Below is a comprehensive listing of the eFellows’ research projects, broken down by cohort.

Cohort I

Aim 1: To develop an adaptive exoskeleton controller that will tune the assistance provided by a powered lower-limb exoskeleton, to maximize motor learning rate and retention based on an individual’s changes in task execution performance and lower limb muscle activation. We will use the eksoNR (Ekso BIonics, Richmond, CA) lower limb rehabilitation exoskeleton and develop a custom controller cable of tracking and adapting to task performance, to individualize neurorehabilitation training progress for patients with incomplete SCI. Functional task performance, movement kinematics, and muscle synergies will be monitored from the user and used to compute a composite measure for the controller. While exoskeleton-based training has primarily focused on motor recovery of singular, repetitive movements such as gait, another major limitation of current robotic neurorehabilitation is that there is low transfer of training to activities of daily living, and patient outcomes outside of the lab show little progress. To overcome this, virtual reality (VR) has been explored as a viable method as it can produce an immersive training experience in a variety of tasks (caves (e.g., Motek Caren), and head mounted displays (e.g., Oculus, HTC VIVE)). However, many VR-environments are typically limited to presenting virtual objects to modify user-behavior and are constrained by the lack of physical interaction. Existing physical interactions in VR rely on passive haptics, where simple physical objects align with virtual objects to produce tactile feedback (e.g., vibrotactile devices), thereby producing a sense of touch, but ineffective in reproducing the reaction forces characteristic of physical interactions. There is tremendous un-explored potential for what could be achieved by a force-feedback system integrated with VR, especially when applied to neurorehabilitation. Aim 2: To develop and test a customizable, sensory-immersive, rehabilitation system for activities of daily living to increase neuroplasticity, motor restoration, and task transferability among individuals with incomplete SCI. A body-scale force-feedback system (Forcebot) in VR is being developed for industrial worker augmentation, as part of two ongoing NSF funded projects (NRI-Forcebot and C-Accel-LEARNER). We will leverage these existing efforts and modify the functionality of the Forcebot system to be applied to neurorehabilitation. We will design a range of tasks (activities of daily living) in VR coupled with immersive force feedback to increase the user’s motor learning capabilities across diverse tasks including but not limited to gait, navigating doorways, and walking on varied terrain.

The proposed study provides a useful foundation for advancing a nexus between the long-term research goals of both the project mentor, Dr. J. A. Henderson and fellow, Dr. L. S. Benjamin. Dr. Henderson has forged a notable pathway, exploring factors that impact the attainment of advanced engineering degrees among Black males. A probable secondary study is currently being conceptualized to possibly compare the experiences of Black males in EE PhD programs to the I-T-O quality indicators presented and discussed in the primary study. These data might also be foundational to the development of research-based interventions aimed at supporting Black males in EE PhD programs. We hope that findings will (i) illuminate evidence-based practices for current and future EE PhD students, (ii) provide insights that support the re/design of EE PhD learning outcomes and (iii) possibly even inform specialized and regional accreditation standards. Research will be disseminated at the Association for the Study of Higher Education (ASHE) and the American Society of Engineering Education (ASEE) conferences – first, as works in progress and then as full conference papers. We also aim to publish findings in journals that have diverse readership, for example educational administrators, practitioners, and EE faculty, such as the Journal for Engineering Education.

The overall goal of this postdoctoral research is to investigate how IWS influences microbial communities, DBPs and antibiotic resistance in bulk water and biofilms. The main research objectives are: 1) investigate DBP (e.g., trihalomethanes and haloacetic acids) formation potential under different IWS conditions using low and high molecular weight NOM; 2) assess microbial community structure, ARB and ARGs in bulk water and biofilms exposed to different IWS cycles; and 3) profile microbial communities, the occurrence of DBPs, ARB and ARGs in a full-scale IWS system. For this work, laboratory-controlled experiments will be carried out using simulated drinking water distribution systems (DWDSs) consisting of two pipe-loop systems located at the University of Massachusetts, Amherst. Methodology will include high-throughput sequencing (shotgun and MinION real-time sequencing), qPCR and flow cytometry for assessing microbial communities, ARB and ARGs, and ultraviolet absorbance and gas chromatography for DBP analysis. The use of laboratory pipe-loop experiments will allow us to evaluate the effects of IWS independently and eliminate confounding factors commonly found in full scale DWDSs. During the first year, the pipe loop systems will be started, acclimated and the first set of experiments will be carried out, which will consist of simulations of NOM intrusion between IWS cycles and monitoring of DBP formation and microbial communities’ shifts. These simulations will be carefully designed to represent actual IWS commonly found in real systems. During the second half of the first year and beginning the second year, a second round of experiments will be carried out. The microbial community in bulk water and biofilms in these pipe-loop systems will be exposed to antibiotics found in source waters (e.g., Tetracycline, Sulfamethoxazole and Clindamycin) and microbial shifts will be monitored under IWS conditions. Contingent upon travel restrictions, we will carry out field work in Panama to study these topics in a real full-scale IWS system in collaboration with our partners at the Smithsonian Tropical Research Institute. During the second year, a final set of experiments will be carried to assess the interactions between DBP formation, antibiotic resistance, and microbial communities in simulated DWDSs with IWS. This research effort aims to draw conclusions that can aid in the management of IWS systems and identify research priorities to further understand IWS.

This proposal suggests the development of a mathematical framework for ZLD/MLD systems, which determines the optimal way to combine different technologies to achieve a given salinity in the discharge for minimal energy or cost expenditure. It will select appropriate technologies, order of technologies, sizes of systems, and operational variables. It will look at conventional technologies, such as reverse osmosis (RO) and thermal evaporators, as well as emerging technologies, including high-pressure RO, and osmotically assisted RO, among others. This framework will allow for the systematic design of ZLD/MLD systems with integrated cost and energy analysis of the system design, given constraints on the potential design of the system. The framework is envisioned as a mathematical optimization problem, specifically a Mixed Integer Linear Programming (MILP) problem. Data or functions which are non-linear will be linearized, potentially through piecewise approximation. Binary variables will be selection of technology types, while continuous variables will include flow rate, salinity, electricity usage, heat usage, and cost, among others. Constraints may be final salinity and throughput rate, and could be formulated to be fit-for-purpose. The objective function could be system cost over the lifetime, unit cost of water, total energy usage, or unit energy usage. Data for the optimization will be sourced from research papers, governmental or NGO reports, and other publicly available data.

Tracking and pursuit of moving targets is a key challenge in mobile robotics, relating to tasks such as reconnaissance, rendezvous, and evasion, for applications from military operations to marine research. Theoretical work (modeling and game theory) and lab-based studies using existing robotics systems have spurred advances. However, these methods can be limiting when confronted with the complexity of real-world applications, such as unpredictable target behavior, movement in 3 dimensions, and environmental complexity. In these contexts, bioinspired design is pivotal; insects, which produce complex behaviors with simple neural circuitry, have proven valuable models. I will study 3D aerial pursuit of varying target types using a highly effective model system: the dragonfly. These predators achieve up to 95% success, capturing predictable, erratic, and evasive targets by integrating updated sensory information in fractions of a second, while balancing costs and the likelihood of success. They surpass manmade systems in their speed, unconstrained motion, and adaptability to target behavior, and their pursuit strategies diverge from theoretical models. I will use dragonflies as a model to examine fundamental pursuit-evasion questions, including: Do optimal pursuit strategies differ depending on the unpredictability of target motion or the likelihood that targets will detect and respond to pursuers? What decision-making algorithms guide changes in pursuit strategy as target behavior changes, pursuit costs rise, or the likelihood of success declines? I will use multiple, calibrated high-speed cameras to analyze how a perching dragonfly (Pachydiplax longipennis) accelerates from rest to pursue an overhead, artificial prey item (a bead) in unconstrained, 3D space. Dragonflies employ a circuitous, “sneaking” strategy to avoid detection when approaching predictable prey, but preliminary work suggests they adopt a more direct approach to rapidly apprehend evasive targets. I will construct an automated, microcontroller system with pulleys and DC motors to produce different target behaviors, integrated with sensors to produce rapid changes in target behavior shortly after dragonflies initiate pursuit. This system will simulate three types of targets: unresponsive with predictable motion, unresponsive with erratic motion, or responsive, switching from predictable to erratic motion shortly after pursuit begins. I will quantify success rate and collect hundreds of videos for each target type, then digitize 3D trajectories using automated, deep-learning methods. From these data, I will categorize pursuit strategy, measure the delay between changes in target and pursuer behavior, and quantify metrics related to cost (e.g., peak acceleration, time to capture), which will be integrated into a decision-making algorithm for pursuit of varying target types. This research will reveal how a highly effective model pursuer maximizes the likelihood of target acquisition while minimizing costs, and will reveal the timeline of decision-making that occurs during 3D aerial pursuits that occur in less than half a second. By using new methods of eliciting and analyzing complex, aerial interactions, I will generate a massive dataset of successful and unsuccessful pursuits of different target types, providing insight into fundamental but hard-to-study aspects of pursuit and evasion. Broader Impacts: Biomechanics and bioinspired design are areas of engineering with unique potential to increase diversity within the engineering community. Many science students who are initially intimidated by competitive, equation-heavy engineering classes or discouraged by negative experiences during these classes (as was the case for me as a woman in STEM) are drawn to biology, and representation of women and underrepresented minority (URM) researchers is considerably higher in biology than in other STEM disciplines. Biomechanics provides a bridge between biology and engineering, linking engineering tools and theory to tangible, everyday experiences. At the undergraduate level, positive research experiences can bolster student confidence and maintain enthusiasm for STEM fields at later career stages12-13. I will leverage the interdisciplinary nature of this project to mentor undergraduate researchers from both engineering and biology, providing benefits to both. For biology students, this research will provide a taste of engineering that may motivate further coursework. For engineering students, exposure to biological research can spur creativity by introducing novel ways of thinking about and answering questions. I will focus on recruiting URM undergraduates through programs at U.C. Davis such as the NSF California Alliance for Minority Participation and Mentorships for Undergraduate Research Participants in the Mathematical and Physical Sciences. I will also help expose a larger student population to engineering methods by contributing to course planning and delivering guest lectures for a new Comparative Biomechanics course being offered at Davis in Spring 2022.

We propose to investigate algorithms for fast, robust online distributed optimization for trajectory planning among multiple vehicles in autonomous driving scenarios. Task 1 [Collaborative Planning]. We will investigate distributed optimization methods such as the Alternating Direction Method of Multipliers (ADMM), distributed gradient descent (DGD), and Distributed Sequential Convex Programming in application to joint trajectory planning. We will pose the optimization as a Model Predictive Control (MPC) in which the cars will be tasked with optimizing a joint cost function on the trajectories of all the cars, subject to constraints such as collision avoidance constraints, dynamics constraints, input constraints, and roadway and legal constraints. Task 2 [Collaborative Prediction]. We will also investigate distributed optimization methods for jointly predicting the future motion of observed agents who are not part of the V2V network. For example, if a human driven car is among the agents in the scene, the autonomous cars will co-optimization their prediction of the human-driven car’s future motion using a distributed optimization. We will mathematically formalize the problem as a maximum a posteriori optimization, in which we will seek the future trajectory that maximizes the likelihood of the observations of all the collaborating autonomous cars. Task 3 [Simulations and Experiments]. Finally, we will validate our distributed optimization approach in simulations with high-fidelity open-source car simulation environments such as CARLA. We will also test our algorithms on small-scale autonomous car models in Prof. Schwager’s Multi-Robot Systems lab at Stanford. Finally, we will test our algorithm with a full-scale experimental autonomous car available in Prof. Schwager’s lab. To ensure safety, the full-scale autonomous car will interact with multiple simulated cars to emulate a realistic multi-car interaction scenario. In all cases we will compare the performance of our distributed optimization-based approaches with a V2V network versus traditional predict-then-plan and game theoretic approaches without a V2V network. We expect that our algorithms will give trajectories with fewer collisions, fewer evasive maneuvers to avoid collisions, will require less computation time, and ultimately give more naturalistic driving behavior for the autonomous cars.

Despite calls to improve engineering student wellbeing and success, engineering students continue to suffer from a culture of “suffering and shared hardship”, “meritocracy of difficulty”, and “chilly climate” (especially women and minoritized groups). Student thriving continues to be a focus in higher education and has been shown to improve wellbeing, retention, and academic success. However, existing surveys of thriving focus broadly on higher education and do not translate well to the distinct engineering culture and population. Thus, developing the first survey to measure thriving specifically for engineering students is essential to supporting the development of diverse engineering students and informing the practices of engineering educators. Invite the eFellows cohort and mentors from multiple engineering programs to collaborate on recruiting a nationally representative sample of at least 300 undergraduate engineering students for this study. Conduct online Zoom interviews with at least 20 undergraduate engineering students (at least ten women and minoritized students) to generate an item pool that represents the most salient aspects of thriving for students. Determine the format for measurement and response format. Refine the item pool based on expert’s feedback on the item content’s relevancy, clarity, conciseness, and bias. Pilot the survey items using Qualtrics with at least 300 undergraduate engineering students who are at least in the second year of their engineering programs (to target students acculturated in engineering programs). Refine the survey by examining item correlations, means, standard deviations, and standard errors. Conduct exploratory factor analysis (EFA) to generate the latent factors that underlie the set of survey items. Interpret the latent factors based on the broader categories in the model of engineering thriving. Recruit another round of at least 300 students for confirmatory factor analysis (if survey is substantially refined). Conduct confirmatory factor analysis to assess how well the factor structure fits the data. Establish the final survey with about 60 items that takes under 15 minutes to complete. Share survey with eFellows cohort and mentors for feedback on utility. Disseminate findings in at least two peer-reviewed journal manuscripts and/or conference papers. Write a competitive, multi-institutional, and federally funded research grant using findings as pilot data.

One mechanical characteristic that has been understudied in terms of cardiomyocyte research is the effect of viscoelasticity of the material they are seeded on. Viscoelastic materials exhibit stress relaxation, or the ability to remodel and dissipate stress when constant strain is applied. Recent literature highlights how the stress relaxation rate plays an important role in cellular differentiation and morphology. Another important parameter is the type of cell binding sites of the material. Cells can interact with a material through a variety of cell binding peptides and proteins. This adhesion presentation is another key parameter to investigate along with stress relaxation, as cells must properly bind to their material before they are influenced by its mechanical properties. Both properties are known to change in cardiomyopathies, and cells have been shown to induce a disease phenotype when exposed to certain mechanical cues like stiffness. Therefore, we hypothesize that stress relaxation and integrin engagement influence hiPSC-CM function, and that the changes of these properties in a diseased cardiac tissue promote disease progression. We will interrogate this hypothesis through exploring the following aims: Aim 1 How does stress relaxation change hiPSC-CM’s gene expression and function? Although viscoelasticity of a tissue is known to change during disease progression, it remains an understudied parameter in cardiomyocyte biology. Therefore, we hypothesize that by tuning the viscoelastic properties of a material we can influence healthy and disease hiPSC-CMs function. Aim 2: How does stress relaxation in conjuncture with integrin engagement effect hiPSCCM disease phenotype. The protein composition of cardiac tissue changes during disease progression, however it is unknown how this, in conjunction with the changing stress relaxation, influences cardiac cell behavior. We hypothesize that engineering alginate hydrogels with stress relaxation values and cell binding peptides reflective of a disease state versus a healthy state will influence both healthy and diseased cardiomyocytes. Both these aims will utilize many of the CNSI resources, including rheometer and nano characterization of our alginate biomaterial, protein patterning on the alginate to dictate cell shape and alignment, as well as cell proteomics analysis to characterize biological differences.

My goal is to establish the physical and chemical principles that govern the rate and range of polariton-mediated energy and charge transport. Towards this goal, I aim to (1) investigate exciton diffusion in organic films using transient absorption (TA) microscopy and (2) investigate the energy and charge transfer in mixed donor/acceptor films using photoluminescence (PL) and TA spectroscopies. The proposed work lies at the frontier of the research interests of NSF’s CBET. I will investigate ultrafast polariton-mediated exciton transport in microcavities using TA microscopy. In the initial tens of femtoseconds after optical pumping, excitons are predicted to diffuse coherently, after which point the transport transitions to an incoherent hopping regime. I will investigate exciton transport during short (~10 fs) and long (~10 ps) timescales to understand both the coherent and incoherent transport regimes. Initial studies have indicated polariton-mediated exciton transport is ~100x slower than the group velocity expected from the polariton dispersion.5 Through systematic control of the material properties, I will uncover the origin of the mismatch between the expected and measured transport velocity. In addition to exciton diffusion, I will investigate energy and charge transfer in mixed donor/acceptor microcavities. Energy/charge transfer from donor to acceptor can be resolved in time using PL or TA spectroscopy by identifying the decay and growth of spectral signatures associated with the donor and acceptor, respectively. I will investigate the energy/charge transfer from a strongly coupled donor species to an uncoupled acceptor species. These investigations will reveal how the delocalized donor-based polariton interacts with localized acceptor molecules. As an initial experiment, I will assemble microcavities with varying concentrations of acceptor dopants in bulk donor films, and I will perform Stern-Volmer analysis to determine the influence of the polariton quenching radius on energy/charge transfer. Next, I will fabricate multilayer structures, where the position of the acceptor layer is varied relative to the cavity field, to elucidate the role of the photonic mode in mediating transfer. With regards to energy transfer, I will demonstrate that polariton formation not only increases the energy transfer radius but also enables energy transfer between donor and acceptor species that are spectrally incompatible. With regards to charge transfer, I will demonstrate that polariton formation can enable through-space charge transfer, where the delocalized exciton-polariton is converted into a localized electron and hole that are separated by a long distance.

Understanding how Low-level jets (LLJs) are formed is of the upmost importance to predict extreme rain events, and their possible connections with hurricanes in the Caribbean and tornadoes in the Great Plains, as well as their impacts on coastal erosion. Therefore, the overarching goal of this research is to understand the dynamics of LLJs and their impacts on climate formation and energy resources where they are observed. The project aims to devise a conceptual model to explain the mechanisms of formation of LLJs. A unified theory will be created with LLJ specificities included. We seek to integrated theory, simulations and experiments including field data. Massive population displacement, especially into poor areas, stresses water reserves of receiving communities. By understanding the mechanisms of phenomena involved in humidity transport, those countries can predict cycles of rainfalls and droughts, thus facilitating planning and management of water reserves and emergency services. As LLJs can transport gases and particulates across long distances, understanding LLJ’s development allows to plan the siting of industries that release environmentally hazardous materials to the atmosphere (one example being offshore oil rigs), placing them away from LLJ trajectory. Broader knowledge dissemination will be accomplished via educational YouTube videos and publications in high-impact journals as well as with summer school.

The main challenge in focal cooling is controlling the temperature gradient in a small zone so that the neural dynamics in the other regions of the brain remains unaffected. In this research, I will realize a microscale thermocouple probe based on an array of vertical high aspect ratio (200 µm to 1 mm) metal needles (Fig. 1A) developed in Dr. Krishna Jayant’s lab that is capable of accurately cooling a small cortical region with spatial resolution of as high as a few hundred microns. Needles will be printed using nanoscale electroplating using a novel µAFM tool. Our proposed platform will comprise of transparent flexible parylene substrates onto which 3D printed copper/gold needles are integrated. The needles will be insulated with parylene except for the tips which will be Platinum coated. The probe will be bonded to the surface of a heat sink to maintain the temperature at the tip. We will insert the Needle probe into the cortex (Fig. 2B) and perform temperature measurements, two-photon calcium imaging of network activity, silicon probe recordings to ascertain electrical activity, and two-photon targeted intracellular recordings to map single cell properties – methods that are standard in the Jayant lab. Overall, we will ascertain the design space of our technology, the biophysical basis of cooling induced changes in cortical activity in vivo, and the spatial extent of modulation. To justify the design, I performed finite element simulations for heat conduction in the brain using COMSOL Multiphysics to assay the spatio-temporal resolution of needle probe-based cooling. The probes (base diameter of 200 µm, tip diameter of 2 µm, and a length of 400 µm) were simulated with a constant boundary condition (32 ºC) applied at the base. I found that the temperature at the tip drops by 5ºC within the first 10 ms and then becomes steady while the temperature is near constant 400 µm above the tip. This allows us to focally cool select regions whilst temperature away from the test site remains unaffected. Overall, I will substantiate my experiments with theory.

This project aims to help industry develop more objective metrics for determining whether potential candidates are a “good fit” in terms of alignment between the organizational reward structures and the career orientations (e.g., work-related attitudes and subjective career meaning) of engineers. Career orientations are differentially motivated and expressed among groups (i.e., gender and race/ethnicity). Awareness of these orientations provides insights on decision-making and has implications for career management in terms of anticipated challenges and opportunities for developmental support. Essentially, the meaning that underrepresented workers bring to career has implications for their career enactment (i.e., behavior, expectations, and ultimately, whether they will stay with an organization). The findings of this project will be significant to the extent that they establish best practices on how to attract, support and develop individuals with diverse career orientations. I propose the following research question(s) to accomplish the project aim: 1. How do organizational expectations, benefits, and rewards align with the different career orientations of engineering interns from historically underrepresented groups? What are the biases toward work-related attitudes (penalizing or more supportive for certain orientations)? 2. How do engineering students perceive their career-fit related to current engineering workforce needs? The intellectual merit of this project will be the extension of career psychology theories to elucidate the experiences of historically underrepresented groups in engineering, specifically for members from Latinx and Black/African American ethnic and racial demographic backgrounds. Additionally, the proposed work builds on the expertise developed during my dissertation research. This project will highlight both the shared experiences among sub-groups with respect to career meaning and the influence that meaning has on career decision-making, and the within-group nuances related to professional experiences to inform recruitment and retention efforts. By leveraging career psychology for industry use, the broader impact of this project will be organizational change that improves inclusion and career development support for potential candidates and current workers with non-traditional career orientations. A timely endeavor to ensure that efforts to sustain an equitable and diverse engineering workforce are realized amidst a paradigm shift in employee expectations in the post-pandemic workforce. The findings will help identify biases in organizational rewards and benefits that differentially support (or penalize) the work-related attitudes of talented and demographically diverse candidates, resulting in improved recruitment and retention efforts as well as enhanced career satisfaction of underrepresented racial, ethnic and gender minorities in the engineering workforce.

The goal of the project is to address two questions: 1) What are clear evidence-based pathways to achieve racial equity, and address formation and population parity of African Americans engineers within and across academia and industry? 2) How can challenges of identity, resilience, resistance, and other barriers to success be diminished for Blacks. The success of project activities will be measured by formative evaluation activities. Catalytic activities that will examine research trends, and developmental and ecological system theories such as Spencer’s phenomenological variant of ecological systems theory (PVEST) that addresses formation of Black American Engineers along the engineering pipeline while considering socially constructed categories such as class, race, gender and stressors such as risk, stress, and coping mechanisms as variables for the prediction of negative learning attitudes and persistence in engineering. For contingencies, we have developed a remote mentorship plan that will enable the unimpeded implementation of the research plan and enhance the postdoctoral experience via access to technological tools for communication and research facilities. This plan covers access to several career skills such as writing grant proposals, teaching students, writing articles for publication and communication skills to develop her skills, knowledge and experience needed to excel in his/her career path. The project will contribute to the society by developing a shared vision for a partnership that will support Black American engineers’ formation, equitable access to engineering careers and broaden the participation of Blacks in engineering. My advisor and I will work collaboratively with GlobalMindED to cultivate strong partnerships and connections to inclusive leaders with the capacity and expertise to create a partnership of minority institutions that will move the needle for Black Americans in engineering. A powerful communication plan will incorporate the resources and reach of GlobalMindEd’s newsletters, press releases, and podcasts to disseminate and build awareness of project activities. Strategies for promoting equity and persistence in engineering will be identified and shared with ASEE and the NSF Network through participation in affinity groups, discussion threads, and webinar.

With Magnetic Particle Imaging (MPI) it is possible to generate real-time in-vivo results from dynamic organs (e.g., a beating heart, blood flow in veins). This allows for producing 4D medical images (videos) of the dynamics of the internal organs, which will be indispensable in diagnosis and treatment planning of many serious diseases such as cancer or cardiovascular ailments. However, the conventional medical image analysis techniques are incapable of fully utilizing the hidden information in 4D data. Most 2D or 3D image analysis techniques (including our previous work on brain MRI segmentation) utilize the properties within the static images to detect abnormalities. Detecting patterns and abnormalities from 4D data requires signal analysis methods that go beyond 3D image analysis and integrate the temporal variations to achieve 4D signal analysis. The accuracy of such 4D signal analysis is affected by the resolution of 4D data (both spatial and temporal resolution). Given that generating high resolution 4D data from sensor readings incurs a high computational complexity, a major challenge is designing low-cost devices that provide high resolution 4D data. We propose to address the challenges in 4D signal analysis through: 1) developing reconstruction and filtering methods for high resolution MPI images; and 2) developing temporal interpolation methods for 4D spatiotemporal MPI data. Our proposed methods do not incur extra hardware cost of replacing sensors and will utilize our recent findings on DNN design and performance improvement as described next.

This proposal seeks to assess the interactions between city infrastructure and urban air quality and climate. This research will explore the following hypotheses: 1) The compounded effect of urban infrastructure distribution plays a significant role in the effectiveness of mitigation measures; and 2) The synergistic or competing effects differ across major US cities and regional climates. It is expected that the results of this proposal will provide answers to the following the questions: i) What are the compounded effects of multiple emissions sectors, city layout and weather patterns on air quality? ii) Are there common patterns in these effects across cities? iii) Which components of city infrastructure and layout have the most significant impacts? iv) Will we be able to predict future effects of city infrastructure and design on the effectiveness of air quality regulations? v) What are the implications for disproportionately impacted communities, and what measures can be taken to achieve environmental justice? : I expect that the results of this project will lead to more effective emissions mitigation strategies, and reduced urban pollution exposure and adverse health outcomes, particularly for historically disadvantaged communities that tend to experience a higher burden of pollution exposure. On a microscale, I plan to mentor graduate students and include them in research tasks. It is also my intent to engage with the scientific and engineering community at UC Berkeley and get involved with local communities and schools by making presentations, facilitating programs, and leading summer activities.

Metamaterials exhibit different responses due to their material composition, geometry, and configuration. Their use as acoustical treatment has been well studied, however, what has not been widely studied is how interfaces, which serve as connectors of structures as indicated in Figure 1, can be engineered to reduce vibrational and acoustical energy. The expertise of Dr. Haberman will be leveraged to develop and design this research idea 3. The objective of this research will be to design interfaces with specific surface topologies to absorb/diffuse acoustical energy. In Phase 1, engineered textured interfaces will be used to benchmark the effects of texture and feature dimension on vibro-acoustic absorption. These experiments done within an anechoic chamber will use Shakers, high precision Laser Doppler vibrometry, accelerometers and microphones to capture the structural-acoustic response of a Two-wall-interface structure. These measurement systems are available at UT-Austin within the Texas Acoustics group which includes Dr. Habermans’ laboratory. Different interface topologies will be designed and manufactured at the Center for Additive Manufacturing and Design Innovation (CAMDI) with collaborations with Dr. Carolyn Seepersad and other interdisciplinary researchers at UTAustin. At the conclusion of Phase 1, an experimental toolkit with an optimum set of interfaces topologies will be used to inform Phase 2, where different placements of interfaces along the Two-wall-interface structure will be explored. The tools used in this research will be both experimental and numerical. In Phase 1, the characteristics of the optimum structured interface (waviness, roughness, lattice periodicity) will be developed in COMSOL, a Finite Element tool. In Phase 2, different spatial distributions of interfaces will be explored to maximize the dispersion of acoustic energy across the broadest range of frequencies, which directly impacts the acoustic properties of the structure. Analysis of these changing boundary conditions will be done using Digital Signal Processing and Machine Learning tools.

Examining the impact of source water disinfection on biofilm formation, morphology, ecology, and cellular detachment kinetics will be a critical first step in understanding the role of UV as a potential alternative secondary disinfectant. Three research objectives are planned as follows: Objective I – Examine differences in biofilm morphology between biofilms formed in chlorinated drinking water (CW) and UV-irradiated water (UVW) across wavelengths. Biofilms will be cultivated in microscopy flow cells (MFC) designed for monitoring biofilm processes. MFCs will be used to examine naturally forming biofilms from different source waters (unchlorinated tap water and groundwater), and those from spiking biofilm forming bacteria (i.e., Pseudomonas sp.). Each reactor will receive water disinfected by different methods (e.g., free chlorine, UVC at 222, 254, 280 nm). Throughout the growth phase, biofilm morphology will be monitored using confocal laser scanning microscopy (CLSM) to characterize structural differences (extracellular polymeric substances, morphology) between UVW biofilms and CW biofilms on pipe coupons. Objective II – Determine if transitioning the secondary disinfection method (CW to UVW) affects biofilm morphology and cell detachment kinetics. To determine the effects of transitioning disinfection strategies on morphology and cellular detachment kinetics, CW and UVW biofilms will be cultivated in MFCs. Green fluorescent protein (GFP) tagged Legionella pneumophila cells will be introduced into the MFC until settled onto the biofilm. The source water will then be transitioned to cell free UV-irradiated water. Transitions in biofilm morphology and GFP cell detachment rates will be characterized as previously described using atomic force microscopy and CLSM. Objective III – Determine the impacts of transitioning between chlorinated water and UV-irradiated water on the microbiome and opportunistic pathogen proliferation in complex environments. Dissertation work by the applicant, Ley observed increases in pathogen concentrations following a shift from chlorinated to UV-irradiated water in building plumbing. It is critical to study the impact of disinfection methods on OP concentrations within both bench-scale reactors to reveal underlying mechanisms and pilot-scale building plumbing loops to emulate real-world flow conditions. Biofilms will be developed using source waters in a pipe loop rig, across pipe materials over a longer term. Parallel pipe loop studies will explore the potential use of embedded LEDs along the pipe for biofilm control. Following the transition from CW to UVW, biofilm samples will be collected temporally and analyzed (16S rRNA sequencing and qPCR) to examine microbial community shifts and succession patterns in response to changes in disinfection.

The Macfarlane lab at MIT controls the microstructures of nanoparticle superlattice-based macroscopic solids by “sintering” preassembled superlattice single crystals (i.e., low-temperature mechanical pressing to induce particle reorganization and densification of the composite). I will use techniques like compression molding and hot working on preassembled superlattice crystallites to further refine microstructures while producing macroscopic objects with arbitrary 3D shapes. It is well established that temperature modulates the reversibility of supramolecular bonds binding and allows NCT reorganization. Regulating these processing conditions will enable control over grain size, structure, and defects while the processing mold shapes the macroscale material structure. These parameters critically impact the properties of traditional bulk metals and ceramics but their effects on nanoparticle superlattice assemblies are unknown. The fundamental information gained from studying how reversible supramolecular bonds affect ordering and reorganization at the nano- and microscale will enable tailorable materials properties in subsequent aims and benefit both the soft matter and mechanical properties communities. In this aim, I will investigate the impact of controlling microscale structural features on fatigue resistance, a bulk mechanical property important for durability against repeated deformation cycles. Cyclic tensile tests on macroscopic materials fabricated using methods in aim 1 will examine how the achieved grain size affects crack initiation. I anticipate that analogous to metals, modulating grain size will alter grain boundaries’ ability to pin dislocations and prevent fatigue. Furthermore, based on prior evidence that the concept of sacrificial bonds is effective in creating tough polymer-based materials, supramolecular interactions between nanoparticles will dissipate energy, thus hindering crack propagation. I will achieve orientation dependent mechanochromic response, a property enabled only by microscale structural features. Such response typically results from deformation induced changes in the average interparticle distance between randomly arranged optically active filler. Here, I will demonstrate the importance of microscale structural control by fabricating a prototyptical plasmonic mechanochromic sensor by using anisotropic nanoparticles with both magnetically and plasmonically active compositions. The local distribution and orientation of building blocks within the macroscopic material will be varied with magnetic alignment during the compression molding step. Absorption profile will be modulated by alterations to interparticle distance and orientation under applied load, as organization of particles within the material affects the degree of plasmon coupling.

The study and analysis of Electrical Microgrids performance is of extreme importance. State Estimation, Optimization and Cybersecurity are part of the most relevant aspects to explore as part of microgrids structure. The vulnerability of energy grids is evident with attacks and failures due to increased demand caused by climate change. In order for the grid system to be resilient there has to be a hybrid combination of solutions and microgrids represent one of these solutions. The Massively Open Online Courses will be taken by students and professionals who manifest interest in the study of microgrid operation and the role that these microgrids play for secure and optimal performance of energy grids. The courses will become an advanced tool for application in the continuous process of research carried out by universities and utility companies. The publishing of the scientific papers will increase intellectual production and make a wider impact to the discipline through intellectual merit. Moreover, the teaching and technical skills of the researcher will improve, and this aspect constitutes a positive factor of positively influencing the academic career of many students.

My research plan is to investigate the resiliency of transportation networks and to develop optimal strategies for deployment in the era of CAVs to address disruptive events that are relatively of short duration (e.g., cyber-attacks) or long duration (e.g., hurricanes). The research will duly recognize and account for the three phases of resilience planning. In the pre-event phase, the transport agency reinforces the infrastructure to enable resistance to damage and efficient response including evacuation. The research will develop optimization programs including network modeling and simulation, to develop pre-event plans for the movement of people. With regard to the response phase, the research will develop optimal policies to mitigate the disaster-related congestion and optimal evacuation strategies such as route guidance for CAVs through V2I, dynamic resource allocation, and emergency rescue. The research will also develop protocols to increase the integrity of real-time information in an environment of possible erroneous information sent to CAVs by malicious agents or due to communication system malfunction. With regard to the recovery/restoration phase, the research will enhance existing frameworks for the transport agency to assess and mitigate the impacts on affected transportation and communication networks. The framework will involve the use of demand modeling and network optimization modeling. As the specter of climate change and other disruptive events continue to threaten infrastructure longevity and operations, it is beneficial to identify sustainable and technology-enabled solutions to reduce the consequences of such threats as they become manifest. In leveraging emerging technologies to help address longstanding problems associated with infrastructure resilience, the proposed research is expected to have significant societal impacts. Most importantly, the objectives of the proposed research are consistent with the vision and activities of Georgia Tech’s Infrastructure Research Group led by Professor Amekudzi-Kennedy, a reputable expert in transportation system resilience.

This is fundamental research combining the primary understanding of the role of interfacial interactions with multi-phase flow functions through porous media. The potential outcome of the proposed project is to understand the underlying flow behavior relevant to underground hydrogen storage for application in Environmental Engineering Energy Sectors. Increase in worldwide energy consumption and greenhouse emissions has driven the energy industry towards environmental-friendly technologies, such as carbon capture, utilization, and storage (CCUS) and usage of hydrogen as a clean source of energy. Hence, underground hydrogen storage is considered as a promising technology to store and extract hydrogen for future applications. To identify potential geological formation for the purpose of hydrogen storage, a systematic experimental investigation is required to study the dominant parameters affecting hydrogen behavior in the targeted reservoirs. However, there are very limited experimental studies available in the literature to address the influential factors pertinent to the hydrogen flow behavior in subsurface media. Therefore, there is a significant demand in the scientific and industrial communities to provide such analysis. The Keywords in this work are as follows: Underground hydrogen storage, reservoir engineering perspective, interfacial phenomena, and multi-phase flow functions through porous media. This proposal is prepared for the subtopic “Chemical, Bioengineering, Environmental, and Transport Systems”. The main contribution from this work is to improve the understanding and application of interfacial phenomena inside naturally occurring porous media resulting in an efficient underground hydrogen storage. The results of this work will have a major positive impact on the reduction of unwanted pollutions in the environment caused by fossil fuels and greenhouse emissions, while providing aids to respond the increase in global energy consumption.

Systems thinking has been defined as “synergistic analytic skills used to improve the capability of identifying and understanding systems, predicting their behaviors, and devising modifications to them in order to produce desired effects”. Research has shown a general deficiency in systems thinking capabilities among undergraduate engineering students. There is a need to improve engineering students’ systems thinking abilities to prepare them for their careers. Product teardown as an active learning strategy may improve students’ systems thinking capabilities based on the learning theory known as constructivism. The objective of this proposed research is to investigate the effects of product teardown activities on undergraduate engineering students’ mental models of systems and systems thinking abilities. This proposal details a two-year plan for developing the necessary research materials, implementing the learning activities in undergraduate classrooms, measuring changes in systems thinking abilities, and exploring the broader application of the results and their implications. By exposing students to an activity built on constructivism, this work aims to improve the students’ systems thinking skills, which is necessary for success in industry. The following 3 hypotheses are considered: Hypothesis 1: A product teardown activity will improve the completeness of students’ mental model representations for the same given system. Hypothesis 2: A product teardown activity will improve the completeness of undergraduate engineering students’ mental model representation for other systems that share analogous functionality to the given system. Hypothesis 3: Mental model representations of engineering systems by 4th -year undergraduate engineering students will be more complete than 1st -year undergraduate engineering students before and after the product teardown activity.

My previous work has been on quantum optics for quantum information and communication, largely in the realm of light-matter interactions. Some projects I have worked on during my PhD studies involve the quantum Zeno effect, a phenomenon whereby a quantum state is prevented from changing due to repeated observations of the state. The effect is not necessarily destructive, and as such allows for careful control of quantum systems in some circumstances, particularly with quantum photon states. Photons as information carriers have the advantage that they do not strongly interact with the environment, nor do they directly interact with one another. Thus, they are highly sought after for quantum communications purposes and distributed computing. Unfortunately, this also means they require strong nonlinearities to interact with one another, making it difficult to perform logic operations with them without ancilla and heralding. Nor are they immune to loss in fiber channels as well as polarization mode dispersion among other issues, so the information they contain is susceptible to corruption. The quantum Zeno effect has been used to alter the way photons interact, even mimicking fermionic behaviors, allowing for the construction of universal quantum logic gates. It has also been theoretically shown that it can be used as a method to perform logic operations on macroscopic qubits formed by coherent photon states. Experiments have already been performed which show the Zeno effect to be realizable in the lab. There has been relatively little research into the Zeno effect as a tool for quantum information. Under the eFellows program, I will continue my work on utilizing the Zeno effect in light-matter interactions. I will begin by expanding upon the previous research in the context of optical waveguides such that they will be effectively interchangeable with more common optical devices such as polarizing beam splitters and wave plates. Following that, I will apply these techniques to coherent states, allowing for the potential creation and manipulation of Schrödinger’s Cat states as quantum information resources. If successful, this work will pave the way for many new techniques for optical quantum information and even impact the broader field of photonics.

In this proposed work, we hypothesize that enhancing the complexity of the behavioral task can reduce a subject’s habituation to a task and retain the signal strength associated with the movement-related neural features. We propose to ask able-bodied subjects to perform a reaching task where they are instructed to track a target with arm and hand movements. During this task, brain rhythms will be recorded with scalp electroencephalography (EEG) while hand movements will be tracked with motion tracking hardware. We will impose two types of complexity to the motor task. In the first experiment, the trajectory of the target will either have a repetitive and predictable pattern or a complex trajectory with unpredictable changes. In the second experiment, subjects will perform the reaching task to various targets and, at random trials, experience a visuomotor perturbation with the controlled cursor. This will be done by applying a different angular mapping between the hand position and the controlled cursor. Our first objective is to characterize how EEG features associated with the reaching movements change over trials depending on the imposed conditions with varying task difficulty. Source localization will be used to focus our analysis on sensorimotor areas, and the strength of the EEG modulations will be compared to behavioral task performance. We anticipate that movement-related cortical modulations will be stronger in the first study where trials are more complex and unpredictable, and stronger in the second study in trials that recently contained perturbations. Our second objective is to assess if the imposed complexity enhances the machine learning models used to predict hand movements based on EEG signals. These predictive models will be used to classify the presence and absence of movements, as well as the direction of the hand movements. We anticipate that stronger movement-related neural modulations in trials with complex target trajectories or perturbations will yield higher classification accuracies.

The proposed research seeks to address the following questions: 1) How do 2DMPs nucleate? This includes the 2DMP’s initial rupture and subsequent rapid growth. The factors influencing the 2DMP’s equilibrium structure will be investigated for the first time through the following series of studies. 2) How do pleats interact with the environment? Factors like temperature and gas medium can drastically affect the mechanical behavior of 2D materials. The effect of these external factors on the control of 2DMPs is a substantial knowledge gap. Our goal is to quantify how pleat nucleation depends on multiple critical factors, such as the maximum indentation load, number of 2D material layers, orientation of the tip facets relative to the crystal lattice of the 2D material, and the initial width of the pleat (to be controlled by forming pairs of initial scratches at defined points via AFM). Pleats nucleated and grown at a range of temperatures (120 – 450 K achievable with the RHK Technology AFM in the Carpick lab) will be studied for length, fold width, lattice orientation, and strain. In collaboration with Dr. Cross’s student, I will update the model to integrate thermal activation and temperature-dependent properties. Comparison with experimental results will reveal the controlling mechanisms and permit applying the knowledge to other 2D materials. Using established experimental methods, Gr/SiO2 samples will be exposed to water to form a Gr/H2O interface, and then AFM used to form and characterize 2DMPs. Gr/SiO2 samples will also be submerged in other chemicals with varying surface energies, providing further dimensions to tune to the interfacial properties of the 2DMP. The proposed tunabilities of the 2DMP interface will also affect the fold energy, tearing energy, and interfacial friction, which adds complexity to the analysis, but also broadens the parameter space of pleat energetics to be explored. This will be the first investigation of how pleat equilibrium structure depends on and can be controlled by interfacial energies, allowing us to test and improve the continuum model, and to understand pleat behavior more deeply.

The goal of this research is to determine a reliable procedure for in-situ formation factor measurement of concrete. Such a procedure would mitigate possible discrepancies between laboratory and field concrete and also be inclusive of alternative cements – including rapid repair materials and carbon capture products – that do not hydrate in the same manner as portland cement (PC) systems. There are three aspects of formation factor testing that require further investigation for in-situ measurements, as outlined below. The following research plan will use both PC and non-PC cement systems. Part 1 – Methods of Accounting for Field Conditions (Year 1): The first challenge to address is how best to account for the variable factors in surface resistivity measurement of field concrete such as degree of saturation (DOS) and the presence of rebar or cracking. Recommended conditioning procedures for laboratory-prepared hydraulic binders maintain saturation by submerging samples in simulated pore solution. However, saturation is not maintained for in-service concrete and the DOS can dramatically influence electrical resistivity measurements. Finite-element modelling methods for the relative humidity of in-service concrete will be used to estimate the DOS of samples in this study and will be compared to in-situ internal relative humidity sensor readings, and the moisture content determined ex-situ. Methods from prior studies will be used to account for the presence of cracks or rebar. Part 2 – Pore Solution Analysis Methods (Year 1): Secondly, procedures for pore solution analysis will be investigated. The pore solution conductivity is required to calculate the formation factor and must be experimentally measured in cases where it cannot be reliably estimated, e.g., in-service concrete and/or alternative binders. However, the conventional approach for pore solution extraction – utilizing high pressure – cannot be readily adapted for field use. Literature suggests that cold-water leaching procedures could be a reliable alternative to high pressure extractions. The proposed research will evaluate if in-situ or ex-situ leaching methods can be used to accurately determine the formation factor. Part 3 – Service–Life Modelling and Validation (Year 2): Samples will be cast during year one and stored at an outdoor exposure facility to replicate in-service conditions. During year two the developed procedures will be used to determine the formation factor in-situ. Diffusion models for chloride ingress based on the formation factor and chloride binding profiles obtained from concurrent research will be compared to the results of salt ponding tests.

Cohort II

Ln³⁺ ions are ideal candidates for spectral conversion, due to their high luminescence efficiencies and rich energy level structure that allows for great flexibility to undergo upconversion and downconversion of photons in a wide spectral region (UV-VIS-NIR). Lanthanide Super Crystals (X₀.₅(NO)₃X₀. ₅(NO)₃•(Byp)₂) are of interest due to these materials potential to increase energy conversion for solar cell technologies in the future. Currently, efficiencies of solar cells are limited close to the Schockley-Queisser limit (30%) for c-Si based solar cell devices. Development of light absorbing meta-materials could be very helpful to enhance photonic activities and could be essential for solar cell advancements. Current commercial solar cell devices mainly absorb narrow portion of visible light and unfortunately, most VIS and IR light photons are lost due to thermalization and transmission losses. In this research we studied and observed the lanthanide-based up conversion phenomenon, the underlying energy transfer processes, the sensitization strategies taking advantage of organic linker/ligand molecules, the emerging hybrid Ln-organic UC materials absorption and transmission of light, and finally the potential applications of such materials in a broader context.

The research will use a novel approach combining fluid mechanics engineering, industrial engineering human factor concepts and usability methodologies guided by Reason’s Model of Human Error for studying poor outcomes related to human work and how systemic defenses failed. Our central hypothesis is that clinically relevant setup practices of IVSP systems result in high rates of flow rate inaccuracy, alterations in IVSP pressure outputs, and these errors can be ameliorated through human factors and engineering design principles. Study objectives are as follows: (1) Determine how variability of IVSP setup, such as lengthening tubing, modifying height of IV bags, adding connectors to tubing, amongst other frequently observed ad hoc modifications, can alter pressure within the IVSP system and affect flow rates yielding incorrect medication dosing. (2) Through use of established laboratory methods, determine how flow rate accuracy is impacted by current clinical IVSP setup practices related to IV infusion bag height and IVSP height. (3) Using my knowledge of working in clinical settings plus the engineering background of my mentors, we will create an innovative and novel IV pole design to reduce the degrees of freedom for IV infusion bag and pump setup to more closely match manufacturer’s recommendations and improve flow rate accuracy and drug delivery. (Sub-Objective 3) Experienced clinicians will test our IV pole to determine ease of use and drug delivery improvements as compared to a standard pole.

Vocal communication is a fundamental tool necessary to survive and thrive. However, each year, hundreds of thousands of people will experience a total loss or reduction in their ability to communicate. Neuroprostheses — electronic devices that directly decode the neural correlates of a person’s desired actions, thereby bypassing the injured part of the nervous system — stand to radically improve the quality of life for individuals with lost motor, speech, and language function. However, there has yet to be a clinically viable neuroprosthesis that can fully meet patients needs. Our proposal will develop the tools necessary to create clinically viable neural prostheses capable of conversation. Previous studies were limited by low channel-count neural interface technology, suboptimal brain area coverage, and overly constrained behavioral task design. Traditionally, these studies limit their subjects’ vocabulary and externally control when the subject initiates speaking. This has yielded promising results within limited assistive systems— such as fixed responses to simple questions — but are far from restoring natural conversational speech. To fully meet patients’ needs, communication prostheses must not only decode the words the user is trying to say, but also: ● Capture their full intended prosody and tone ● Stay quiet when listening to their conversational partner (even during rapid back-and-forth ● Monitor how well the system working (and update accordingly) without external supervision We propose developing and deploying novel interactive experimental paradigms combined with large-scale neural recording to achieve a next-generation communication neuroprosthesis. I will record naturalistic speech while simultaneously developing systems and metrics that enable direct exploration of the feedback control system. This will inform the design of prototype devices that can leverage error signals to automatically correct its mistakes (based on a “teaching signal” extracted from the user’s perception of communication performance) and improve its decoding algorithm.

Antimicrobial resistance (AMR) is a growing public health issue1. Animals used in husbandry pose a unique threat as reservoirs of AMR bacteria or genes (ARB/Gs) due to the use of antibiotics for growth promotion2. Animal husbandry at the household scale is widespread in low-and middle-income countries (LMICs). AMR in these settings is compounded by inadequate sanitation infrastructure and unregulated antimicrobial usage3. Although antibiotics’ effects have often been connected with industrial-scale food animal production, little is known about the impact of household-level animal exposure. This research proposal is part of a collaborative project led by researchers at the University of Washington and Universidad San Francisco de Quito. In this study, I will leverage DNA extracts obtained from their ongoing investigation in Ecuador and bring my expertise in metagenomics and bioinformatics to explore the impact of animal husbandry on antibiotic resistance burden in the household environment. Specifically, [Aim 1] I will sequence and assemble metagenome-assembled genomes (MAGs) with antibiotic resistance genes, [Aim 2] then compare their abundance, diversity, and genetic similarity in animal heavy exposure households to households with limited animal exposure. This data will be used to test the hypothesis that more animals will result in more MAGs carrying resistance genes which may increase the ARB/G transmission between animals and humans.

Onsite sewage treatment and disposal systems (OSTDS), or septic systems, are commonly used for domestic wastewater treatment in Florida (FL), with an estimated 2.6 million systems in operation (FDEP, 2022). However, conventional OSTDS have low nutrient removal performance, achieving about 30% N removal (Toor et al., 2020). These systems are a major source of nitrate (NO3 – ) pollution to waters, presenting harmful effects, such as methemoglobinemia and eutrophication (USEPA, 2022a; WHO, 2017). Horizontal sub-surface flow constructed wetlands (HSSF CWs) are a cost-effective method for OSTDS NO3 – management (Vymazal and Kröpfelová, 2008). Water discharged from an OSTDS can pass through a HSSF CW to undergo denitrification, where microorganisms convert NO3 – into nitrogen (N2) gas. Laboratory experiments suggest that HSSF CWs can be amended with low-cost materials, such as sphalerite (Zn0.628Fe0.372S) and oyster shells to improve denitrification performance (Dasi, 2022). Sphalerite serves as an electron donor that denitrifying microorganisms use to produce energy (Equation 1). This process can be supplemented with oyster shells, which supports the growth of denitrifying microorganisms on its surface (Tong et al., 2017). Previous research has not evaluated the denitrification performance of sphalerite and oyster shells in HSSF CWs; therefore, it is unknown whether this system produces water safe for discharge to surface waters (i.e., surface water discharge). : The goal of this project is to develop a low-cost HSSF CW for OSTDS NO3 – management. The specific objectives are to: (1) Compare the long-term NO3 – removal performance of a pilot conventional and amended HSSF CW for domestic wastewater treatment; and (2) Evaluate whether the systems produce treated water that meets surface water discharge guidelines.

CNT-BP has a microstructure that resembles a dense mat of interlocked tows of nanofibers. Deformations to CNT-BP induces micro-strains in these interlocked CNT bundles that alter the CNT-BP electronic structure, which can be expressed as electrical resistance. This is the essence of piezoresistivity [7]. Through microscopy and controlled sensing experiments, this research intends to explore and establish relationships between the microstructure of the CNTs in BP and the piezoresistive responses of the CNTBP film in various sensing situations. In practicality, sensors will need to operate for extended periods of time in unpredictable environmental conditions. This has not yet been explored. Our previous work is promising; however, knowledge gaps remain as it pertains to the structure-property-performance relationships of sensing. The action plan, which will be primarily conducted at the High-Performance Materials Institute (HPMI), is as follows.  Design and manufacture fiber reinforced composite panels according to ASTM standards D7136 and D7236 for impact and flexural testing, respectively.  Fabricate CNT-BP sensors using an ink-jet printer and according to our previous work [5]. It is noteworthy that the manufacturing process has been patented by Florida State University. A key objective is to train undergraduate students in device manufacturing.  Perform microscopy analysis and durability experiments to explore the CNT microstructure with respect to key sensor metrics that drive the piezoresistive sensing response. This will be conducted at the National High Magnetic Field Laboratory and HPMI.  Perform temperature-controlled experiments to reveal influences that temperature changes have on the sensing response.  Establish parameters and develop a mathematical model that explain and predict the sensing behavior of continuous CNT networks like Buckypaper. The results will lead to new patent applications and 2-5 high-impact journal publications. Independent projects for both undergraduate and graduate students are expected to spawn from this, and the intention is to involve 2-3 minority students. By investigating the reliability of CNT Buckypaper in long-term sensing operations, untapped applications, such as monitoring the mechanical behavior of structures and projecting the progression of physical therapy, are more attainable. CNT BP is a novel nanomaterial that may possess unfamiliar features that experienced microscopists can identify and possibly relate to performance. This research will be conducted at HPMI; however, microscopists at the National High Magnetic Field Laboratory will be consulted.

According to a recent report (National Academies of Sciences, Engineering and Medicine, 2019) “Minority Serving Institutions (MSIs) are valuable resources for producing talent to fulfill the needs of the nation’s current and future STEM workforce. The educational outcomes and STEM readiness of students of color will have direct implications on America’s economic growth, national security, and global prosperity”. Significant disparities between representation in the engineering workforce versus the general population are observed for all except Black and Asian females (American Society for Engineering Education, 2018; McGee, 2020; National Academies of Science, 2011; National Science Foundation, 2017). The goal of the project is to address two questions:1) What are clear evidence-based pathways to achieve equity, and address formation and population parity of female African Americans engineers within and across academia and industry? 2) How can challenges of identity, resilience, resistance, and other barriers to success be diminished for underrepresented groups in engineering along the pathway. The project will focus on two elements of collaborative infrastructure: Shared Vision and Partnerships. The project intends to develop a shared vision of scientific innovation for addressing identity / developmental challenges which diminishes participation of underrepresented groups in engineering and advanced education/career and to crystalize partnership between Howard University, Hampton University, California State University, Los Angeles, and Tennessee State University other MSIs, nonMSIs, nonprofits, funders, and industry allies that share a similar vision. The project will advance understanding of the barriers faced by minorities in engineering at MSIs and contribute critical knowledge about solutions for addressing them. The project will contribute to society by developing a shared vision for a partnership that will support black American engineer’s formation and equitable access to engineering careers and broaden the participation of Blacks in engineering. Strategies for promoting equity and persistence in engineering programs and advanced careers will be identified and shared with the NSF Network through participation in affinity groups, discussion threads, and webinar. The success of project activities will be measured by formative evaluation activities.

Perfluoroalkyl substances (PFAS) are anthropogenic chemicals that are very persistent, non-biodegradable and bio-accumulable chemicals in the environment. To completely remove PFASs destructive technologies are required. Electrochemical treatment technology can be operated without chemical addition and easily scaled up to degrade PFASs. The objective of this research proposal is to conduct a comprehensive study to enhance existing PFAS removal adsorption technologies through their hybridization and process intensification with electrochemical technologies. The adsorption process can be used to minimize mass transfer limitations and maximize selective contaminant removal by electrocatalytic processes, while providing quick spent nano sorbent regeneration in situ. This novel hybrid process can minimize effects of PFAS laden from landfills and decrease CO2 emissions associated to sorbents pyrolysis. The design of electrified adsorption hybrid systems can set the foundation to develop similar strategies for other micropollutants (e.g., pesticides, arsenic etc.). Findings will be disseminated via regional and national conferences, peer-reviewed publications, and direct communication to water utilities. We will develop an outreach activity to showcase benefits of adsorption to K-12 and general audience that will be presented at ASU Night of the Open Door (audience ~ 800/year) to know about the human health impacts of PFAS in drinking water. Besides, undergraduate students traditionally underrepresented in STEM will be mentored in this project and will be engaged to participate in the described outreach activities. These activities will position Dr. Ersan as a role model for young women in STEM.

The evolution of locomotion in multicellular organisms is a result of multiscale coupling of actuation at different levels involving mechanical, electrical, and biochemical signaling. For instance, exercise or injury at the tissue or organism scale alter biochemical signaling pathways within single muscle fibers, leading to downstream effects on muscle metabolism and contractility. Developing multiscale computational models is a powerful way to quantitatively explore this coupling across scales. Such models can be used to elucidate the fundamental cause-effect relationships between mechanical stress and changes in cell signaling, allowing for prediction of the effects of different types of mechanical loading on the behavior of skeletal muscle. Throughout the many signaling pathways in muscle fibers, intracellular Ca 2+ plays a key role. In fact, Ca 2+ release from the sarcoplasmic reticulum is the key event which leads to contraction following the depolarization of muscle fibers. However, less is understood about how forces experienced by muscle fibers can feedback to alter Ca 2+ signaling pathways. Here, I propose the development of multiscale computational models to characterize the coupling between mechanical loading during different types of exercise and changes in intracellular Ca 2+ signaling. I plan to draw on my previous experience examining the overlap between cell mechanics and Ca 2+ signaling in human neutrophils, in which I developed computational models of cell deformations and global Ca 2+ dynamics during phagocytosis. Dr. Rangamani’s research group has a strong record of developing detailed computational models of biochemical signaling and mechanics in several different cell types. These approaches can be readily applied to skeletal muscle, and will be powerfully paired with data from experimental collaborators – Profs. Stephanie Fraley and Samuel Ward. Knowledge gained from this research can impact the understanding of exercise physiology at a cellular level, with potential implications for muscle strengthening and regeneration. I plan to share findings from this research with the general public through online engagement (e.g. blog posts on my website, and through mentoring high school and undergraduate students, as is common in Dr. Rangamani’s group. In these outreach efforts and throughout my professional career, I am committed to engaging with diverse audiences and creating equitable and inclusive spaces within STEM research, especially for those lacking privilege.

The research objective of this proposal is to understand the importance of long-range electrostatic correlations on the polymer conformation and coacervation behavior of bioadhesive polypeptides. Bioadhesives have promise to solve the long-standing problem of providing durable and strong adhesion in aqueous environments. The failure of current glues to adhere to wet surfaces can be attributed to many reasons such as the breakdown of the internal structure of the adhesive (cohesive failure) or of the contact region between the adhesive and the surface (adhesive failure). In addition, bioadhesives tend to be biocompatible, which is important for medical adhesion applications where conventional closure techniques, such as suturing and stapling, have many disadvantages like tissue damage and inflammatory responses. Thus, a mechanistic, molecular understanding of bioadhesives is paramount for future development of new adhesive materials for use in aqueous environments and in biomedical applications. We will glean insight into the detailed molecular mechanisms of biological adhesives based on coacervates through the phase diagram as a function of salt and polymer concentration. In particular, I will examine the influence of chain length, composition (e.g., number of charged groups), sequence (i.e., arrangement of charged groups), and salt type (e.g., valency of ions) as these play an important role in the performance of bioadhesives. In addition, charged residues present in bioadhesives are pH regulated through an acid-base equilibrium (i.e., charge regulation), which has practical relevance as it is well known that animals modulate their local environment when secreting the adhesive. I plan to extend the RGF theory to include this charge regulation effect. I expect the proposed research will allow for new and better understanding of the coacervates found in bioadhesives. It will also provide new insights into the molecular and biophysical principles underlying condensate assembly in liquid-phase organelles. Thus, the support provided through the postdoctoral fellowship will allow for broad impact in not only the polymer community, but also at the experimental and industrial level.

Individuals with chronic hemiparesis following stroke often have reduced ankle propulsive forces. These reduced forces can be detrimental to an individual’s overall quality of life and mobility as reduced ankle propulsion forces have been correlated to reduced walking speed and gait asymmetry. A promising solution for retraining paretic ankle propulsion force generation is biofeedback. This training strategy provides the user with quantitative information on a specific performance variable to induce behavioral changes. Preliminary studies have shown that biofeedback that targets anterior ground reaction forces (AGRF) can improve propulsion-related variables in older adults and the chronic stroke population. But, despite these promising results, AGRF gait biofeedback training remains a relatively uncommon gait retraining strategy in clinical practice. There exists little research on barriers limiting clinical use, but we posit that there are three key potential barriers; (1) current AGRF-based biofeedback equipment is bulky and expensive (2) there is a lack of understanding of the underlying neurophysiological, biomechanical, and motor learning mechanisms limiting the development of evidence-based protocols (3) there is a lack of knowledge around the clinical characteristics of responders and non-responders to gait biofeedback. To address these barriers, we will complete the following three objectives in this work: (1) Validate the efficacy of an IMU-based strategy for gait biofeedback training (2) Examine the change in maximum torque and motor evoked potential activity of the soleus, and gastrocnemius muscles (3) Examine the relationship between ankle proprioceptive acuity and responsiveness to biofeedback training. : Benefits of biofeedback training are modest with large variability. But, these benefits could potentially be enhanced if we can determine how to select the patients who respond best to a particular treatment. Better patient stratification will improve with a better understanding of the underlying mechanisms of biofeedback training, and this work will help develop knowledge of these mechanisms. Further, gaining this mechanistic understanding of how biofeedback works and the changes due to biofeedback will allow for developing more evidence-based biofeedback protocols. Validation of the IMU-based strategy would provide preliminary evidence on the feasibility of wearable sensor approaches. This would significantly enhance the accessibility and application of AGRF biofeedback training to other training environments. This work will provide basic scientific knowledge on the underlying changes to biomechanical and neurophysiological mechanisms due to biofeedback and provide novel insight into the design of wearable systems for novel biofeedback training. Such interdisciplinary work will provide unique research opportunities for undergraduate and graduate students. Further results will be disseminated at conference workshops, presented at research conferences, and published in peer-reviewed journals. Research participation will be broadening by working with underrepresented students through ongoing programs available at George Mason University, such as the Undergraduate Research Scholars Program, Summer Impact Program, and the Aspiring Scientists Summer Internship Program.

Despite decades of research into effective teaching practices, many STEM classes are taught using conventional transmission-based approaches. In order to transition to instructional methods that are proven to better support student populations from increasingly diverse backgrounds, there is a pressing need to understand how STEM instructors make decisions about course design. 1,2 In this study, I will use ethnographic methods to understand the system that drives faculty decision-making and prioritization, with a focus on how assessment of teaching influences implementation of research-based teaching practices. My training as a chemical engineer will enable me to serve as the lead instructor of a chemical engineering course and thereby conduct a true ethnographic study as a member of the studied instructor community, while also placing me in a position to act on the structural changes I propose. Through immersion in the course design process and through structured and informal interactions with faculty in the Department of Chemical and Biological Engineering at Tufts University, an R1 institution with a stated focus on undergraduate education, I will characterize the factors that influence how faculty make instructional and course design decisions. In particular, I will investigate the extent to which research-based pedagogical approaches are implemented. The long-term objective of this project is to use insights into instructional decision-making to rationally design approaches that will catalyze a shift in teaching practices in line with educational research. As an authentic ethnographic study conducted by an integrated member of the chemical engineering instructional community, my research will provide the first “inside look” into STEM faculty decision making in higher education. My positionality within the studied group will allow me to observe the ecosystem in which instructors operate and to provide a variety of data sources for the formation of a cohesive, contextualized characterization of the instructor activity system. Insights into the complex systemic interactions that impact course design and delivery will enable further research into policy and programmatic changes. Ultimately, the findings of this study will contribute to a shift in methods of assessing teaching effectiveness and an increase in incorporation of research-based teaching practices in STEM courses at research-focused institutions. To make progress towards serving underrepresented racial and gender minority students and students from under-resourced high schools, universities must overhaul their colonial cis-hetero-patriarchal course structures and instructional approaches. By identifying the factors that influence instructor decision-making, this work will mark a critical step towards the implementation of innovative research-based pedagogical practices that serve the diverse set of students whose backgrounds do not align with exclusionary expectations of more traditional lecture based instructional approaches. 11,12 By identifying practical structural changes to shift faculty instructional practices, the knowledge produced by this study will be foundational to future work on systemic change, both in my future research group as a faculty member and in the wider community.

The Center for the Enhancement of Engineering Diversity (CEED) was founded in 1992 to improve the recruitment and retention of underrepresented undergraduate engineering students (also known as an engineering student support center or ESSC [1]). As an organizational entity, CEED has undergone dynamic change over time, in response to educational systems. In 2019, CEED expanded to include graduate students and faculty/staff in addition to pre-college and undergraduate communities. The NSF, in their Science of Organizations funding opportunity [2] states that “Organizations …are critical to the well being of nations and their citizens. They are of crucial importance for producing goods and services, creating value, providing jobs, and achieving social goals.” The expansion of the CEED organization identified a critical need to examine how to improve the design, development, management and sustainability of this multifaceted engineering student/faculty/staff support organization. The goal is to enable development and enhancement of an ability to create a research portfolio suitable for an academic position; a portfolio that enables CEED to establish itself as a premier research to practice organization for broadening participation. This body of work will contribute to creating a research portfolio for an engineering diversity center that has been operational for 30+ years addressing systemic issues in engineering education. Insight into the far-reaching influence of this center will provide further insight for explorations in “expanding opportunities in STEM to people of all racial, ethnic, geographic, and socioeconomic backgrounds, sexual orientations, gender identities, and persons with disabilities” [4]. It will also contribute to the understanding of how organizations like CEED are designed, managed and grow, as well as the behavior of individuals within the organization and the relationships that exist among the system, the people and the organization itself. As attention to diversity increases nationwide, there are numerous organizations like CEED within academic environments that would benefit from understanding how to manage their growth, both in responsibilities and in communities served. However, limited research-based analysis has been done to understand this type of organization despite them having been a cornerstone for recruiting, retaining, and graduating engineers from historically underrepresented groups. By establishing a research portfolio within CEED, it will encourage knowledge management practices and demonstrate how an organization can adapt as vital knowledge centers in our changing ecosystem of engineering education. The results of this work will inform and educate on how to better position an organization for growth and sustainability. Furthermore, the development of a research portfolio will enable the continuation of successful efforts to broaden participation through CEED, allowing greater numbers of minoritized students and adults to benefit from CEED’s activities.

Environmental justice (EJ) is defined as the fair treatment and meaningful involvement of all people regardless of race, color, national origin, or income, with respect to the development, implementation, and enforcement of environmental laws, regulations, and policies (EPA, 2022). Communities of color are often marginalized, and disproportionality incur the effects of environmental issues such as polluted air quality, poor drinking water, and inequitable access to sustainable resources (Montoya et al., 2021). Too often people of color are excluded from fair treatment and meaningful change that directly impact their communities. This intentional neglect and bias have been referred to as discrimination and anti-blackness. To address these inequities, civil and environmental engineers should be instructed in environmental justice as part of their core learning when creating solutions to address overarching environmental problems (Martin, 2019). As environmental engineering has grown to include interdisciplinary research, the need to engage communities has emerged as a pressing issue. At the Civil and Environmental Engineering Department at the University of South Florida (USF), the local East Tampa community has been engaged in projects that address areas of the city that are being impacted by the presence of environmental injustice. The goal of this proposed research is to co-create and transform civil and environmental engineering curricula and workshops to combat anti-blackness and infuse environmental justice into core environmental engineering courses. This proposed research will transform engineering education and contribute to decreasing inequity in engineering and science spaces. Project assessments will generate quantitative and qualitative data that can be used for the development of education programs at colleges and universities. : Undergraduates and graduate students will be mentored by Drs. Michelle Henderson, Maya Trotz and other faculty included on the NSF grant. Underrepresented minority undergraduate and graduate students will be recruited and mentored to incorporate environmental justice into their research. I will also serve as a guest lecture for previously mentioned environmental engineering courses. In the event of limited face-to-face interactions, courses will be adapted for online and socially distanced learning.

Nucleic acids can revolutionize human health outcomes, as evidenced by mRNA lipid nanoparticle (LNP) vaccines used to combat COVID-19. Despite the success of these systems, their structural and dynamical material properties are not well understood, making them difficult to tune and optimize. LNPs require a delicate balance of properties to deliver nucleic acids: structural integrity to protect from degradation and/or exchange with biomolecules during storage and delivery, but also facile dissociation of components once inside the cell.1–3 Optimizing this tradeoff could make future advancements possible by allowing more flexible storage requirements, lower dosages, and increased therapeutic tolerances. Such optimization could be enabled by retrosynthesis, where the procedures needed to make new molecules or materials with target properties are identified using a synergistic combination of automation, characterization, and AI to reverse the usual process of synthesis. As an eFellows postdoctoral fellow, I will develop a retrosynthesis process for LNPs with Prof. Lilo Pozzo at University of Washington using materials focused characterization. Specifically, I will develop a high-throughput method to create and characterize LNPs with programmable conditions. Then, I will use an engineering framework of experimental design and machine learning (ML) to target a set of LNP properties. Finally, I will study component exchange kinetics to assess LNP stability. : Our proposed research advances the mission of NSF’s engineering directives through an integration of engineering and life sciences by leveraging techniques which have advanced polymer and colloidal physics with machine learning (ML)-informed experiments for a biomedical application. The research will enable rapid mRNA therapeutic design by incorporating high-throughput and time-resolved experiments in the creation of LNPs. The culmination of our proposed work will translate materials characterization tools and an engineering framework to the drug delivery community, providing an important perspective on a system frequently envisioned more statically. Our methods will serve as a platform to quickly adapt LNP processing when, for example, a targeting ligand is introduced to the formulation or new in vivo results indicate enhanced efficacy for a differently sized nanoparticle. Additionally, the CV-SANS results will lead to a mechanistic understanding of how molecular exchange could occur in LNP processing, degradation, and endosomal escape. Current times have limited access to neutron beams, though Prof. Pozzo has experience using international facilities that can be leveraged if necessary. The high-throughput system we create will be used in further future work to do a full mapping of structure-to-function correlations, leveraging the same infrastructure with new AI interface. In conclusion, the proposed research will benefit society by improving LNP therapeutics and demonstrating the importance of accurate materials.

Today systems industries face significant challenges to bring products to market. Companies struggle to integrate into complex designs a large number of components designed by various manufacturers. Integrators report that most system failures arise from unexpected interactions among components. Thus, designing components to meet clear interface specifications would go a long way towards simplifying system integration. Unfortunately, system specifications are often extensive, ambiguous, and informal, hindering the analysis of their completeness and consistency. As a result, system engineers regularly rank the handling of specifications for the lifetime of the product as one of the top challenges in their field. In this project, we will develop algorithms and software that leverage the theory of contracts to support the rigorous, correct-by construction design of complex systems. Using specifications of components, our software will help designers to verify whether a given decomposition of a system into subsystems will meet its objectives. When this is not the case, the tool will help designers to characterize the specification of a subsystem that needs to be added to the design in order to meet the system’s objectives. The algebraic aspects of contracts have been developed with vigor over the last several years. Today we know, among others, operations to compose system specifications, find missing specifications to complete a design [MEMOCODE18], and merge specifications of multiple aspects of a design [TECS19]. These and other operations [UCB22] can be used to address wellunderstood system design and analysis tasks. To advance the science of rigorous system design and streamline the development of complex systems, we will provide software support for contracts. This project will require research of efficient algorithmic manipulations of formal specifications expressed in various formal languages. We will apply the methodology of contracts to design problems in two domains based on compositional design: autonomous systems and synthetic biology. To do this, we will collaborate with domain experts in the research collaborations of Murray’s lab. A successful application of contracts in these fields will allow us to advance formal system design and to identify classes of specifications which capture the requirements expressed in autonomous systems and in synthetic biology. Given (i) that contracts were devised to aid systems industries to streamline the integration process and (ii) that we seek to provide engineers and scientists with a tool to implement a compositional specification approach to system design, it is important for us to develop this software in close contact with developers of complex systems. The existing collaboration between Murray’s group and NASA JPL will allow us to obtain feedback and case studies from JPL engineers working on complex space-exploration systems. We will teach contract-based design in Caltech’s CS142 (distributed systems). The software support for contracts developed in this project will be used to generate homework and project ideas for the students in the class. We have links with aerospace and automotive companies who are interested in contract-based design. In addition to our interaction with JPL, we will contact these systems companies to receive feedback and to facilitate the adoption of our tool by engineers in industry.

In recent decades, climate change has increased the frequency and intensity of natural disasters, such as hurricanes and flooding, which enhance the chance of release from Superfund sites, the most contaminated hazardous waste sites in the US. Federal data indicates that nearly 60% of Superfund sites in the US are located in areas that will be directly affected by natural hazards exacerbated by climate change [1]. However, there is limited understanding of the effects of climate-change-induced perturbations, including sea-level rise (SLR) and corresponding saltwater flooding, on the mobilization of contaminants from these sites. This proposed work aims to bridge this knowledge gap through the synthesis of novel experimental analysis and machine learning approaches. To that end, I will join an interdisciplinary team of researchers at the University of Texas at Dallas to fulfill the following primary research objectives: Objective One ‒ Understanding the impacts of SLR-induced flooding on contaminants and Objective Two ‒ Developing a model to predict contaminant fate during flooding. By combining unique experimental data and machine learning approaches, we will provide insight into the interactions of physical (i.e., shear stress) and chemical (i.e., saltwater intrusion due to saltwater flooding) processes acting during climate-change-induced perturbations. The impacts of these interactions on the fate of pollutants will thus be evaluated in detail for the first time. The experimental data produced in this research will also be leveraged to develop predictive models using machine learning techniques to determine the influences of climate change on the cycling of contaminants on larger scales to help inform lawmakers. The findings of the proposed research project will facilitate further understanding of the mechanisms by which contaminants behave in dynamic contaminated environments. This research will, therefore, provide insights into how pollution can be managed under projected climate changes. The outcomes of the research will not only benefit the scientific community but will also be beneficial at public and policy levels because the employed methodology applies to various pollutants. These research results can be used by environmental divisions of national and local agencies, such as the Department of Energy (DOE), the US Environmental Protection Agency (USEPA), and the US Geological Survey (USGS), to assist in the development of effective coastal pollution management strategies. Furthermore, the model proposed in this research will improve the ability to predict and mitigate the adverse impacts of climate change on pollution fate on a larger scale. Most flooded Superfund sites (in Texas and around the US) are in lower-income neighborhoods and communities of color [4]. The results of this research will, therefore, reflect on the potential long- and short-term concomitant risks of rising seas for these communities.

Falls are a common occurrence in older adults and individuals with neuromuscular pathologies. In fact, one in three older adults fall each year. 1. These falls can lead to debilitating and life-threatening injuries, and are financially burdensome, costing the United States health care system approximately $50 billion annually. 2. While robotic exoskeletons have the potential to improve balance, nearly all exoskeletons are designed and controlled to restore steady-state walking rather than prevent falls. This is despite serious demand from stakeholders who consider fall prevention the most important design goal not yet addressed 3. My postdoctoral research aims to develop and test robotic exoskeletons to prevent falls during both standing and walking. Recently, a few exoskeleton controllers have been developed to improve balance in response to perturbations 4,5. However, there are limitations to current controllers. Exoskeleton controllers have only been validated against a single type of perturbation during a specific phase of the gait cycle – even though loss of balance can be induced randomly during standing and walking and by different perturbation events. My central hypothesis is that a multi-joint exoskeleton driven by a machine-learning enabled, physiologically inspired task level controller can robustly stabilize the body in response to postural perturbations during standing and walking. This work will improve our understanding of balance control strategies in healthy individuals and establish principles of human-robot interaction in the context of balance—a new direction in the field. Applying machine learning to rapidly estimate salient physiological states establishes a novel, generalizable method for exoskeleton control based on physiological principles, which can be applied to other non-steady-state behaviors. I will also make data sets and algorithms from each publication available on GitHub. Ultimately, this work will inform strategies for the development of exoskeletons aimed at improving balance and reducing the risk of falling in older adults and individuals with neuromuscular pathologies. Development of physiological-inspired control schemes that leverage task-level control—one-to-many rather than one-to-one mapping to individual joint-levels—could also be applied to simplify control of lower-limb prostheses and autonomous bipedal robots.

Rare and low abundance taxa have been shown to have keystone roles in large diverse microbial communities. 1. We are on a hunt to identify rare, low abundance taxa which play an important role in maintaining the microbial diversity and function in complex biologically engineered systems such as wastewater treatment facilities. 2. Unfortunately, current techniques such as 16S rRNA gene amplicon sequencing and metagenomic sequencing are more of a lottery draw with a bias in overemphasizing the most prolific and abundant organisms. We are left with the challenge to understand the metagenomic functionalities of the organisms which are literally one in a million (or less) draw of luck to potentially reconstruct a genome to understand a taxa’s functional potential. This research will focus on encapsulating niche biologically active organisms in hydrogel beads to proliferate while protected in-situ which will significantly increase the odds of identifying and sequencing rare taxa and their functional role in the wastewater community. Our unique strategy to enrich rare taxa focuses on using BONCAT (bio-orthogonal non-canonical amino acid tagging) to distinguish metabolically active microbial populations at different redox cycles during wastewater treatment operations. These metabolically active populations can be further separated based on microbe size using FACS (fluorescence activated cell sorting) at the Cellular Analysis and Cytometry Core hosted at Georgia Tech. Using BONCAT + FACS will narrow down the search with groups of metabolically active microbial populations and enriching these populations in the safety of encapsulated hydrogel beads will promote cellular growth in-situ for higher quantities of these niche taxa. This proposal highlights the best aspects of our expertise(s) in unique ways to uncover low abundance active organisms in activated sludge. These low abundance taxa are crucial to understanding how numerous micropollutants are efficiently broken down during the short hour-long timescales to treat domestic wastewater and will help reveal the rare taxa which have been hypothesized for years without detection.

Due to an increased number of polluted water and wastewater systems within the United States, more innovative and feasible solutions must be explored to address contamination due to urban runoff, agricultural waste, and wastewater. Water quality is of paramount importance nationally and internationally, making it a priority for both science and engineering fields alike. This research aims to investigate environmentally sustainable water remediation technique using a controlled release chemical oxidation polymer system (US Patent No.: US 8,519,061 B2) to remediate aqueous and soil environments. In conjunction with the oxidants, the biodegradable polymer effectively reduces bacteria concentrations in aqueous systems and improves color and odor for extended periods. y. This research is expected to contribute to the understanding of the controlled release of oxidants, improve environmental remediation technologies, reduce environmental, health, and safety risks, and minimize human health threats. The specific objectives of this research include (1) analyzing the characteristics and behaviors of the polymer system, and (2) evaluating its use as a novel environmental engineering remediation application. The tasks will be accomplished through laboratory experimentation, data collection, and data analysis. Furthermore, a modeling component will be investigated to test the application at a larger scale. The intellectual merit of this research includes an innovative approach and examination of the controlled release of the established chemical oxidant polymer for the treatment of various water systems. Although some research has investigated the use of controlled release for drug delivery in the field of medicine, there is a dearth of information for use in the soil and water remediation. This research of a potentially transformative environmentally sustainable water remediation technique would benefit society and provide solutions for environmental challenges of the future. The broader impact of the proposed research aims to explore solutions of environmental challenges of increased risks to human health and society in mind. The research will be conducted at a leading Historically Black College & University (HBCU), North Carolina A&T State University, and run by a postdoctoral fellow, mentor, and faculty partnership from groups traditionally underrepresented in the engineering research field. With the help of my mentor and the department, I also plan to create summer outreach programs such as a 2-week summer camp for middle to high school girls in STEM to empower and expose them to engineering based solutions for the environmental remediation. The camp will provide hands-on experiences, learning items, and mentoring activities, which will inspire participants to pursue careers in STEM fields. This research also aims to conduct seminar/workshop presentations at least twice a year to water industry professionals such as Tetra Tech, Arcadis, RTI International, and other industry-leading companies in local area. I will present my research outcome in national conferences and publish in highly-ranked peer-reviewed journals.

Surface fouling of marine vessels that are exposed to sea water represents a costly problem for military, commercial (sea instruments, hulls, internal seawater system, keels of ships, residential boats), and naval submarines. The accumulation of microorganisms, plants, algae, and barnacles leads to surface biofouling and corrosion, as well as increased drag as the vessel moves through water, resulting in higher fuel consumption. To overcome this problem, ship hulls are often painted with foul release (FR) or anti-foul (AF) coatings. Traditionally, these paints contain copper or other biocides that pollute marine environment and kill marine organisms. However, environmental concerns have caused these paints to be banned. The bio-inspired and foul release concept behind this innovation is environmentally friendly and anchors on improved surface fouling-release mechanism. : The innovation proposes the research and development of an enhanced hybrid polymer antifouling and fouling release coating formulation matrix for sea instruments, hulls, and keels of ships. The critical factors influencing adhesion and fouling-resistance in antifouling coatings are surface energy, hydrophobicity, bonding energy, surface roughness and elastic modulus these properties will be studied and understood. This project will synthesize and characterize plurality of polymers and specifically fumed silica and hydrogel-based polyethylene glycol, polyurethane diacrylate polymer matrix for enhanced antifouling and anti-corrosion properties. Models will be developed in the laboratory to probe these factors implementing computational and experimental techniques. One of the broader societal value and impact of this innovation is environmental sustainability, through elimination of copper load, in the formulation to prevent the destruction of marine life and human poisoning, and the resistance of bioaccumulation formation on subsea instruments and hulls of ships reducing fuel consumption. The superior coating formulation will reduce the cost of maintenance down time of ships all over the world and reduce the current fuel cost in boats and ships. I anticipate developing educational materials and initially involving graduate, undergraduate and high school students during the research. The economic benefits for the proposed use of the technology will generate direct job creation to the US economy and contribute to GDP growth.

Systems thinking has been defined as “synergistic analytic skills used to improve the capability of identifying and understanding systems, predicting their behaviors, and devising modifications to them in order to produce desired effects”. Research has shown a general deficiency in systems thinking capabilities among undergraduate engineering students. There is a need to improve engineering students’ systems thinking abilities to prepare them for their careers. Product teardown as an active learning strategy may improve students’ systems thinking capabilities based on the learning theory known as constructivism. The objective of this proposed research is to investigate the effects of product teardown activities on undergraduate engineering students’ mental models of systems and systems thinking abilities. This proposal details a two-year plan for developing the necessary research materials, implementing the learning activities in undergraduate classrooms, measuring changes in systems thinking abilities, and exploring the broader application of the results and their implications. By exposing students to an activity built on constructivism, this work aims to improve the students’ systems thinking skills, which is necessary for success in industry. The following 3 hypotheses are considered: Hypothesis 1: A product teardown activity will improve the completeness of students’ mental model representations for the same given system. Hypothesis 2: A product teardown activity will improve the completeness of undergraduate engineering students’ mental model representation for other systems that share analogous functionality to the given system. Hypothesis 3: Mental model representations of engineering systems by 4th -year undergraduate engineering students will be more complete than 1st -year undergraduate engineering students before and after the product teardown activity.

An anomaly-based intrusion detection system (IDS) is an effective mechanism for detecting cyberattacks such as the denial of service (DDoS) attack, but the detection performance depends on a useful dataset used for training. There are synthetic or simulation-based datasets available with various limitations. To overcome the limitations of existing datasets, a realistic IDS dataset will be developed for various use cases using Spirent’s CyberFlood-CF20 emulator and full network configurations. Deliverable of the Project (Team of two Post Docs) 1. dataset: Using CyberFlood in a full network configuration, realistic benign and attack traffic will be emulated and captured considering various attack/use cases, including malware attacks 2. Feature extraction algorithm: Develop an algorithm to extract features from raw data 3. IDS dataset: Create a labeled dataset 4. Develop/compare machine-learning algorithms to detect anomalies behavior and identify/predict attacks ( using Spirent Systems ) 5. Prepare a research proposal to be submitted to NSF/DOD.

Ordinary portland cement (OPC) concrete is the second most widely consumed material on Earth, and it contributes to approximately 8% of CO2 emissions worldwide. Approximately 70% of these emissions come from the calcination of limestone, so searching for alternative raw materials is of the utmost importance. I propose utilizing alternative materials that mimic both ancient Roman concrete and geophysical cementation processes to replace limestone as a feedstock for cementitious binders. Drawing inspiration from the natural cementation occurring in volcanic fields and the durable and longstanding Roman marine concrete (RMC), I will synthesize new near-zero carbon binders. Furthermore, I will assess properties such as strength, serviceability, and durability to produce a material comparable in performance to existing cements but with a reduced carbon footprint. The proposed work has been divided into 3 major tasks, each with a set of sub-objectives. Task 1 is the nano- and micro-material characterization of RMC and naturally cemented volcanic ashes utilizing characterization techniques such as microcomputed tomography (microCT) and scanning electron microscopy (SEM). Task 2 is to design a new cementitious feedstock and to develop a feasible synthesis process. Task 3 is the upscaling of synthesis techniques to produce novel mortars to test physico-mechanical properties. In this work I will leverage the characterization capabilities of three different laboratories at Stanford in addition to multiple shared facilities (e.g., SLAC Linear Accelerator Laboratory) and a potential collaboration with Pennsylvania State University to explore novel cementitious binder chemistries originating from distinct, widely available materials such as kaolinite. I will focus on processing techniques and their affect on the nano- and micro- structures of these binders and the resulting macro-scale properties, thus creating a feedback loop for the production of optimal chemistries. This work leverages the collective expertise of a multidisciplinary team to produce a novel carbon neutral material. This research has the potential to be globally transformative, helping society achieve their long-term CO2 reduction goals. The work focuses on directly targeting carbon emissions from the upstream, while additionally providing a mechanism for carbon uptake. This work impacts the education and training of students at all levels by creating a broader, inclusive, and interdisciplinary scientific context where history meets geophysics, chemical engineering, and traditional civil engineering to create a nature-inspired material. Communicating this work to the general public, industry partners, and the scientific community will illuminate the relationships between the humanities and science.

This project will develop a simulation in eXtended Reality (XR) of a technical interview. XR is an umbrella term that includes virtual reality, augmented reality, and mixed reality. This tool will be able to provide students with standardized practice technical interviews without the need for human technical interviewers, thus allowing non-biased technical interviews at scale. After development, a series of experiments will be carried out to answer the following research questions: a) “Will XRPTI reduce anxiety during technical interviews for engineering students?”, b) “Does XRPTI engagement change the students’ perception of their technical abilities?”, and c) “How can data generated by XRPTI enhance the instruction and support of engineering departments?”. It is hypothesized that XRPTI will a) reduce anxiety for students in technical interviews, b) increase awareness of technical weaknesses unbeknownst to the students, and c) increase student support provided by engineering departments. The goal of this research is to develop an evaluate a technology that can enhance how professors evaluate engineering curriculum and how students prepare for technical interviews. The objectives of this research project is to a) build a corpus of mock interviews for which to use for the ML algorithm, b) develop a working prototype of the application, c) test the usage of the application with a sample of undergraduate engineering students, d) collect data from the student participants on their experience, and e) collect data from engineering faculty on how their thoughts of the XRPTI application. Due to COVID-19 most of the mentoring, data collection and application development activities for the postdoc will be virtual whenever possible.

The coordinated contraction of cardiomyocytes (cardiac muscle cells) drives the action of the heart to pump blood throughout the body. When injured, cardiac tissue has a limited capacity for self-repair, mainly due to the non-proliferative nature of adult cardiomyocytes. Thus, interventions are needed to assist the heart in repair after injury, such as with a myocardial infarction (MI) (heart attack). Due to the massive cell death after MI, the heart compensates for lost function with a process of structural remodeling, which includes inflammation and ultimately the deposition of extracellular matrix and formation of a scar (1). Beyond the rapid restoration of blood supply to the heart, there are few approaches to address this cascade of deleterious remodeling that can result in fatal heart failure, and exploratory therapeutics such as cell delivery have been unsuccessful in clinical trials. I propose the use of engineered biomaterials to repair the heart post-MI, an approach that can leverage the intricate engineering of biomaterial properties such as injectability, porosity, and drug delivery to guide endogenous repair. Specifically, I will engineer new injectable granular hydrogels to provide biological cues after MI, to 1) limit the damage to the infarcted area and 2) promote vascularization and repair to guide function. To achieve this goal, I will fabricate granular hydrogels from anisotropic hyaluronic acid (HA) particles loaded with stromal cellderived factor-1 (SDF-1) for delivery to the heart (Fig. 1). I will complete this work as a postdoc in the Burdick Laboratory, to leverage their extensive expertise in biomaterials engineering for tissue repair, including in the heart. crease vascularization and cardiac function over granular hydrogels alone. The innovation in the project is the use of biomaterials engineering to increase pore anisotropy and chemokine delivery for rapid cell invasion, to mitigate adverse ventricular remodeling and improve overall cardiac function after MI. Upon completion of the work, the importance of structure on cellularization of injectable biomaterials in the heart will be understood, as well as factors to rapidly recruit cells to the heart. There is limited understanding of how different particle aspect ratios affect packing, injectability, cell invasion, and tissue reconstruction and this project will fill this knowledge gap. A successful outcome of this work will be the first steps towards new treatments for patients after MI. Further, the knowledge gained using this approach could extend to other biomedical applications such as the treatment of volumetric muscle loss. Additionally, this work will provide opportunities for not only the training of myself in new techniques towards my independent career, but also opportunities to train junior students in research and to conduct outreach to underrepresented groups in STEM disciplines.

Shared Autonomous Vehicles (SAVs) are on the horizon, and they would have the potential to make a revolution in the negative environmental effects of transportation by decreasing traffic congestion dramatically. Although there is no doubt that we will see them on the roads in the future, it is unknown when exactly they will launch in the market. Also, still, it is not crystal clear what would be their Market Penetration Rate (MPR) will be in the next decades. Although there have been some studies that investigated the MPR of AVs, there is a gap in the literature for a reliable and comprehensive MPR model for SAVs. For instance, Litman (1) predicted the market penetration of AVs based on the general fleet market and also the previous vehicle-related technologies adoption procedure, i.e., automatic transmission, airbags, hybrid vehicles, and vehicle navigations systems. The study found  that a technology such as automatic transmission needed 50 years to be affordable and reliable, and still, after almost a century from the first time it was invented, it could only reach a market penetration of 5modelm0% in Europe and Asia. The main goal of this study is to investigate different aspects of the deployment of the SAVs. To reach this goal, the following objectives will be undertaken: • Launch a Stated Preference (SP) survey to collect the public and the specialist ideas about the SAV • Develop a mathematical predictive model that can predict the MPR of the SAVs during the next decades. This project’s output will provide a systematic approach to understanding the potential implications of SAV technologies and systems on travel behavior. Additionally, the results of the SP survey data analysis provide important inputs to policy analysis on the impact of SAV systems and guide planning.

Climate change is already costing taxpayers dearly. According to a recent Government Accountability Office report, the federal government has spent $350 billion in the last ten years responding to climate-related disasters, with costs expected to reach $35 billion annually by 2050. Climate change will continue to cause more intense heat waves, droughts, and wildfires; increasingly severe storms, flooding, and hurricanes; and more severe winter storms that threaten every region of the country. There are several regions in the US that suffer from severe weather conditions. The Buffalo-Niagara area is one such region, with more than 30 such winter events annually. Road accidents in Erie County, New York (NY), increase by 25%–50% during the winter months. According to the city of New York, hurricane Sandy resulted in 19 billion dollars in losses. It is projected that similar hurricanes, due to the rising sea levels and ocean, will cause 90 billion dollars in losses to this city in 2050. This indicates the importance of transportation network performance during disasters and recovery activities after disasters. The severe winter weather has also had negative impacts on social equity. According to the Federal Emergency Management Agency, severe winter weather caused disaster declarations in counties with median incomes that were 3.58 percent lower than the national average. While upstate NY is accustomed to this weather in the winter, due to ongoing climate change, more and more regions in the U.S. have started to regularly experience different winter events with much less experience and facility to confront them. Connected technologies have rapidly evolved in recent decades and are a key component in facilitating winter road maintenance through utilizing big data technology to enable the collection, fusing, and analysis of weather and road condition information in a timely manner. The outcomes of such actions can be used by the system operator to allocate available resources (e.g., plow trucks) for road maintenance in a more efficient way. Hence, I propose an information framework that specifies the process of collection, fusion, and processing of real-time information during extreme weather events to obtain a better understanding of the road safety conditions that facilitate road maintenance. Finally, the road condition information is used to develop the optimal road work schedule with the goal of promoting equity (using accessibility as a surrogate measure). The successful implementation of this project will contribute to the theoretical framework development that allows us to incorporate data from the information center to understand and predict road safety during snow events; suggest optimal resource allocation using equity as a major performance measure; and design of information delivery systems for relevant decision-makers. The output will include prediction and optimization models and specification of the necessary software components. The end product of this research effort can assist urban planners and local and regional transportation engineers to develop new models, policies, regulations, and, more importantly, information-based platforms for severe weather events and road maintenance procedures. The expected contributions of the follow-up large-scale implementation of the proposed framework across the regions with a large number of severe weather conditions are (i) increased efficiency of resource usage; (ii) developing and incorporating equity measures into the transportation operation decision process; and (iii) increased road safety during severe weather events. The broader impacts of the project include: (i) Improve winter road maintenance and safety across the country: the proposed framework and its implementation in the pilot area will serve as an example of community-based road data collection using connected technology to monitor and fine-tune road winter operations and increase road safety, (ii) operational research advances: the project implementation will provide a unique case study that can be used by the practitioners, who implement a similar framework to design the most efficient operational procedures for road maintenance; and by the operational researchers, who are exploring optimization problems with dynamic traffic information and dynamic resource allocation during evacuation.

This research project explores how experiential learning opportunities for doctoral students shape their professional identity and motivation to imbed social justice in their scholarly work. While Black doctoral recipients are pursuing career pathways in academic settings upon graduation (NSF, 2019), they are under-represented in non-academic sectors (McGee et al., 2020) and seek more professional development opportunities within their doctoral experiences (Sowell et al., 2015). This project will focus on developing and executing an internship program for doctoral students in engineering education. The importance of experiential learning opportunities has been highlighted at the undergraduate level for engineering students, but there is an opportunity to expand on its significance at the graduate level. The development of the internship program will be guided by the elements of Kolb’s Experiential Learning Cycle Model (2005) and Esteban-Guitart & Moll’s Funds of Identity concept (2014). The Graduate Student Explorations of Non-academic and Inclusive Experiences (GENIE) program seeks to build on the success of an NSF INTERN experience that supported the first Journal of Engineering Education (JEE) Diversity, Equity and Inclusion (DEI) graduate intern. GENIE internships would establish a sustainable and accessible internship program for engineering education doctoral students through professional societies such as the American Society for Engineering Education (ASEE), the National Society for Black Engineers, and the Society for Hispanic Engineers (SHPE). GENIE internships would provide graduate students in engineering education with mentored early career experiences with the premier professional organization in their field, placing their voices in the center of engineering education professional initiatives and programs. A cohort of GENIE interns would have the added benefit of communal experiential learning and reflexivity that will help break down the notion of insider knowledge in the field through shared knowledge (Secules, 2020). GENIE internships will give engineering education graduate students opportunities to strengthen their identity and professional formation as engineering education scholars. These internships will raise graduate students’ awareness of the field beyond what is possible through doctoral education alone, particularly for those who are new to the field. In addition, bringing graduate students’ expertise to their professional society will strengthen the innovation, diversity, and professional development that the society embodies and provides. Strengthening graduate students’ professional affiliation will increase the likelihood of completing their doctoral programs (Ross et al., 2021) and shape their career trajectories, which is particularly relevant for students from marginalized populations (NSF, 2019). Providing engineering education graduate students with professional mentors who are not in academic positions will facilitate exploring non-academic career paths.

Modeling and simulation of the flow past fast-moving objects is vital for many important applications from space travel to military applications. Of particular interest within this context is the accurate characterization and reproduction of hypersonic flow in a laboratory environment. This task is challenging because of the difficulty in replicating a myriad of physico-chemical changes that occur simultaneously and at different time and length scales within a typical hypersonic flight. The complexity of this event is further compounded with the generation of significantly high temperatures, which impacts the interaction of the fluid and the object structure (fluid structure interaction or FSI). Consequently, the modeling of hypersonic flows is still in development, with active research involving many theories across many subject areas. Subsequently, the simulation of high temperature, high stress hypersonic flows is yet to be fully realized. Previous simulation attempts in available literature have been mildly successful in accurately reproducing field data due to a lack of complete physico-chemical information and intensive computation required [1]. The study of hypersonic flows in ground testing facilities have also been reported to be marred with a shortage of capabilities and other important limitations [2]. Nevertheless, significant progress has been made in advancing the understanding, modeling and simulation of hypersonic flows since its inception [3]. For example, the effect of Shock-Shock Interaction (SSI) between a launcher and its boosters moving at Mach 5 speed, and 0-degree angle of attack is provided in Fig. 1. This study seeks to investigate the modeling and simulation of such high temperature, high stress hypersonic flows using advanced techniques in computational fluid dynamics (CFD) for the purpose of advancing the science. Specifically, in this study, the CFD simulations of a high temperature hypersonic flow (for a prescribed geometry) will be conducted using grid refinement and modified codes in commercially available software packages including Ansys, STAR-CCM+, OpenFOAM, and MATLAB (for optimization study). This study will be fundamental in nature in that it will be reviewing (and improving, where necessary) available basic theories of hypersonic CFD and all current methods of numerically analyzing such event. The focus of the study will be on improving the predictive capabilities and computational efficiency of hypersonic CFD models for extreme conditions exhibiting phenomena such as turbulence, boundary layer transitions, high temperature, shock/boundary layer interactions, ablation, and structural interactions. The result of this study is expected to accurately represent the physics of a fast-moving object (at hypersonic speed) with computationally efficient models.

This research proposal is aimed towards selective chemical vapor etching (CVE) of material in a vacuum environment. Selective CVE is a new field in etching that should provide selective removal of materials for building 3D devices with improved performance. This research aims to expand on three different areas of selective CVE within the current work in this lab: 1) Wet etching through the formation of H2O multilayers in vacuum, 2) the effects of ALD overlayers towards increasing SiO2 etch rates using H2O and HF, and 3) selective etch of SiNx relative to SiO2 using HF-Pyridine. 1) Wet Etching Through the Formation of H2O Multilayers in Vacuum This project proposes a hybrid approach between solution-based and vacuum-based etching techniques. At high pressure and low temperatures, H2O multilayers are able to adsorb onto SiO2 surface.1 The H2O multilayer creates a solvent for HF, however the characterization of the multilayers is necessary before performing further experiments. The multilayer thickness as a result of the pressure and temperature conditions will be studied in order to create working conditions for using wet layers in the vacuum environment. Subsequent experiments will focus on SiO2 etch using HF and water multilayers and eventually the study will study the water multilayer with other reactants for etching. 2) The Effects of ALD Overlayers Towards Increasing SiO2 Etch Rates Using H2O and HF Studies have reported an increased spontaneous etch rates of SiO2 using H2O and HF. The adsorbed material is said to promote increased concentrations of H2O at the surface necessary for the formation of HF2 – via the reaction 3H2O + 6HF → 3HF2 – + 3H3O + . 2 The current data shows that Al2O3 ALD overlayers on SiO2 increases etch rates. However, the current pathway is unclear, and necessitates experiments to elucidate mechanisms involved with regards to increased etch rates in presence of the Al2O3 overlayer. The effects of temperature and pressure of the H2O and HF constituents on SiO2 etch rates in the presence of adsorbed Al2O3 will be monitored. The possible enhancement from different overlayers will also be examined. 3) Selective Etch of SiNx Relative to SiO2 Using HF-Pyridine Current experiments in this lab have shown selective etch towards SiNx using HFpyridine. In aqueous media, HF produces HF2 – which is selective towards SiO2 etch. In nonaqueous, or non-proton-accepting media, HF does not dissociate into HF2 – . Absent of this ionic species, etching is favorable towards SiNx. 3,4 The work will focus on characterization of the HF multilayers and the use of other non-aqueous, non-proton-accepting media as multilayers for enhanced etching selectivity.