Research Projects for Summer 2024

Duke's Pratt School of Engineering is offering research experience opportunities across each its four academic departments:

Grand Challenge REU participants have the opportunity to conduct research across a large spectrum of interdisciplinary topics broadly organized into five areas: energy, environment, health, national security, and learning—

Five topic areas of Grand Challenges REU program

The following is a list of research projects available during the summer 2024.


Machine Learning-Guided Development of New Adsorptive Materials for Climate Change Mitigation  

Grand Challenge: Develop carbon sequestration methods

Faculty Mentor: Liang Feng , Assistant Professor in the Mechanical Engineering & Materials Science Department

The project will aim to address the excessive carbon dioxide emissions fueling the climate crisis. The project with the Feng group looks to revolutionize the conventional methods of carbon capture by investigating new materials. Using applications of GPT-4, this project looks to design, test, and validate new materials and methods for the active absorption of carbon dioxide from the atmosphere. Unlike traditional methods, this approach looks to investigate carbon capture methods that have less energy demand and higher overall efficiency. The project will look to use both computation and practical methods of evaluating material design.

Students will play a vital role in this project, actively participating in research, design, experiments, and analysis. They will be engaged in both small group collaboration and individual tasks, depending on the specific phase of the project. We welcome students from diverse training backgrounds with knowledge in Chemistry, Materials Science, Chemical Engineering, or related fields. Students in the advanced years of their undergraduate studies or those pursuing postgraduate degrees will be given preference. Prior experience in laboratory settings and familiarity with related research will be advantageous. The capability to critically analyze results, draw insights, and contribute to the development and refinement of the project's methodologies. Good written and oral communication skills for effective collaboration and reporting.

Description of work

Literature Review: A deep dive into existing literature to understand the current state of carbon capture technologies. Text Mining: Use computer-aided design and simulations to study the adsorption and adsorbent material screening. Laboratory Experiments: Conducting controlled experiments to test and validate the proposed technology. Analysis and Reporting: Comprehensive evaluation of results and drafting recommendations for potential industrial applications.  

For additional information, please visit 

Understanding DNA-nanoparticle interactions with applications in autoimmune disease

Grand Challenge: Engineer better medicines

Faculty Mentor: Christine Payne , Professor of Mechanical Engineering & Materials Science

Many autoimmune diseases involve DNA-bioparticles, but the relationship between these particles and the disease is unclear. In addition, bioparticles are highly heterogenous and difficult to characterize. The goal of this project is to create well-controlled, synthetic, DNA-particles to address mechanistic questions related to autoimmune disease. This research is done in collaboration with a clinician-scientist at Duke's School of Medicine.

Experiments will include nanoparticle characterization (TEM, DLS), biological assays (ELISA, RT-PCR), and live cell imaging with fluorescence microscopy. The REU student must be able to work both independently and as part of team. The student will present research results at group meetings and in the final REU poster presentation.

No previous research experience is required. The REU student must be curious, hard-working, and a good lab citizen. All necessary skills will be taught over the summer.

For additional information, please visit 

Self-rectifying Graphene/Oxides/Graphene Memristor Crossbar Array for Process-in-memory

Grand Challenge: Reverse-Engineering the brain

Faculty Mentor: Tania Roy , Assistant Professor of Electrical and Computer Engineering

The surge of deep neural networks (DNNs) calls for strong computing capability with high energy efficiency. However, data shuttling between the separated computing and memory units in today’s von Neumann architecture consumes unnecessary time and energy. The process-in-memory (PIM) architecture based on memristive crossbar arrays has shown great potential for energy-efficient AI acceleration by integrating the computing and memory functionalities into a single unit. Specifically, the crossbar arrays can perform vector-matrix multiplication (VMM) in DNNs efficiently by encoding the weights of DNNs in different conductance states of the memristive devices. Among different memristive devices, self-rectifying oxide-based memristors for passive crossbar arrays feature scalability, high area efficiency, and effectiveness in dealing with sneak path current. While metal electrodes have been used in self-rectifying oxide-based memristors, incorporating graphene as the electrode has the potential to reduce the read and write energy due to its semimetal nature.

In this project, self-rectifying graphene/oxides/graphene memristors with different active area sizes and their crossbar arrays with different dimensions will be fabricated. Engineering efforts on the oxide stacks will be conducted to achieve a high rectifying ratio, a large number of distinguishable conductance states, as well as low read and write energy. The electrical and structural properties of the fabricated single devices and crossbar arrays will be characterized. Considering the electrical behavior of the crossbar arrays, peripheral circuitry for data processing in the crossbar arrays will be customized to form a PIM macro. The energy consumption and computing speed of the as-designed PIM macro on various AI models will be benchmarked

Short description of student work required 1. Conduct electrical measurements on the output characteristics and the number of conductance states of the single memristive device with different active areas.

2. Conduct electrical measurements on the sneak path current in the crossbar array using half-biasing scheme.

3. Participate in the nanofabrication process of the single device and crossbar array.

Ideal candidate: Interests in nanoelectronics materials and devices. Experiences in nanofabrication and electrical measurements is a plus.

For additional information, please visit 

Evaluating the persistence of overlooked pathogens in water treatment settings

Grand Challenge: Provide access to clean water

Faculty Mentor: Nicole Rockey , Assistant Professor of Civil and Environmental Engineering

Elevated viral RNA levels from several respiratory viruses, including SARS-CoV-2, influenza virus, and rhinovirus, are now regularly detected in wastewater. Importantly, viral RNA detection in wastewater matrices offers no information regarding viral infectivity. For example, no infectious SARS-CoV-2 has been detected in wastewater to date, despite the high SARS-CoV-2 viral RNA concentrations frequently measured in wastewater. The infectivity of many other respiratory viruses, including nonenveloped viruses such as rhinovirus and adenovirus, in wastewater matrices has not been well-investigated. Historically, water treatment has focused on enteric virus removal - respiratory viruses are not typically considered a microbial risk in this setting. However, the presence and persistence of infectious respiratory viruses in wastewater is of particular importance in planned water reuse and de facto reuse settings, where viruses can be aerosolized during showering, drinking, or recreational activities. We hypothesize nonenveloped respiratory viruses can be released as infectious viruses in human waste and can persist in wastewater for extended periods compared to enveloped respiratory viruses like SARS-CoV-2. This project will evaluate this hypothesis by using molecular, cell culture, and microscopy techniques to quantify the prevalence of nonenveloped respiratory virus particles. These findings will shift the current paradigm for assessing microbial risks in water treatment to more holistically consider risks associated with any viruses, regardless of conventional transmission route, that can persist through treatment.

The work will include a variety of laboratory and experimental procedures, including but not limited to mammalian cell culture, nucleic acid extractions, molecular analyses, and microscopy. The student must maintain accurate and detailed experimental records and will be encouraged to present research-in-progress and findings to the group.

No previous research experience is required. Interest in biological or environmental sciences, engineering, public health, or a related field is preferred. Strong communication, organization, and interpersonal skills are needed. The ability to work independently and collaboratively as part of a team is desired. All necessary skills will be taught during the summer.

For additional information, please visit 

Exploring microbial communication within nitrifying communities

Grand Challenge: Manage the Nitrogen cycle

Faculty Mentor: Jeseth Delgado Vela , Assistant Professor of Civil and Environmental Engineering

Biological nitrogen removal has been extensively applied in wastewater treatment systems to reduce the potential for eutrophication, or the release of excess nutrients that harm aquatic life. Conventional processes involving nitrification and denitrification are well known for conversion of nitrogenous compounds to nitrogen gas. Our laboratory is working on isolating and enriching nitrifying (i.e., ammonia and nitrite oxidizing) bacterial cultures. Our research seeks to understand microbial quorum sensing (QS) in these nitrifying cultures. QS is a phenomenon in which microorganisms release different chemical signal molecules known as autoinducers to coordinate many of their physiological and biochemical activities. QS can be thought of as the spoken language of bacteria. Despite the environmental relevance of nitrifying bacteria, little is known about QS within these communities. Therefore, our aim is to evaluate the impact of autoinducers on regulating nitrifying activity. The undergraduate involved in this study will focus on evaluating the nitrogen transformation and biofilm formation potential of nitrifying cultures through varying concentration of autoinducer molecules.

From experimental setup to execution and data analysis, the student will work closely with a Postdoctoral Associate working on the project in Delgado Vela’s lab. Student work may include but is not limited to: • Preparation of synthetic feed for bacterial culture and working solutions • Batch scale setup for biofilm development by nitrifying community • Monitoring of physico-chemical analysis such as Total Suspended Solids (TSS), Volatile suspended solids (VSS), NH4+-N, NO2--N, NO3--N • Genomic DNA extraction from biofilms developed on different substratum • Assistance in biofilm characterization via Confocal Laser Scanning Microscopy (CLSM).

The participant will gain experience designing and executing an experiment, while upholding safe laboratory practices. The ideal participant has a strong interest in learning about how microbes interact with one another. No previous research experience is necessary. The participant will receive all necessary training.

For additional information, please visit 

Intelligent Augmented Reality

Grand Challenge: Enhance virtual reality

Faculty Mentor: Maria Gorlatova , Assistant Professor of Electrical and Computer Engineering

Augmented reality (AR) is a rapidly developing technology area with potential for transforming many daily human experiences. While promising, current AR systems are somewhat limited in their capabilities, in particular in multi-user experiences, high energy consumption, and general lack of robustness and adaptability. The goal of this project is to obtain in-depth experimental understanding of the limitations of current augmented reality experiences, and to establish how these limitations can be addressed. Students involved in this work will experiment with developing and experimenting with different augmented reality applications, experiences, and platforms; to understand the systems and network loads of different operations, key drivers of immersive user experiences, and the potential of edge and cloud computing platforms to address the discovered limitations. The project involves both the development of Unity-based holographic experiences, and real-world holographic deployments with Google ARCore mobile device platforms and Magic Leap One headsets. The project is best suited for students who have an experimental mindset and who enjoy obtaining in-depth understanding of system performance, physical phenomena, and human behavior. Relevant technical preparation includes general software development skills, a background in communications and/or networking (preferred but not required), and familiarity in machine learning (preferred but not required).

For additional information, please visit  

Exploring nanostructure made of novel two-dimensional materials

Grand Challenge: Engineer the tools of scientific discovery

Faculty Mentor: Gleb Finkelstein , Professor of Electrical and Computer Engineering & Professor of Physics

This project will focus on exploring van der Waals heterostructures – layered structures made of two-dimensional materials other than graphene, held together by the van der Waals forces. By stacking atomically thin crystals, it is possible to combine magnets, superconductors, insulators, and optically active materials, thereby creating properties not found in nature. These “designer materials” promise to expand our understanding of quantum materials and to find revolutionary applications as electronic devices and sensors.

The participant will work under supervision of an experienced graduate student. The undergraduate will learn the methods to design, fabricate, and measure the van der Waals nanostructures.

No special skill are required, but some lab experience (e.g. soldering, debugging instrumentation) are a plus.

For additional information, please visit  

Acoustofluidic Supported Sensing of Biomarkers in Alzheimer’s Disease

Grand Challenge: Advance health informatics

Faculty Mentor: Tony Jun Huang , Professor of Mechanical Engineering & Materials Science

The objective of this research project is to harness the potential of acoustofluidics to enhance the sensitivity of electrochemical sensing for disease diagnosis. In the current electrochemical (EC) systems, biomarkers, which are indicative molecules of various diseases, are measured to identify individuals with specific health conditions. One specific biomarker of interest is miRNA, owing to its widespread relevance. However, detecting miRNA poses a considerable challenge due to its low concentration levels. Consequently, the primary aim of this project is to leverage acoustofluidics to concentrate these biomarkers, thereby paving the way for the effective electrochemical sensing of miRNA. To validate this concept, our project will include a proof-of-concept demonstration involving the detection of miRNA associated with Alzheimer's Disease.

Students will help fabricate acoustofluidic devices from scratch, including PDMS fabrication, plasma bonding, basic lab skills like pipetting, etc. Students will also be taught to perform experiments, using electrical equipment like function generators, amplifiers, and impedance matchers. The student will be expected to contribute to the experimental design process, figure development, and manuscript writing. Additionally, the student will be expected to attend weekly group meetings and regularly communicate with their mentor.

Ideal skills: Student must have a strong interest in pursuing a career in research or medicine. Experience with lab equipment is not necessary, but an understanding of the research processes and literature reading skills are encouraged. Ideally, the student should also be interested in the field of acoustofluidics. More info can be found here.

For additional information, please visit 

Closed-Loop Brain Stimulation

Grand Challenge: Reverse engineer the brain

Faculty Mentor: Warren Grill , Professor of Biomedical Engineering

We are developing and implementing in persons with Parkinson's disease closed-loop brain stimulation using feedback signals (local field potentials) recorded directly from the brain. This project is to conduct an analysis of local field potential (LFP) data collected from participants with Parkinson's disease (approximately 60 participants over ten years). The student will implement code to analyze these data, conduct analysis across the patient group, and produce publication-quality figures. This work will help us identify useful biomarkers for symptoms of Parkinson's disease. The student will implement code to analyze these data, conduct analysis across the patient group, and produce publication-quality figures. The ideal candidate will have strong coding skills in MATLAB and/or Python; interest in neuroscience and brain stimulation.

For additional information, please visit