University of Tennessee Chattanooga

NSF Awards · FY 2026 · 2026-09

Viral infections grow and spread through processes that occur at many connected levels, from events inside infected cells, to the body’s defense against infection, and to the spread of disease across communities. However, these processes are often studied separately, making it difficult to understand how changes at one level affect outcomes at another. This project will develop new mathematical and computational tools to connect these levels in one framework. The work will help researchers better understand how viruses grow, how the body responds to infection, how treatments and prevention measures work, and how infections spread in populations. By linking these processes together, the project may provide useful guidance for evaluating strategies to reduce the impact of viral diseases. Educational and outreach activities will provide interdisciplinary training opportunities and introduce students to the application of mathematics in biomedical research and public health. This project will develop and analyze a multiscale mathematical modeling framework for viral infections by integrating intracellular and extracellular dynamics within the human body with disease transmission between host populations. At the intracellular level, the models will describe viral entry, replication, assembly, and release. At the extracellular level, the models will describe viral kinetics and immune responses, including antibody and cellular immune responses. At the population level, the models will incorporate multiple transmission routes, symptomatic and asymptomatic infection, vaccination, waning immunity, and variant emergence. These components will be coupled into unified multiscale systems to study how molecular mechanisms, host immune responses, and transmission processes interact across scales. The project will combine dynamical systems analysis, bifurcation theory, stochastic modeling, data fitting, sensitivity analysis, and numerical simulation. The resulting models will provide a mathematical basis for studying treatment effects, vaccine impacts, variant dynamics, and epidemic outcomes across multiple biological scales. 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

Billions of people around the world live in arid and semi-arid regions that have limited freshwater supplies. Technologies that capture water directly from air can help alleviate water scarcity. This project will transform almond biomass waste into a bio-desiccant. The bio-desiccant will capture moisture from air and release it as liquid water. The process will consume modest amounts of energy. Outcomes from the project will support circular biotechnology and bioeconomy and provide an environmentally responsible approach to securing clean water. The project will also contribute to workforce development in sustainable materials and renewable water technologies. This project will support the development of tunable solid bio-desiccant materials for low-energy renewable water harvesting. The project will design and validate bio-derived sorbent materials that integrate porous biocarbon scaffolds with ionic liquids. The material will capture atmospheric moisture and release liquid water under modest thermal inputs. The scope of the research will span material synthesis, performance evaluation, and system-level validation. The project will 1) synthesize and optimize tunable solid bio-desiccants derived from agricultural biomass; 2) establish quantitative relationships between material properties and durability, sorption capacity, and regeneration energy requirements; and 3) integrate optimized bio-desiccants into a renewable water harvesting prototype to evaluate water production rates, regeneration efficiency, and operational stability. The project will establish design principles for scalable and energy-efficient renewable water harvesting technology. 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

This REU Site award to the University of Tennessee at Chattanooga (UTC), located in Chattanooga, TN, will support the training of 10 students for 10 weeks during the summers of 2026-2028. The program, Interdisciplinary Computational Biology (iCompBio), will provide research training in developing computational approaches across genomics, epidemiology, geology, ecology, evolution, biochemistry, cell biology, and molecular biology. The program will enhance participants' knowledge and research skills in both computing and life sciences, as well as soft skills, thereby better preparing them for careers in interdisciplinary STEM fields. It will also strengthen the campus research community, bring in talented students from other institutions, and raise the university’s visibility as a place where innovative, student-centered research happens. Students will learn how research is conducted, and many will present the results of their work at scientific conferences. Assessment of this program will be done through Qualtrics. Students should apply to the REU site using NSF ETAP (Education and Training Application: https://etap.nsf.gov). The training students will receive is aligned with the NSF priorities in Biotechnology, AI, and Quantum Information Science. The iCompBio research projects integrate computational and biological approaches to address complex scientific questions. Example projects include applying artificial intelligence to metabolite identification, using bioinformatic tools to analyze bacterial proteins, modeling the spread of viral infections, studying biodiversity responses to environmental stress, and exploring quantum approaches for biological data analysis. Participating departments include Computer Science and Engineering, Biology, Geology and Environmental Science, Mathematics, Physics, Chemical Engineering, and Engineering Management and Technology. Students will begin with training in Python, data science, and machine learning, then work closely with faculty mentors on research projects while also participating in seminars, workshops, and presentation training. The program includes instruction in ethics and responsible conduct of research, along with mentoring on communication, teamwork, and the social responsibilities of science. Post-program surveys will be conducted. and overall effectiveness by tracking REU-related research outcomes, including publications and conference presentations, as well as longer-term post-graduation pathways such as graduate study and career trajectories. 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 project aims to serve the national need of preparing and retaining highly qualified STEM teachers to meet critical STEM teacher shortages. Twenty-four (24) prospective and practicing middle and high school teachers from multiple high-need and other districts in Southeast TN and North GA will participate over three years. Teachers will engage in authentic research experiences in microelectronics (for the future of quantum computers) and applications of artificial intelligence (AI), which are STEM disciplines identified as critical national needs. The project will provide teachers with the opportunity to participate in this innovative space through a six-week summer research experience and Project-Based Learning (PBL) curriculum development, integrating microelectronics and AI into middle and high school STEM classrooms. Teachers will participate in academic year follow-up activities, including professional development, classroom visits and mentoring by the research team, industry talks, continued curriculum development support, and presentations at conferences. The benefits of contributing to a domestic semiconductor and AI ecosystem with a pipeline of a large variety of skilled workers and future engineers, scientists will be amplified through industry partnerships, participation in student organizations, and dissemination of findings to a broader audience via the project website, publications, and symposium participation. This project at the University of Tennessee at Chattanooga includes partnerships with Hamilton County Schools, Bradley County Schools, Walker County Schools, and Chattanooga State Community College. Project goals include (1) Increase teachers’ practical knowledge of STEM principles related to microelectronics and AI. (2) Train teachers on project-based learning design and implementation in classrooms. (3) Increase the number of well-prepared teachers who will be committed to stay as STEM teachers. (4) Broaden student participation in STEM areas of microelectronics and AI. The research will focus on microelectronics and machine learning applications. As the present Complementary Metal Oxide Semiconductor technology approaches its scaling limits and the quantum effects become more prominent, quantum dot transistors represent a potential alternative technology for enabling quantum computing and future digital applications. AI applications will address areas such as the effects of extreme weather on TN transportation infrastructure, robotic control, urban water resources engineering, and restoration of power grids after outages. Project evaluation will include pre- and post-teacher surveys to assess perceived levels of achievement in each goal area, as well as follow-up surveys at the end of each project year to assess implementation of lessons. Teachers will complete academic year pre/post PBL surveys of students’ change in knowledge of microelectronics and AI principles and attitudes toward STEM careers. Results will be disseminated via symposiums, publications, and sharing of PBL lesson plans. This Research Experiences in STEM Setting project is supported through the Robert Noyce Teacher Scholarship Program (Noyce). The Noyce program supports talented STEM undergraduate majors and professionals to become effective K-12 STEM teachers, and experienced, exemplary K-12 teachers to become STEM master teachers in high-need school districts. It also supports research on the effectiveness and retention of K-12 STEM teachers in high-need school districts. This project is funded by the Robert Noyce Teacher Scholarship Program and is supported in part by funds from the Micron Technology, Inc. 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 Planning Grant: Advanced Cybersecurity for Power Distribution Infrastructure project is a year-long planning effort to develop a comprehensive strategy to build, deploy, and integrate a “QuantumGrid Innovation Hub” in Chattanooga, Tennessee built upon the EPB Quantum Network, a smart electric grid, and an innovation ecosystem that forms a testbed where startups, researchers, and industry can develop quantum applications for electric grid security and related approaches. Planning activities will encompass stakeholder convenings, identifying technical barriers and potential solutions, engaging startups and industry partners, and concurrent economic and workforce development activities. Together, these planning activities will culminate in a clear, actionable blueprint for launching the testbed that is technically grounded and includes a feature-set and capabilities that advance a critical application space for U.S. national security in the quantum sector. This work — led by the University of Tennessee at Chattanooga (UTC) in partnership with Chattanooga’s Electric Power Board (EPB), CO.LAB, and the Chattanooga Quantum Collaborative — builds upon EPB’s existing quantum networking hardware in the region as well as a growing quantum hardware research efforts at UTC. The testbed’s chosen focus area, quantum grid security applications, is critical to national security and there is a significant need for application development in the sector to protect critical infrastructure — namely, the power grid, which is among the most vulnerable infrastructure components to cyberattack across the nation and the world. EPB’s work to develop a commercially available quantum network, built upon dedicated fiber infrastructure, provides the baseline for the research and testing environment. Additionally, the proposal features strong economic development and startup engagement efforts run by CO.LAB, a non-profit startup accelerator based in Chattanooga. They propose to host design sprints, hackathons, and reverse pitch sessions that will attract researchers and technologists to serve as both initial users of the testing environment and a critical feedback loop for planning future testbed capabilities and services. Finally, the proposal also aims to engage community colleges, workforce boards, and employers to develop a robust workforce development plan to create career pathways in the quantum/grid security application space. The activities supported by this award have a significant chance to advance national priorities, bolster regional economic growth, and advance technological progress. Specifically, the development of quantum applications for grid security are highly relevant to U.S. national security, infrastructure resilience, and global competitiveness. Investment in this project is both consistent and well-aligned with the goals and broad strategic objectives of NSF TIP. This investment is consistent with agency’s goals and the scientific and technological community’s interest in accelerating the development of quantum-enabled applications to strengthen cybersecurity of critical 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 2025 · 2025-09

Respiratory infectious diseases like influenza pose a significant global public health challenge, causing substantial morbidity, mortality, and economic costs. Environmental factors—such as temperature, humidity, and air quality—critically influence influenza transmission by affecting viral survival, host susceptibility, and human behavior. However, most existing influenza models rely solely on disease incidence data for forecasts and early warning signals. This project integrates environmental conditions into a hybrid forecasting and early warning system for influenza outbreaks, enhancing public health preparedness and response to emerging and re-emerging infectious diseases in a changing environment. The interdisciplinary nature of this work—bridging mathematics, statistics, epidemiology, environmental science, and artificial intelligence (AI)—fosters collaboration across fields, accelerating scientific progress and knowledge exchange. Additionally, the project inspires the next generation of mathematical scientists, supports workforce development in quantitative public health, and expands access to high-quality STEM education. This project develops a cutting-edge hybrid framework that combines differential equations, machine learning, and statistical techniques to forecast influenza outbreaks and detect early warning signals driven by environmental conditions. The principal investigator and her team refine inverse methods to estimate time-varying transmission rates, evaluating their robustness across model structures and revealing mechanisms behind observed transmission patterns. By incorporating environmental factors, the team enhances the accuracy of outbreak forecasts and identifies key environmental drivers of early warnings. These tools are validated using real-world influenza and environmental data from New York, California, and Tennessee. The research produces generalizable methodologies adaptable to other environmentally influenced diseases, strengthening our ability to predict complex disease dynamics. In parallel, the project prioritizes education and outreach by providing research opportunities for students, hosting an annual mathematics poster competition for middle and high schoolers, and launching a webinar series on mathematical epidemiology. 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 · 2025-08

This project is about creating a special type of material called doped calcium manganite (DCM) perovskite oxides, which could be used for high-temperature thermochemical energy storage (TCES). The main goal is to improve Concentrated Solar Power (CSP) technology, which produces electricity from sunlight. As the world faces increasing energy demands, more renewable energy solutions are being developed, including CSP. However, CSP has a challenge: it relies on sunlight, which is an intermittent energy source. This makes it hard to use CSP all the time. To solve this, scientists are working on ways to store heat energy so it can be used when the sun isn’t shining. One promising solution is TCES, which allows CSP plants to store heat at high temperatures and release it when needed. This project will focus on developing and testing DCM materials to create the best possible heat storage solution. It will also help students who work on the project improve their critical thinking and problem-solving skills, preparing them for future careers in STEM fields. Currently, CSP plants use molten salt-based heat energy storage systems, but their low decomposition temperatures (600 to 650°C) limit plant efficiency by capping the operating temperatures of the power generation cycle. TCES leverages the heat generated from a reversible chemical reaction that facilitates the reduction (charging step) and re-oxidation (discharging step) of metal oxides such as DCMs. Compared to sensible heat energy storage, TCES possesses a substantially higher energy density and a more extended storage period. In this project, DCMs will be synthesized using the solution combustion synthesis approach by co-doping both the Ca and Mn sites with suitable metal cations. The primary objective is to attain a TCES capacity of greater than 500 kJ/kg within the optimal temperature range of CSP systems (700 to 1200°C). The design of DCMs will emphasize achieving superior enthalpy of reduction, extent of redox reactions, and cyclability, directly contributing to the attainment of the maximum TCES capacity. Density Functional Theory (DFT) analysis will provide theoretical insights into the potential of DCMs concerning oxygen vacancy formation energy (OVFE). Detailed characterization of DCMs will allow correlating their physical and chemical attributes with their ability to facilitate the redox reactions in TCES. 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 · 2025-06

This project explores how quantum technology can improve the way we measure and detect small changes in our environment, such as temperature shifts, pollution levels, or even tiny vibrations, across large areas like cities. Researchers at the University of Tennessee at Chattanooga will use a special kind of light, a so-called "squeezed light", to create a network that senses these changes more precisely than current methods allow. By testing this innovative approach on a real-world fiber-optic network in Chattanooga, built in collaboration with industry partners like the Electric Power Board (EPB) and IonQ, Inc., the project demonstrates how quantum science can move beyond laboratory experiments into practical, everyday use. Imagine a system so sensitive it could help monitor air quality in neighborhoods or ensure clocks worldwide stay perfectly in sync; those are the kinds of possibilities this work opens up. This effort funded by NSF will push scientific boundaries while offering real-world benefits. Beyond the technology, the project trains students and professionals in cutting-edge skills, preparing them for future careers in quantum information science and engineering. It also strengthens ties between universities and local industries, showing how federal investment can spark innovation, improve lives, and inspire the next generation to tackle big challenges with creative solutions. This research focuses on achieving sub-shot-noise-limited (sub-SNL) distributed quantum sensing using continuous-variable (CV) entanglement on a commercial metropolitan-scale quantum network. The team will construct a table-top CV-entangled network utilizing two-mode squeezed states, generated through four-wave mixing in atomic rubidium-85 vapor, to measure distributed phase shifts with sensitivity surpassing classical limits. Deep learning, specifically Q-learning, which is a reinforcement learning technique, will be employed to suppress excess noise without requiring pilot tones or training sequences, by adapting similar noise mitigation strategies from CV quantum key distribution (CV-QKD). This approach leverages homodyne detection and real-time phase estimation to optimize local oscillators across the network, addressing noise introduced by beam splitters and environmental interactions. A single-mode squeezed light source at the telecom wavelength of 1570 nm will extend this methodology to the EPB Bohr-IV Quantum Network, a software-reconfigurable fiber-optic infrastructure deployed by IonQ, Inc., featuring a hybrid ring/spoke topology with scalable quantum nodes. The project’s intellectual significance lies in its novel integration of machine learning (ML) with CV quantum sensing, offering the first practical demonstration of sub-SNL distributed sensing on a deployed commercial metro-scale quantum network. Through partnerships with Arizona State University and industry collaborators like EPB and IonQ, Inc., this work advances quantum information science and engineering, providing a scalable framework for future quantum networking applications and contributing to both theoretical and experimental progress in the field. 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 · 2025-02

Aerogels are nanoporous materials composed mostly of air trapped inside a web-like nanoporous structure. These materials have a very low thermal conductivity, which makes them potentially useful in thermal insulation applications. Improved materials for thermal insulation could lead to significant energy savings in the US. However, the complexity of aerogels at the nanoscale presents challenges to describing and manipulating heat transfer inside the material, which could limit its potential use in practice. This project will use computational simulations to better understand the relationships between the structure of aerogels and their physical properties, which could provide designers with knowledge to optimize their behavior. Results from the project will yield direct economic benefits such as energy-efficient nanomaterials, as well as societal benefits in agricultural, environmental, and healthcare applications. Educational activities supported by the project will raise nanotechnology awareness across educational levels and help expand the future science and engineering workforce. Aerogels' properties, such as pore size, porosity, and solid-network, can be tailored during synthesis, but predicting the resulting heat transfer behavior is challenging due to the nanoscale size of the porous system and the solid skeleton. Current theories fail to explain nanoscale heat transfer accurately, and experimental methods lack precise structure-property correlation. To address this knowledge gap, this project will provide a detailed characterization of true nanoscale heat transfer behavior as a function of corresponding physical parameters. The pore-level interfacial and nanoscopic transport mechanisms contributing to aerogel heat transfer will be resolved to obtain a multi-scale structure-property correlation for aerogel design, assisted by machine learning. An accurate description of nanoscale heat transfer mechanisms requires molecular-level calculations. To address this, molecular dynamics simulations will be used to accurately model solid and gas conduction and their combined effective behavior, considering nanoscale mechanism. This project will advance knowledge on heat transfer behavior of (i) complex solid nano-networks with structural characterization, (ii) nano-confined gases under molecular surface effects with rarefaction characterization, and (iii) gas/solid interfaces. Unlike existing limited machine learning studies that suffer from insufficient multiscale data and related physical descriptions at the molecular-level, this project will use a novel bottom-up approach to train machine learning algorithms using molecular pore-level calculations for structure-property predictions. 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 · 2025-01

The rapid evolution in the power grid is driven by sustainability concerns, with focus shifting significantly towards renewable energy sources and away from fossil fuels. Meanwhile, the rise of electric vehicles (EVs) has accelerated the electrification of transportation. Accordingly, this project proposes an interaction-aware management framework to improve the efficiency and sustainability of these two independent and self-interested systems. Since both the EV transportation system and the power-grid system are managed by distinct stakeholders and operate within different domains, they function independently and without coordination, impacting their overall efficiency and causing issues such as voltage instability, frequency fluctuations, high financial costs, and long charging durations. Past work has accumulated abundant knowledge on how to design each system independently; however, strategies to achieve synergistic outcomes beneficial to both parties remain under-explored. It is therefore crucial to develop a collaborative framework that considers how each system responds to the other's actions, such as how power grids adjust electricity prices based on EV charging demands and how EVs choose charging stations based on price and availability. This collaboration is expected to benefit both EV drivers and power-grid operators, reducing costs and improving sustainability. In terms of broader impact, the project also includes capacity-building, education, and outreach initiatives to promote the participation of underrepresented minorities in the modern EV-related workforce. Technically, the project will focus on the following three key components: (1) a robust multi-agent reinforcement-learning control model for power systems to dynamically adjust electricity prices and charging-power rates, which integrates a human charging-behavior model for enhanced accuracy and efficiency; (2) a mean-field game-based control method for large-scale EVs to autonomously select charging stations in a decentralized fashion with awareness of potential charging rates; and (3) an incentive-driven collaboration mechanism to facilitate socially optimal actions between the power grid and EV operators using graph-based multi-agent reinforcement learning and Shapley value. The project will use real-world data to validate its approach and ultimately contribute to the development of a more sustainable transportation and power 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 2024 · 2024-10

This project aims to serve the national interest by developing tools for acquiring data from university instructors and students about faculty use of evidence-based teaching practices, and to learn about challenges faculty face in using these best practices in their teaching. This project expands upon previous work that developed and validated the Faculty Inventory of Methods and Practices Associated with Competent Teaching (F-IMPACT). The F-IMPACT tool helps instructors evaluate their teaching methods. The current project plans to develop a similar survey for students to fill out about what practices they see their instructors utilizing in their classes called S-IMPACT. This new survey tool aims to give fair feedback to teachers that provides actionable items to help improve their teaching. This project also intends to expand upon identifying faculty-perceived barriers to using evidence-based practices. Towards this end the investigators plan to interview up to 240 teachers at two collaborating universities to create and validate the Observed Barriers to Successful Teaching Questionnaire for Lecturers (OBSTQL). Using better teaching methods has been shown to improve student performance and to increase retention. The combination of data obtained could drive action/intervention, ultimately improving retention and recruitment into the STEM pipeline in a manner replicable across multiple institutions. This project plans to utilize a Concerns-Based Adoption Model (CBAM) with three diagnostic dimensions: (1) the Model of Success, (2) Measures of Behaviors, and (3) Measures of Attitudes. The Model of Success is a collection of evidence-based classroom practices developed by a review of the literature on the scholarship of teaching and learning. The Measures of Behaviors used in this project include faculty and student reports via the F- and S-IMPACT surveys, respectively, and validation of reports using the Classroom Observation Protocol for Undergraduate STEM (COPUS). The Measures of Attitudes include the development and deployment of an attitudinal survey (OBSTQL) based on previous grounded-theory research on faculty-perceived barriers to the implementation of evidence-based practices. Specifically, the project plans to continue development and measure congruent validity of the S-IMPACT through correlation with the F-IMPACT and COPUS, from which both inter-rater and parallel-forms reliability could be determined. Combined with OBSTQL development, the project plans to answer the following research questions: (1) What differences are there between student-observed and faculty-perceived usage of evidence-based teaching practices? (2) What are the most common barriers to implementation of evidence-based teaching practices? (3) Can student observations of instructor actions reliably measure instructional style? and (4) Does a guided-observation design produce less implicit bias and higher reliability compared to affect-based student evaluations of teaching? The NSF IUSE: EDU Program supports research and development projects to improve the effectiveness of STEM education for all students. Through its Engaged Student Learning track, the program supports the creation, exploration, and implementation of promising practices and tools. 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 2024 · 2024-10

Non-technical Abstract: The project aims to establish a comprehensive Quantum Information Science and Engineering (QISE) program at the University of Tennessee at Chattanooga (UTC). Focusing on research, education, workforce development, and community participation in quantum technologies, the project partners with Texas A&M University (TAMU) to investigate a novel theoretical and experimental scheme for demonstrating distributed quantum sensing on a metropolitan-scale fiber-optic quantum network in downtown Chattanooga. The research focuses on creating and distributing multi-photon entangled states across multiple distant nodes on the quantum network and demonstrating distributed quantum sensing with Heisenberg scaling with the number of involved nodes on the network. In collaboration with several industry partners, UTC has established a Quantum Node Lab connected to the world’s first software-reconfigurable commercial quantum network, powered by Qubitekk, Inc., and deployed by the Electric Power Board (EPB) of Chattanooga. This lab serves as the testbed for the project on the deployed fiber network infrastructure. This cross-sector interdisciplinary collaboration among UTC, TAMU, Qubitekk, and EPB significantly expands UTC’s QISE research capacity and broadens surrounding communities’ participation in QISE. The research findings and results are in turn used to enhance experiential learning experience for students enrolled in the newly launched QISE certificate program at UTC, developed for upskilling technology professionals in surrounding communities. Additionally, PI Li is part of an ongoing NSF ExLENT project to establish an inter-institutional QISE curriculum in the Southeastern US. This ExpandQISE project naturally feeds into the ExLENT effort by offering experiential training to the next generation of QISE professionals to meet the increasing regional demand for quantum talents. Technical Abstract: Most current experimental quantum sensing demonstrations are limited to measuring physical quantities at a single location or distributed sensing of multiple physical quantities within a controlled lab environment. There is no concrete manifestation of distributed quantum sensing on a deployed commercial network infrastructure thus far. The objective of this project is thus to bridge the gap between proof-of-concept in-lab demonstration and practical real-world implementation. Specifically, the project aims to experimentally demonstrate distributed quantum sensing with Heisenberg-scaling among 8 distant nodes on a deployed metropolitan-scale fiber-optic commercial network infrastructure. Theoretically, the project utilizes both Fisher information matrix (FIM) and quantum Fisher information matrix (QFIM) to provide feasible measurement procedure for attaining Heisenberg scaling with coincidence photon counting. Experimentally, the project creates, characterizes, and distributes a 4-photon entangled Greenberger-Horne-Zeilinger (GHZ) state across 8 distant nodes on the deployed commercial quantum network, and demonstrates distributed quantum sensing with Heisenberg scaling via measurements of 8 unknown local phases in the 8 nodes. This project serves as an excellent example of advancing quantum information science and engineering (QISE) across both academia and the private sector. 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 2024 · 2024-09

This Cyber-Physical Systems (CPS) project will investigate cognitive and cooperative sensing and imaging systems for rapid near real-time subsurface infrastructure monitoring and mapping. This research advances the frontier of subsurface sensing to a new paradigm enabling practical large-area surveys not possible by existing means. By significantly enhancing subsurface knowledge acquisition, the systems in this project will have far-reaching implications for maintenance and planning related to urban subsurface infrastructure, improving resilience, security, emergency response and urban renewal. The interdisciplinary nature of this collaborative research project broadens inter-institute and institute-community interactions. The research activities and outcomes will enhance and enrich existing STEM education curricula, CPS research efforts and summer programs for K-12 students, undergraduates, graduates and underrepresented groups in both Burlington, Vermont, and Chattanooga, Tennessee, leading to the development of a highly competitive and diverse STEM workforce for Internet of things, smart cities, public safety, and transportation industries. The goal and scope of this project are to create faster and more accurate subsurface infrastructure sensing systems using teams of coordinated autonomous ground penetrating radars (GPRs) equipped with innovative and feedback-controlled cognitive slant sensing (CSS) capabilities. The research methods will be a collaborative and integrated development of hardware, communication networking, data acquisition and analytics, fundamental algorithms and models. The research approaches are to: 1) Build autonomous mobile GPR agents with slant scanning and edge-enhanced communication and computing. The CSS-GPRs can operate in both distributed and collective modes, with agents scanning individually or as a team in a scalable architecture; 2) Create synergistic multi-agent monostatic and multistatic teaming to map and construct 3-D images of subsurface infrastructure using novel slant imaging methods; 3) Assemble teams of autonomous GPRs with networking capabilities to enable adaptive switching between distributed and collective sensing modes; and 4) Validate with laboratory and field tests in challenging urban environments. The potential contribution of this research is advanced sensing systems that swiftly traverse designated terrains, employing data-driven adaptive methodologies to yield high-fidelity and scalable tomographic renderings of subsurface conditions and built 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 2024 · 2024-09

Early detection of breast cancer is critical to decreasing mortality, and breast-ultrasound imaging is commonly employed in early diagnosis due to its widespread availability, portability, and affordability. Yet, breast ultrasound is inherently noisy and of low contrast, characteristics that challenge its effectiveness at breast-cancer diagnosis and hinder application of automated deep-learning methods. Furthermore, the training such state-of-the-art deep learning requires extensive training samples equipped with costly manual labeling by radiologists. Therefore, this project aims to develop breast-cancer detection using deep learning that can function effectively with minimal annotations as well as within the substantial noise inherent to breast ultrasound. Beyond boosting public-health outcomes, broader-impact activities include the participation of women and minority students in the research. Specifically, this project aims to develop a weakly-supervised breast-cancer detection using active and weakly-supervised learning to help doctors diagnose breast cancer in ultrasound images. Weakly-supervised object localization will be applied to avoid reliance on bounding-box-level annotations, instead employing a transformer-based classification network to detect breast cancer. Furthermore, active learning will select the most informative images for training an improved breast-cancer detection model by iteratively choosing the most relevant breast-ultrasound images based on detection outcomes to successively refine the model's capabilities. The success of this project will bring both social and technological benefits, its outcomes enhancing women's health by assisting early detection of breast cancer, particularly benefiting underrepresented groups. 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 2024 · 2024-09

This IRES project provides undergraduate and graduate students from the University of Tennessee at Chattanooga and other institutions with the opportunity to gain international research experience in Chile. A unique aspect of this program is that it supports collaborations among students with backgrounds in biology, geology, and math. In collaboration with non-NSF funded investigators at four universities in Chile, Biology and Geology students are investigating how the environment affects the behavior and reproduction of the degu (Octodon degus), a rodent found only in Chile. Math students use data shared by Biology and Geology students to model the effects of environmental conditions on degu social behavior and reproduction. These projects are essential to understand how animals respond to changes in climatic conditions. The program benefits both students and local communities in numerous ways. Students learn how to write grant proposals and learn some Spanish language skills. Meetings with international mentors and U.S.-based investigators prior to IRES prepare students for 6-25 week-long projects in Chile. Some projects are remote, allowing students with limited ability to travel to participate. This IRES serves a broad constituency in the U.S. IRES students contribute to a remote seminar series on animal behavior and participate in educational activities for K-12 students. The project supports a STEM camp for underprivileged high school students (20 students) from the greater Chattanooga, TN area. Such activities enhance the training of a diversity of students. They also build bridges from secondary school to university. IRES students present their work to researchers and members of the local community in Chile. These activities build international cooperation and understanding as well as enhance the international networks of IRES students. In this program, 30 U.S. students (10 undergrad, 20 graduate) are collaborating with non-NSF funded investigators at four Chilean institutions (Universidad de Chile, P. Universidad Católica de Chile, Universidad de los Andes, Universidad Mayor) to determine how environmentally mediated changes in social organization and social structure influence the reproductive success of the degu (Octodon degus), a social rodent found within a wide geographical range in Chile. The project supports field-based projects in which Biology and Geology students use a combination of behavioral (e.g., live-trapping, telemetry, data-loggers) and geospatial (e.g., GPS, spatial mapping) methods to collect data. Specific project designs test alternate hypotheses within an overarching conceptual framework that has emerged from a nearly two-decade study of degus in Chile. Data generated by Biology and Geology students are used by Math students to generate predictive models for the effects of environmental change on animal societies. These models generate ideas for additional field-based projects led by Biology and Geology students. The program supports lab-based projects to estimate the impact of the environment on mating strategies and reproductive success. These and other activities supported by this program are designed to develop the professional skills and cultural understanding of students, increasing the likelihood for successful future projects and their capacity to compete and collaborate in the international research arena. Each cohort of students is engaged in research, educational, and cultural activities for up to two years. Prior to their IRES field-based travel and research, students participate in animal and research training, grant proposal development, and Spanish language courses. Pre-trip meetings with U.S. and international mentors facilitate the development of successful and productive projects. During their international experience, students develop behavioral, geospatial, and math modeling skills, giving them a broad research toolkit. Collaborations with Chilean counterparts and non-NSF funded collaborators from Colombia and the U.S. are meant to enhance their international network of peers and potential collaborators. After the IRES field work, the program supports student-led publications in peer-reviewed journals and presentations at regional and national meetings. An important aim of the program is to broaden the impacts of the project by promoting international research and STEM education in the greater Chattanooga, TN area. To this end, IRES students are participating in a remote seminar series with an international audience of behavioral ecologists. The project also supports a year-long STEM training program for under-privileged high school students participating in outreach programs supported by the University of Tennessee at Chattanooga. In this program, students develop scientific and critical thinking skills. The program culminates in a week-long STEM camp during which students learn data collection, geospatial analysis, and math modeling skills like those used by IRES students in Chile. 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 2024 · 2024-08

Biodiversity faces threats from fluctuating salinity levels, influenced by climate shifts and human activities such as water extraction, agricultural runoff, and pollution. These changes pose significant challenges to aquatic ecosystems, stressing and endangering resident species. However, some organisms exhibit remarkable resilience to varying salinity levels through physiological adaptability and genetic mechanisms. How do these species thrive in fluctuating salinity conditions, and what can studying their evolutionary, genetic, and physiological processes reveal about resilience and adaptation in changing environments? This project will investigate these questions using microscopic invertebrates—monogonont rotifers, whilst nurturing a new generation of scientific experts in this ecologically important yet often understudied group of invertebrates. This project will support a postdoctoral associate, a Ph.D. student, two M.S. students, and 15 undergraduates, primarily recruited from underrepresented groups. Additionally, 40 undergraduate students will participate in hands-on research activities through two summer programs. Researchers will engage the public through collaborations with universities, university museums, and the Tennessee Aquarium, promoting a broader understanding and appreciation of the challenges biodiversity faces in a changing world through the lens of rotifer evolutionary ecology. Monogonont rotifers play critical roles in aquatic ecosystems by influencing bioturbation, bacterial denitrification, and the transfer of energy from producers to larger consumers. Their short generation times and varying salinity tolerances within the same clade, make them ideal subjects for studying ecological, physiological, and evolutionary processes related to salinity tolerance. This project will: (1) enhance understanding of rotifer taxonomy and ecological distribution by sampling across various salinities using an integrated taxonomic approach. Despite extensive documentation of marine biodiversity in the United States, there is no formal survey of saltwater rotifers from the Southeast U.S. Focusing on this area is particularly relevant due to significantly increasing coastal flooding, sea-level rise and salinification interacting with dense human populations; (2) investigate genomic and transcriptomic differences among salinity-tolerant species through common garden experiments, revealing differential genomic arrangements, gene expression patterns, and phenotypic responses to changing environments; (3) utilize phylogenomic methods and advanced phylogenetic comparative methods to determine ancestral habitat preferences among targeted genera, assess niche conservatism impacts on current species distributions, and analyze rates of adaptation for salinity-tolerant traits. 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 2024 · 2024-08

Project Summary/Abstract Bacterial responses to fatty acids include, but are not limited to, degradation for metabolic gain, modification of membrane lipids, alteration of protein function and regulation of gene expression. Vibrio species exhibit significant diversity with regard to the machinery known to participate in the uptake and incorporation of fatty acids into their membranes. Both aquatic and host niches occupied by Vibrio are rife with various free fatty acids and fatty acid-containing lipids. The roles of fatty acids in the survival and pathogenesis of bacteria have begun to emerge and are expected to expand significantly. Compared to the import mechanisms of minerals (e.g., iron) and sugars (e.g., lactose), the process and significance of scavenging and handling fatty acids is punctuated with gaps in knowledge. Gaining a better understanding of how microbes harness environment-specific resources will shed light on several themes of pathogenesis, such as environmental persistence, transmission to humans, and course of disease. The varied abilities of bacteria to recognize, uptake and utilize lipid molecules from their environment certainly represents a worthwhile research endeavor. It is hypothesized that Vibrio cholerae undergoes significant structural modifications to its membrane phospholipids depending on the exogenously available polyunsaturated fatty acids (PUFAs), and that these alterations affect membrane permeability sufficient to change the susceptibility to membrane active antimicrobials. The proposed project seeks to advance a specific mission of the NIGMS: to increase our understanding of biological processes and lay the foundation for advances in disease diagnosis, treatment, and prevention. Accordingly, the research aims are to i) interrogate the nature and degree of polyunsaturated fatty acid (PUFA) incorporation into membrane phospholipids and ii) examine the altered membrane dynamics and antibiotic susceptibility conferred by exogenously acquired PUFAs. Bioanalytical methodologies (thin-layer chromatography and UPLC/MS) will quantify and structurally characterize exogenous PUFA-mediated phospholipid modifications. Interrogation of membrane permeability will be performed in vitro using established dye-based assays and in silico with atomistic modeling and simulation analyses. Collectively, the data will lay the foundation for deciphering a versatile pathway in Gram-negative bacteria by defining PUFA assimilation capability and its impact on membrane permeability, a critical cellular attribute that must be characterized in the interest of developing strategies to combat infection. The results of these studies should contribute to our current understanding of how and why bacteria have evolved to utilize a variety of exogenous lipid molecules, thus providing insight into their survival both outside and within the host, as well as unlocking pathways vulnerable to antimicrobial attack.

NSF Awards · FY 2024 · 2024-07

The University of Tennessee Chattanooga (UTC) and Cleveland State Community College (CSCC) have partnered to develop EXPAND TN: Explorations: Experiential Learning in Advanced Manufacturing towards Novel and Diverse Career Opportunities for Rural Tennessee (TN) Students. EXPAND TN will provide at least 120 dual enrollment high school students from rural counties in southeast Tennessee with the opportunity to explore careers in advanced manufacturing. EXPAND TN is designed to improve postsecondary education outcomes for dual enrollment students, specifically for engineering and technology degree programs. This will be accomplished through a comprehensive experiential learning program that allows students to explore career opportunities in advanced manufacturing – an emerging discipline that continues to transform the US manufacturing sector. ExPAND TN is a comprehensive experiential learning (EL) program that includes a six-week EL activity integrated into a dual enrollment mathematics course offered to high schools in three rural school districts primarily serving low-income, first-generation students. EL program participants will also engage in advanced manufacturing (AM) career exploration through industry tours, an AM Career Fair, and AM professional development activities. Through the EXPAND TN Scholar Program, 30 of the dual enrollment students will have the opportunity to extend their EL experiences through a year-long AM mentorship, rotational job shadow, and scholarship program supporting students’ transition into engineering or technology majors at CSCC or UTC. Objectives of the program include: (1) Increase participants’ interest and motivation to pursue a degree in engineering and technology; (2) Increase participants’ research skills and practical knowledge of advanced manufacturing; and (3) Demonstrate that this approach represents a viable, sustainable workforce pipeline. 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.