GIS course

Forests provide a wide array of goods and services on which humans depend for their well-being in both urban and rural locations. Unfortunately, land clearing for both urbanization and agriculture have fragmented much of the world's forests with unknown consequences for their ability to continue providing these services. Trees that are located on the edge of a forest are exposed to a different suite of environmental factors than trees on the inside of a forest. Edge trees are impacted by environmental factors such as higher exposure to light, wind, and temperature gradients. Other factors include effects from neighboring land depending on type and use. The purpose of this project is to quantify differences in the edge effect on trees in rural settings compared to urban settings that may lead to different levels of ecosystem functioning that have previously been unaccounted for.

 

Continuous Analysis of Many CAMeras (CAM2) 

Professor:
Yung-Hsiang Lu
Preferred major(s):
ECE, CS
Desired experience:
ECE 264 or CS 240

This project develops the technologies to analyze real-time images and video streams from hundreds of cameras. The purpose is to detect anomaly (such as traffic accident) or emergency (such as a natural disaster). The participating students will learn computer vision, machine learning, computer system management.

More information: https://www.cam2project.net/

 

Cooling Technologies Research Center (CTRC) 

Professor:
Justin Weibel

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.

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

 

Data-driven analysis and modeling of disaster impact on businesses using large scale mobility data 

Professor:
Satish Ukkusuri
Preferred major(s):
Civil Engineering, Computer Science, Data Science
Desired experience:
- Proficiency in programming languages such as Python, R, Java (preferably Python) - Some experience on handling large scale (~GB) data of any kind - Knowledge on statistical and machine learning models - Interest in solving important societal problems using novel data and techniques

With the recent increasing trend of both the frequency and intensity of natural hazards, there is significant global attention on improving the resilience of cities against disasters. Among the various dimensions of urban resilience, the recovery of businesses is a critical component that contributes to the economic performance of cities after disasters. Past studies have analyzed the recovery of businesses through surveys and interviews, however, observations are limited to a few timesteps, failing to give a continuous and longitudinal understanding of the recovery process of businesses. With the emergence of novel and large-scale data collected from mobile sensors and online social media platforms, we are now capable of observing and analyzing the dynamics of people, goods, and information at an unprecedented spatio-temporal granularity. Using the detailed individual mobility patterns, we are able to estimate the number of visits to each point of interest (POI), and infer the performance of each business located in that POI. Despite this significant opportunity, no studies have used such data to analyze or model the recovery of businesses after disasters.

The objectives of this project are to fill the aforementioned research gaps by:
a) Developing statistical analysis methods to estimate the longitudinal recovery patterns of business-related POIs using large scale mobility data including mobile phone records and online social media data.
b) Identify how the pre-disaster characteristics of businesses such as the category (e.g. retail, pharmacy, restaurant) and the size (e.g. average daily customers, popularity) affect the recovery trajectories of businesses after disasters.
c) Understand how the spatial attributes of businesses (e.g. rural, urban) accelerate or decelerate the economic recovery of businesses.

The student will work with the Principal Investigator (Professor Satish Ukkusuri) and graduate research assistant (Takahiro Yabe) by assisting them in data collection, data analysis, visualization of results, and documentation of the project outcomes.
Therefore, proficiency in programming languages such as Python and some experience on handling large scale (~GB) datasets would be strongly desired.

More information: http://www.satishukkusuri.com/

 

Design and Fabrication of a Novel Membrane Heat Exchanger for Substantial Energy Savings 

Professor:
James Braun
Preferred major(s):
Mechanical Engineering
Desired experience:
Course Work: Thermodynamics (ME 20000), Graphics for Mfg (ME 16300), ME Design Innovation (ME 26300). Potentially beneficial: Heat and Mass Transfer (ME 31500). Other Skills/Experience: Interest in sustainability/energy. Some experience with 3D modeling and prototyping is required. Experience with machining will be very useful (though not required) since certain parts will need to be custom designed and manufactured. Having coursework in thermodynamics and heat/mass transfer will allow the student to better understand the larger scope of the project and thus may enable them to make additional contributions.

Air conditioning/heating accounts for nearly 20% of primary energy consumption in the United States, and the long-established vapor compression cooling technologies we use for air conditioning rely on energy intensive overcooling and re-heating in order to meet the required indoor temperature and humidity values. The “membrane heat exchanger” is a novel device that can simultaneously transfer heat and moisture using thermally conductive, porous membranes to separate water vapor out of air. Studies on similar technologies have predicted that these systems can save several quadrillion BTU’s of primary energy consumption in the US alone, and we expect the membrane-HX to achieve system COP’s >2x that of current air-cooling systems.

The student who joins this project will be responsible for designing, fabricating, and assembling an experimental test apparatus that will be used to test the membrane materials for the membrane heat exchanger. The student will create a detailed 3D model of the membrane chamber, will create and source a parts list, and will manufacture/assemble the test apparatus. This project will give the student experience in product development, 3D design/prototyping, team collaboration, scientific methods, and literature review (all applicable across industry and academia).

 

Development of Riverine Energy Harvesting Device 

Professor:
Jun Chen
Preferred major(s):
Mechanical Engineering
Desired experience:
Mechanical design (CAD), prototyping development (basic machine/fabrication skills), data analysis (Python or Matlab), strong hands-on skills, good team spirit, good GPA.

The SURF student will join a research team with Purdue professors and graduate students to develop a energy device to harvest energy from river currents. He/she will work on the survey of river sources and design/test of part of the system.

 

Development of a Custom Electromechanical Tester for Characterizing Microelectronic Materials 

Professor:
Ganesh Subbarayan
Preferred major(s):
ME, MSE, EE
Desired experience:
The following skills are desirable but not required, all are encouraged to apply. CAD, machining experience, LabVIEW, MATLAB

The experimentation characterization component of HiDAC Lab focuses on characterizing the mechanical and electrical properties of materials critical to microelectronic devices. Currently, two new testing systems are being developed to test (1) the electromigration resistance and (2) the strength and fatigue properties of solder microbumps.

More information: https://sites.google.com/view/hidac/

 

Drinking water safety and sampling in buildings 

Professor:
Andrew Whelton
Preferred major(s):
Open
Desired experience:
Science or engineering background Prior lab or field experience with chemical or microbiological analysis preferred, but not required. Students will be trained with all necessary methods. Clear motivation to make a difference Able to effectively work in diverse teams Work hours will be based on the time of day and actual date of prescheduled sampling

The student will assist graduate students, a postdoctoral research association, and the professor conduct drinking water sampling in buildings. The project's focus is to better understand how drinking water quality changes during a plumbing system's age and also differences in drinking water across buildings. This project will be a mix of field and laboratory work. One study site is located in West Lafayette, IN while others are elsewhere. The student would accompany the researchers to those sites. Prior study can be found here: https://www.ncbi.nlm.nih.gov/pubmed/29253792

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

 

Elastically-driven flow focusing in micro-channels 

Professor:
Vivek Narsimhan
Preferred major(s):
Chemical Engineering, Biological Engineering, Physics, Chemistry, Applied Mathematics
Desired experience:
Basic understanding of MATLAB

Separation of biological suspensions (e.g., cells, bacteria, macro-particles in solution) find wide use in the detection, diagnosis, and treatment of disease. Traditional techniques such as centrifugation and filtration (size-exclusion) are common, but for many point-of-care applications, it is desired to use strategies that are more gentle, cheap, portable, and low-volume. Here, microfluidics has emerged as an attractive method to address these concerns. Using channels with minimal power sources or moving parts (i.e., only syringes), several laboratory studies have demonstrated that one can purify and isolate cancer cells, leukocytes, or bacteria samples from diluted whole blood without the use of specific biomarkers. The scientific premise behind these studies is that various components in blood have different shapes, sizes, and deformability, and this variability in physical properties allows one to isolate/purify these components using flow forces.

In this project, we propose to improve focusing-based microfluidic techniques through the addition of long-chain, charge-neutral polymers (e.g., PEO or PVP) to the biological suspension. If added in dilute amounts (~1% wt. or below), these bio-compatible polymers impart additional flow forces to the particles in the fluid. These forces depend sensitively depend on the particle’s size, shape, and deformability, and hence can be used to fractionate particles by shape and size. The student will do the following: (a) fabricate non-spherical microparticles, and (b) visualize these particles flowing in a microfluidic device through microscopy or holography. The student will learn basic synthesis and image processing for this project.

More information: https://viveknarsimhan.wixsite.com/website

 

Electron Beam Fluorescence for Supersonic Flow Diagnostics 

Professor:
Alina Alexeenko
Preferred major(s):
AAE
Desired experience:
Hands-on design, build, test experience.

The project is focused on experimental studies in high-vacuum facility of supersonic plumes by electron beam fluorescence.

 

Enabling next generation manufacturing via digital twins 

Professor:
Michael Sangid
Desired experience:
Students should be familiar with programming skills, such as Matlab and/or Python.

Manufacturing is an engine that drives the economy of many countries. The global economy is entering its fourth industrial revolution - digitalization and cyber-physical systems – including an evolving manufacturing sector. Complementing the next generation of manufacturing is the concept of digital twins, which act as digital representations of a serializable component or system to predict future performance based on current knowledge. These digital twin simulations are periodically or constantly updated with new data and information from disparate sources, including physics-based simulations, real-time sensor information, and human/machine interfacing. There are fundamental research topics that need to be addressed, prior to fully realizing the idea of the digital twin, including data fusion of the various modalities of information streams, the role and impact of human actors in a cyber-physical production system, and decision-making in the presence of uncertainty. The present post-doctoral position will investigate the model-based definition of a component across its entire product lifecycle. A framework to capture, fuse, and manage data from Purdue’s digital manufacturing testbed will be created and incorporated with predictions from physics-based simulations, which will form a high fidelity digital representation of behavioral and contextual phenomena. The associated tsunami of data will be used in a decision making framework to provide a quantitative assessment or prediction given the various sources of uncertainty from the data and model streams.

 

Engineer a synthetic neuron 

Professor:
Chongli Yuan
Preferred major(s):
CHE/ABE/BME
Desired experience:
GPA > 3.5

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.

 

Enhancing Li-Ion Battery Performance Using Ion Implantation & Irradiation 

Professor:
Janelle Wharry
Preferred major(s):
Materials Science & Engineering; will also consider students with background in Electrical, Mechanical, or Nuclear Engineering
Desired experience:
Familiarity with crystal structures and x-ray interactions with materials is desirable, though not required. Most important qualifications are a willingness to learn, ability to work in a team, and clear communication skills.

Energy storage is one of the greatest challenges facing our planet's clean energy future. We need drastically improved energy storage capacity for carbon-neutral electricity generation such as wind and solar, as well as for advanced energy-efficient vehicles and electronic devices. Lithium-ion batteries are leading energy storage technologies due to their safety and reliability, but their charge storage capacity and lifetime must be improved. Li-ion batteries operate by intercalating (i.e. cycling) Li ions in and out of an anode such as TiO2. In this project, we aim to use ion implantation and irradiation to enhance the intercalation capacity, over a larger number of intercalation cycles. We are specifically studying the ionic and electronic conductivities of TiO2, following different ion irradiation doses and species. The student's contribution will include: (a) assist with crystal structure measurements, using techniques such as x-ray diffraction, Raman spectroscopy, and transmission electron microscopy (TEM); (b) assist with microstructure characterization using TEM; and (c) conduct simulations of ion implantation and irradiation into TiO2, using various ion species, energies, and fluxes.

More information: http://engineering.purdue.edu/NuclearMaterials

 

Evaluation of Online Learning with Data Analytics  

Professor:
Kerrie Douglas
Preferred major(s):
College of Engineering
Desired experience:
SEED lab is looking for an undergraduate student researcher who is passionate about using data analytics to improve the quality of education for diverse engineering learners. We desire someone who enjoys working with quantitative data but is also willing to learn qualitative methods as needed. The student researcher must be teachable and also capable of working independently to accomplish goals.

The SEED (Science and Ethics of Educational Data) lab is currently conducting research on advanced technical engineering courses that are available for professional engineers through online platforms. Online forms of education are quickly advancing in innovative ways, and it is necessary to understand the quality of the learning opportunities. Our research combines both learner interaction/behavior data with traditional forms of educational assessment (e.g., homework, exams, etc.) We use data analytics to visualize patterns of behavior and connect them to course materials. Our goal is to better understand how to improve the quality of the learning opportunities provided through online courses and support continuous improvement of the courses.

More information: https://engineering.purdue.edu/ENE/People/profile?resource_id=79518

 

Geodesic convolution with various applications in 3D data analysis 

Professor:
Min Liu
Preferred major(s):
ME, ECE, CS
Desired experience:
python, c++ code, experience with convolutional neural networks

The scope this project is to explore the mechanics of geodesic convolution (in contrast to the standard Euclidean space convolution) for deep neural networks. The objective is to research for a more efficient, robust and shape-aware filter to support various applications for 3D vision data analysis, E.g. Autonomous CAR, robot navigation, and Augmented realities.

More information: https://engineering.purdue.edu/cdesign/wp/

 

Harmful Algae Blooms in the Wabash River 

Professor:
Greg Michalski
Preferred major(s):
Chemistry Biology Computer Science
Desired experience:
Wet Chemistry, biology, programming

Studying harmful

 

High Performance Concrete from Recycled Hydrogel-Based Superabsorbent Materials 

Professor:
Kendra Erk
Desired experience:
Enthusiasm for chemistry and an interest in materials research. Prior experiences with cement and concrete would be a benefit to the project but are not required.

Concrete that is internally cured by water-swollen superabsorbent polymer (SAP) particles has improved strength and durability. Widespread adoption of SAP-cured concrete is hindered by the lack of commercial SAP formulations that maintain their absorbency in cement’s high-pH environment. Most commercial SAP formulations are designed for disposable diapers and other absorbent hygiene products (AHPs), which account for ~12% (3.4M tons) of all non-durable goods in landfills. Over 70% of a diaper’s weight is composed of absorbent materials – mainly cellulose and polyacrylamide(PAM)-based SAP particles – the latter being chemically equivalent to the SAP particles that perform well in concrete research. Thus, a sustainable strategy to create effective concrete curing agents is to recycle the absorbent materials from AHPs and reprocess for use in concrete. AHP recycling efforts are already underway, including a plant in Italy with a 10,000-tonne annual capacity for AHP recycling. However, synthetic strategies must be developed to convert recycled AHPs into absorbent particles that perform well in concrete. Hypothesis and Objectives: We hypothesize that the PAM and cellulose components of AHPs can be separated and chemically crosslinked to form particles that display high absorption capacity in alkaline environments. The SURF student will: (1) obtain recycled absorbent materials and characterize the structures of the materials including composition, particle morphology, and swelling behavior; (2) design and synthesize absorbent particles by combining different ratios of recycled absorbent materials with a crosslinking agent and grinding/sieving to create particles with dry sizes of 10-100 micron; (3) identify the dosages of absorbent particles required to create internally cured concrete with good workability and mechanical strength; and (4) perform cost-benefit analysis of concrete cured by recycled particles and commercial SAP.

More information: https://soft-material-mechanics.squarespace.com/home/

 

Highly Selective Nanoengineering Polymer Membranes for Air Revitalization in Astronaut Life Support Systems 

Professor:
David Warsinger
Preferred major(s):
Mechanical Engineering, Materials Science, Chemistry, and related fields
Desired experience:
Junior standing or higher, experience with MATLAB or EES is desired but not required, interest in continuing project in future semesters

One of the most significant technological barriers to putting humans on mars is reliable carbon dioxide removal in air revitalization for life support systems. Health effects from high CO2 levels, occurring when CO2 partial pressures exceed 2.0 mmHg, are among the most common complaints and challenges from astronauts. CO2 poisoning causes drowsiness, headaches, and dangerously impairs cognitive function. If astronauts lose their ability to perform task that require immaculate attention to detail, especially in emergencies, it could cost their lives. Despite the potential of membrane technology to mitigate health concerns and address challenges of current materials, this technology has not been extensively explored [1]. There is great opportunity to leverage transformative advances resulting from research efforts to reduce CO2 emissions in gas processing and apply them to air revitalization. High performance facilitated transport polymer membranes with a significantly enhanced affinity for CO2 have shown to be effective for the selective removal of CO2 in combustion by-products. Applied to air revitalization in space, membrane technology could provide a lower energy replacement for current heat-driven technologies. It also has the potential to reduce mass, power, and volume, and improve reliability and efficiency in comparison to current systems. However, membranes for selective removal of CO2 in combustion processing are typically designed to achieve a 95% purity. A higher purity level is instrumental for potential application in producing a breathable atmosphere in space with acceptable CO2 levels.

More information: http://www.warsinger.com

 

Human Body Communication 

Professor:
Shreyas Sen
Preferred major(s):
ECE, BME

The student will work on theory and device design related to using the human body as a communication medium to improve Healthcare and HCI.

More information: https://engineering.purdue.edu/~shreyas/SparcLab/home/media.html

 

Human Factors Considerations: Older Adults and Autonomous Vehicle Systems 

Professor:
Brandon Pitts
Preferred major(s):
Industrial Engineering
Desired experience:
Human Factors, Matlab, Transportation, some experience in statistics, some computer programming experience (in any language)

Automobiles are becoming increasingly autonomous. At the same time, the demographics of drivers using these advanced vehicles is changing. In particular, adults aged 65 years and older are the fastest growing age group worldwide and are expected to benefit from vehicle automation. However, age-related perceptual and cognitive difficulties may limit the extent to which these systems are useful for individuals in this age category. The goal of this project is, therefore, to quantify interactions between (older adult) drivers and autonomous driving systems in order to develop approaches that enhance roadway safety for various aging populations.

The SURF student will assist with collecting and analyzing data from human-subject experiments (using a laboratory driving simulator) and with writing any project publication. In addition, the student will meet regularly with faculty and graduate mentors to communicate his/her progress.

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

 

Human Factors in Laparoscopic and Robotic Surgery 

Professor:
Denny Yu
Preferred major(s):
Industrial Engineering
Desired experience:
Human Factors, Machine Learning, Sensors, Programming

Fatigue and injuries among surgeons are becoming more common. The purpose of this project is to examine the ergonomic risks and test possible interventions to surgeons during laparoscopic and robotic surgery. This work will leverage sensing technology (e.g., motion tracking, pressure map, electromyography, or other sensors) to monitor surgeons to ultimately develop interventions for augmenting human performance in surgery and how to better design an operating room.

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

 

Hybrid halide perovskite nanomaterials for next generation photovoltaics and electronics 

Professor:
Letian Dou
Preferred major(s):
interdisciplinary, chemistry, chemical engineering, and materials science

Modern society relies on electronics and optoelectronic devices (e.g. transistors, light emitting diodes, lasers, solar cells, and detectors). Semiconductor materials are the basis of these devices. Currently, the state-of-the-art devices are dominated by conventional inorganic materials, which are expensive to produce and hard to be incorporated into the next-generation flexible/wearable and bio-compatible devices. While organic materials are advantageous in terms of costs and mechanical flexibility, their electronic properties are usually not as good as inorganic materials. Organic-inorganic hybrid materials provide a promising solution, if the best of the two worlds can be combined.

The design of new hybrid materials for the next generation of optoelectronic and sensing devices and the elucidation of their fundamental structure-property-performance relationships are the key focus of the Dou research group. Specifically, we aim to assemble organic and inorganic materials together through non-covalent and covalent interactions. We tailor the properties of these materials at the nano scale and molecular level in order to deliver new fundamental insights regarding the semiconducting organic-inorganic interface. In turn, this will allow for improved performance of solar energy harvesting and solid-state lighting, and chemical/biological sensing devices. Our research is highly interdisciplinary as it bridges chemistry, chemical engineering, and materials science such that new research paradigms that cut across traditional science and engineering disciplines can be established.

More information: https://letiandougroup.com/

 

Hypervelocity impact screen recording diagnostics and spectroscopy 

Professor:
Vikas Tomar
Preferred major(s):
mechanical, aerospace, materials, mathematics, statistics, computer

Need undergraduate researchers to help perform meteor impact experiments, hypervelocity (> 1 km/sec) impact damage measurements, space based additive manufacturing, space self healing material experiments, and damage tolerant sensor designs.

 

Illumination of Damage through Microtomography 

Professor:
Michael Sangid
Preferred major(s):
AAE, ME, MSE, EE, CSE, or IE
Desired experience:
Students are expected to work with Image Processing and Visualization tools, as well as Matlab.

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 microtomography, 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, microtomography scans will be reconstructed and the damage in the form of voids or cracks 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.

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

 

Image analysis of vesicle membranes 

Professor:
Vivek Narsimhan
Preferred major(s):
Chemical Engineering, Biological Engineering, Physics, Chemistry, Applied Mathematics
Desired experience:
It is desirable for the student to have a background in MATLAB, Python, or equivalent.

Vesicles are elastic and highly deformable sacs of fluid enclosed by a lipid bilayer. These entities are critical for the intracellular compartmentation and molecular trafficking that underlie the signaling, defense and nutrition vital for an organism’s survival. Similar lipid architectures are also used in industrial applications ranging from drug and gene delivery to fabric softeners. Lastly, vesicles are model systems to understand fundamental processes that occur in all cellular membranes (e.g., budding, fusion, membrane-protein interactions). For these reasons, there is immense interest to characterize the physical properties and mechanical behavior of vesicular systems under various conditions.

In this project, the student will develop and modify image processing codes to analyze microscope images of vesicles. The goal of these codes is to extract elastic properties of the lipid bilayers through thermal fluctuations of the vesicle shape over time. Ambitious students will also have the opportunity to synthesize vesicles in lab, examine more complicated membrane architectures (multicomponent vesicles), and solve equations describing the shape dynamic of these entities under weak flow.

More information: https://viveknarsimhan.wixsite.com/website

 

Improving Aircraft Engine Performance 

Professor:
Paul Bevilaqua
Preferred major(s):
Aeronautics or Mechanical Engineering
Desired experience:
The ideal candidate will have had courses in fluid mechanics and propulsion, as well as experience building and flying model aircraft. Practical experience on a design, build, fly or drive team is a plus.

Recently, there has been renewed interest at NASA and the major aircraft and engine companies in increasing the thrust per horsepower of aircraft engines by ingesting the boundary layer wake of the aircraft’s fuselage. Although there are analytical predictions of this benefit, it has never been demonstrated. The purpose of this SURF project will be to measure the thrust and fuel consumption of two propulsion systems, one ingesting the boundary layer wake from a model aircraft fuselage and another ingesting free stream air. The experiment will be conducted in the low speed wind tunnel at the Purdue Aerospace Research Laboratories. The ideal candidate will have some experience designing or flying model aircraft with glow plug engines. This research project will be performed under the guidance of Visiting Professor Paul Bevilaqua, a Purdue graduate and retired Chief Engineer of the Lockheed Martin Skunk Works.

 

Lake Michigan Ecosystem Modeling 

Professor:
Cary Troy
Preferred major(s):
Civil, Environmental, or Mechanical Engineering
Desired experience:
Proficiency in Matlab; Good communication skills, written and oral; Exposure to differential equations

This is an NSF-funded project examining the role of turbulence in the Lake Michigan ecosystem. Particularly, the project is quantifying the interactions between water column turbulence and the ability of invasive quagga mussels to filter nutrients and plankton out of the water column. The SURF research will involve the development of a 1-D biogeochemical model that models the temporal and vertical distribution of nutrients (e.g. phosphorus), phytoplankton, and zooplankton in Lake Michigan. The successful SURF applicant will be responsible for the coding and development of the model in Matlab, as well as potentially participating in data collection on Lake Michigan and the analysis of this data.

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

 

Lake Michigan shoreline erosion and sediment transport 

Professor:
Cary Troy
Preferred major(s):
Civil or Environmental Engineering
Desired experience:
The desired student would have experience on the water or in the field, as well as basic abilities in Excel and Matlab. The student must be able to swim and ideally have some experience on boats.

This project aims to quantify historical and ongoing erosion along Lake Michigan's shoreline, using drones and water survey data. With Lake Michigan water levels currently at near-record highs, property damage along the lakeshore is immense, and the student will work to better understand the magnitude and mechanisms of shoreline erosion. This project will involve field work and potentially some computer simulations of Lake Michigan shoreline erosion.

More information: https://engineering.purdue.edu/CE/People/Faculty/Troy

 

Li-ion Battery Thermal Stability Analytics 

Professor:
Partha Mukherjee
Preferred major(s):
Mechanical, Chemical, Chemistry
Desired experience:
Undergrad level heat and mass transfer, modeling and analysis, hands-on

Lithium ion (Li-ion) batteries are ubiquitous. Thermal characteristics of these systems are critical toward safer and high-performance batteries for electric vehicles. As part of this summer research, analysis of thermal stability mechanisms under normal and anomalous operating conditions of Li-ion cells will be performed based on a mix of experimental and theoretical studies.

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

 

Low-cost user-friendly biosensors for animal health 

Professor:
Mohit Verma
Preferred major(s):
Biomedical engineering, biological engineering, electrical engineering, mechanical engineering, or other relevant fields
Desired experience:
To be successful at this position, you should have a GPA>3.5, prior experience working in a wet lab (ideally experience with bacterial culture and DNA amplification), experience building electromechanical devices, and the ability to work in a team.

Infectious diseases are a leading cause of economic burden on food production from animals. For example, bovine respiratory diseases lead to a loss of ~$480/animal. 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.

The SURF student will have three objectives: i) design primers for detecting pathogens associated with bovine respiratory diseases, ii) build a device for processing the sample and extracting DNA that can be amplified by the biosensor, and iii) build a device for detecting colorimetric/fluorometric output from the biosensor.

More information: www.vermalab.com

 

Lyophilization Research at LyoHUB 

Professor:
Alina Alexeenko
Preferred major(s):
AAE, ABE, CHE, CS, ECE, ME
Desired experience:
Varies.

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.

 

Machine Learning Guided Modeling for Concrete Strength Prediction using Piezoelectric sensor based Electromechanical Impedance (EMI) Technique 

Professor:
Luna Lu
Preferred major(s):
civil engineering, computer science, electrical engineering, mechanical engineering
Desired experience:
Python, Matlab or related coding experience

The objective of this work is developing a machine learning guided model to predict concrete compressive strength gain using electromechanical impedance (EMI) method coupled with piezoelectric sensor. EMI method can monitor the compressive strength gain of concrete through electro-mechanical coupling effect of piezoelectric sensors. Our previous work has approved the feasibility of using this technology through a series of lab test with various type of sample incorporated with different type of cement, supplementary materials (SCMs) and water-to-cement ratio. The correlation results we obtained using EMI-root mean square deviation (RMSD) index, which are highly correlated with the compressive strength of concrete. However, the field condition is quite different compared with lab such as ambient temperature, humidity, internal temperature of concrete and sensing zone etc. Thus, these factors have to be taken into consideration to develop the reliable mathematical model on concrete strength prediction.
To reach this goal, data sets of impedance and compressive strength data of large concrete slab samples will be collected to train the predictive model using convolution neuron network (CNN). The aim of this project is to establish the function via machine learning algorithms to improve the accuracy for concrete strength monitoring and prediction.

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

 

Measurements of parameters of T-100 Hall thruster  

Professor:
Alexey Shashurin

Hall thrusters are widely utilized for spacecraft propulsion. The technology has been originally developed in Soviet Union and got adopted on the West in 1990s. In Hall thruster, neutral gas propellant is ionized and accelerated in the cross-field accelerator to reach high propellant exhaust velocities in the range 10 - 50 km/s.

In this project student will work with Hall thruster T-100. The project will include operating the thruster, measurements of electrical parameters of Hall discharge for various anode flow rates and magnetic fields, Langmuir probe measurements in the Hall thruster plume and indirect thrust measurements

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

 

Metabolic Signals Influencing Gene Expression 

Professor:
Ann Kirchmaier
Preferred major(s):
Biochemistry, Biology, biomedical engineering, chemical engineering or similar
Desired experience:
general chemistry plus, molecular biology, genetics, and/or biochemistry. Previous laboratory experience a plus.

Undergraduate Research Opportunity in Epigenetics. Student will conduct primary hypothesis-based research, learn to design experiments and interpret results. Student will utilize genetic, biochemical, or nutritional strategies to quantify metabolites and to assess functions of evolutionarily conserved metabolic enzymes and how their metabolic intermediates influence gene expression.

 

Metal Nanofoam Fabrication and Characterization 

Professor:
David Bahr
Preferred major(s):
MSE
Desired experience:
Minimum 1 year chemistry. Prefer some experience with microscopy or materials testing.

Metallic nanofoam structures (with ligament and pore diameters on the order of 100 - 400 nm) have been formed using templates formed from electrospinning. Starting with a polymer precursor, we oxidize and then reduce a non-woven fibrous mat to create a 3D metal foam. Metal foams have extremely high strength to weight ratios, we aim to increase this by creating core-shell foams (where we deposit additional metals onto the ligaments). The student on this project will be responsible for materials processing, carrying out electron microscopy to characterize the structures, electroplating the foams, and quantifying the structure of the foam. The work will be primarily experimental, and requires a working knowledge of chemistry and materials characterization tools.

 

Micro/nano Scale 3D Laser Printing  

Professor:
Xianfan Xu
Preferred major(s):
Mechanical Engineering, Physics, Materials Engineering, Chemical Engineering, Electrical Engineering
Desired experience:
Junior or Senior standing, GPA>3.6

The ability to create 3D structures in the micro and nanoscale is important in many fields including electronics, microfluidics, and tissue engineering and is an emerging area of research and development. This project deals with the development and testing of a setup for building microscopic 3D structures with the help of a femtosecond laser. A method known as two photon polymerization is typically used to fabricate such structures in which a polymer is exposed to laser 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. In order to make a solid and stable structure, investigation of better materials and optimization of the process parameters is needed. Besides, possible improvements to the control algorithms used in the setup can be done to increase the efficiency of the process and build the structures faster.

 

Monitoring Bacterial Contamination in Biologics 

Professor:
Arezoo Ardekani
Preferred major(s):
Chemical Eng, Biological Eng, Biomedical Eng, Physics, Mechanical Eng
Desired experience:
Microfluidics, cell culture, and microscopy OR computational fluid dynamics

Biologics comprised 22% of major pharma companies in 2013 and is expended to grow to 32% of sales in 2023. Biologics are large complex molecules that are created by microorganisms and mammalian cells. They are polypeptides or proteins such as monoclonal antibodies, cytokines, fusion proteins used in vaccines, cell therapies, gene therapies, etc. Impurities such as aggregates, cell debris, bacterial and viral contamination can negatively impact the manufacturing process. In this project, we will focus on developing methods for monitoring bacterial contamination.

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

 

Nanoscale Heat Transfer 

Professor:
Xianfan Xu
Preferred major(s):
Mechanical Engineering, Physics
Desired experience:
Heat transfer, junior standing and above, GPA > 3.7

This project deals with study of heat transfer in thin film materials using Raman Spectroscopy and Ultrafast laser systems. Heat transfer in nanoscale materials including 2D materials (very thin layered materials bonded by van der Waal’s force) shows different characteristics for their thermal transport behaviors 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 develop experimental method and analyze the experimental results.

 

Novel Ethnographic Investigations of Engineering Workplaces to Advance Theory and Research Methods for Preparing the Future Workforce 

Professor:
Brent Jesiek
Preferred major(s):
Engineering (any field) and/or Social Sciences (Sociology, Anthropology, Science and Technology Studies, etc.)
Desired experience:
Any amount of exposure to qualitative social science research methods is highly desirable for this position.

In this exploratory project we will undertake innovative approaches to collecting, analyzing, and archiving empirical data related to engineering practice. This project will involve ethnographic research at multiple field sites representing multiple industry sectors using novel methods such as agile ethnography, trace ethnography, and network ethnography. These methods are new and evolving, and thus have scarcely been used to study engineering practice. Yet they appear highly promising for many reasons, including their potential to generate research findings more rapidly and with a greater focus on specific problems and questions. Such methods have started to gain traction in industry precisely due to such advantages, especially in software engineering and related fields where work is already very digital and distributed in character. The field studies proposed for this project are especially concerned with how work is coordinated and aligned within and across teams, including through the use of digital data and tools. The student selected for this research project would be partially embedded in, and working with, a technical team in an industry or university lab setting in order to collect data and generate insights for our study.

 

Open Source Analysis and Modeling of Experimental Ferroelectric Data 

Professor:
Dana Weinstein
Preferred major(s):
ECE, MSE, CE/CS
Desired experience:
familiarity with Python programming, version control, basic electronics/circuits Preference to those with some of the following: familiarity with scientific python libraries (pandas, matplotlib, numpy), ferroelectric films, electrical characterization, device modeling

Since the discovery of CMOS-compatible ferroelectric films in the early 2010's, interest in these materials for improved memory and logic devices has dramatically increased. With this increased demand comes the need for widely available tools to bridge the gap between experimental data and theoretical modeling. The applicant will familiarize themselves with the different models applied to ferroelectric devices and work to improve the existing Ferro package (https://github.com/JAnderson419/Ferro). Possible improvements based on student interest include but are not limited to: txt file parsing to support data from additional test instrument sources, implementation and fitting of theoretical models to experimental test data, and automated generation of SPICE-compatible output files based on experimental devices for further simulation.

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

 

Open to Many Topics 

Professor:
John Howarter
Preferred major(s):
Any

Interested in multiple project areas.

 

Operation and characterization of SPT-100 Hall thruster 

Professor:
Alexey Shashurin

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 the 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 documentation for AAE 590 Plasma Lab including description of Hall thruster theory and instructions for the lab.

 

Optimization of Quantum Circuits for Noisy Environments 

Professor:
Andrew Weiner
Preferred major(s):
Electrical Engineering, Physics or any closely related major
Desired experience:
(a) Experience using Matlab or Python for instrument control is strongly preferred. (b) Electricity and Magnetism coursework preferred

Our research group works on encoding and processing quantum information in the frequency domain. The platform we work with – biphoton frequency combs (BFCs) – are photon pairs that are entangled in time and energy (frequency). We use commercial hardware like phase modulators and pulse shapers for quantum state preparation and manipulation. Some recent demonstrations include measurement of high dimensional frequency-bin entanglement and tunable quantum gates, among others. Our current efforts are focused on developing quantum circuits to simulate the dynamics of molecules.

The SURF student’s contribution would be as follows:
(1) Develop an instrument control interface to automate the process of quantum state preparation. In particular, we often use commercial pulse shapers to “carve” BFCs from a continuous down conversion spectrum. However, carving a BFCs requires precise positioning of frequency bins in order to ensure that one passes energy-matched (anti-correlated in frequency) comb line pairs. The student would automate this process by interfacing with the pump laser, pulse shaper, and single photon detectors and implementing appropriate instrument control.

(2) What we often measure in our quantum experiments is coincident single photon detection events or, simply, coincidences. However, the number of coincidences depends on factors like loss in the experimental system, the timing jitter of single photon detectors, and the resolution of the timing electronics. The student will carry out a systematic study to evaluate the effect of these factors on the coincidence rate out of a quantum circuit and make recommendations on how to optimize the detection system for high coincidence rates or high coincidence-to-accidental ratios (analogous to signal to noise ratio).

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

 

Performance evaluation of Thermoelectric-concrete panels 

Professor:
Ming Qu
Preferred major(s):
Mechanical Engineering or Architectural Engineering or Material Engineering
Desired experience:
Heat transfer, Thermodynamics, Numerical Methods for Engineering Statistics Good hands-on experience

The NSF research project aims to create a new strategy to reduce building energy consumption while enhancing thermal comfort by using thermoelectric concrete-envelope to heat or cool indoor spaces with no or a little need for power.

In this summer internship, an undergraduate will assist the graduate student in examining the Thermoelectric-concrete (ThermoConc) panels. The undergraduate student will be involved in the test of the TE concrete module and modeling work.

Expectations for the undergraduate student with the help of the graduate student
1. Literature review
2. Fabricate the prototype of the ThermoConc panel.
3. Test performance of the ThermoConc.
4. Data acquisition and analysis for performance evaluation for the ThermoConc panel’s functions: the power generation and heat pumping.
5. Reporting

 

Photonic Component Design for Quantum and Classical Information Processing 

Professor:
Andrew Weiner
Preferred major(s):
Electrical Engineering, Physics or any closely related major
Desired experience:
(a) U.S. citizenship, (b) Electricity and Magnetism coursework preferred

Photons are ideal carriers of quantum information because they are robust against decoherence and are compatible with fiber optic networks. Our research group works on encoding and processing quantum information in the frequency domain. One limitation of conventional or bulk optical equipment is that these devices have high optical losses, which is a major issue for applications in the quantum regime. We recently designed photonic integrated circuits to implement elementary quantum gates and carry out operations like parallel single qubit rotations.

The SURF student’s contribution would be as follows:
(1) Design and simulate photonic elements (microresonators for generation of Kerr and quantum frequency combs, pulse shapers, etc.) for our next round of chip fabrication. The student will be given performance specifications and be expected to use analytical expressions, as well as FDTD and/or FEM simulation tool, and come up with recommendation for appropriate device geometries.

(2) Characterize on-chip optical devices/systems and relate actual performance in our first batch of chips to the original design specifications. Depending on the student’s level of experience, he/she will collect data from our testbed, compare it to the design specifications, and draw appropriate inferences from the data.

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

 

Piezoenergetic Material Development 

Professor:
Steve Son
Preferred major(s):
Engineering or science major
Desired experience:
Likely sophomore or higher.

Undergraduate would help with the research of piezoelectric materials and their uses within energetics such as propellants, thermites, and other reactive materials. Currently we are investigating using piezoenergetics as igniters for solid rocket propellant. This involves taking advantage of piezoenergetics unique properties such as their photoflash and spark sensitivity. The primary focus of this research will be studying the combustion and burning characteristics of energetics with piezoenergetic inclusions. Additionally, this research involves working with nanomaterials that are used in piezoenergetics, as well as various techniques to produce them. Over the course of the summer the student will gain hands on experience manufacturing these materials, additively manufacturing them using 3D printing, and running burn tests to study the combustion of propellants with these materials.

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

 

Preliminary Design of a 10-passenger Regional Electric Aircraft 

Professor:
Nicole Key
Preferred major(s):
AAE
Desired experience:
Students who have taken Aircraft design related courses are strongly encouraged to apply.

With the development of battery technology, the all-electric airplane for thin-haul application is becoming a reality in the foreseeable future in the 2020s. Comparing to the traditional fossil-fuel-dependent airplanes, the electric-powered airplane offers lower cost in term of operation and maintenance, and also generates less greenhouse gas footprint. In a previous study, a conceptual design of a 10-passenger aircraft for thin-haul application was conducted. The airplane falls in the category of general aviation, according to the Federal Aviation Administration (FAA) regulations. The range of the aircraft is 500nm VFR, and it features a joint-wing design for better aerodynamic performance and is powered by two newly designed duct fans. The maximum takeoff weight (MTOW) of the airplane is 15,400 lb, and the cruise speed of the airplane is Mach 0.42 at an altitude of 30,000 ft. The takeoff and landing distances are 2500 ft and 1650 ft, respectively. To achieve the range requirement, the battery weight is approximately 52% of MTOW for the existing design while using a pack level battery density of 300 Wh/kg, which is excepted to be available at the early-to-mid 2020s.

The goal of the project is to advance the previous conceptual design of the Electric Aircraft into the preliminary design phase with a focus on wing design, stability and control, and mission optimization

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

 

Real-Time process monitoring in Continuous Tablet Manufacturing using spectroscopy Process Analytical Technology  

Professor:
Gintaras Reklaitis
Preferred major(s):
Chemical Engineering
Desired experience:
Basics of chemical engineering, statistics and MATLAB is preferred.

The pharmaceutical industry addresses the health care needs of the world’s population. The industry has been dominated by batch manufacturing. However, the limitations of batch manufacturing and advances in Process Analytical Technology (PAT) have led to the shift towards continuous manufacturing (CM). But the implementation of continuous manufacturing has its challenges and continues to be an area of intense research interest. Online measurement of drug concentration in low drug load Oral solid dosage (OSD) product in CM setup is a recognized challenge. Spectroscopy methods using Near Infrared (NIR) and Raman spectrometers could provide the potential solution for real-time measurement of Active Pharmaceutical Ingredients (API) in such a mixture of API and excipients. The objective of the project will be to perform the required experiments and develop appropriate models for predicting the API concentration in real-time during the production of tablets in continuous manufacturing pilot plant at Purdue University, where are the necessary tools and resources are available.
This project will provide with good insight into the Pharmaceutical industry. It will help the students in understanding the concepts of spectroscopy and using the industry-standard NIR and Raman tools. This will also provide the students with the opportunity to work and get familiar with the different unit operations in tablet manufacturing, to learn about the different FDA guidelines for drug product manufacturing and to develop the mathematical models using multivariate analysis for the prediction of the concentrations in real-time. The project has a good balance of experimental and modelling work. The students will get a chance to interact and work with Renowned Professor Dr Reklaitis.

 

Reducing Ocean Pollution By Understanding the Formation and Stability of Shipboard Emulsions 

Professor:
Kendra Erk
Preferred major(s):
MSE, ChemE, EEE, and related fields of science and engineering
Desired experience:
Enthusiasm for chemistry, and interest in materials, environmental, or chemical engineering. Prior experience with emulsions would benefit the project but are not required.

Bilge water is a collection of waste fluids onboard a ship and is a major source of pollution in marine environments. All waste fluids onboard (including oil, grease, fuel, detergents, etc.) are collected in the ship’s bilge until it can be treated and released into the ocean. Treatment techniques remove some of the pollutants but have a difficult time removing oil when it is in the form of an oil-in-water “shipboard emulsion” with nanoscale droplets. Consequently, oils and detergents are released into the environment. The goal of this project is to study the formation and composition of bilge water emulsions to ultimately prevent emulsion formation and improve treatment techniques. In this project, the SURF student will create and characterize model bilge water emulsions with emphasis on understanding the formation mechanisms and stability of these emulsions. The SURF student will have the opportunity to learn many different characterization techniques specific to nanoscale oil-water emulsions, including optical microscopy, dynamic light scattering, zeta potential measurements, interfacial tension measurements and more, by working closely with a current Ph.D. student in MSE and faculty in MSE and EEE (Profs. Erk, Howarter, and Martinez).

More information: https://soft-material-mechanics.squarespace.com

 

Resilient Extraterrestrial Habitat Engineering (with the NASA-funded RETH Institute) 

Professor:
Shirley Dyke
Preferred major(s):
Mechanical, Aerospace, Civil, Industrial, Electrical or Computer Engineering
Desired experience:
Students interested in this project should have good experimental skills, some programming skills, and some experience in MATLAB, Simulink or Python. Any engineering background is acceptable.

There is growing interest from Space agencies such as NASA and the European Space Agency in establishing permanent human settlements outside Earth. However, even a very cursory inspection of the proposals uncovers fatal flaws in their conceptual design. The buildings may not be able to support the load demands, which should include potential impact from meteorites and/or the seismic motions induced by such an impact, and perhaps most importantly, the materials used as cover for radiation protection may be radioactive themselves. Ongoing research interest focuses on mitigating astronauts' health and performance in space exploration and has neglected the largely unexplored needs regarding the habitat and infrastructure required on extraterrestrial bodies. Their design and sustainability represents a multidisciplinary engineering and scientific grand challenge for humanity. In a context of extreme environments, it is especially important to design buildings whether for habitation, laboratory or manufacturing, that are capable of responding to prevailing conditions not only as a protective measure, but also to enable future generations to thrive under such conditions.
NASA has recently funded a research institute focused on the resilient design and operation of extraterrestrial habitats, including system monitoring and health management, safety controls, and robotic interventions. Participating undergraduate researchers would be tasked to design, modeling and development of a novel cyber-physical testbed for analyzing, exploring and comparing the behavior and growth of various subsystem extraterrestrial habitat system designs subjected to working and extreme conditions.
We are looking for 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.

More information: https://www.purdue.edu/rethi/

 

Roll-to-Roll Fabrication of High Performance Conformal Thermoelectric Generators 

Professor:
Luna Lu
Preferred major(s):
chemical engineering, mechanical engineering or materials science
Desired experience:
Previous lab or experience is desired but not required.

Thermoelectric generator (TEG) is a solid-state technology that can convert thermal energy directly into electricity through the Seebeck phenomenon. Over 2.5 quadrillion BTU/year of energy generated in US is wasted as a form of heat, which can be reclaimed as electricity using flexible TEG to power sensors and other microelectronics for civil communications and Internet of Things (IoT) technologies. Unfortunately, the current TEG technology is suffering from its rigid device structured, low efficiency and high cost in both device fabrication and installations.
In this work, a novel roll-to-roll production line of conformal thermoelectric generator (cTEG) will be reported. In-line fabrication includes several micro-deposition processes on a roll-to-roll equipment for a continuous manufacturing platform. The specific activities include: (a) depositing top metal contact layers using screen printing technique; (b) creating micro-porous channels on polymer substrates using pulsed laser irradiation system; (c) filling of micro-channels with p- and n-type TE materials using pipet dispensing systems or similar technique for nanoparticles depositions; (d) laser sintering of p- and n- type TE materials for in-situ crystallization with minimal thermal damage, followed by screen printing the top layer metal contacts to achieve high power output of conformal TEG as power sources for sensors. Thermoplastics with low thermal conductivity (i.e. Kapton, PDMS, polyamide etc.) will be used as substrate and insulating materials between p-n legs.
The cTEG with polymer substrate and insulating materials lead to maximum heat gain to reach high efficiency at the device level for power generation. The performance of cTEG will be discussed with regards to the materials quality and manufacturing process. The fundamental science developed here will have a broad interest to flexible electronic and nanomanufacturing community.

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

 

Smart manufacturing using digital twin and AI based VR/AR applications 

Professor:
Martin Jun
Preferred major(s):
Mechanical Engineering, Computer Science, Computer Engineering
Desired experience:
CAD, VR programming, Arduino, C++, Python

This project involves obtaining continuous data from manufacturing systems such as robots and CNC machines and creating real-time digital twins in VR environment, which can be used as an AR for humans who operate the manufacturing systems within the VR environment. The manufacturing systems will also be continuously monitored using IoT sensors and devices and analyzed data will be displayed in the VR for humans to make better decisions. The student will be working on data communications, data analytics, VR programming, robot control, etc.

More information: https://web.ics.purdue.edu/~jun25/

 

Synthesis of energetic materials, explosives, and related precursors. 

Professor:
Davin Piercey
Preferred major(s):
Materials Engineering, Chemical Engineering, Chemistry
Desired experience:
Organic chemistry lab experience.

The Piercey Research Group focuses on the development of new energetic materials (propellants, explosives, pyrotechnics) for both military and civilian uses, as well as studies the fundamental chemistry and reactivity of high-nitrogen energy-dense compounds.

If you join our group as a SURF student you will be paired with a graduate student or staff scientist mentor You will be mentored in the synthesis of high-nitrogen compounds and over time your responsibility will be to prepare relevant safe non-explosive precursors to the work being carried out. As your skills improve and we verify your lab safety, you may be able to handle the energetic materials

 

System on Chip (SoC) Team 

Professor:
Mark Johnson
Preferred major(s):
Electrical and/or Computer Engineering
Desired experience:
Education and experience in one more of the following is desirable: (1) Verilog/System Verilog coding skills for logic synthesis and test bench design, (2) Analog and digital integrated circuit design background including circuit simulation and layout, (3) Microcontroller programming in C and assembly language, (4) Compiler design, (5) Operating systems and especially Real Time Operating Systems.

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. In fact, the 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. Currently the team is focused on creating an SoC that is optimized for very small scale and low power machine learning applications. Our current design includes a processor that has been customized to greatly reduce the number of instructions that must be executed during neural network computations, and it includes small test circuits for certain integrated circuit failure mores and for improving the security of SoC designs. Upcoming work on the project includes incorporation of low power features, memory self-test, computation accelerators for neural network applications, development of software to demonstrate the capabilities of our design.

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

 

Testing Evolutionary Hypotheses on Collective Memory of Past Disasters through a Large-Scale Crowdsourced Behavioral Experiment 

Professor:
David Yu
Preferred major(s):
Computer science, industrial engineering, civil engineering, Purdue Polytechnic (computer information & technology), electrical engineering, etc.
Desired experience:
Software programming for online/web application (e.g., PhP).

This project will develop and conduct an online behavioral game that will crowdsource participants through Amazon Mechanical Turk. In this behavioral experiment, a group of real human subjects can complete a relevant task online (solving a social dilemma in the face of environmental shocks) and communicate their knowledge and experience to a next group of online participants (next generation). Many groups of online participants will be crowdsourced into the experiment to generate high fidelity behavioral data on a mass scale. Participants' decision data will be analyzed to test several hypotheses about human behavior.

 

Thermal Conduction in Heterogeneous Media 

Professor:
AMY MARCONNET
Preferred major(s):
Mechanical, Chemical, or Materials Engineering
Desired experience:
Courses in heat transfer and/or fluid mechanics, experience in the machine shop, and experience with Matlab is advantageous

The operating temperature of commercial grade electronic chips used in laptops, modems/routers, gaming consoles, hand-held devices such as smartphones, tablets, and supercomputers can reach dangerous levels (>80 C) as computing tasks intensify. If unchecked, this can lead to material degradation and hamper the performance of the device. Thermal interface materials (TIMs) are used for efficient heat dissipation from junction to ambient in such devices as contact thermal resistances impede efficient heat conduction to the outer surface, to be dissipated to the surroundings. Examples of different types of TIMs are pastes/grease, gels, pads, metallic TIMs, phase change materials and thermal adhesive tapes. Thermal pastes contain high conductivity filler particles in a polymer matrix. Prior research has explored filler particle chemistry (e.g., ceramic, metal, carbon black), morphology, filler loading or volume fraction, state of dispersion and fabrication strategies (i.e., functionalization, particle alignment, self-assembly) to fully exploit the high conductivity property of the microscopic filler and the highest reported value is in the range of 5-10 W/m-K.

Industry grade thermal pastes generally contain high loading of particles in the polymer matrix. Beyond a certain loading known as the percolation threshold, thermal conductivity is known to increase and to evaluate this enhancement, an experimental study involving cylindrical particles-filled epoxy is proposed. Effective thermal conductivity of different types of particle arrangements, up to the percolation threshold, will be measured using an infrared (IR) microscope. Conduction patterns in the different arrangements will be assessed for better thermal management. For the purpose, a rig that can hold the particle-epoxy medium needs to be fabricated. Additionally, novel experimental rig designs may be required depending on the specific choice of materials for various arrangements of the particles within the epoxy.

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

 

Ultrasound transduction and imaging on a flexible printed platform 

Professor:
Dana Weinstein

Roll-to-roll printing of thin film piezoelectric materials on flexible substrates has opened the doors for large-area, low cost, portable ultrasound transducers capable of biomedical imaging, structural monitoring, environmental sensing, and more. In collaboration with Prof. Mukerrem Cakmak, this project involves the electromechanical characterization of new polymer-based piezoelectric films fabricated at the Purdue Birck Nanotechnology Center and the design of supporting board-level circuitry to demonstrate transduction (driving and sensing acoustic waves) ad beam steering in an array of microscale devices.

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

 

Using Unmanned Aircraft Systems (UAS) to Monitor Crop Development 

Professor:
Keith Cherkauer
Preferred major(s):
Agricultural Engineering, Environmental Engineering, Civil Engineering
Desired experience:
Knowledge of programming (e.g., MatLab or Python), electrical circuits, and digital imagery is desired but not required.

Global food production must continue to increase in order to support a growing world population. The integration of data science, sensors and automated sensing platforms into breeding programs allows science and engineering to work together to increase the speed and accuracy of seed selection for future development into commercial products. Unmanned Aircraft Systems (UAS) provide a platform to collect very-high resolution remote sensing image data from fields frequently during the growing season. The student selected for this project will work with an experienced team of graduate students and faculty to collect imagery and supporting ground reference data from multiple crop fields. They will learn how to setup ground targets, collect additional ground reference data (including soil moisture and leaf porosity measurements), manage large datasets, and process imagery to extract metrics per plot, which can be correlated to the specific seed variety in each plot within the field containing each experiment. As part of this project, the student will be responsible for collecting ground reference data and processing UAS imagery. They will use their data to assess the usefulness of one metric used for monitoring crop development that will be selected at the start of the summer.

More information: https://www.agry.purdue.edu/hydrology

 

Virtual reality simulations of blood flow in capillaries 

Professor:
Hector Gomez

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 hired
on the SURF program will produce a virtual reality animation using our 3D simulations of blood flow in capillaries.

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

 

Vortex breakdown on a delta wing 

Professor:
Alexey Shashurin

Delta wing planform is important aerodynamic configuration used for many high-speed vehicles. The project will experimentally study delta wing at low-speeds. Specifically, the effect of vortex bursting on delta wing will be evaluated in low-speed wind tunnel. Student will be involved in design of the wing model, utilization of smoke generator, conducting experiments in the wind tunnel and flow visualization. The objective of the project is to demonstrate that vortex bursting can be prevented and determine strategies to achieve this.

 

Water-energy micro-grids for remote communities in Latin America 

Professor:
David E M Warsinger
Preferred major(s):
Mechanical, Civil, Environmental, Chemical, or Materials Engineering.
Desired experience:
Spanish experience a plus. Ideally looking for students who also want to participate on the project in future semesters.

Peru and Cusco have a major challenge in water, both from waterborne disease (a common challenge throughout Latin America) and water contaminated by mining practices (which has even included using mercury). The integration of renewable energy with reverse osmosis desalination is a promising technology to handle both issues. The overall goal of this project is to develop a portable clean water and renewable energy co-production microsystem for the Cusco region using a new hybrid power generation system—microgrids (integrating solar, wind energy & energy storage) integrated with a novel water desalination based on reverse osmosis. By effective integration of renewable energy sources in a portable microgrid system with water, we will enable electricity production and water purification with minimum energy requirements—typically evident of such water treatments. Moreover, by using a data-driven look-ahead and real-time planning and operation framework, the proposed system will enable vast accessibility to self-sufficient clean electricity and water systems for rural and remote communities in the region, while providing city planners with unique tools for optimum use of resources for the community.

Students will help build these water energy micro-grids. We will bring as many of them as he can on a study abroad trip to Peru, with travel expenses and a trip to Machu Picchu covered. This will include design, construction, and testing of the water-energy apparatuses, and also examination of the health side of things: impacts of heavy metals contamination and waterborne disease that can be prevented with the membranes.

More information: www.warsinger.com

 

Wind Energy Over Complex Topography near Machu Pichu 

Professor:
Luciano Castillo
Preferred major(s):
Mechanical Engineering, Aerospace engineering or electrical engineer
Desired experience:
thermodynamics, calculus, basic programing

Access
to electricity remains a challenge in mountainous regions due to difficulties in power grid interconnection. Wind turbines carry the opportunity to resolve the power deficit in remote areas while also saving water; however, wind farm performance in complex terrain remains largely
unknown. This study explores the viability of exploiting wind energy in the mountainous regions of Peru along the Andes by investigating the impact of complex topographic characteristics on turbine wake recovery and power generation.

More information: https://engineering.purdue.edu/ME/People/ptProfile?resource_id=173054

 

Zeolite Catalyst Design for Pollution Abatement Technologies 

Professor:
Rajamani Gounder
Preferred major(s):
Chemical Engineering

Zeolites are crystalline materials that are used as catalysts for NOx abatement in diesel exhaust after treatment, however, improved catalyst materials are needed to meet future emissions regulations. Their catalytic performance is linked to their atomic-scale properties, and 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.

More information: https://sites.google.com/site/rgounder/