University of Kentucky Research Foundation

NSF Awards · FY 2025 · 2025-02

This doctoral dissertation research project examines how corporate consolidation in the rental market is impacting the fiscal landscape of cities. The project specifies the evolving portfolio management strategies of institutional investors, assesses the impacts of these strategies on tax valuation, assessment and administration, and investigates the connections between corporate portfolios and local strategies to lower tax assessments. The research contributes to knowledge on the housing shortage by analyzing how shifting concentrations of investment in rental housing are impacting urban tax bases and city budgets. The project also contributes to the education and training of an early-career scientist. This research proposes that increasingly consolidated ownership structures associated with rental housing are disrupting longstanding patterns and trends in tax assessment. The researchers conduct expert interviews, augmented by observations at real estate and tax assessment conferences, to assess how rental management strategies seek to offset investors’ largest yearly operational expense in local property taxes. This analysis is complemented by extended fieldwork to document how the portfolio management strategies of rental investors capitalize upon the specificities of local tax systems, such as using local tax consultants to file property tax appeals or in advocating for project-based tax abatements. Through its focus on the infrastructural dimensions of taxes and the tax code, this project brings together scholarship from Geography, City and Regional Planning, Science and Technology Studies, and Urban Studies, to specify how housing rentiership is redefining the fiscal landscape of US cities. 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

High-entropy alloys (HEAs) are an exciting new type of material made by combining five or more different elements in nearly equal amounts. This creates a wide range of possibilities at the atomic level, allowing scientists to experiment with many different combinations. As a result, HEAs have unique internal structures that give them special properties. Unlike traditional metals that often face a trade-off between strength and ductility as strain rate increases, HEAs demonstrate the capacity for simultaneous enhancement of both strength and ductility during high strain rate loadings. This makes them particularly promising for use in protective armor designed to withstand high-velocity impacts, such as in defense and aerospace applications. This project seeks to understand HEAs specifically employed to meet the challenges posed by these extreme loads. The results will help improve safety, durability, and performance in critical applications such as armor plating, spacecraft shielding, ballistic protection, and structural components exposed to impact loads. In addition to its scientific contributions, knowledge gained by the PI and graduate researchers will be shared with students at various educational levels, both locally and at the PI's institution. Moreover, the experimental data will be made publicly available, encouraging further research and innovation within the scientific community. The overarching goal of this two-year project is to advance the understanding of resilient high entropy alloys (HEAs) capable of effectively mitigating high-velocity impact loads. Despite extensive research efforts aimed at comprehending the fundamental dynamic failure mechanisms, notable challenges persist, including the exploration of microstructure-property-performance relationships of HEAs for high-velocity impact scenarios and the design of new HEAs tailored for impact protection. To address these challenges, the proposed work will be conducted in collaboration with Dr. Kaliat (K.T.) Ramesh's research group at Johns Hopkins University (JHU), leveraging their cutting-edge facilities not available at the PI's home institution. The project will unfold in three key phases. Initially, we will conduct high-velocity impact experiments at velocities up to 2 km/s, utilizing JHU's Hypervelocity Facility for Impact Research (HyFIRE). This phase will yield high-speed images of HEA targets from different perspectives, providing invaluable insights into HEAs' performance under high-speed impacts. Subsequently, the second phase focuses on characterizing the dynamic mechanical properties of HEAs at high strain rates up to 1000/s, utilizing JHU's Kolsky bar facility. This investigation will illuminate how deformation mechanisms, flow stress, and strain-hardening are influenced by strain rate, microstructure, and temperature. Finally, in the third phase, we will comprehensively characterize the microstructures of HEAs before and after the high-velocity impact experiments, utilizing Scanning Electron Microscopy (SEM) and Transmission Electron Microscopy (TEM) at the PI's home institution. This comprehensive analysis will establish complete microstructure-property-performance relationships. The outcome of this project will significantly advance our scientific understanding of material failure mechanisms in HEAs and similar complex alloys under impact loading conditions. 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

This grant supports research that will advance investigations of lithium transport in multilayer systems with nano-sized layers, under realistic operating conditions. Lithium is of immense importance in the development of a diverse set of applications, such as drug release for pharmaceuticals, quantum technology and electronics, and energy storage devices such as batteries. Interfaces are more prevalent in nanosized systems and can drastically change the functionality of a material for lithium transport. Understanding lithium transport through the interfacial systems can have a significant impact on the design and applications of nano-scaled multilayer systems. This joint project between the University of Kentucky of USA and Technische Universität Clausthal of Germany will develop a specialized measurement technique of neutron reflectometry and numerical modeling to investigate lithium transport in nano-scaled multilayer systems. Results from this research will help to strengthen U.S. standing and influence in the development of electric vehicles and hybrid electric vehicles, increase the safety and reliability in pharmaceuticals, quantum technology, and energy storage devices, as well as help the American workforce become more competitive in these industries through the training of graduate and undergraduate students. The PI will also actively recruit and mentor women and minorities to foster their interest in the fields of nanotechnology and energy storage. There is a lack of systematic investigations of atomic transport through interfaces and confined systems by standard diffusion measurement techniques. The primary goal of this joint research project between the University of Kentucky of USA and Technische Universität Clausthal of Germany is to develop advanced in-situ techniques for investigating atomic transport that can clarify the rate mechanisms responsible for anomalous atomic transport through interfacial systems and aid in the design of nano-scaled multilayer systems of high quality. Specifically, the research group from the University of Kentucky will develop and parameterize models for Li+ transport in nano-scaled Si/Li3NbO4 multilayer systems and aim to find approximate and numerical solutions for the nano-scaled multilayer systems studied using neutron reflectometry by the research group from the Technische Universität Clausthal of Germany. The dependence of Li+ transport on mechanical stress and space charge zones at the interfaces will be examined to elucidate the mechanisms responsible for anomalous atomic motion through interfacial systems. Students will be trained in the development of in-situ neutron reflectometry and modeling analysis to understand the rate mechanisms controlling atomic transport in multilayer systems with nano-sized layers and to design nano-scaled multilayer systems of high quality. 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 · 2024-12

Commonly known as the “spotted wing Drosophila,” Drosophila suzukii has become one of the most captivating cases of rapid worldwide invasion. Originally from southeast Asia, this fruit fly was first recorded in California in 2008 and has since spread to 48 of 50 states in the US, with parallel expansions occurring in Europe. In addition to its intriguing invasion biology, D. suzukii is a significant pest that causes up to $500 million in annual losses to US agricultural efforts. The proposed work will create long-term collaborations with local farmers and naturalists across Vermont and Kentucky to understand the genetic, physiological, and ecological underpinnings of D. suzukii’s success as an invasive species in the context of a rapidly changing world. Of particular interest to our work is D. suzukii’s capability to develop into summer-specialized and winter-specialized “morphs,” a physiological feature that plays a key role in their success and hardiness as invaders. We will combine physiological experiments, genomics, and computer simulations to predict how these traits will evolve under various climate change projections. We will focus on the capacity of the fly to expand its habitat into northern latitudes as colder winters, an ecological delimiter for D. suzukii, continue to weaken due to climate change. We will also develop a summer science module for K-12 students focused on horticulture, invasive species, and climate change, and lesson plans from these modules will be published in peer-reviewed science education journals. The project will train multiple undergraduate interns, two graduate students, and a postdoc. Global climate change has introduced novel stressors to many habitats, and it is unclear which species may emerge as winners or losers in a changing world. To date, most efforts in climate change biology have focused on traits important for coping with extreme heat events. Yet, winter temperatures are warming twice as fast as summer temperatures in North America, and the evolutionary consequences of more heterogeneous winters remain understudied. This is a critical knowledge gap given that species distributions are often limited by minimum winter temperatures. Quantifying factors that shape winter biology is critical for predicting organismal responses to changing climates. This proposal investigates the relative contributions of plasticity, local adaptation, and seasonal adaptive tracking in fine-tuning key overwintering traits in D. suzukii. We will use flies from Vermont and Kentucky to quantify genetic variation in cold tolerance, overwintering survival, and post-winter reproduction, as well as the reaction norms of these traits in summer/winter seasonal morphs. We will use whole genome resequencing to determine whether D. suzukii has persistent overwintering populations and the extent to which genetic structure is shaped by adaptive tracking. Lastly, we will create a novel set of simulations to explore how plasticity, local adaptation, and adaptive tracking evolve in a metapopulation that experiences fluctuating stressors. We will also incorporate projections into our simulations to predict how reaction norms for overwintering traits evolve in response to climate change. This project is jointly funded by Integrative Ecological Physiology (IOS/IEP) and the Established Program to Stimulate Competitive Research (EPSCoR). 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 · 2024-11

Biological phenomena are often driven by complex dynamic regulatory networks. In natural or engineered systems, complicated structures can be generated from simpler building blocks, or modules. This notion of complex systems built from modules is also prevalent in modern systems biology. However, a clear theoretical foundation of modularity, including useful definitions of basic concepts and mechanisms, is still missing. This research project will fill this gap by defining modular structures in biological systems in a mathematically rigorous way. The research will determine why modularity can be advantageous to an organism and elucidate how modularity can be leveraged to advance our understanding of molecular systems. Studying the modularity of specific gene regulatory networks underlying salamander limb regeneration as well as hormone regulation in plants harbors the potential to reveal novel biological insights. Through involvement of students in all aspects of the research, this project contributes to the interdisciplinary training of STEM workforce. The dissemination of results through a dedicated project website and webinars enables anyone to analyze biological network models. The foundation of this project is a rigorous, structure-based definition of modularity in the context of Boolean networks, a common modeling framework in systems biology. Through computational, experimental, and theoretical studies, it will be shown that this definition of modularity (i) is biologically meaningful, (ii) implies a decomposition of the dynamics of Boolean networks, which can be employed to efficiently compute their dynamics, and (iii) that modular networks can be controlled effectively. The theoretical results, including theorems and implemented algorithms for practical computation, will advance the body of knowledge in the fields of network analysis, systems biology, and developmental biology. The validity of the project will be demonstrated through (1) in vivo analyses in the model plant Arabidopsis, (2) in silico analyses in an emerging animal model, axolotl. This will yield novel biological insights regarding (1) the interplay between phytohormones during Arabidopsis organogenesis, and (2) gene regulatory networks directing fibroblast reprogramming in axolotls. 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 working to shift the narrative nationally in engineering education from surviving in a culture of stress to a narrative of thriving. Higher education is facing a mental health crisis, with increasing rates of mental health and well-being (MHWB) challenges both nationally and globally. This change towards a culture of thriving in engineering is particularly important because MHWB impacts every aspect of engineering culture. Most approaches to support MHWB use faculty training in recognizing students in distress, which is a deficit approach instead of a proactive, asset-based approach. This Level 2 Institutional and Community Transformation project is designed to build on an existing community of practice that includes over 100 people, including faculty, staff, graduate students, postdoctoral fellows, and university administrators. This group empowers members to discuss MHWB in engineering and share strategies to support student, faculty, and staff MHWB. Strengthening and expanding this community will have an asset focus on how to promote and celebrate well-being, normalizing these conversations in engineering education. This project has the potential to directly support the MHWB of students, faculty, staff, and administrators in engineering education. The main research question is: How can faculty, staff, and administrators act as agents of change for cultures of well-being in undergraduate engineering education? The design consists of (1) a programmatic expansion of the Wellness in Engineering Community of Transformation (WE-CoT) with well-being advocate mini-grants to support the integration and assessment of MHWB interventions as well as (2) qualitative research that assesses the experience and advocacy development of participants via focus groups, interviews, and photovoice, a method centering members’ voices. The programmatic expansion plans to include a monthly meeting series and development of a sustainable community structure. The project includes support for development of a MHWB intervention, assessment plan, and data analysis for mini-grant recipients. Each project will receive mentorship support with experts in educational research methods and/or intervention development and counseling. This programmatic expansion is positioned to support the continuation of MHWB workshops for engineering faculty, staff, and administrators nationally. The project plans to utilize a qualitative research design focused on centering the voice of engineering faculty, staff, and administrators to answer the research questions. These will consist of photovoice interviews and focus groups with both members in the WE-CoT and also with non-community students, faculty, staff, and administrators who are engaged in engineering education. If successful, the results from this research will inform WE-CoT initiatives to increase the effectiveness of the community in supporting change agents around mental health in engineering. Research results will be disseminated in scholarly publications as well as through WE-CoT monthly meetings and conference MHWB workshops. The NSF IUSE: EDU Program supports research and development projects to improve the effectiveness of STEM education for all students. Through the Institutional and Community Transformation track, the program supports efforts to transform and improve STEM education across institutions of higher education and disciplinary communities. 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 National Science Foundation Innovations of Graduate Education (IGE) project at the University of Kentucky will enhance mining engineering graduate programs by integrating Artificial Intelligence (AI) knowledge and skills. The project aims to address fundamental issues such as data analytics, machine learning, and generative AI in the mining industry by creating a new course titled "Applications of Artificial Intelligence in the Mining Industry" and significantly modifying an existing course on "Mine Automation." These courses will cover critical AI applications, such as data collection, predictive analytics, safety and risk management, and ethical considerations in AI. By collaborating with leading mining companies and academic institutions, this initiative seeks to bridge the gap between AI advancements and mining education, ensuring graduates are well-equipped to tackle modern challenges in mining operations. This project will contribute to the scientific knowledge base and societal well-being by fostering innovation and promoting sustainable mining practices. The hypothesis driving this project is that integrating AI-focused coursework into the mining engineering curriculum will enhance students' problem-solving abilities and adaptability to technological advancements in mining operations. The project seeks to align educational content with the practical, technological, and innovative demands of the industry, ensuring graduates are well-prepared to enter and excel in the workforce. The project's first step involves conducting a comprehensive analysis of current and emerging AI technologies impacting the mining industry through stakeholder engagement via a detailed questionnaire. This questionnaire will gather insights on AI applications, required competencies, and curriculum gaps, ensuring a diverse range of perspectives. With these insights, a flexible and comprehensive curriculum framework will be developed, covering essential AI elements from foundational concepts to advanced applications. This framework will guide the design of the syllabi for the proposed courses, balancing theoretical knowledge with practical applications. Each course will emphasize project-based learning and interdisciplinary collaboration, ensuring students gain hands-on experience with AI tools and technologies relevant to mining. A robust assessment plan will incorporate continuous feedback from academic and industry advisory boards. Semi-annual reviews will ensure the curriculum remains dynamic and responsive to technological advancements and industry needs. By aligning the educational experience with practical demands and evolving technologies, this initiative aims to prepare graduates to lead and innovate in the AI-enhanced mining sector. The Innovations in Graduate Education (IGE) program is focused on research in graduate education. The goals of IGE are to pilot, test and validate innovative approaches to graduate education and to generate the knowledge required to move these approaches into the broader community. 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

Most people in the USA consume disinfected drinking water. While disinfection is vitally important to prevent waterborne disease, disinfection by-products (DBPs) form as an unintended consequence. The U.S. Environmental Protection Agency currently regulates 11 DBPs in drinking water. In 2022, a new class of DBP called halocyclopentadienes (HCPDs) was discovered in chlorinated and chloraminated drinking water from three U.S. cities. These DBPs were found to be very toxic and likely to accumulate in tissues. Given these findings, more information is needed to assess the occurrence of these compounds in drinking water and identify conditions that give rise to them. The goal of this research is to conduct a national occurrence study of HCPDs across the USA to uncover the factors that influence HCPD formation and investigate their genotoxicity (potential for cancer). The results will be used to determine whether HCPDs pose a risk to human health. This goal will be achieved by measuring HCPD concentrations in drinking water collected across the USA, conducting laboratory experiments to understand how HCPDs are formed, and conducting genotoxicity experiments in cells and in laboratory test animals (nematodes). Societal benefits result from a better understanding of the potential risks of these new DBPs. This information will facilitate long-term engineering solutions to enhance drinking water safety and sustainability. Additional benefits result from science training opportunities for high school and college students to increase scientific literacy and improve the Nation’s STEM workforce. Water disinfection was the greatest public health achievement of the 20th century. However, chemical disinfection has raised a public health issue resulting from the potential for cancer and reproductive/developmental effects associated with DBPs. In 2022, HCPDs were discovered in chlorinated and chloraminated drinking water from three U.S. cities. These DBPs were found to be highly cytotoxic and likely to bioaccumulate. Thus, it is important to determine their occurrence in drinking water, along with factors that influence their formation. While the cytotoxicity of three HCPDs is known, their genotoxicity is currently unknown. The goal of this interdisciplinary research project is to address these knowledge gaps through a three-part study to: i) assess HCPD occurrence in drinking water from across the USA, ii) determine important factors in HCPD formation, and iii) investigate their genotoxicity. To achieve this, HCPD DBPs will be quantified using gas chromatography-mass spectrometry. Genotoxicity will be assessed using two model organisms. Single cell gel electrophoresis using Chinese hamster ovary cells will be used to assess genotoxicity in vivo, and long amplification quantitative PCR and transgenerational assays will be used to assess genotoxicity in the nematode C. elegans. Results will advance our understanding of the potential risks of this new class of DBP and enable engineering solutions to enhance drinking water safety and sustainability. The research team will disseminate results to relevant scientific communities, as well as to the lay public and key stakeholders via established outreach programs. Graduate students will be trained to provide research experiences for undergraduate and high school students. These student research and mentoring activities will encourage participation of underserved groups in STEM. 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 will contribute to the national need for well-educated scientists, mathematicians, engineers, and technicians by supporting the retention and graduation of high-achieving, low-income students with demonstrated financial need pursuing degrees in chemical engineering at Prairie View A&M University, the University of Houston, and the University of Kentucky. Despite efforts to improve retention and graduation rates in engineering, challenges persist across these three institutional contexts (a Hispanic-serving Institution, a Historically Black College & University, and a Predominantly White Institution in an EPSCOR jurisdiction), as well as in the broader engineering community. Data from these institutions show a relationship between financial stress, mental health issues, and reduced academic performance among engineering students. This planning grant will use student focus groups to enable the identification of services that would provide financial, engineering identity, and wellness support for students enrolled in chemical engineering programs. Interventions will effectively account for the culture of chemical engineering as a course of study and incorporate students as co-creators of knowledge around what it takes to support student success, well-being, and retention. The planning activities will inform a future Track 3 S-STEM proposal that will support scholars across the three collaborating institutions. The overall goal of this project is to understand how to increase STEM degree completion of low-income, high-achieving undergraduates with demonstrated financial need. Over its one-year duration, this Collaborative Planning Grant project will identify interventions to support the financial stability, engineering identity, and wellness of undergraduate students enrolled in chemical engineering programs. Existing interventions to improve student support are often institution-centric, lack supporting evidence, or do not consider the unique aspects of disciplines. They also tend to overlook student insights as an important part of developing new practices and generating knowledge. This project will enable the development of the infrastructure, programmatic supports, and campus-level relationships necessary to facilitate the development of a robust student support network. Action research will be used to identify and develop stakeholder-driven interventions to support student success and sense of belonging. These interventions will be integrated into a future Track 3 S-STEM proposal that will provide financial, engineering identity, and wellness support for students. This project is funded by NSF’s S-STEM program, which seeks to increase the number of low-income, academically talented students with demonstrated financial need who earn degrees in STEM fields. It also aims to improve the education of future STEM workers and to generate knowledge about academic success, retention, transfer, graduation, and academic/career pathways of low-income students. 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

Society has witnessed revolutionary growth of capabilities in data capturing, through the wide-spread deployment of Internet of Things, and intelligent analytics, through advances in Artificial Intelligence (AI). To fully realize the potential of such a transformation, this project addresses the bottleneck of bringing data and advanced analytics together. Specifically, the project’s novelties are the development of enabling technologies for integrating distributed intelligence into the network edge to support next-generation smart applications. The project's broader significance and importance are its potential to transform how high-impact application domains such as smart energy systems and smart agriculture leverage AI to open unexplored frontiers. The project also includes educational and outreach activities for graduate, undergraduate, and K-12 students, and builds new research capacity that significantly benefits the EPSCOR jurisdiction of Kentucky. This project has three main thrusts. The first develops techniques for the life cycle of distributed intelligence, including data curation, decentralized learning/fine-tuning, and distributed inferencing, with a focus on optimizing the efficiency in a resource-constrained edge environment and adapting to the requirements of the supported application. The second thrust analyzes exemplary applications that can benefit from edge intelligence and identifies their requirements, with an initial focus on smart energy systems and smart agriculture. The third thrust focuses on the planning activities. The objective is to engage stakeholders from the target application domains and to perform preliminary studies for evaluating candidate technologies and formulating future research questions. Activities include two in-person workshops, one held at the University of Kentucky and the other at the Pennsylvania State University, and meetings with domain experts who are potential Co-PIs in a future CISE Large proposal. 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

Strengthening American Infrastructure (SAI) is an NSF Program seeking to stimulate human-centered fundamental and potentially transformative research that strengthens America’s infrastructure. Effective infrastructure provides a strong foundation for socioeconomic vitality and broad quality of life improvement. Strong, reliable, and effective infrastructure spurs private-sector innovation, grows the economy, creates jobs, makes public-sector service provision more efficient, strengthens communities, promotes equal opportunity, protects the natural environment, enhances national security, and fuels American leadership. To achieve these goals requires expertise from across the science and engineering disciplines. SAI focuses on how knowledge of human reasoning and decision-making, governance, and social and cultural processes enables the building and maintenance of effective infrastructure that improves lives and society and builds on advances in technology and engineering. The United States sits on the precipice of historic new investments in transportation infrastructure. These investments have the potential to remake the transportation system in a way that supports the American economy, reduces greenhouse gas emissions, and promotes opportunities for all segments of society. This SAI project enables decision-makers in state and local governments to make informed choices about transportation investments by generating new evidence on how people change their commutes in response to changes in the physical infrastructure around them. Past research on commuting behavior typically observes people at only one point in time. That approach lends valuable insight, but also imposes severe limitations on the kinds of inferences that can be made about commuting behavior. This new project overcomes those limitations by drawing on data collected in the American Community Survey (ACS). The ACS is an annual survey of about 2 million households conducted by the US Census Bureau since 2005. Among other data, the ACS records where people live, where they work, what they earn, and how they get to work. Utilizing a secure data center to protect privacy, this project identifies and links the responses of individuals who happen to be surveyed in more than one year, creating a longitudinal data set. By observing the change in commuting behavior over a period long enough to observe changes in transportation infrastructure, the resulting data set overcomes the limitations of surveys that observe people at only one point in time. These new data are used to address four questions: 1) By how much can changes to the residential built environment reduce car commuting? 2) How does teleworking affect commuting distance? 3) How does teleworking affect wage growth? And 4) Who is leaving transit-rich areas, who is replacing them, and how do their commutes change? The data and analyses are made widely available through the network of 33 Federal Statistical Research Data Centers, enabling new directions for future research on a broad range of transportation, economic and social outcomes. This award is supported by the Directorate for Social, Behavioral, and Economic (SBE) Sciences and the Directorate for Engineering. 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

The goal of the Pandemic Environmental Surveillance Center for Assessing Pathogen Emergence (Pandemic ESCAPE) is the timely detection of emergent pathogens across a variety of settings through cost-effective and easy-to-implement environmental surveillance (ES). ES uses environmental samples, to discover and monitor pathogens. Pandemic ESCAPE will advance ES technology, data interpretation, and adoption to promote its widespread deployment across the United States. Pandemic ESCAPE will adopt multiple strategies to tackle this challenge. These include research and engineering to design portable and easy-to-use ES devices, development of new methods for ES and genome sequencing, modeling of disease transmission using ES data, co-production of ES knowledge through community partnerships and participatory science, and engagement through public outreach and education activities. The Center will work closely with public health experts and the private sector to ensure that its solutions are practical and can be easily integrated into existing infrastructure. The long-term strategic goals of Pandemic ESCAPE are to 1) Create simplified tools to advance environmental surveillance (ES) pathogen monitoring and prediction capabilities; 2) Train the next generation of scientists in ES; 3) Accelerate the adoption of ES as a pandemic prevention tool for everyone; 4) Communicate ES data effectively, efficiently, and inclusively to support knowledge to action; 5) Empower communities to build local ES capacity; 6) Advocate for pathogen detection and response strategies that include ES. Through collaboration with partners in low resource communities, the Pandemic ESCAPE team will develop the necessary tools to grow an extensive ES network that can be used to monitor for pathogens. Achieving these long-term goals will have a transformative impact on how communities identify, monitor, and mitigate the impact of emerging pathogens. 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

Place-based innovation (PBI) plays a crucial role in driving regional economic development and addressing community needs by leveraging existing research institutions, universities, and industries. However, critical gaps remain in our understanding of the duration and investments required to bring products from initial research and development through translation phases to market availability. Similarly, the impact of workforce development approaches on participant career trajectories and earnings potential remains unclear. This project aims to provide novel modeling solutions to quantify and predict PBI-related quantities, considering the complex interplay of geography, technology domain, and cross-sector partnerships. The outcomes of this research may benefit various stakeholders, including companies, nonprofits, governments, universities, and individuals, by providing crucial insights into the timelines, investments, and contextual dependencies associated with converting ideas into societal impacts. The proposed study will develop a deep graph neural network-based foundation model to quantify and predict PBI-related quantities, addressing the challenges posed by data scarcity and the multi-faceted complexity of diverse geography and technology domains. The model will be pre-trained using widely accessible public datasets and fine-tuned on place- or domain-specific datasets, enabling effective handling of data scarcity. The research design focuses on three main aspects of PBI: modeling the time and capital requirements for product development and commercialization; assessing the impacts of workforce development and diversity, equity, inclusion, and accessibility (DEIA) factors on career outcomes; and accounting for the influence of contextual factors such as geography, technology, and cross-sector partnerships. The methodology will leverage cutting-edge AI techniques, such as multi-view learning and graph Transformers, to provide an integrated model for PBI-related modeling and predictions. The project has high potential to significantly advance our understanding of PBI dynamics and complex interplays of factors influencing innovation, workforce development, and regional economic growth. The developed foundation model can serve as a powerful tool for decision-makers, enabling them to make informed choices regarding resource allocation, strategy development, and policy implementation. Moreover, the project's innovative approach will help open up new research avenues in the field of PBI, fostering further advancements in our understanding of innovation ecosystems and their societal impact. 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

Flash floods impact communities throughout the US each year, causing loss of life, property, and livelihoods. Rural communities, especially those in the Appalachian region, are particularly vulnerable to flash floods. This, in part, is due to the limited infrastructure to understand, predict, and prepare for flash floods in these regions. To address these challenges, the project will bring together civil engineers, environmental scientists, and social scientists to work alongside community research partners from the region. A key outcome will be an improved ability to understand, predict, and prepare for flash floods under different conditions. This will be achieved with new models, strategically placed sensors, regional flood analyses, and insight from those most affected by flash floods, community members. Researchers and community members will work together to identify specific issues related to flash floods, such as flooding knickpoints and locations where models may perform poorly. By integrating engineering, environmental science, and social science, this project will create solutions tailored to community goals, serving as a model for resilience planning in vulnerable communities across the US. The project's workforce development plan will guide over 500 middle and high school students in the Appalachian region through college and into their careers. Activities will include field experiences, tree plantings, and environmental sensor trainings.. This plan will be put into action with the help of community partners throughout Appalachia, including local citizens, non-profit organizations, and watershed associations. Flash flooding has caused the highest number of fatalities of any flood type in the last two decades. Communities in central Appalachia are especially vulnerable to flash floods. The goal of this project is to gain fundamental knowledge of flash flooding under a variety of weather events and mitigate its impacts in vulnerable rural communities by advancing research capacity, interdisciplinary collaboration, and scientific literacy across Kentucky and West Virginia EPSCoR jurisdictions. Using increased hydrologic research infrastructure and an evidence-based community engagement model, the project will integrate three research tasks to meet this goal: 1) advance the hydrologic sciences to understand controls of flash floods in disturbed and forested stream systems; 2) facilitate community-engaged research to increase resilience and flash flood technology uptake; and 3) develop a community-led science model for increasing knowledge of flash floods. The project will couple catchment-scale hydrologic models (process-based, machine learning), on-the-ground data collection, regional flooding analysis, and hydrologic sensing technology with evidence-based participatory action research to co-create new flash flood knowledge, tools, technology, and subsequently, tailored solutions. The project will provide insight on heavily disturbed landscapes across the US; how to measure, monitor, model, and predict flash flooding with sufficient time for communities to respond in understudied and infrequently monitored headwater systems; and what current and future flash flood risks look like in stream-adjacent communities. 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

The broader impact of this I-Corps project is based on the development of a sustainable method for recycling expired lithium-ion batteries. This advance represents a significant improvement over current recycling methods, offering a more effective and environmentally friendly solution, crucial for sustainable resource management. This technology employs green solvents that reduce hazardous waste and resource consumption, emphasizing resource efficiency and minimizing ecological impact. Additionally, this technology enhances public health and safety by cutting down the use of dangerous chemicals in the recycling process, thereby reducing health risks to workers and preventing environmental contamination. Overall, this lithium-ion battery recycling technology meets critical industry needs while also serving broader societal objectives, fostering a healthier, safer, and more sustainable future for all. This I-Corps project utilizes experiential learning coupled with a first-hand investigation of the industry ecosystem to assess the translation potential of the technology. The solution is based on the development of a deep eutectic solvent (DES)-based technology for recycling critical metals from expired lithium-ion batteries. This innovative technology efficiently recovers critical metals, such as lithium, cobalt, nickel, and manganese, with high purity and selectivity. By selectively extracting and recycling these critical metals, this technology can reduce environmental impact and promote a circular economy. In addition, this technology uses a solvent that can be derived from low-cost plant materials and is recyclable, which means low cost and minimal wastewater generation. This solution also operates under mild reaction conditions, so it has fewer safety concerns and environmental hazards. This advance represents a significant improvement over current recycling methods, offering a more effective, efficient, and environmentally friendly solution for critical metal recovery from lithium-ion batteries. 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 award supports three separate experimental programs, each aimed at exploring the structure of our universe at the most fundamental level. The first two programs will investigate how the quarks and gluons that are liberated in high energy proton-proton and electron-proton collisions spawn new quarks and gluons that eventually combine to form the particles that comprise the visible universe. These studies will shed light on the nature of Quantum Chromodynamics (QCD), a theory that describes the strong force, one of the three fundamental forces that are in the Standard Model of Particle Physics. The third program aims to discover signals of new forces and particles that are not yet included in the Standard Model. The g-2 experiment at Fermi National Laboratory is on track to make the world’s most precise measurement of the anomalous magnetic moment () of the muon, a particle very similar in nature to the electron, but a factor of 200 times heavier. A discrepancy between the measurement and theoretically calculated value of would point to possible contributions to the muon's anomalous magnetic moment from Beyond the Standard Model (BSM) forces and/or particles. The experiments supported by this grant will provide undergraduate and graduate students with the necessary tools and experience to either continue their work in basic research or to enter the technical workforce and lend their problem-solving expertise to a myriad of technical fields, including the areas of finance, big data analysis, patent law, the development and application of machine learning algorithms and medical physics. Undergraduate students funded by this award will continue to have the rare opportunity to experience the scientific culture and basic research performed at U.S. National Laboratories. The QCD program will use the STAR detector at the Relativistic Heavy Ion Collider to measure jet fragmentation functions and energy-energy correlators, from distributions of hadrons inside of fully reconstructed jets. Measurements of the differential cross-sections of identified charged hadrons within reconstructed jets in center-of-mass 200 GeV proton-proton and proton-Ion collisions will provide unique constraints on the collinear and transverse momentum dependent quark and gluon fragmentation functions and new insights into hadronization effects in nuclear matter. Measurements of energy-energy correlators in central and forward rapidity jets in proton-proton collisions at 500 GeV will giveinsights into how this hadronization process evolves in time, providing complementary information to similar measurements at the Large Hadron Collider. Finally, measurements of the Collins asymmetry in forward jets, using the newly upgraded STAR detector will provide sensitivities to the transverse spin distributions at high x as well as the Collins fragmentation functions. The BSM component of the program will allow the PI and her group to continue their current efforts in the muon g-2 experiment (E989) at Fermilab and contribute to the analysis of the final datasets (Runs 4-6). Contributions will include the development and support of the g-2 GEANT based simulation software package, simulation production and evaluation of select beam dynamics systematic errors using the simulation 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

Dialog systems provide the most natural, convenient, and expressive interfaces for humans to use computers to accomplish tasks, regardless of their background, technical ability, or age. Dialog systems that are custom built for a specific task are usually limited to predefined domains, basic database interactions, and lack personalization, which restricts their real-world impact. While large language models have demonstrated remarkable capabilities in open-domain dialog, they struggle with controlled, collaborative multi-turn interactions needed for effective task completion. This project aims to develop advanced dialog systems that seamlessly adapt to new, unseen user intents, provide comprehensive responses beyond structured databases, and personalize interactions according to user traits. These advancements will directly impact numerous industrial and educational applications, including personalized tutoring, customer support, and healthcare consultation. By enabling the most intuitive and natural way of communication, this project aims to transform how individuals engage with computing systems for performing a wide range of tasks, leading to improved productivity, increased accessibility, and enhanced user experiences. This project aims to significantly enhance the adaptability, flexibility, and personalization of task-oriented dialog systems by advancing the capabilities of large-scale language models with reinforcement learning. First, the project will focus on developing mixed-initiative dialog models. These models will leverage pre-trained large language models, integrate interactive feedback from reinforcement learning algorithms to dynamically adjust their responses based on inferred task semantics during interactions. Then, this project will extend these dialog models to autonomously utilize a variety of tools, such as web searches and calendars, to handle user requests that fall outside the scope of existing databases or predefined tools. The models will execute actions in interactive settings by comprehending the documentation of appropriate tools and integrate feedback from tools to enhance their proficiency. Finally, the project will expand the functionality of dialog models to personalize interactions based on both explicit and implicit user needs. This includes grounding conversational flows in personality traits derived from historical chat logs and unstructured data. Ultimately, this project seeks to establish task-oriented dialog systems as highly practical, human-like tools capable of effectively supporting diverse real-world applications. 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 project will stand up the Engineering Wellness Center (EWC) at the UK Pigman College of Engineering (COE). The EWC seeks to improve engineering students' mental health and well-being by providing and investigating the impact of increased access to resources that enhance social development, emotional well-being, and sense of belonging. The increasing prevalence of mental health challenges among college engineering students poses a threat to broader engineering workforce development and retention efforts by impacting student academic performance. In turn, these impacts extend into industry, where engineers are leaving the sector due to mental health challenges such as burnout. Acknowledging the critical link between mental health and the success of engineers, this project emphasizes a proactive approach to mental health, encompassing well-being beyond the absence of illness to include sense of belonging and social-emotional skills that are vital to student achievement. Integrating this skill development into engineering education is crucial for cultivating well-rounded, successful engineers prepared to navigate diverse professional settings. Further, the EWC will ensure that all engineering students have the structural support necessary to prioritize their mental health as undergraduate engineering students. This project will enable the EWC to translate research to practice with a goal of enhancing students’ mental health as they navigate their experience within the COE at UK. The EWC will be a welcoming environment within the COE where all students feel supported and empowered to proactively identify their needs and access resources to meet them. Ultimately, we hope to catalyze a culture change to redefine what it means to be a successful engineering student by prioritizing the development of technical, social and emotional skills that not only improve student mental health but also their effectiveness as engineers. This evidence-based EWC will tailor activities to the unique needs of engineering students, informed by specific research on their mental health. The project will measure the impact of these interventions on both mental health and academic outcomes, addressing a critical gap in mental health intervention literature within engineering. By promoting evidence-based interventions, this project aims to reshape engineering identity to prioritize mental health, supporting student success both in academia and the workplace. The EWC is expected to have a strong impact on those students who are historically underserved in engineering that are at highest risk for mental health distress and are least likely to seek professional support for their mental health. 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

Non-technical summary Organic semiconductors form a special class of soluble inks that can be used to create new forms of electronic components such as transistors, diodes, and solar cells. As the performance of these materials improves, there are ready markets for their unique properties: For example, Organic Light Emitting Diodes (OLEDs), where the organic semiconductor is used for its light-emitting properties, are now a mainstream high-end display technology. For organic semiconductors to have impact in other key innovative technologies - such as transistors for sensing, computation, and control elements for flexible electronics, the performance metric that must be optimized is charge transport. Charge transport is related to the speed that charges (positive or negative) can move through these solids - the faster the movement, the better the performance. It is well known that when electric current moves through a material, the material tends to get hotter (think: clothes iron or electric stove burner). When things get hotter, the molecules in those objects begin to move around. Scientists researching organic semiconductors have proposed that this movement of molecules can have a very negative impact on charge transport - however, no definitive evidence of this impact has been shown. Supported by the Solid State and Materials Chemistry program in the Division of Materials Research at the NSF, the object of this proposal is to make a series of molecules that can each move around in different ways as the material heats up. By studying the impact of these movements on charge transport, we will be able to create design rules for the development of next-generation charge transport materials, a key enabling step in the use of organic semiconductors in technologies for lower-cost display, communication, and sensing applications. In the process, students from a wide array of educational stages (from high school through post-doctoral) and backgrounds (particularly students from rural regions of Kentucky) will have hands-on experiences making and measuring cutting-edge materials for flexible electronic technologies. Furthermore, outreach to researchers at Eastern Kentucky University will assist faculty there to kickstart their research efforts through workshops on proposal submission to the NSF. Technical summary Recent studies suggested that in crystalline organic semiconductors there are certain highly disruptive (killer) phonon modes – vibrations particularly disruptive to charge transport – that limit maximum performance in devices such as transistors. While it has been known for many years that thermal dynamic disorder is detrimental to charge transport, the degree to which these motions disrupt transport in organic semiconductors has not been demonstrated. Through the synthesis and study of three new classes of organic semiconductors, each designed to inhibit particular phonon modes, and in collaboration with physicists, spectroscopists and device engineers across the U.S., we will elucidate the relationships between phonon modes and charge transport (as measured through field-effect mobility in organic transistors). Along with providing critical data to advance our understanding of organic semiconductors, the students from these collaborative research groups will receive training in the analysis of previously unknown electronic materials. From these data, we will determine the real degree of impact of, in particular, long molecular axis vibrations on charge transport, and then develop design rules to mitigate the impact of thermal disorder. The simplest of the synthetic targets will be prepared by high school students, introducing them at this critical early stage to collaborative research in organic electronics. Along with outreach to recruit students from Appalachian-serving institutions in Kentucky, workshops on preparing and submitting NSF proposals will also be presented at Eastern Kentucky University, as they begin to start a program in manufacturing engineering, and desire to reignite their long-dormant research programs in the basic 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 2024 · 2024-09

Computational models enhanced with machine learning have the potential to significantly improve our ability to simulate biological processes at multiple length scales. This includes various disease conditions that affect organ structure and function. One such disorder is pathological fibrosis, which is the excess accumulation of extracellular matrix (stiff fibers) within the organ tissue. While this process is compensatory initially, prolonged fibrotic activity can cause increased stiffness leading to dysfunction. This project combines techniques and insights from several disciplines, including engineering, computer science, applied mathematics, and physiology, to develop advanced computational models of the heart. The work aims to build fundamental understanding of heart-disease progression and could aid in the evaluation of potential treatment strategies. Since cardiovascular disease is the leading cause of death in the United States, discoveries from this research could have a significant impact on society. Additional broader-impact aspects of the work include incorporation of technical examples of cardiac engineering into computational-mechanics courses along with the development of open-source software tools and databases. The goal of this research is to develop a computationally-efficient multiscale modeling framework that integrates machine learning and artificial intelligence to predict the structural and functional changes that occur in the presence of heart disease. Specifically, it will create a multiscale finite element framework that uses innovative computational techniques to couple (1) network models (myocardial perfusion), (2) agent-based models (myocardial fibrosis), and (3) timescale separation schemes (myofiber growth). All of these developments integrate concepts from mathematics and engineering to define two-way interactions between system-level biology and molecular mechanisms. To enhance this computational framework even further, a physics informed neural networks will be developed to provide instantaneous calculations of these complex interactions, via efficient replication of the multiscale framework output. This approach will leverage emerging ideas from computer science and applied mathematics. Finally, the computational tool kit will enable a deeper understanding of how microstructural changes in tissue composition affect whole heart function, as well as the effects of potential therapies. 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

This award funds the research activities of Professor Sumit R. Das at the University of Kentucky. The construction of a consistent framework combining the laws of Quantum Mechanics, which describes microscopic phenomena, and General Relativity, which describes gravitational phenomena, remains one of the most important unsolved problems in physics. In recent years it has become clear that a unique property of Quantum Mechanics called "entanglement" plays a key role in achieving this synthesis. This project aims to understand the connection of entanglement and gravitational phenomena in a precise fashion. Quantum entanglement is at the heart of a variety of physical phenomena observed in nature and is one of the basic principles used in the construction of quantum computers. Consequently, the results of this research will serve the national interest by advancing fundamental science in the US. The PI will also involve graduate students and postdoctoral scholars in this research, thus providing them the valuable training necessary to develop into independent scientists and educators. In addition, the results of this research will be used to enhance classroom education both at the graduate and undergraduate levels. The PI also plans to give public talks about his work in various forums and lectures, and at the “Osher Lifelong Learning Institute” which offers courses and enrichment programs to members of the community. In addition, the PI plans to engage in “Science for Everyone, KY” which is an outreach program based in Lexington with an aim to increase scientific awareness in the community. More technically, Professor Das will study an approach to combining the laws of Quantum Mechanics and the laws of gravity which is based on the “holographic correspondence”. In this correspondence, gravitational phenomena in a certain number of spatial dimensions are related in a precise fashion to non-gravitational phenomena in a smaller number of dimensions. Previous research by the PI has uncovered the role of a new kind of quantum entanglement which involves the “internal” degrees of freedom of field theories in the holographic correspondence. The first goal of this project is to explore properties of this kind of entanglement and relate to notions of entanglement in the gravitational description. The second goal is to study how entanglement and other quantum information theoretic features like complexity behave in cosmology, particularly near the big bang, with the hope that this would teach us how to think of this epoch of the universe in a controlled fashion. The third goal concerns the application of these concepts to situations in many-body physics which are driven out of equilibrium by an external disturbance. This latter class of phenomena are intimately related to the quantum physics of black holes which displays chaotic behavior. This should provide insight into various puzzles posed by the Hawking radiation of black holes. 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

The research project aims to advance organic solar cells (OSCs) to support the shift from non-sustainable to sustainable energy sources. While OSCs have achieved significant power conversion efficiency, their large-scale production faces challenges due to the use of toxic solvents that are harmful to both the environment and public health. This project will develop greener manufacturing processes using eco-friendly, biomass-derived solvents such as Cyrene and γ-valerolactone (GVL). These bio-based solvents offer reduced toxicity compared to the currently used solvents. One challenge is that the current components for OSCs are not compatible for processing with these two greener solvents. This project will use an innovative combination of experiments and molecular simulation to rationalize the design of new chemical functionalities with characteristics to improve their solubility. This collaborative project will provide outstanding opportunities for training graduate students in interdisciplinary approaches that integrate experiments and simulations. The project will also support educational opportunities and promote diversity in STEM by participating in local outreach activities such as the "Harvesting Sun Light" workshop for the Expanding Your Horizons conference, designed to inspire middle school girls to engage with STEM. The primary objective of this project is to design and synthesize zwitterlated conjugated polymers and organic molecules that can be processed using the biomass-derived solvents Cyrene and GVL. The researchers hypothesize that tuning the charged group pairs in zwitterionic sidechains will control the solubility and assembly of these materials in the selected solvents. The research approach combines computational and experimental research: molecular simulations will explore the thermodynamics of solvation and assembly of the polymers and molecules in Cyrene and GVL, while experimental efforts will focus on synthesizing these zwitterlated materials and characterizing their assembly and film morphology. The researchers aim to develop manufacturing processes that produce high-performance OSCs with power conversion efficiencies exceeding 15%. This research will advance understanding of the role of zwitterionic sidechains in solubility and assembly, illustrate their impact during the nonequilibrium solvent evaporation process, and result in the development of multiple high-performance zwitterlated polymer systems and manufacturing processes. The outcome of this collaborative project will be the molecular principles for designing zwitterlated polymers and molecules that can be used to produce high-performance OSCs using Cyrene and GVL-based processes and several such polymers and molecules. 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

The Southeast Region Chemical Engineering Chairs’ meeting is an annual conference held by department heads and chairs of Chemical Engineering in the area nominally bordered by Texas in the west up to the Ohio River in the north. This is an important meeting that aims to promote collaboration and innovation by sharing best practices and research advancements, enhance equity and inclusion within departments, and align educational programs with industry needs. In prior years, topics discussed included modernization of chemical engineering curriculum, machine learning and artificial intelligence in the chemical engineering field, mentoring of junior faculty, among many others. Additionally, the meeting aims to support faculty development through workshops and mentoring, and advance research by identifying funding opportunities for projects that address global challenges. As such, this meeting has the greatest impact when as many department chairs from the southeast region can participate. However, due to financial barriers, the attendance of minority serving institutions has been limited. The goal of the next conference is on facilitating interactions with minority serving institutions to support minority students in chemical engineering through joint programs, research collaborations, and exchange opportunities. A travel grant will be used entirely to support department chairs or program leaders from minority serving institutions, who otherwise may not have the financial resources, to attend the meeting. Personalized invitations will be sent to potential speakers and attendees, describing the conference, stating the benefits of attending, and informing them of the availability of travel grants to cover transportation, accommodation, and meals. After the meeting, data will be collected on the participant demographics, and attendee feedback. This data will be evaluated for the effectiveness of the recruitment and support strategies, assessing the impact of the travel grants towards broadening participation. Using this information, we will provide recommendations on how to improve recruitment and participation for future events. 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

The U.S. lacks resources for many minerals that contain elements critical to the national economy and to national security. This project will explore new sources of the chemical elements called the “rare earth elements” (REEs). These are metallic elements with strong magnetic properties and are critical to the energy transition. There is only one currently producing domestic U.S. source of REE. The majority of world production of REEs is from a single mine in China. The proposed research will explore for REE mineral deposits in bedrock, stream sediments, and sedimentary rocks in the Piedmont and Coastal Plain provinces. The mineral monazite is the most common REE-bearing mineral in the crust. Preliminary research demonstrated that monazite is widespread in the region. This new project will address the following questions. (1) What is the source of the monazite currently transported by rivers from the Appalachian Mountains to the Atlantic coast? (2) Is the source metamorphic rocks or igneous rocks, or both? (3) Has the source changed over geologic time? (4) What are the best methods for locating these deposits? Methods used to answer the questions include mass spectrometry and scanning electron microscopy. The answers will permit mineral exploration companies to focus exploration and development on the highest-grade source regions. The project will train graduate and undergraduate students at the University of Kentucky and Syracuse University. Upon graduation these students will be prepared to join the workforce in critical mineral exploration. The project will involve a high school earth science teacher to create science education content. The research will improve the ability of the U.S. to address the critical mineral needs outlined in Executive Orders 13953 and 14017. Research will test hypotheses for the provenance of detrital monazite in clastic systems in the Atlantic Coastal Plain that were demonstrated by the USGS to be critical REE placer mineral deposits. The research addresses the GEO-CM charge of research “leading to advanced understanding of geologic and geochemical processes through which critical minerals form and are concentrated into economically viable deposits”. The Atlantic Coastal Plain province has proven REE potential based on detrital monazite in Upper Cretaceous to modern clastic systems between the Fall Line and the Atlantic littoral zone. The long-hypothesized source of monazite in these deposits are belts of middle to late Paleozoic medium- to high-grade metapelites and granitic magmatic rocks in the crystalline Piedmont and Blue Ridge Provinces. Rather than using detrital zircon as a proxy for the heavy mineral suite, the researchers will date the ore mineral of interest (monazite) for testing provenance models. High-throughput monazite laser ablation-split stream-inductively coupled plasma-mass spectrometry (LASS-ICP-MS) will be used to obtain U-Pb dates and REE concentrations. They will conduct a geochronologic survey of monazite in potential bedrock metamorphic and magmatic sources, not already characterized by their prior research, which will be used to assess provenance and sediment delivery paths from inferred Appalachian sources to Coastal Plain clastic systems. In addition to monazite age and REE variations, the researchers will measure Nd isotope compositions in detrital monazite and source rocks as additional tests of provenance. Samples will include crystalline bedrock (metamorphic and granitic units), modern sediment in headwater streams and trunk streams flowing to the coast (e.g., the Broad River drainage basin), and Upper Cretaceous to Lower Tertiary coastal plain sediments at the Fall Line that are exceptionally high in detrital monazite. The project will train graduate and undergraduate students at the University of Kentucky and Syracuse University. Upon graduation these students will be prepared to join the workforce in critical mineral exploration. The project will involve a high school earth science teacher to create science education content. The research will improve the ability of the U.S. to address the critical mineral needs outlined in Executive Orders 13953 and 14017. 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

Understanding the dynamic response of economic outcomes to a variety of changes is crucial for studying fluctuations in economic activity and designing fiscal or monetary actions aimed at reducing these fluctuations. This Award funds a research project that will develop econometric methods to estimate the impulse response function that are central to the study of how the economy responds to various changes in nonlinear environments. The research project will also illustrate the performance of the estimators in large and small samples. The results of this research will provide empirical researchers with a general framework for the estimation of dynamic responses in many environments. The research will also develop a statistical software to implement the new estimation method. In addition to its contribution to econometric methods, the results of this research will improve macroeconomic decision making, which will reduce economic fluctuations, increase economic growth, and improve the living standards of citizens. This award funds a research project that will contribute to the literature on impulse response functions in a large class of nonlinear structural VARs (SVARs). This study will discuss different notions of impulse response functions, study the validity and performance of local projections, and propose new estimation methods for the average impulse response function and the conditional average response function. The project will develop nonparametric estimation methods that allow researchers to be agnostic about the functional form in models with nonlinear regressors, the process driving the switch between states in state-dependent models and the form of interactions between shocks and initial economic conditions in models with interactions. It will also develop estimation methods for time-varying SVAR models and state-dependent FAVAR models. The project will also develop an econometric software to implement the new method. Besides improving estimation of structural VAR models, the results of this research will improve macroeconomic decision making, which will reduce economic fluctuations, increase economic growth, and improve the living standards of citizens. 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.