University Of Tennessee Knoxville

NSF Awards · FY 2026 · 2026-10

Scientific artificial intelligence systems increasingly depend on datasets collected, cleaned, and transformed across multiple institutions. The integrity, provenance, and authenticity of these datasets determine whether scientific results are valid and reproducible. Current approaches such as logs, checksums, and anomaly detection provide useful evidence, but they do not guarantee that data transformations preserve the scientific properties required by downstream analyses. The CERTIFY project creates a framework for verifiable integrity in artificial-intelligence-ready scientific data pipelines. The project enables scientific data objects to carry machine-checkable proofs that specified integrity and provenance requirements are satisfied. It also uses temporal contracts to describe required properties of data curation, cleaning, and transformation steps, and compiles those contracts into lightweight operating-system-level monitors that prevent buggy or compromised code from violating declared policies. Large language model-based agents help configure and manage these components, reducing the expertise required to apply formal verification methods in scientific workflows. The project is evaluated through two science drivers: agricultural sensor networks used to study livestock health and crop yield, and electronic health records that require strong provenance and privacy safeguards. The project produces open-source software, documented example datasets, course modules, and training materials that help prepare researchers and students to secure AI-driven scientific workflows. This structure supports reproducible discovery while making integrity checks practical for collaborative, cross-institutional scientific teams. The results strengthen trust in data-intensive science by helping researchers verify where data came from, how it changed, and whether it remains suitable for scientific use. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2026 · 2026-09

Scientific research today generates data at a scale and pace that far exceeds what most research institutions can manage on their own. Petabytes of measurements from telescopes, particle accelerators, weather sensors, and medical imaging systems sit in disconnected storage systems across the country, out of reach of many scientists who could use them to drive scientific discoveries and AI innovation. Universities and colleges with limited computing resources are seldom able to participate in large-scale, data-driven research, limiting who contributes to scientific progress, workforce training, and AI readiness. This project operationalizes the National Science Data Fabric (NSDF), a national data infrastructure service funded through the NSF Integrated Data and Systems Sciences (IDSS) program, that connects researchers at institutions of all sizes to scientific data wherever it resides at national laboratories, experimental facilities, cloud platforms, leadership-class computing centers, campus clusters, or laboratory instruments while reducing the need for costly and time-consuming data movement. By removing infrastructure gaps that limit participation in national-scale research, NSDF advances the NSF's mission to promote the progress of science and advance national health, prosperity, and welfare. The project also builds the next generation of data and AI-capable scientists through a national Fellows Program, summer training institutes, and hands-on workshops open to students and early-career researchers at universities and colleges across the country. This Category II IDSS project transitions the successful NSDF pilot (NSF Award #2138811) to a full production-grade national cyberinfrastructure and service. The core technical approach replaces the costly data-to-compute model with a federated architecture that connects computation directly to data across varied environments through standardized NSDF Entry Points deployed at campuses, national laboratories, computing centers, and cloud systems. Entry Points standardize identity management, data ingestion, metadata capture, and workflow integration across independent sites while preserving local data governance and access policies. Five integrated technical innovations drive the system: interoperable federation through open APIs, Findable, Accessible, Interoperable, and Reusable (FAIR)-aligned schemas, persistent identifiers, and federated identity services (CILogon, InCommon); AI-ready workflows and standardized AI benchmarking through MLCommons/MLPerf-integrated pipelines and containerized execution environments; automated data quality, provenance, and lineage tracking to support reproducible and verifiable AI outputs; a national user support system built on a structured five-phase user lifecycle model, a dedicated helpdesk, and AI-assisted documentation services; and continuous community co-design through domain liaisons, advisory boards, and an annual All-Hands Meeting. The NSDF-Catalog indexes datasets across scientific repositories using FAIR Digital Object standards and Croissant metadata schemas, facilitating the creation of data cohorts for AI training. As the federation grows, the project deepens integration with the National AI Research Resource (NAIRR) ecosystem, and advances long-term sustainability through consortial, academic, and industry partnership models. The project is led by the University of Tennessee Knoxville in partnership with the University of Utah, Purdue University, the Texas Advanced Computing Center (TACC), and MLCommons, with collaborating institutions across academia, national laboratories, and industry. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2026 · 2026-08

A central goal of biology is to understand how and why diverse structures evolve. This highlights a long-standing question: do new forms arise mainly because natural selection directly favors them, or because they emerge indirectly as a consequence of how organisms grow and develop? Tackling these questions requires knowledge of how new forms originate and their fitness implications. This project uses ferns, an ecologically important and evolutionarily persistent group of plants, to address this fundamental problem by exploring the evolution of vascular architecture (the set of tubes that move water and nutrients). Ferns are a powerful system for this work because they have evolved some of the most diverse vascular systems on Earth. The project tests two leading, and potentially complementary, explanations for this diversity: that vascular patterning evolved as a direct adaptation to drought (the Drought-Driven Hypothesis) versus that it reflects developmental linkage with changes in overall plant growth (the Ontogenetic Hypothesis). By integrating physiology, development, and large-scale evolutionary analyses, the project will clarify the unresolved problem of how developmental mechanisms generate new anatomical variation and how selection acts on this new form. Understanding these mechanisms advances fundamental knowledge about how plant form evolves, while also improving the scientific basis for anticipating how plants may respond to drought - fundamental to agricultural productivity. Moreover, this work can help influence broader processes from agricultural crop breeding to artificial intelligence, both of which operate under evolutionary frameworks. The project also strengthens public engagement with science through widely disseminated educational videos, which transparently document the research process and its findings. This will occur by creating and sharing short, engaging videos through Let’s Botanize, an educational organization focused on plant science communication. By making complex botanical science accessible and story-driven, this outreach will spark curiosity and connect broader audiences with how research unfolds. This project is organized into three integrated aims. Aim 1 tests the Drought-Driven Hypothesis by quantifying whole-plant vulnerability to drought-induced embolism across leaves, rhizomes, and roots. Anatomical traits will be quantified for each species, linking hydraulic function to vascular construction across three scales: architectural (vascular bundle arrangement), histological (tissue organization and connectivity), and cellular (conduit size and wall traits). Aim 2 tests the Ontogenetic Hypothesis by examining whether variation in stelar morphology covaries with whole-plant developmental traits (e.g., rhizome diameter, internode length, phyllotaxy, leaf area) across a broad comparative dataset (>300 fern species). This will combine traditional histology with non-destructive micro-computed tomography (microCT) to capture 3D vascular networks, and it will leverage AI-based image segmentation to rapidly quantify vascular features from large image datasets. Aim 3 places the results from Aims 1 and 2 into macroevolutionary frameworks using phylogenetic comparative methods and climate data from species occurrences to evaluate how hydraulic function and development scale up to explain macroevolutionary patterns. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2026 · 2026-08

Across many rural and resource-constrained communities in the United States (including agricultural regions, Appalachian and coal country areas), resident-led groups and civic organizations work to address local concerns ranging from water safety and soil health to food production and land conditions. Yet gathering and acting on local data remains a persistent challenge. Existing tools for such tasks often assume reliable internet connectivity, use data platforms owned and managed by outside companies, and need specialized technical expertise that resource-constrained communities simply do not have. This leaves dedicated community groups without practical tools that work under real-world field conditions, regardless of how committed or knowledgeable they are about their local needs. This project develops a new class of low-cost, portable Edge Artificial Intelligence (EdgeAI) tools that embed machine learning directly into tiny, inexpensive computing devices that can operate without internet access or specialized equipment. These EdgeAI tools will enable grassroots communities with limited technical infrastructure to understand local conditions by creating, adapting, and managing their own data systems. Field research with civic organizations in Appalachian Tennessee will guide the design of these tools and generate design principles applicable to similar communities nationwide. By placing practical EdgeAI tools in the hands of community members, the project strengthens local capacity to detect and respond to concerns about issues such as water safety, soil health, and food production, supporting the health and economic welfare of communities that currently lack access to advanced technology. The project also engages undergraduate and K-12 students from rural and other low-infrastructure settings in hands-on, place-based computing, helping prepare the next generation of technologists who understand and serve the needs of their communities. This project investigates how participatory design methods and human-centered interfaces can enable non-expert users to deploy, adapt, and govern machine learning systems running on small, inexpensive embedded computing devices with tight memory and processing constraints (on the order of 256 kilobytes of memory). The research pursues four interconnected aims. First, participatory co-design methods will be developed and validated through field workshops, community mapping, and hands-on prototyping sessions where residents and civic organizations identify local data priorities and shape the design of EdgeAI tools for their specific contexts. Second, non-expert authoring interfaces will be created, allowing community members to collect and label data on-device, adjust detection thresholds, and retrain machine learning models directly on embedded devices without relying on desktop computers or internet connectivity. Third, these systems will be deployed at community gardens, water monitoring sites, and rural locations, where residents work with the tools under real field conditions over extended periods, revealing how community-led adaptation shapes system performance, trust, and local decision-making. Finally, education pathways will be built through undergraduate coursework and K-12 outreach, connecting students from rural areas and households without prior college experience to hands-on computing work rooted in the needs of their own communities. All tools, datasets, and curricula will be released as open-access resources to support adoption across a broad range of communities with limited technical infrastructure. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2026 · 2026-08

Many important problems in science and engineering involve moving interfaces that separate different materials, fluids, or biological structures. Examples include tumor growth, tissue development, solidification in advanced materials, battery technologies, and complex multiphase fluid flows. Accurately simulating these systems is difficult because the interfaces are often highly irregular, dynamically evolving, and geometrically complex. This project develops new mathematical and computational methods that allow such interface problems to be simulated efficiently and accurately on modern computers without requiring expensive, complicated geometric reconstruction techniques. The resulting tools will support scientific advances in biotechnology, materials science, and advanced manufacturing by enabling faster and more reliable large-scale simulations of complex physical and biological processes. The research will also contribute to the broader scientific computing infrastructure needed for future data-driven and artificial intelligence-assisted modeling technologies. In addition, the project will support the training of graduate students in applied mathematics and scientific computing, produce openly available software for the research community, and expand public access to advanced mathematical education through textbooks and freely available online lecture courses. This project develops and analyzes diffuse domain methods for solving partial differential equations posed on complex and evolving geometries in two and three spatial dimensions. The research focuses on the construction of diffuse approximations of characteristic and surface delta functions associated with curved interfaces and boundaries, together with the derivation of accurate diffuse formulations for one-sided and two-sided interface problems. Analytical tools, including matched asymptotic expansions, weighted Sobolev space techniques, and Gamma-convergence methods, will be used to study consistency, convergence, and stability properties of the resulting approximations as the diffuse interface width tends to zero. The work will also develop fully discrete finite difference and finite element schemes for nonlinear interface problems with nonlinear boundary conditions, emphasizing efficient implementations on regular meshes together with fast multigrid solvers. Freely available software implementations and benchmark datasets will be developed to facilitate the application of these methods to challenging multiphysics problems arising in materials science, biological systems, and complex fluid flows. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2026 · 2026-07

Understanding how extreme weather varies in type, magnitude, and frequency is critical for anticipating future risks to the social, ecological, and economic security of the nation. This project will use novel methods of tree-ring analysis to produce detailed records of past rainfall and temperature extremes during the last millennium across temperate North America. The project will also develop accessible visual materials and tutorials, connecting students across wide-ranging fields to create a unique project-based learning environment that enhances participation and capacity in the STEM workforce. With this CAREER award, the project will apply novel dendrochronological techniques in quantitative wood anatomy (QWA) to develop high-resolution temperature and precipitation proxy records to augment an existing network of baseline paleoclimate information. QWA parameters will then be used to improve the temporal precision of reconstructed environmental volatility over the Common Era, with an increased emphasis on the trends and drivers of extreme weather conditions. In doing so, the project will address these specific research objectives: 1) increase the spatiotemporal coverage of high-resolution paleoclimate records across North America, 2) use QWA methods to improve the resolution and robustness of paleoclimate estimates from tree-rings in the temperate latitudes of North America, and 3) identify and quantify the natural forcing mechanisms on extreme weather conditions prior to the Industrial era. The education plan of this CAREER award emphasizes integrating QWA research with creative visual methods to 1) motivate young students to pursue STEM-based inquiry through easily accessible and engaging opportunities for in-depth exploration of paleoenvironmental and atmospheric science, and 2) involve interdisciplinary groups of undergraduate students in hands-on, engaging experiences where they learn STEM skills through the development of paleoenvironmental science tutorial videos and a visual art exhibition. These education and outreach tools will be developed using data and images from the research. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2026 · 2026-06

Developing a cross-protective vaccine to control Leptospira infection Project Summary Leptospirosis is a leading zoonotic cause of human morbidity and mortality, occurring in diverse epidemiologic settings, with estimates of 1.03 million cases and 58,000 deaths per year. It is one of the underrecognized and neglected diseases of humans and animals with similar clinical manifestations. The presence of numerous Leptospira species/serovars, infecting a broad range of animal reservoirs and the resulting environmental contamination, makes control and prevention in humans and animals a cumbersome task. The bacterin-based vaccines available for animals do not offer complete protection or prevent renal colonization and urinary shedding. Due to these limitations and adverse effects, vaccination of humans is not practiced. A broader cross-protective vaccine is urgently needed to prevent Leptospira infection in humans and animals. The availability of a large number of genome sequences and advanced bioinformatics tools has allowed us to use reverse and structural vaccinology approaches to identify a repertoire of conserved antigenic epitopes from Leptospira. Probing these epitopes using a high- throughput microarray and serum from naturally infected animals resulted in identifying proteins that are exposed to the host immune system. In the proposed research, we will confirm their surface exposure and evaluate their protective efficacy in hamsters and mice. Our proposed strategy will overcome the traditional linear, time-consuming, and cost-prohibitive process of antigen discovery for vaccine design. We expect that the vaccination with a cocktail of selected candidates will induce a focused protective response toward relevent immunogenic protein regions. Our long-term goal is to develop a safe and cross-protective vaccine that provides long- lasting immunity against Leptospira infection irrespective of infecting Leptospira serovar and affected host species. Leptospirosis is a globally significant human and animal health problem and a perfect paradigm for an infectious disease of “one health” importance. Development of a cross-protective multispecies vaccine is highly desirable to control this disease, and such a product will have public health, economic, and social impact.

NSF Awards · FY 2026 · 2026-06

Designing synthetic, beneficial microbes and microbial communities to inoculate into soil, and manipulation of the microbes around plant roots (the rhizosphere microbiome) to promote plant growth and productivity are current strategies to ensure food security with sustainable agricultural practices. These strategies rely on understanding how bacteria sense and respond to plant hosts. Bacterial signaling networks detect signals, process information, and drive behaviors like movement toward plant roots. These networks allow the bacteria to monitor and adapt to their surroundings with responses such as colonizing a plant surface, exerting beneficial effects on a colonized host plant, or implementing stress responses if conditions worsen. These systems tend to be studied in isolation, a practice needed to understand basic function, but this approach limits the development of effective predictive models and can lead to unexpected challenges for improving bacterial performance in biotechnological applications. This project will investigate a strategy by which beneficial soil bacteria integrate sensing and responses from distinct signaling networks to mediate colonization of plant roots and exert beneficial effects on plant growth. The project will evaluate the implications of its findings for biotechnology by consulting with industry partners. The project will also contribute to workforce development by engaging and training college students and junior scientists in research relevant to biotechnological applications. This project will elucidate molecular mechanisms linking bacterial chemotaxis signaling, flagellum structure and function and general stress responses in a beneficial soil bacterium, Azospirillum brasilense, used as a commercial bio-inoculant. The project will define the general stress response signaling network in this model soil bacterium, elucidate how the general stress response network alters the structure-function of the bacterium’s polar flagellum and identify how the chemotaxis signaling network integrates with the general stress responses network. Examining the molecular signatures that comprise the general stress response will help identify a subset of targets for synthetic engineering applications for the design of next generation bioinoculants. Characterizing how chemotaxis signaling alters the expression of a minor polar flagellin will provide new paradigms that should be applicable to other soil bacteria with similar genome architecture. How chemotaxis signaling intersects with other regulatory networks remains poorly understood, and preliminary data have suggested entirely novel mechanisms and new avenues for producing strains for soil inoculation that have enhanced stress resistance. Characterization of novel molecular pathways that integrate bacterial motility and chemotaxis signaling with regulation of stress responses will advance our understanding of how bacteria adapt in the soil and as part of a rhizosphere. The project will provide research opportunities for undergraduate students who have limited access to research opportunities and provide graduate students and a postdoctoral fellow with opportunities to apply their research communication skills in a breadth of outreach activities, as well as interact with industry partners. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2026 · 2026-06

Energy efficiency and scalability are emerging as fundamental constraints for advanced computing systems such as artificial intelligence infrastructure and quantum computing platforms. Many quantum technologies already operate at cryogenic temperatures, creating a growing need for digital logic that can function reliably and efficiently in these environments. Superconducting electronics provide a promising foundation for such systems, yet existing superconducting logic approaches have remained difficult to scale into flexible, general-purpose digital architectures. This project addresses that challenge by developing a voltage-controlled superconducting logic framework designed for modular construction, reliable signal cascading, and compatibility with modern digital design methodologies. By bridging emerging superconducting device concepts with practical circuit implementation, the project seeks to enable scalable cryogenic computing technologies. In parallel, the project integrates research and education through a structured pathway that spans early high school exposure, undergraduate training, and graduate research. These activities include hands-on modules, research-integrated coursework, and openly accessible digital resources designed to expand participation in extreme electronics and strengthen the microelectronics workforce. The technical vision centers on a hybrid superconducting logic platform that integrates ferroelectric-superconducting quantum interference devices (FeSQUIDs) and heater cryotrons (hTrons). In this framework, FeSQUIDs enable voltage-controlled modulation of superconducting critical current, while hTrons provide amplification and output restoration needed for robust digital operation. The research pursues three connected thrusts. First, it investigates material and device physics in both conventional superconductors and compositionally complex superconducting alloys to understand how material composition and ferroelectric effects influence switching behavior and controllability. Second, it develops semi-physical compact models suitable for circuit simulation and implementation within established modeling frameworks, enabling systematic device-to-circuit translation and supporting compatibility with automation-oriented design practices. Third, it designs, simulates, and benchmarks voltage-controlled Boolean logic gates and multi-stage circuits to evaluate scalability. Quantitative success will be assessed using clearly defined performance criteria at the circuit and system levels, including: (i) stable rail-to-rail voltage swing suitable for reliable multi-stage cascading, (ii) switching speed and energy dissipation measured consistently across benchmark circuits, and (iii) robustness to realistic variability as reflected in model-informed variation analysis. The education plan complements the research by delivering scaffolded learning experiences and measurable dissemination outcomes, including multilingual digital content with tracked engagement and adoption. By unifying device physics, compact modeling, and benchmarked circuit design within a multi-level co-design framework, this project establishes the foundation for scalable superconducting digital logic platforms for future cryogenic computing systems. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2026 · 2026-05

Antarctica holds vast, hidden reservoirs of salty groundwater beneath its ice and frozen soils; an extensive network that may influence earth system variability, ocean ecosystems, and ice sheet stability. This project will directly measure groundwater discharge and potential associated gas seeps along the Antarctic coast, revealing how these subsurface waters transport nutrients, trace metals, microorganisms, and atmospheric-reactive gases to the Southern Ocean. Understanding these exchanges is vital because they can shape marine productivity, influence carbon cycling, and control the release or storage of gases. The project strengthens U.S. and New Zealand scientific collaboration in alignment with the “Antarctica InSync” initiative, supporting coordinated, sustainable research in one of the world’s most logistically challenging environments. Insights from this work will help improve predictions of how Antarctica both responds to and influences global environmental variability. This collaborative RAPID project investigates how Antarctic groundwater drives ecosystem connectivity across the McMurdo Sound coastal zone, focusing on the Cape Evans and New Harbor regions of the Ross Sea. The team will identify groundwater discharge using in situ gamma radiation sensors, deploy seepage meters and OsmoSamplers for fluid and gas flux measurements, and collect water and sediment samples for detailed geochemical and microbial analyses. These data, combined with land-based geophysical, SCUBA, and ROV surveys by New Zealand partners will quantify groundwater pathways, flux rates, and biogeochemical properties. The project tests the hypothesis that Antarctic groundwater significantly affects coastal geochemistry, microbial diversity, and glacial flow, influencing the sensitivity of Antarctic coastal margins to earth system dynamics. The findings will provide foundational data for future multinational monitoring, modeling, and management of Antarctic critical zones. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2026 · 2026-05

The project supports the 54th Barrett Memorial Lectures at the University of Tennessee, Knoxville, centered on the mathematical foundations of data-driven scientific discovery. Many problems in science and engineering depend on extracting reliable information from high-dimensional, noisy, or limited data, requiring advances in mathematics and statistics to ensure models are accurate and trustworthy. This project brings together researchers, students, and early-career scientists to exchange ideas on methods with applications spanning biology, materials science, and engineering. Through lectures, poster sessions, and collaborative discussions, the project promotes interdisciplinary training, broad participation, and workforce development. By strengthening the mathematical foundations underlying artificial intelligence and data-driven modeling, the project contributes to national priorities in AI and biotechnology, and advances the national interest through scientific innovation, education, and the development of a diverse and skilled workforce. The project convenes researchers to investigate core mathematical and computational challenges in data-driven scientific modeling, with emphasis on uncertainty quantification, Bayesian inference, geometric and topological data analysis, and multiscale modeling. The Lectures integrate advances in probabilistic computation, Monte Carlo methods, numerical analysis, and scientific computing to address high-dimensional and data-scarce regimes. Particular focus is placed on unifying physical principles with machine learning through mathematically grounded frameworks that improve generalization, interpretability, and robustness. Activities include plenary and invited talks, poster presentations by junior participants, and structured breakout sessions targeting key research directions such as optimization, topological learning, and stochastic modeling. These interactions are designed to catalyze new collaborations and identify open problems in computational mathematics and data science. Anticipated contributions include advancing foundational theory for data-enabled scientific discovery and strengthening connections between mathematics, artificial intelligence, and domain sciences. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2026 · 2026-05

The Practice and Experience in Advanced Research Computing (PEARC) conference has traditionally included a robust student program. The program exposes students and scientists to career opportunities, people networks, and other resources that help students connect, build skills, and accelerate research. This proposal provides travel support to students to attend the PEARC26 conference (Minneapolis, MN, July 26-30, 2026) and participate in the Student Program. Students will be paired with mentors from a community of experts who will provide support and network opportunities to the students after the conference. Students will also have the option to present their research through lightning talks to all attendees. PEARC26's size (about 1000 participants) provides students in the Student Program with a unique opportunity to gain hands-on experience in advanced research computing and encourage their interest in pursuing a career in the field. The student program committee will recruit students from a broad range of academia, ranging from research-intensive R1s to teaching-focused community colleges. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2026 · 2026-04

Many of the most consequential decisions in energy, transportation, and supply chains must be made in the face of uncertainty (e.g., fluctuating demand, disruptions, extreme events), yet the resulting optimization problems can be too large for current methods to solve quickly and reliably. This EArly-concept Grant for Exploratory Research (EAGER) project will provide empirically grounded guidance on when and how quantum computing resources available in the near future can help with these uncertainty-driven decisions, producing practical “guardrails” and open tools that show which problem structures are amenable to these methods. The work advances NSF’s mission by promoting progress of science in quantum-enabled optimization under uncertainty, strengthening national health, prosperity, and welfare through more resilient planning and operations, and informing methods relevant to national defense logistics; it will also train graduate researchers and release open-source benchmarks and software to broaden participation and accelerate impact. The project will develop and validate stochastic formulations for two-stage stochastic programs, emphasizing the common case where uncertainty enters linear terms (e.g., right-hand-side and linear cost uncertainty) to enable compact quadratic unconstrained representations suitable for hybrid quantum–classical workflows. The researchers will implement and evaluate linear vs. logarithmic scenario encodings; design and benchmark soft non-anticipativity enforcement via penalty and relaxation strategies; create incumbent-generation procedures that use quantum (or quantum-simulator) sampling to produce high-quality candidate solutions for classical refinement; and perform systematic scaling and noise-robustness studies across benchmark families to produce phase-diagram style summaries linking instance structure, encoding choice, penalty strength, and achieved solution quality. Deliverables include an open-source code base and benchmark suite, along with evidence-based guidance on where quantum sampling provides measurable value for stochastic optimization. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2026 · 2026-04

PROJECT ABSTRACT Cells sense and interact with their surroundings through signaling processes that take place on the plasma membrane. Signals transduced through the membrane are integrated by the cell to make decisions about cellular responses and cell fate. Antigen engagement by immune receptors, for example, can provoke dramatic cellular activation and downstream effector responses, while defects in signaling can lead to autoimmune disease or immunodeficiency. Signal transduction often depends on spatial re-organization of receptors that leads to new interactions with downstream signaling partners. This occurs within the plasma membrane which itself is a complex mixture of proteins and lipids with a heterogeneous and dynamic structure. As a result, receptors and signaling molecules encounter various kinds of membrane microenvironments that can organize, compartmentalize, and locally regulate the biochemical steps of signaling. In T cells, activation through the T cell receptor (TCR) is clearly highly coordinated in space. TCRs engaged with an antigen-presenting surface first form microcluster assemblies that initiate the activation signal. But, key mechanistic details of receptor triggering and regulation are lacking. A major obstacle is that both microclusters and the surrounding heterogenous membrane are structured on sub-micron length scales that are inaccessible to conventional microscopy. My research program aims to understand how receptor signaling complexes are assembled and regulated by the heterogeneous membrane using super-resolution fluorescence imaging. Super-resolution localization microscopy (SMLM) is a powerful tool to study the spatial regulation of membrane receptor signaling because it can measure organization of biomolecules on length scales that are relevant to both signaling assemblies and membrane structure. In multi-color live cell experiments, we can produce quantitative measurements of interactions and dynamics of membrane components that are sensitive enough to detect lipid-mediated organization of plasma membrane components. We will use this technique to address the questions of how membrane signaling “compartments”, or local membrane microenvironments, are generated in the plasma membrane, how receptor signaling cascades couple to membrane compartments, and how membrane compartments influence the activity of signaling proteins. In addition to native TCRs, the research outlined in this proposal uses engineered immune receptors known as chimeric antigen receptors (CARs) as a simplified, modular, modifiable, model receptor to study spatial regulation of immune signaling. In parallel, we will develop new super-resolution probes and imaging modalities to access spatial information about signaling activity (e.g. kinase activity, phosphorylation) and physical properties of membrane microenvironments (e.g. surface charge, and lipid composition).

NIH Research Projects · FY 2026 · 2026-03

Project Summary/Abstract Our research focuses on deciphering the higher-order structure (HOS) and function of human integral membrane proteins (IMPs) in their native cellular and tissue environments. IMPs are central to many biological processes, such as transport, catalysis, and signal transduction. They are the primary targets for 60% of FDA-approved drugs. However, their HOS and functional structural properties (e.g., conformational changes, ligand interactions, solvent accessibility, and dynamics) under physiological conditions remain poorly understood, particularly for the transmembrane (TM) regions. Traditional structural approaches (e.g., cryo-EM and crystallography) often require the extraction of IMPs from membranes using detergents, which disrupt their native conformations and limit the ability to study these proteins in their native state within living systems. Our hypothesis is that the membrane environment is crucial for maintaining the native structure of human IMPs, which is essential for regulating their physiological functions. Capturing the structural information of IMPs in their native settings provides a more accurate reflection of their functional states and more effectively guides drug development. To address this critical knowledge gap, our lab is pioneering innovative mass spectrometry (MS)-based footprinting techniques to capture the native states of IMPs in cells and tissues. MS-based footprinting is emerging as a powerful means to answer biological questions about IMPs by affording sufficient structural information on their dynamics, conformations, and interactions. However, major challenges—such as the unreactive and protected TM domains and the low footprinting efficiency in membrane environments—have hindered its biomedical application. We propose to design and improve novel footprinting methods that provide high coverage of IMPs in native settings. We will then apply these methods to various human IMPs (e.g., enzymes, transporters, and transducers) to investigate their structural properties and reveal drug interactions. Additionally, we propose to integrate these approaches with MS imaging (MSI), creating a novel paradigm—spatial structural proteomics—which will provide insights into IMPs' distribution, content, and HOS within their biological contexts, thereby revealing their roles in disease progression. Over the next five years, we will develop a suite of robust MS-based tools for investigating the HOS, spatial localization, and functional interactions of IMPs in their native environments. By combining structural biology, spatial structural proteomics, and data science, our research will pave the way for transformative biomedical applications, including the discovery of new therapeutics and a deeper understanding of IMP-associated diseases.

NSF Awards · FY 2026 · 2026-01

The Tennessee CHIPS STARS project at the University of Tennessee, Knoxville, is creating pathways for high school teachers and students to engage with microelectronics and semiconductor technologies. Aligned with national investments in chip design and manufacturing, the program supports the growing demand for a skilled U.S. workforce in these critical sectors. It brings together university researchers, local schools, and industry partners to offer an industry-aligned curriculum, hands-on learning experiences, and paid internships. Over the project’s duration, 20 STEM teachers will be trained to deliver cutting-edge content, reaching over 1,000 students. Through lab tours, guest speakers, and experiential learning, the initiative strengthens the talent pipeline from high school to careers—positioning Tennessee as a leader in advancing the nation’s microelectronics workforce. This project responds directly to the national call for a robust semiconductor workforce by creating scalable, high-impact pathways from high school to industry. This three-year initiative combines intensive teacher training, a standards-aligned microelectronics curriculum, and authentic industry experiences with partners such as Texas Instruments, Siemens, and Oak Ridge National Laboratory. The program will train 20 high school STEM and Career Technical Education (CTE) teachers to deliver flexible, problem-based modules grounded in real-world applications. Over 1,000 students will engage in classroom projects, with 45 selected for paid summer internships. Students can also earn industry-recognized micro credentials through a new dual-enrollment model. Core deliverables include a sustainable implementation framework, open-source curriculum resources, and a statewide network of trained educators. Rigorous evaluation will assess gains in educator capacity and student interest in microelectronics careers, positioning Tennessee as a leader in the semiconductor talent pipeline. The ExLENT Program, supported by the NSF TIP and EDU Directorates, seeks to support experiential learning opportunities for individuals to increase their interest in and access to career pathways in emerging technology fields. This project is co-funded by the Innovative Technology Experiences for Students and Teachers (ITEST) program, which supports projects that build understandings of practices, program elements, contexts and processes contributing to increasing students' knowledge and interest in science, technology, engineering, and mathematics (STEM) and information and communication technology (ICT) careers. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-10

Pathogens originating from wildlife pose an increasing threat to agriculture and public health. Yet, predicting when and how these pathogens spill over into domestic animals or humans remains a significant challenge. This is mainly due to limited knowledge about which reservoir hosts interact with potential recipient species, how long those interactions last, and which types of contact are most likely to result in pathogen transmission. Recent advancements in animal biologging and pathogen genetic sequencing are opening new opportunities to map contact networks and trace pathogen transmission across species. This research integrates animal tracking data, pathogen genomics, and mathematical modeling to better understand and predict cross-species transmission dynamics. The project focuses on blood-feeding vampire bats, key wildlife reservoirs for rabies virus, and the livestock upon which they feed. By analyzing interactions between these species, the project aims to identify patterns of contact and transmission that can inform more effective surveillance and control strategies. Given rising concerns over emerging zoonotic pathogens, the research is timely. It not only advances methods to integrate diverse data streams to study spillover but also provides practical insights into managing vampire-bat livestock conflict, an issue of growing concern. The research will also broaden participation by mentoring trainees, strengthening scientific capacity in our study areas, and engaging communities through school programs and public outreach on infectious disease prevention. This project aims to (1) characterize dynamic, multi-species contact networks between vampire bats and livestock using animal-borne proximity sensors; (2) map pathogen-sharing networks by analyzing genetic similarity among common viral, bacterial, and protozoan pathogens (e.g., coronaviruses and rabies virus, hemoplasmas, and trypanosomes); and (3) develop statistical and simulation models to identify likely transmission routes, understand epidemiological dynamics, and inform rabies virus control strategies. The outcomes will advance our fundamental understanding of pathogen spread in complex host communities. Additionally, our integrative approach, combining animal tracking with pathogen diagnostics, will offer a valuable framework for studying cross-species transmission in other systems. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-10

The Dark Matter Data Commons project addresses a pressing need in experimental astrophysics by creating an open, reusable, and accessible data infrastructure to support discovery in dark matter research. There has been an important investment in dark matter detection experiments, but the field still lacks standardized mechanisms for sharing data and analysis workflows. This gap hinders transparency, reproducibility, and innovation in science. By aligning with FAIR principles (i.e., Findable, Accessible, Interoperable, and Reusable), the project builds a data platform that broadens access to high-value experimental datasets, enabling participation of students, early career researchers, and institutions with limited infrastructure. The project advances scientific progress and national research capacity through this data infrastructure while supporting educational development and the research community. It contributes to national interest by accelerating discovery in a federally prioritized scientific area and fostering access to cyberinfrastructure for data-intensive science. The project establishes an end-to-end infrastructure for curating, storing, accessing, and analyzing dark matter experiment data. It delivers a FAIR-compliant repository enriched with standardized metadata, integrated with scalable AI and machine learning (AI/ML) workflows, and accessible through user-friendly command-line and Python interfaces. Built atop NSF-funded resources, the commons begins with time-series data from the Cryogenic Dark Matter Search and DELight experiments. It also includes tutorials, training materials, and an affinity group to foster community engagement and reproducibility. This work is a template for similar data infrastructure across rare-event physics, providing a foundation for long-term open science and AI-enabled discovery. This award by the Office of Advanced Cyberinfrastructure is jointly supported by the Physics at the Information Frontier (PIF) Program within the Division of Physics in the Directorate for Mathematical and Physical Sciences. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-10

The SAFARI project (Scientific Analytics, Forensics, and Reproducibility for Workflows in CI) advances scientific research by integrating forensic data analytics into the workflows used for large-scale data analyses. These analytics (i.e., tools that track the origin, processing, and reliability of data) are directly incorporated into the workflow systems used by researchers in Earth science and related fields. The integration ensures that workflow artifacts (data and software) are reliable, reusable, and reproducible and that the scientific results are trustworthy and of high quality. SAFARI enables researchers to construct and execute workflows that are modular, transparent, and explainable, making it easier to adapt and share methods across diverse computing platforms with collaborators and the public. The benefits of this project are demonstrated in applications such as weather forecasting, irrigation modeling, and wildfire prevention, with broader potential impacts in bioinformatics, physics, and materials science. SAFARI enhances the reliability, security, and resilience of data from high-throughput scientific workflows by embedding forensic data analytics into cyberinfrastructure services. Rather than applying post-hoc analysis, it integrates provenance tracking, automated verification, and artifact modularization into the Pegasus Workflow Management System. SAFARI targets three core challenges: ensuring trustworthiness through transparent and secure execution, improving reusability via workflow decomposition and containerization, and supporting reproducibility with standardized provenance capture and validation. The project addresses threats such as incomplete documentation, tampering risks, and execution variability—issues commonly observed in workflows that utilize Artificial Intelligence (AI) and heterogeneous geospatial data to predict environmental conditions and natural hazards such as soil moisture, wildfire risk, and crop yield estimation. By restructuring workflows into reusable components and forming an Earth science-focused group aligned with the high performance computing community, SAFARI delivers scalable and secure Cyberinfrastructure (CI) services. Through impactful Earth science applications, such as AI-powered soil moisture modeling and wildfire prediction, SAFARI demonstrates practical societal benefits directly aligned with national AI priorities. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-10

This project advances foundational robotics research by developing a miniature, artificial intelligence (AI)-enhanced soft robotic system that mimics key protective functions of the human eye such as blinking, tear flow, and corneal sensing to autonomously maintain visual clarity during minimally invasive surgeries. In these procedures, surgeons rely entirely on laparoscopic cameras for visual guidance, but lens contamination from smoke, fluids, condensation, and debris frequently impairs visibility. These visual obstructions often disrupt the procedure and require repeated manual or instrument-based interventions to restore clarity. The system integrates soft actuation, programmable fluidics, and real-time AI control to enable autonomous lens cleaning, adaptive airflow shaping, and vision enhancement within a compact robotic platform. In addition to addressing this important medical need, the knowledge gained in this project will enable applications in robotic perception for space exploration, disaster response, and advanced industrial automation. The project also contributes to STEM education through interdisciplinary training in robotics, AI, and engineering applications via curriculum development, summer camps, and other outreach activities. This research aims to develop a bioinspired, multifunctional soft robotic platform that integrates mechanical actuation, fluidic control, and embedded AI to autonomously manage visual clarity in constrained surgical environments. Inspired by ocular physiology, the platform includes five components: a concealed, eyelid-like soft robotic wiper; a cornea-like cover for sealing the camera lens and imaging sensors; an array of soft tubing that mimics the lacrimal gland to introduce airflow and tear-like liquid; a lacrimal-passage-like soft robotic nozzle connected to the tubing channels; and AI algorithms that emulate corneal nerve functions to enhance vision in real time. The research is structured around three technical aims: (1) the design and fabrication of a miniature soft robotic wiper capable of autonomous contamination removal; (2) the development of a programmable, closed-loop soft robotic nozzle to dynamically modulate airflow in response to intraoperative conditions; and (3) the development of AI-driven control strategies to coordinate actuation and deliver real-time image enhancement using techniques such as joint super-resolution and deblurring. The approach uses parameterized model predictive control, optimized via deep reinforcement learning, to ensure persistent feasibility and robustness to uncertainty. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-10

Conference: Workshop on Atomic Scale Fabrication for Quantum Information This award will support a workshop to explore research opportunities in quantum information and sensing based on emerging approaches including silicon and semiconductor-based qubits, macromolecules, conjugated molecular systems, and other experimental realizations. Workshop topics will include both top-down fabrication techniques and bottom-up methods that can offer atom-by-atom assembly for selected materials, ranging from sequential low-throughput approaches to higher-throughput methods enabled by classical semiconductor workflows and synthesis. The workshop will serve as a forum to engage traditionally distinct communities whose common goal is to build quantum structures with near- and atomically precise fabrication and assembly in currently non-standard materials. It aims to address relatively less explored approaches to quantum information that are now enabled via advances via ML/AI, synthesis, and ion, electron, and beam probes. Topics to be covered will encompass silicon-based approaches that center around nuclear spins as well as electron spins; nuclear spins of donor atoms such as phosphorus and arsenic that require manipulation at the atomic scale; and fundamental research in manufacturing methods that will accomplish this. Other topics of interest include electron/hole spin-based approaches that utilize silicon as well as other semiconductors such as carbon nanotubes, and also fabrication technologies that are at the nanometer scale and need to be further developed. Macromolecules, and organic systems more generally, are also of interest since they encompass an exciting area of research that combines materials research, precise manipulation at molecular length scales, and studies of spin and quantum light generation. Both soft (organic) and hard matter (Ex. Carbon nanotube) pi-electron systems will be considered in the workshop. A broader outcome that is expected from the workshop is fostering of a collaborative ecosystem across multiple disciplines empowered to translate academic breakthroughs into scalable, real-world quantum technologies. Support for the workshop is provided by the NSF Directorate for Engineering (ECCS, CMMI, EFMA) and the NSF Directorate for Mathematical and Physical Sciences (DMR). This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2025 · 2025-09

Project Summary/Abstract Dr. Thompson, as PD/ PI, Professor and Director of CAFSP and Co-Director of the TN COE, UTK is submitting an application to continue support of FDA’s efforts to modernize the approach to training IFSS human and animal food safety professionals and addressing capacity to design and develop content for new or existing classroom and web courses, virtual events, reference resources, job aids, or other tools needed to design and develop engaging regulatory curricula utilizing adult learning principles. She has assembled a strong team with experience in food safety, instructional design, information technology, 508 compliance, and video development. Requested funds will support OTED priorities and mutually agreed upon projects to address these 2 focus areas: 1) Course Planning, and 2) Course Design, Development and Evaluation. UTK has the following specific aims: 1) Support OTED’s course delivery needs and ensure that participants experience a quality learning outcome; 2) Support course design, development and evaluation projects to meet OTED’s needs and priorities using high quality, innovative and cost-effective approaches; 3) Implement a flexible approach to establishing workplans and timelines to adjust for unanticipated circumstances based on changes to OTED needs and priorities; 4)Explore innovative approaches to enhance participants’ overall learning experience while also considering cost effectiveness and timeliness; and 5) Fulfill all necessary reporting and meeting requirements to ensure lines of communication are open and that effective coordination occurs with all FDA personnel to implement timely and high-quality outcomes. UTK will provide the following deliverables. Under Focus Area 1 on Course Planning, UTK will continue its successful delivery of FD190 Food Current Good Manufacturing Practice, Application, and Evidence Development and its related Train-the-Trainer and will cover all aspects of a delivery including: 1) Recruiting IFSS SMEs and providing instructor skills training and other OTED mandated training to SMEs identified as potential course instructors; 2) Ensuring instructors are trained on how to deliver all course components including scheduling TTT sessions as needed; 3) Evaluating instructor performance to assure course content is delivered in compliance with FDA/OTED instructional standards; 4) Maintaining and submitting all necessary course records; 5) Developing annual work and project plans in collaboration with OTED; and 6) Developing a course delivery schedule in concert with OTED's work planning. Under Focus Area 2 on Course Design, Development and Evaluation, UTK will support OTED’s identified priorities and will implement innovative and cost-effective approaches to: 1) Develop or revise course design documents, storyboards, assessments, knowledge checks, graphics, videos, exercises, and instructor, administrator, and participant manuals; 2) Establish project plans/ timelines in collaboration with OTED; 3) Develop annual work and project plans; 4) Review and pilot classroom course content; and 5) Review and pilot web-based course content (content/functionality testing/508 compliance testing).

NSF Awards · FY 2025 · 2025-09

This award supports fundamental research into mathematics that will enable a better understanding of the connection between neural rhythms and cognitive function. The research represents a necessary first step in the development of new treatment options for brain disorders caused by abnormal brain rhythms including schizophrenia, autism spectrum disorder, attention deficit hyperactivity disorder, and Parkinson's disease, promoting science and advancing national prosperity. Results of the project will contribute to the understanding of the biophysical mechanisms that support short-term memory, enabling a better understanding of how patients with disorders such as Alzheimer’s disease lose their ability to remember where they are. The award will support graduate students and researchers at the interface of neuroscience, mathematics, and engineering. The research plan includes two outreach efforts to encourage high school students to pursue careers in engineering and mathematics: 1) Week-long, hands-on research experiences with activities coordinated by the University of Tennessee, and 2) National mathematical modeling contests for regional high school and undergraduate students at the University of Florida. This project will advance the mathematical understanding of emergent behaviors in populations of coupled neural oscillators. Due to the size and dynamical complexity of most neural systems, model order reduction is often an imperative first step for mathematical analysis. However, the underlying assumptions required for the implementation of current approaches (e.g., weak coupling, symmetry, repetitive firing with a steady frequency) result in idealized phenomenological models that often give an incomplete and/or incorrect picture of the mechanisms governing the aggregate behavior. This fundamental limitation leads to gaps in our general understanding of how individual neurons organize to produce brain rhythms that ultimately support essential cognitive functions. The project aims to fill this gap in knowledge, significantly advancing coupled oscillator theory in neuroscientific contexts to accommodate strongly coupled neural networks, neural bursters, systems with nonnegligible heterogeneity, and higher order N-body interactions. Successful completion of this project will yield comprehensive, interpretable, mechanistic, and tractable techniques for the analysis of coupled oscillators, facilitating a deeper understanding of the mechanisms governing synchronization, rhythmic activity, and information processing in the brain. These techniques will be integrated with archival microelectrode data from rat entorhinal cortex to investigate the dynamical mechanisms that govern theta phase precession, a phenomenon that allows an animal to keep track of its precise location in space. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-09

This CSSI project is a multi-university collaboration between Tennessee Technological University, the University of Tennessee, Knoxville, Stony Brook University, and the Illinois Institute of Technology. This project improves how massively parallel computers run large-scale artificial intelligence (AI) applications by enhancing the Message Passing Interface (MPI), a widely used standard for coordinating work across many computers in parallel programs. Currently, the enabling data-transfer software used in AI, for communication between computers enhanced by Graphical Processing Units (GPUs), are often proprietary and/or limited in scope; they cannot be expanded or enhanced by an open community. That situation restricts innovation, making it harder for scientists to collaborate and enhance their science output on limited computer resources, while also creating dependency on a few vendors. By contrast, this project builds on and advances Open MPI, a major open-source implementation of MPI with a long history of broad impact, to make it more efficient, flexible, and better suited for modern AI tasks. In addition to improving the Open MPI implementation, MPI4AI aims at standardizing extensions to MPI so all implementations and users of MPI will benefit from this project's outcome. MPI4AI introduces key improvements to Open MPI, including native support for GPU communication, enhanced collective (group) communication operations including those that are AI-algorithm specific, compute stream integration, and optimized data movement. Specifically, these advances target performance bottlenecks in three AI patterns: neural architecture search with transfer learning, key-value prefix caching in large language model inference, and large-scale data-parallel training. The project improves resilience and malleability through fault-tolerant mechanisms, enabling AI applications to adapt dynamically to system changes and to use resources more efficiently. By forwarding these enhancements toward adoption in the upcoming MPI-5 and MPI-6 standards, the project ensures long-term impact across both academic research and industrial AI workflows. These contributions will lower the cost of running large AI workloads and broaden access to scalable AI infrastructure. MPI4AI's capabilities will enable researchers exploring new modalities of AI computation to express their algorithms and code efficiently and more effectively as compared to existing solutions that work within the confines of current MPI features and vendor-specific message-passing libraries. Underlying improvements devised for Open MPI will also be broadly beneficial to other use cases and users of this parallel programming system. Overall, key strengths of this effort are a strong commitment to standardization and emphasis on performance-portability across various hardware platforms with particular focus on AI-enablement. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-09

With the support of the Macromolecular, Supramolecular, and Nanochemistry Program in the Division of Chemistry, Dr. Brantley of The University of Tennessee, Knoxville will explore how electricity can be used to convert plastics into new chemical building blocks. Currently, there is a critical need to develop new methods to recycle or upcycle the large quantities of waste plastics that are generated every year. Dr. Brantley and his team will investigate the use of electrochemistry to change the physical and chemical properties of plastics in an effort to create valuable products from what would otherwise be considered waste. The introduction of new functional groups onto otherwise inert polymers has the potential to make them useful for a variety of new applications, including as coatings, adhesives, or even high-performance materials. Dr. Brantley and his team will also explore how electrochemistry can be used to generate reactive groups that will spontaneously break waste plastics down into discrete building blocks. These building blocks are important because they can be used to make a wide range of new materials, such as new plastics, fine chemicals, and possibly even therapeutic agents. The overarching goal of this project is to understand fundamental electrochemical reactions that could one day be used to transform large quantities of plastic waste into new, valuable products. This program will also aim to enhance public awareness of science through a social media outreach program that explains scientific principles with demonstrations. Dr. Brantley will also develop new educational initiatives to help students better understand challenging chemistry concepts. The development of novel methods for polymer backbone editing are crucial to not only prepare advanced materials with bespoke properties, but also to transform extant macromolecular substrates into value-added products. Dr. Brantley will expand the polymer modification toolbox by exploring the mechanism and scope of polymer (for example, polyolefins, polyesters, and polyurethanes) editing strategies involving radical ions. This program aims to probe how electrochemically generated radical-cations can promote polymer functionalization via H-atom transfer, with an emphasis on reactions that can install stimulus-responsive motifs. Dr. Brantley will also investigate electrochemical reductions of polyesters and polyurethanes to access macroradical-anions. He will seek to understand how spontaneous mesolytic cleavage of these species can promote polymer deconstruction into monomers and other valuable synthons. This research is expected to significantly expand the chemical space available for polymer functionalization and reveal new fundamental insights into the chemistry of underexplored reactive intermediates in polymer science. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.