Research Projects - Summer 2019

The Pratt School of Engineering is offering research experience opportunities across all its departments: Biomedical Engineering, Civil and Environmental Engineering, Electrical and Computer Engineering, Mechanical Engineering and Materials Science. Grand Challenge REU participants have the opportunity to conduct research in 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 2019. 

Active Monitoring of Mechanical Systems

Grand Challenge: Restore and Improve Urban Infrastructure

Faculty Mentor: Wilkins Aquino , Anderson-Rupp Professor of Civil and Environmental Engineering, Associate Professor in Mechanical Engineering and Materials Science

The goal of this project is the development of a dependable, autonomous or semi-autonomous (i.e. low human involvement), and minimally disruptive framework for monitoring equipment and components in mechanical structures (e.g. nuclear reactors, wind turbines, etc.). One of the main challenges in monitoring components or structures is to obtain sufficient information through sensing so that reasoning systems can uniquely and unambiguously detect anomalies. Information obtained from a component or structure is usually limited due to sparse and static sensor networks. We will develop a robust active sensing framework through the integration of model-based inference and mobile actuating/sensing robots. Our approach is to address the monitoring problem from a holistic view in which inference from data and data acquisition (actuation and sensing) are tightly integrated. Our system will be general enough that could be adapted to various monitoring problems where operational anomalies are to be detected under uncertainty. For instance, operational anomalies could include the onset of mechanical damage (e.g. cracks, distributed damage), emergence of undesired sound sources, and sudden appearance of contaminant sources, among others. The student will be involved in different activities that will include numerical modeling using the finite element method of mechanical waves and their interaction with damage, inverse problem techniques to identify sources and damage, machine learning algorithms, and the integration of these techniques with mobile robot sensors. Strong programming skills using scripting languages such as Matlab or Python is required. Knowledge of other low-level languages such as C++ or Java is a plus, but not required. Basic or intermediate courses in solid mechanics, linear algebra and vector calculus. Strong passion for learning new concepts.

Coarse Grained Simulations of Viral DNA Packaging

Grand Challenge: Engineer better medicines

Faculty Mentor: Gaurav Arya , Associate Professor in Mechanical Engineering and Materials Science

Many viruses package their genome into viral procapsids to near-crystalline densities. Because DNA has a negatively-charged phosphate backbone, very strong electrostatic repulsive forces arise that oppose DNA confinement. To mitigate these effects, it has been proposed (and supported by simulations) that positively-charged multivalent ions (such as Mg2+ and spermidine3+) screen charges and reduce the electrostatic self-repulsion of the DNA backbone. However, single-molecule force microscopy experiments show that high concentrations of multivalent ions could also induce stalled packaging dynamics, which cannot be explained by current theory or simulation results. We will attempt to bridge this gap between theory and experiment by considering the electrostatics of the viral capsid itself, which has not been accounted for in any prior simulation. Positively-charged multivalent ions may cause DNA to stick to negatively charged regions of the capsid and induce stalling. The student will develop a coarse-grained Langevin dynamics simulation code for simulating the packaging dynamics of DNA into viral capsids, based off an existing code that models the dynamics of DNA in solution. Additionally, the student will learn to existing simulation packages (such as LAMMPS or GROMACS). The student should have interest in computational sciences, biophysics, or biology. A strong coding background is preferred. Biochemistry knowledge is beneficial but not necessary.

GUI Development for Faceted Particle Energy Calculation

Grand Challenge: Engineer the tools of scientific discovery

Faculty Mentor: Gaurav Arya , Associate Professor in Mechanical Engineering and Materials Science

In this project, the student will develop an application with graphical user interface in which the recently developed faceted particle potential energy calculation algorithm will be implemented. The users can create faceted particles of arbitrary shape and the application will output their interaction energies depending on the particles' positions. The GUI will involve both 3D and virtual reality implementation. The energy calculation algorithm is already developed. The student will work on implementing this algorithm in a user-friendly graphical application. Familiarity with programming (preferably C#). Ability to use Unity3D or Unreal Engine.

Network based approaches to understand neurodegenerative diseases

Grand Challenge: Reverse engineer the brain

Faculty Mentor: Alexandra Badea, Associate Professor of Radiology, Biomedical Engineering

The goal of this project is to familiarize students with interdisciplinary approaches to understand the brain. The main thrust is on developing computational approaches using graph analysis and visualization of diffusion tensor imaging and derived tracts. We will use mouse brain high resolution diffusion tensor imaging. The goal is to to use simple animal models to help better understand human neurodegenerative diseases, such as Alzheimer's disease. The student will use R, MATLAB, or python programming for graph analysis (or his /her choice of a programming language), and compare several options for visualizing and assessing the quality of reconstructed tractography data. I am interested in supporting the development of computational skills in engineering students, with a particular focus on supporting female students.

Additional information available here.

Computational design of new materials for solar energy harvesting

Grand Challenge: Make solar energy more economical

Faculty Mentor: Volker Blum, Associate Professor in the Department of Mechanical Engineering and Materials Science

The Blum group offers an active research environment in computational materials science, addressing topics ranging from simulations of materials for energy research (e.g., photovoltaic materials), to development of new methods and computer software for materials based on the first principles of quantum mechanics. The group currently consists of two post-doctoral researchers and three Ph.D. students. An undergraduate researcher would interact closely with these experienced group members in addition to Prof. Blum himself. Prof. Blum co-founded and leads the internationally developed FHI-aims software for computational materials science. Undergraduate researchers will benefit from the group’s embedding into an international community (Berlin, Munich, London, Hefei/China, Helsinki, and others) of other leading groups in computational materials science. It is important to note that students from engineering disciplines may not have previous experience with quantum mechanics, and undergraduate projects do not necessarily require such experience. The ideal candidate would have some programming experience, a keen interest in computational materials science; and they should not be shy to engage and solve computational problems that arise during their research by programming (e.g., using scripting languages like python or high-level programming languages.

Data-driven Polymer Nanocomposite Design via the Materials Genome Initiative

Grand Challenge: Engineer the Tools of Scientific Discovery

Faculty Mentor: Catherine Brinson , Professor in the Department of Mechanical Engineering and Materials Science

The Brinson laboratory specializes in both computational and experimental characterization and design of nanostructured polymers and polymer nanocomposites. A major challenge in this area is the nearly infinite design space due to the enormous range of materials selection, surface chemistries, processing methods, etc. The Materials Genome Initiative (MGI) is a large-scale, data-driven project with the goal of predicting properties through a combination of data mining and physics-based modeling in order to reduce the deployment time of new materials compared with the traditional trial-and-error experimental approach. The Brinson Lab has pioneered MGI efforts for the informed design of polymer nanocomposite materials through the collaborative development of an open-source database and data mining tools. The database contains repositories of curated literature and lab-generated data formatted for data mining and statistical analysis. This materials informatics approach illustrates an example of utilizing advancements developed in the era of “Big Data” to solve practical materials science problems.

The interested undergraduate researcher will work with PhD students toward data archiving and curation that converts raw data entries into a directly usable form, performing regression analyses to uncover the relative significance of material descriptors, as well as designing new schemes for statistical methods for machine learning applications of materials data. An ideal candidate should have strong interest in data science, have robust organizational skills, and be both creative and attentive to detail. Knowledge of polymers and polymer nanocomposite terminology is a plus. For more information refer to this website.

Nanoscale characterization of interfaces in additively manufactured components

Grand Challenge: Restore and Improve Urban Infrastructure

Faculty Mentor: Catherine Brinson , Professor in the Department of Mechanical Engineering and Materials Science

The Brinson Laboratory specializes in experimental and computational characterization and design of nanostructured polymer systems produced by various techniques. Additive manufacturing is emerging as a new paradigm in the rapid development and manufacture of engineering components. New technologies such as fused deposition modelling (FDM), stereo lithography (SLA), and inkjet printing are emerging as viable options to create metamaterials for structural and acoustic applications. The ‘pixel-by-pixel’ nature of these manufacturing techniques enables high structural heterogeneity at the microscale, with some inkjet printers allowing for patterned material properties. For this project, a student will utilize the flexibility of inkjet printers to design polymeric metamaterials with a focus on the optimization of viscoelastic (VE) properties of the resulting structure. In addition, they will complete nanoscale mechanical analysis using atomic force microscopy (AFM) to determine local effects from weld lines or discontinuities at the interface between two materials. At the end of the project, the student will have completed the following tasks: • Viscoelastic characterization of a range of compatible materials from a commercial inkjet printer such as the Stratasys Polyjet • Dynamic mechanical analysis (DMA) characterization of a selection of printed metamaterials to determine how the VE properties of the metamaterials relate to its components • A study that chooses a metamaterial design and then uses finite element analysis (FEA) simulations to predict the impact of adjusting unit cell parameters. The FEA predictions will then be compared to experimental data. • Characterization at the nanoscale across the interface between adjacent resins using AFM and scanning electron microscopy (SEM) to determine if there is any nanoscale heterogeneity in the printed part (e.g. weld line, a zone with mixed resin, etc.) - a preliminary study to observe how the interface changes depending on what resins are used. The ideal candidate would have a strong interest in mechanics of materials, polymers and experimental material characterization. Previous experience with dynamic mechanical analysis (DMA), atomic force microscopy (AFM), Python and FEA software (particularly ABAQUS) would be beneficial but is not a requirement.

Patient-derived organoids for precision medicine

Grand Challenge: Engineer the tools of scientific discovery

Faculty Mentor: Xiling Chen , Hawkins Family Associate Professor in Biomedical Engineering

The REU student will assist in developing state-of-the-art organoid technology from clinical patient samples to screen for gut microbes and CRISPR-mediated genomic and epigenomic screening for precision medicine. The patient-derived-organoids will also be used to screen drugs to guide chemotherapy decisions for an ongoing clinical trial. The student will work with an experienced postdoc (the Duke GI fellow) to optimize new 3D and 2D organoid technology and perform screening of CRISPR, gut microbiome, and cancer targeting therapy. He/she may also assist with computational data analysis. The ideal candidate should have background and interest in tissue culturing, genetics, and molecular biology.

Shake Table Testing for inelastic structural dynamics

Grand Challenge: Restore and Improve Urban Infrastructure

Faculty Mentor: Henri Gavin , Professor in the Department of Civil and Environmental Engineering

Rapid assessment of the integrity of infrastructure systems after hurricanes, tornadoes, and earthquakes requires a method to translate measurements obtained during the damaging event to an assessment of the damage state. In settings where damaged elements can be hidden from view, direct visual inspection is time-consuming, expensive, and potentially inconclusive. Recent discoveries at Duke have led to a technique to use measurements obtained from a few sensors to develop a global picture of the loads sustained by the structure. This ability can be incorporated into a triage framework to inform decisions on urban restoration. All federal buildings in the US, and many more within the US and around the world are instrumented with a small number of acceleration sensors. Until now the utility of these sensors has been questionable because the data analysis methods needed to assess dynamic loads on the structure from only a few acceleration measurements have been missing. Recent research at Duke has bridged this gap and we are now ready to test our results in dynamic shake table testing. This method has application in any context in which dynamic responses are measured on a sparse grid. This project involves (a) experiments on 3D shake tables to measure responses simultaneously in multiple directions, (b) data acquisition and signal processing of the sensor measurements, and (c) nonlinear modeling of this complex behavior based on a combination of data analysis and mathematical modeling. Students will design 3D shake-table models in metal and using 3D printing technology. These models will be shaken on a three-axis shaking table and dynamic responses will be measured using MEMS accelerometers and strain gages. The objective of the project is to test methods to predict strain measurements at the base of the structure (which indicate the dynamic loads sustained by the structure) from sparse measurements of the floor accelerations. Students in this project will learn how to break big data down into fundamental components in order to construct dynamic responses from unmeasured locations. The student will also be asked to study structural dynamics, signal processing, data acquisition, and dynamic measurement techniques. The desired Math background is a course in ordinary differential equations and linear algebra. The desired Engineering course background is: solid mechanics and dynamics. Lab skills to be developed in the research include working with sensors, signal conditioning, data acquisition and servo-controlled actuation systems.

Optically screening large libraries of fluorescent protein mutations

Grand Challenge: Engineer the tools of scientific discovery

Faculty Mentor: Yiyang Gong, Assistant Professor in the Department of Biomedical Engineering

The Gong lab (Neurotoolbox) studies the function of the brain by using optical technologies to record or perturb neural activity. Currently, we employ genetically encoded calcium and voltage sensors to directly record various dimensions of neural activity in live animals. The genetically encoded fluorescent proteins at the heart of these sensors have undergone many rounds of improvement through mutagenesis, or directed changes to the DNA code of the protein. While some fluorescent proteins have some favorable properties such as high brightness or high monomericity, these properties are not universal in all fluorescent protein families. We look to develop technologies that can rapidly screen large numbers (> 10^6) of mutants of existing fluorescent proteins. These hardware and software methods will find mutations that improve brightness or monomericity at an order-of-magnitude higher rate than existing techniques. The resulting improved fluorescent proteins can be immediately inserted into known sensor designs and improve neuroscience experiments by providing higher signal-to-noise. Similarly, the optical technologies developed in this project can lead to methods that impact screening in many biomedical fields. In one part of the project, students will employ molecular cloning to achieve the desired mutation rate in the cloned mutants and generate the appropriate number of mutants. In the other part of the project, students will employ optical fluorescence microscopy and machine-learning data processing to quickly assess the mutants’ figures-of-merit with high accuracy. The ideal candidate would be interested in developing skills in molecular biology, system control, fluorescence microscopy, and neuroscience.

Visit this website for additional information.

Enhance Virtual Reality: Intelligent Environmentally-aware Mixed Reality

Grand Challenge: Enhance virtual reality

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

We are investigating how modern mixed reality experiences -- e.g., experiences generated by Microsoft HoloLens sets -- can be improved via additional intelligence and via integration with sensor-based "smart objects". This project involves the integration of new remote sensing capabilities with existing mixed reality experiences, and the demonstration of the effectiveness of these techniques in creating environmentally-aware mixed reality experiences. Our preliminary work on this project has recently been covered by the Network World.

The project requires setting up a multi-sensor system that collects environmental data, and an ARCore-based and/or a Microsoft Hololens-based system that uses sensor information to change the properties of generated holograms. The project involves experimental evaluation of the developed system. The project requires general software-development skills. Knowledge of Unity, C#, and/or Android-based app development would be advantageous, but is not required.

Optimized Parameters of Brain Stimulation

Grand Challenge: Re-engineering the brain

Faculty Mentor: Warren Grill, Professor of Biomedical Engineering

The Grill lab specializes in analysis and design of devices that use electrical activation of the nervous system to restore function to individuals with neurological impairment. This project focuses on design and testing of temporal patterns of brain stimulation to treat the symptoms of Parkinson’s disease. The project will make use of computer-based modeling combined with measurements of both neural activity and behavior in an animal model of Parkinson’s disease. Students working in this research project will develop an understanding of the principles of brain stimulation, the application of computer modeling to study brain stimulation, as well as practical aspects of conducting experiments using brain stimulation. The students will work directly with a PhD student and have the opportunity to interact with a diverse group of students and staff working across a broad array of neural prosthetic devices using electrical stimulation to restore function following neurological disease or injury. Students with interest in neuroscience and medical devices are ideal candidates for this project.

Environmental Remediation of Contaminated Soil and Sediments

Grand Challenge: Provide access to clean water

Faculty Mentor: Heileen Hsu-Kim, Mary Milus Yoh and Harold L. Yoh, Jr. Associate Professor of Civil and Environmental Engineering

The sediments of lakes, rivers, and estuarine ecosystems at Superfund sites and other areas are often contaminated with toxic metals such as mercury, zinc, lead, and copper. We study technologies for remediation and methods to assess the risk of contaminated sediments. The student researcher will participate in lab-based and field studies to develop passive samplers that quantify the bioavailable forms of metals in sediments and also study how activated carbon and other chemical amendments can be used to reduce the toxicity of metals in the sediments.

Resource Recovery from Coal Ash Wastes

Grand Challenge: Provide access to clean water

Faculty Mentor: Heileen Hsu-Kim, Mary Milus Yoh and Harold L. Yoh, Jr. Associate Professor of Civil and Environmental Engineering

The goal of this project is to develop and evaluate methods to extract rare earth elements from coal ash wastes. Rare earth elements (REEs) are a group of metals that are critical components of modern technologies such as magnets, LEDs, hard drives, and guidance systems. Despite the demand for these materials for critical technologies, the global supply market for REEs is unstable and monopolized by China. While the development of new mines is a way to increase the long term supply, the monetary and environmental costs of mining and the growing demand for REEs provides an opportunity to identify non-traditional and environmentally sustainable sources. Coal ash is the solid waste generated at coal-fired power plants and represents one of the largest industrial waste streams in the U.S. These wastes are known to be enriched in REEs and may provide an alternative source of REEs. Economical methods to recover REEs from coal ash, however, are not well developed and are the subject of this project.

Engineering Nanoparticles to Cross the Blood-Brain Barrier

Grand Challenge: Reverse-engineer the brain

Faculty Mentor: Christine K. Payne, Associate Professor in Mechanical Engineering and Materials Science

Developing drugs and nanoparticles that cross the blood-brain barrier is a major challenge in the treatment of brain cancers. We will use a cellular model of the blood-brain barrier to identify nanoparticle surface properties that facilitate transport and then use robotic approaches to functionalize nanoparticles with the desired surface properties. This research is highly interdisciplinary. An ideal researcher will be curious about many different science and engineering disciplines and highly motivated to learn new schools. The student will work as part of team to grow cells, characterize nanoparticles, and functionalize nanoparticles.

For more information visit this website.

Energy-Related Materials and Device Characterization for Thin-Films Deposited by Resonant-Infrared Matrix-Assisted Pulsed Laser Evaporation (RIR-MAPLE)

Grand Challenge: Engineer the tools of scientific discovery

Faculty Mentor: Adrienne Stiff-Roberts , Professor of Electrical and Computer Engineering

RIR-MAPLE is a laser-based thin-film deposition technique appropriate for organic and hybrid organic-inorganic materials, including polymers, colloidal quantum dot/polymer hybrid nanocomposites, and hybrid perovskites. These materials are investigated for optoelectronic and energy-related devices, such as light emitting diodes, solar cells, and supercapacitors. In this project, the student will investigate the materials properties and device performance of organic/hybrid materials deposited by RIR-MAPLE using atomic force microscopy, UV-visible absorption spectroscopy, photoluminescence spectroscopy, external quantum efficiency, solar cell fill factor measurements, or light emitting diode luminance measurements.

Water Sanitation and Hygiene (WASH): Engineering a Better Toilet

Grand Challenge: Reinvent the toilet

Faculty Mentor: Marc Deshusses, Professor of Civil and Environmental Engineering

4.5 billion people do not have access to safely managed sanitation. Simply put, this means that all or part of their fecal waste somehow ends untreated in the environment. Our lab is working on a range of projects addressing this issue consistent with Sustainable Development Goal 6 in the fields of water and sanitation. These range from low-cost anaerobic digester toilets currently piloted in Kenya and in the Philippines, high-tech supercritical water oxidation reactor to convert sludge to clean water, water filters for household use, to odor control and more. Our approach blends process engineering, fundamental engineering sciences (transport, kinetics), design, computation (different modeling), microbiology. Our work is very hands-on and multidisciplinary (e.g., microcontrollers, microsensors for monitoring air and water). There are no specific prerequisites to join our team. If you are passionate about water and sanitation, enjoy applied research and technology, and like to think that your work will make a difference, join us for the summer 2019. For more information, please visit http://sanitation.pratt.duke.edu/