University of Kentucky Research Foundation

NSF Awards · FY 2025 · 2025-09

Water infrastructure in the U.S. faces many challenges for long-term sustainability. In Kentucky, particularly in the Appalachian region, water distribution systems, which transport water from the point of treatment to the point of consumption, are unique and complex. The various challenges include topography of the region, insufficient revenue from a declining customer base, and decreasing numbers of licensed operators – the people who work daily to make sure water safely gets to customers. Collectively, these deficiencies have led to unsustainable water infrastructure systems, both in terms of the physical infrastructure and the workforce required to operate it. This project seeks to advance the sustainability of physical water workforce pipelines while strengthening new and existing pipelines for individuals to join the water workforce. This work will identify what drives the sustainability of water distribution systems while simultaneously engaging the workforce who could use this information for decision-making. This work is timely due to unprecedented investments in U.S. infrastructure, which should be capitalized on by prioritizing sustainability to ensure long-term benefits for the physical infrastructure and the workforce needed to design, operate, and manage it. This project will develop sustainable pipelines – advancing U.S. water infrastructure sustainability by (1) integrating sustainability assessments into water distribution system planning and management and (2) preparing a sustainability-minded workforce for the nation’s water infrastructure. The first goal – to integrate sustainability assessments into water distribution system planning and management – will be addressed by developing life cycle environmental and economic models for water distribution systems and leveraging Kentucky Infrastructure Authority’s existing datasets to evaluate hundreds of systems, identify sustainability drivers, and develop a state-level screening assessment for informed decision-making. The second goal – to prepare a sustainability-minded workforce for the nation’s water infrastructure – will take a multi-level approach to water workforce development by engaging new entrants to the workforce through partnerships with prisons in Kentucky and training the current and future workforce in sustainability thinking. This project will advance fundamental understanding of drivers of sustainability for water distribution systems while addressing unique challenges for water distribution systems in Kentucky. Ultimately, this work can increase U.S. economic competitiveness by providing a path to consider long-term sustainability when investing the billions of dollars planned for drinking water infrastructure. This project is jointly funded by the ENG/CBET Environmental Sustainability program 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 2025 · 2025-09

This project develops a method, based on cryptographic protocols, that can be used in emerging radio spectrum markets so Federal agencies can acquire spectrum quickly when needed without disclosing information that may compromise their missions. Radio spectrum is a vital resource for wireless communication and sensing. Market approaches such as the Pay-As-You-Go (PAYG) model and the Spectrum Bux currency have been proposed to more efficiently use the congested radio spectrum. Since Federal agencies are a significant fraction of the spectrum ecosystem, Federal agency use of these emerging market mechanisms is necessary for the market mechanisms to provide the desired overall benefits for the nation. However, Federal agency missions sometimes require accessing spectrum without delay; most market designs require pre-registration and other slow steps. Agency missions sometimes require protecting information about sensitive operations; most market designs disclose the identity of the buyer to the seller so the seller can enforce payment terms. The cryptographic methods developed in this project overcome these constraints and thereby help emerging radio spectrum markets succeed. The architecture supports policy adaptation and microeconomic experiments to inform future spectrum policy and market design decisions. The project also helps educate the next-generation spectrum workforce. The core of the project is development of novel protocols based on cryptographic credentials and zero-knowledge proof (ZKP) technologies. The research effort has three primary thrusts. Thrust one creates a ZKP-capable spectrum credential system to enable efficient attribute-based authentication for spectrum access. This allows users with valid spectrum credentials to request access without prior registration or disclosure of sensitive identifiers. Thrust two develops a secure and auditable Spectrum Bux payment system in the PAYG model. The payment system enables asynchronous settlement that guarantees the band manager receives payment after a user successfully obtains a spectrum access assignment, without disclosing private details about the user. Thrust three develops a simulator for studying the interactions between markets using the new credential and payment systems, user and agency pricing strategies, service-level agreements and other contract types, and Federal spectrum policy choices such as rules for protecting spectrum incumbents. The simulator is used for microeconomic experiments under diverse scenarios, ultimately identifying optimal policy and pricing strategies for specific spectrum-sharing contexts. The outcomes of this research will be made publicly available online, including publications, tutorials, and open-source software. 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 will advance fundamental mathematical knowledge by developing new tools to understand the deep connections between addition and multiplication within number systems. It bridges two major areas of pure mathematics: additive combinatorics, which studies patterns formed by adding numbers, and multiplicative number theory, which focuses on properties related to multiplying numbers, especially prime numbers. The PI will create novel techniques to solve complex problems where these additive and multiplicative structures interact. Findings of the project will benefit fields beyond mathematics, particularly cybersecurity, where improved understanding of number-theoretic functions could lead to stronger encryption methods protecting digital information. The combinatorial methods developed will also have the potential to enhance the reliability of data transmission and storage systems. Additionally, the project will train undergraduate and graduate students and contribute to educational outreach through math competitions and K-12 programs to inspire future scientists. The project will tackle challenging open questions involving prime numbers, smooth numbers, and general multiplicative functions. More specifically, the PI will develop new methods in additive combinatorics that can be applied to questions involving primes, such as finding narrow progressions in primes and finding solutions to linear equations in subsets of primes. The PI will analyze the behavior of multiplicative functions in various additive contexts, such as obtaining Gowers norm estimates for multiplicative functions in short intervals and in arithmetic progressions. The project aims to advance techniques in additive combinatorics, especially those based on higher order Fourier analysis and the quantitative theory of inverse sumset theorems, to tackle problems involving both additive and multiplicative structures. By studying how multiplicative functions behave in additive structures, the project seeks to uncover deeper insights into their distribution properties and connections to prime numbers and other key objects in number theory. The project is also expected to forge new connections across mathematics and theoretical computer science, stimulating progress in these fields. 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

Lattice polytopes are convex polytopes where every vertex is an integer vector. They include simple examples like two-dimensional polygons with corners on a rectangular grid and extend to high-dimensional examples beyond our ability to visualize. Lattice polytopes are ubiquitous throughout mathematics and the sciences, including combinatorics, optimization, commutative algebra, algebraic geometry, transportation problems, and many other fields. A graph, also called a network, is a collection of objects that are connected pairwise. Two common examples are cities connected by interstates, or people connected when they are friends. Graphs and networks are a source of important lattice polytopes that allow us to study the structure of the pairwise connections among objects. In this proposal, the principal investigator will study three types of lattice polytopes and their structure. First, flow polytopes will be analyzed, which model the flow of material through transportation networks. Second, graphical Hermite normal form simplices will be studied, which are a new class of lattice polytopes whose structure is determined by a graph. Third, faces of symmetric edge polytopes will be investigated, which are lattice polytopes with connections to solving equations related to the behavior of coupled systems of oscillators. For these three classes of lattice polytopes, this project will study geometric and algebraic properties such as volume, faces, and integer point counting. These properties of lattice polytopes play a fundamental role in both pure and applied mathematics. In addition to contributing directly to the expansion of our understanding of lattice polytopes, the principal investigator will continue his ongoing work in advising doctoral students. Further, by contributing mathematical expertise to research teams in mathematics education and history of mathematics, the project will have an impact on the culture and teaching of mathematics. Finally, the PI will continue to lead workshops and seminars dedicated to teaching and learning in the mathematical sciences. More technically, the goal of this project for flow polytopes is to understand how combinatorial properties of directed acyclic graphs influence volume and refined volume information obtained via Ehrhart theory. By studying the relation between invariants such as degree sequences of networks and volumes of their flow polytopes, the principal investigator expects to increase our collective understanding of how the edge structure of networks influences flow polytope volumes. The goal of this proposal for graphical Hermite normal form simplices is to classify the simplices having the Gorenstein and integer decomposition properties, and to obtain bounds on the values of vectors arising in Ehrhart theory for these objects. Further, by focusing on the recently-initiated study of Ehrhart h-star distributions of these polytopes, the principal investigator will contribute to our understanding of limits of these distributions. For symmetric edge polytopes, the goal of this project is to study the relationship between clustering metrics on graphs and facet numbers of the polytopes, in pursuit of a proof for a conjecture regarding facet maximizing and facet minimizing graphs. The methods used in these projects will involve a mix of computational experimentation and mathematical proof. In prior work of the Principal Investigator in these topics, computational experiments have proven crucial to the process of making conjectures and proving theorems. 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

In this project, recent advances on geometric structures called vector bundles will be leveraged to understand related patterns in several fields of mathematics. Borrowing powerful organizing principles from geometry, the principal investigator aims to establish deep combinatorial patterns and address difficult computational problems involving algebraic equations. This project’s broader impacts include the training of graduate students and opportunities for undergraduates to learn mathematics through rigorous research. The overarching scientific goal of the project is to apply results in the theory of toric vector bundles to investigate several different objects from algebraic geometry and combinatorics. By extending the classical positivity theory of vector bundles to tropical vector bundles, the principal investigator seeks a framework to generalize recent breakthroughs in the study of matroid invariants. The investigator will construct and study moduli spaces for toric vector bundles over various bases, generalizing classical results on the moduli of vector bundles on curves. Finally, the Cox ring of a projectivized toric vector bundle will be studied in a computational manner. The aim of this study is to better understand the construction of the Khovanskii basis algorithm in computational commutative algebra, and the geometric content of the pathological behavior of this algorithm. 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

Social behaviors are integral to the lives of many species and can affect an individual’s ability to survive, thrive, and reproduce. Vasopressin is a neurochemical that influences many types of social behaviors in many species, from communication in electric fish to cooperation in humans. However, vasopressin can influence these social behaviors in conflicting ways, depending on different parameters like age and social context. For example, in birds it can increase aggression for an intruder but also decrease aggression in flocking behaviors. The proposed research will test the hypothesis that vasopressin influences social behavior through its regulation of arousal to social stimuli – that is, how exciting, interesting, or important a social stimulus is to the animal. This research will advance our understanding of how the brain regulates social behavior and provide a framework to guide the development of potential vasopressin treatments for disorders of social development such as autism spectrum disorders. The Broader Impacts of the proposal will provide students with educational and career-building opportunities. The co-PIs will establish a free, online Behavioral Neuroscience conference that will provide undergraduate students, graduate students, and postdoctoral researchers from all over the country the opportunity to present their research and network with faculty and other trainees. The co-PI’s will also establish a science exchange between the University of Kentucky (UKY) and University at Buffalo (UB). This will form a lasting connection between the UKY and UB Behavioral Neuroscience communities that will provide students from these institutions with broad scientific training and expanded academic networks. Vasopressin is among the most consistently implicated neurochemicals in social behavior. A survey of the literature, however, reveals great variability in vasopressin actions, even within the same species. Current hypotheses do not fully address this variability in vasopressin’s actions, leaving a fundamental gap in understanding of how vasopressin regulates social behavior. The proposed research tests the hypothesis that vasopressin influences social behaviors through its regulation of arousal. Arousal influences behavior in an inverted-U shaped manner. If vasopressin regulates social arousal, it could increase, decrease, or have no effect on social function depending on dose or interactions with other factors that impact arousal, like age and context. The PIs will use implantable telemetry and pharmacological manipulations to test whether vasopressin's actions on arousal predict its effects on juvenile social play. The PIs will then determine if manipulating vasopressin projections to arousal centers affects juvenile social play, using a new rat model that restricts chemogenetic manipulations to vasopressin cells. Finally, the PIs will measure autonomic, neural, and behavioral arousal responses of juvenile vasopressin-deficient Brattleboro rats to social and non-social stimuli to determine whether vasopressin’s regulation of arousal is specific to social stimuli. The proposed research may provide a unifying framework for understanding vasopressin’s varied actions on social behavior. This project is jointly funded by Neural Systems Cluster 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 2025 · 2025-08

The rapid development of GPU hardware has promoted scientific supercomputing, enabling exascale data production on heterogeneous supercomputing systems. With GPU dominance in heterogeneous computing, the cyberinfrastructure of GPU-based scientific data compressors is still maturing, and several gaps need to be addressed: existing frameworks lack adaptations to many scientific data analysis requirements; there are no user-friendly interfaces and off-the-shelf solutions for GPU-based scientific data compressors; and the compressors that support non-NVIDIA GPU architectures are very limited. This project develops a user-friendly, high-performance, and portable GPU-accelerated data reduction cyberinfrastructure for all primary GPU-equipped supercomputing systems. It will mitigate data challenges on GPU-equipped supercomputing systems, improve data analysis efficiency, and eventually accelerate scientific discovery. This project will continuously contribute to the education and training of graduate students by enhancing the quality of computing-related curricula in heterogeneous scientific computing, data management, and visualization. This project builds Scientific GPU Compression Cyberinfrastructure (SGCC), a user-friendly end-to-end cyberinfrastructure of GPU-based data compression for scientific data workflows, by porting, extending, and optimizing multiple existing capabilities, including but not limited to: the cuSZ family of error-bounded lossy compressors, GPU-based lossless encoders, QCAT (a CPU-based compression quality assessment toolkit), the Kokkos ecosystem (a multi-backend performance-portability framework), LibPressio (the unified programming interface of scientific compressors), and HDF5. To create SGCC, the project combines three thrusts: (1) SGCC ensures its efficiency and effectiveness in practical scientific data analysis workflows, providing adequate support for diverse data formats and compression quality targets; (2) SGCC improves the usability of the GPU-accelerated data-reduction ecosystem by providing high-level language bindings, command line interface, and user-interface integrated with visualization functionality; and (3) SGCC enables state-of-the-art GPU-accelerated scientific data compressors on multiple heterogeneous computing platforms, such as NVIDIA, AMD, and Intel. 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

Cluster algebras, introduced by Fomin and Zelevinsky in 2001, are a highly active and influential research area. These objects have a beautiful complex structure and are defined through certain specific patterns. Moreover, the same defining patterns appear in many other, seemingly unrelated, areas of mathematics and theoretical physics, enabling one to translate problems into different settings and apply new techniques to study them. In this way, cluster algebras have been instrumental in establishing numerous significant results in mathematics that reach across different fields. This project is aimed at answering fundamental questions about certain objects closely related to cluster algebras and appearing at the intersection of combinatorics and algebra, as well as investigating new connections to other areas of mathematics. The project will also contribute to the advancement of education and research in the mathematical community by working with graduate students and fostering international collaborations. The goal of this project is to study two classes of objects that lie at the intersection of algebra and combinatorics and are closely related to the theory of cluster algebras. The first one is SL_k friezes, certain arrays of numbers that are defined combinatorially and related to Grassmannian cluster algebras. The project aims to develop a thorough understanding of SL_k friezes and tilings, beyond the well-studied case k = 2. The second one is Cohen-Macauley subcategories of Jacobian algebras, which provide additive categorification of cluster algebras. The main objective is to classify algebras of finite Cohen-Macauley type and to derive a new combinatorial model for their Cohen-Macauley categories coming from dimer models. The main tools to answer these questions rely on utilizing existing connections between cluster algebras, representation theory, and combinatorics, as well as establishing new ones to strengthen the relations between the different topics. 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

Phosphorus is finite rock-derived nutrient required by all life on Earth. In natural soils, the breakdown of bedrock controls the amount of phosphorus available. Similar processes are also important for accumulating soil organic matter, which is a major reservoir of nutrients, supporting soil health and capturing atmospheric carbon. As humans expand efforts to accumulate more soil organic matter, they also capture phosphorus, but how phosphorus is involved in soil organic matter stabilization remains a significant knowledge gap. The goal of this project is to understand the role of phosphorus in the stabilization and persistence of soil organic matter. This project will use laboratory and field experiments, spectroscopic, and microscopic tools, to assess the ability of phosphorus on mineral surfaces to bind soil organic matter. The outcomes of this research will provide benefits that support agricultural productivity by providing the basis for future strategies such as targeted plant breeding and soil microbe manipulation that can access the phosphorus within soil organic matter. This research integrates the education goals of improving public understanding of soils and improving soil science education. These education activities will provide continuing adult education through citizen science projects as well as open pathways to careers in science and natural resources for undergraduate students by infusing statistics and data literacy into soil science curricula. The proposed research addresses this central question: what role does phosphorus play in stabilizing soil carbon? To address this question, the specific aims are: 1) Describe the mechanisms by which organic and inorganic phosphorus control mineral associated organic matter adsorption and stabilization by loading common soil minerals with various phosphorus molecules and comparing adsorption of organic matter under laboratory conditions, 2) Determine the phosphorus forms that exist in and relate to soil organic matter in phosphorus-rich and phosphorus-limited ecosystems by performing a series of physical and chemical phosphorus and soil organic matter fractionations in natural soils, and 3) Quantify organic and inorganic phosphorus control of mineral associated organic matter dynamics within phosphorus-rich and phosphorus-limited ecosystems by deploying minerals loaded with common phosphorus molecules in the field. This project will demonstrate how phosphorus acts as an anchor for soil organic matter to stabilize on mineral surfaces, fundamentally controlling soil organic matter persistence, and linking the carbon and phosphorus cycles. This proposal is supported by the Life & Environments through Time (LET) program, the Ecosystem Science (ES) program and The Water, Landscape, and Critical Zone Processes (WaLCZ) program. 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

Proteins are essential for many biological and engineering functions, and their behavior is determined by how their three-dimensional structures change over time. Current computational methods for studying these dynamics, like molecular dynamics (MD) simulations, are limited by their inability to capture long-duration events crucial for understanding processes like protein folding or aggregation. To address this, this project will develop deep-learning models, specifically consistency models, to simulate protein dynamics more efficiently and over longer time scales. These models are expected to predict molecular changes without the tiny time steps required by traditional methods, significantly speeding up the process while maintaining accuracy. The success of this project will provide approaches that scientists can use to unlock insights into protein behavior that are currently inaccessible, paving the way for advancements in medicine, biotechnology, and materials science. This project also combines artificial intelligence with molecular engineering to train the next generation of researchers, fostering a skilled workforce. If successful, the work could significantly accelerate our understanding the dynamic properties of proteins. This project addresses the limitations of molecular dynamics (MD) simulations in capturing the long-time-scale dynamics of protein structures, by focusing on the integration of deep learning-based consistency models. MD simulations rely on Newtonian equations with small time intervals (1-2 femtoseconds), limiting their utility for studies of processes that take place over milliseconds or longer. Consistency models, a recent advancement in generative modeling, offer an alternative by predicting probabilistic distributions of molecular states with significantly larger time steps. The central hypothesis is that well-trained consistency models can replace traditional force fields in MD simulations, enabling long-stride simulations without compromising physical accuracy. The research consists of three primary tasks: (1) developing prototype consistency models trained on MD trajectories of simplified systems like polyalanine peptides, (2) optimizing model architecture and encoding methods to balance efficiency and fidelity, and (3) benchmarking these models on complex systems such as the Pin1 WW domain and Aβ-42 peptide aggregation. This approach is expected to overcome current MD simulation barriers, laying a computational foundation for studying protein folding, aggregation, and other time-dependent processes with high precision and computational efficiency. This award is co-funded by the Directorates for Computer and Information Science and Engineering and Biological Sciences. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-08

The atomic nucleus, composed of protons and neutrons, is about 100,000,000,000 times smaller than is visible by the human eye. Because it is so small, it is not possible to take an ordinary photograph to investigate its basic properties, shape, and structure. Other methods must be employed to “develop a picture” of the nucleus. While we often think of nuclei as spherical in shape, more often they are deformed spheres like an onion or a football. To investigate these structures, the team at the University of Kentucky Accelerator Laboratory uses a Van de Graaff accelerator and a nuclear reaction to produce neutrons, which they scatter from nuclei of interest. The neutron scattering process produces gamma rays that can be used to construct an image of the nucleus. These images contribute to understanding how protons and neutrons interact within the nucleus to form matter in the universe. In addition, students at all levels are provided with opportunities to participate in these studies. Program participants receive hands-on experience and emerge as well-trained nuclear scientists who are capable of important contributions to national energy, medical, and security needs. The University of Kentucky Accelerator Laboratory (UKAL) specializes in producing nearly monoenergetic neutrons. Employing this probe for inelastic neutron scattering studies (INS) yields a wealth of information for nuclear structure including the population of excited states which are often inaccessible with other methods, and the measurement of level lifetimes via the Doppler-shift attenuation method. The primary goal of this work is to provide detailed spectroscopic data that lead to the understanding of the fundamental properties of atomic nuclei. The team at UKAL will continue to investigate open questions about shape coexistence and the onset of collectivity in nuclei within selected isotopic chains as well as study isotopes of interest for neutrinoless double-beta decay, 0νββ. The researchers will also perform complementary measurements in collaboration at other facilities and work together with theorists to provide interpretations of the data obtained. Perhaps the largest advantage of university nuclear laboratories is the ability of learners at all levels to gain hands-on experience with all aspects of the experimental process. These laboratories such as UKAL play a critical role in workforce development as students emerging from them will be well-trained members of the next generation of nuclear scientists necessary for meeting national needs. 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 · 2025-07

The goal of this project is to develop an autonomous robotic system for precision and high-throughput tomato phenotyping in large-scale greenhouses. This system consists of computer vision-based phenotyping with a mobile robot arm that can access the plant canopy for high-quality image acquisition and a dynamic wireless charger that can provide an uninterruptible power source for the entire system. This proposed system is exceptionally distinctive, unique, and advanced compared to the existing autonomous phenotyping systems. This autonomous robotic phenotyping system provides a noninvasive and non-destructive way of accurately obtaining phenotypic information from individual plants. It is also able to accommodate greenhouse workers’ specific needs, perform high-throughput phenotyping by measuring various traits simultaneously, and provide feedback based on autonomous phenotyping evaluation. Automating tasks by using this developed robotic phenotyping system can significantly improve agricultural workers’ well-being by mitigating work hazards and reducing the duration of time they spend in the greenhouse assessing plant status. Four research goals are proposed: (1) Develop deep learning models to localize the tomato plants, perceive the greenhouse environment, and select the target fruits for phenotyping by integrating domain knowledge in phenotyping and greenhouse managers’ specific needs. (2) Develop novel robot motion planning algorithms to take high-quality images for phenotyping and prevent potential damage to the plants and robot. (3) Develop a multi-task learning model to compute the diverse dozens of tomato fruit traits and automatically evaluate the quality of the phenotyping results based on uncertainty analysis and domain knowledge to determine if phenotyping needs to be redone, which will close the loop of this system. (4) Develop an optimized high-efficiency, high-reliability, and low-cost wireless dynamic battery charger concept to provide power to the autonomous robotic phenotyping system operating uninterruptedly in large-scale and humid greenhouses. The fundamental scientific contributions to advance the knowledge in multiple disciplines are::In computer vision, an effective and energy-saving phenotyping recognition method adapting large AI models on relatively small tomato datasets with multi-modality input data (text, image, depth) will be developed. In robot motion planning, a novel motion planning algorithm will be developed to guarantee high-quality image acquisition, avoid obstacles, and prevent plants from damage. In power electronics, a novel circuit topology and a multi-objective design optimization algorithm will be developed to concurrently achieve high efficiency, high reliability, high power density, and low cost of the dynamic wireless charger. From a broader impact perspective, the research approaches will enable greater farm productivity, provide improved safety and health environment for workers, and can be translated to other application domains. 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 · 2025-07

Real-time systems underpin applications such as autonomous vehicles and industrial automation require timely responses to sensor data related to emerging scenarios with a precise coordination. In addition, these real-time applications face significant safety risks due to faulty or even compromised components, or to the interruption of inter-system communication. The project’s novelties center on developing foundational principles that enable multiple real-time systems to carry out coordinated operations correctly and safely with timeliness guarantees. The project's significance and importance lie in the establishment of real-time coordination principles and the creation of new security mechanisms that can be extended to security-critical applications. This project also strengthens the nation’s workforce in network and computer system security through integrated educational activities catered to students in higher education and the public. This project aims to develop a novel architecture called RESONET (REsilient and Secure Operation of NETworked real-time systems) that provides strong fault tolerance and real-time guarantees for real-time multi-systems to carry out coordinated operations. The research is organized into three complementary thrusts. The first thrust focuses on foundational cross-system fault tolerance principles, ensuring that coordinated operations meet timeliness requirements even in the face of component failures. It adopts a layered consensus-based approach enhanced by reinforcement learning that dynamically adapts the consensus parameters to network conditions. The second thrust provides a secure communication layer necessary for the above consensus-based approach by adopting lightweight group authentication and key establishment protocols to secure both intra- and inter-system communications among different components. The third thrust provides the last line of defense by developing an intrusion detection mechanism that can forecast imminent timing violations and detect intrusions and failures. The project uses a drone fleet and an automotive communication network to carry out the validation of developed prototypes and to serve as platforms for hands-on educational activities. All research outcomes and educational materials, including tutorials, presentations, publications, and open-source software, will be made publicly available online. 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 · 2025-07

The University of Kentucky (UK) Center for Applied Artificial Intelligence (CAAI) initiative democratizes Artificial Intelligence (AI) training and access statewide for Kentucky’s geographically isolated and underserved researchers. In partnership with the Council for Postsecondary Education and the Kentucky Community and Technical College System, CAAI conducts workshops across Kentucky bringing together subject matter experts, new and emerging researchers, and higher education and workforce entities to empower researchers to apply AI in their projects and access local and National Artificial Intelligence Research Resource (NAIRR) Pilot cyberinfrastructure. The initiative prioritizes fostering innovation, supporting workforce development, improving research capacity, and advancing safe, secure, and trustworthy AI in research and society. This effort stimulates innovation in traditionally underserved regions by preparing participants with the skills needed to navigate the AI ecosystem, connect with national cyberinfrastructure, and develop transferable skills using production-grade tools. By focusing on tailored mentorship and practical learning pathways, the project supports NSF’s mission to promote the progress of science, advance national prosperity, and foster broader participation in AI research and innovation. The project conducts five AI workshops across Kentucky’s major geographic regions, training five hundred new and emerging researchers. These workshops combine foundational presentations with practical sessions providing guided use of AI models and computational tools. Trainings cover concepts such as large language models (LLMs), machine learning (ML), computer vision, multimodal models, and AI powered data science and analysis. Participants gain practical experience with AI tools that support model training, data analysis, and the application of AI techniques across various domains, including language, tabular, and timeseries models. The skills developed are readily transferable to NAIRR Pilot infrastructure, and participants receive guidance on navigating and submitting research requests for AI compute resources aligned with the NAIRR Pilot Program. Participants engage in exercises using online platforms, Jupyter notebooks, and workflows closely aligned with NAIRR Pilot supported technologies. Workshop content is tailored to participant experience levels and industry interests via pre-event surveys, breakout sessions, and optional technical tracks. After the workshops, continuous learning and connection to the Kentucky AI community is provided through online resources and ongoing communication, such as user guides, video tutorials, office hours, and online forums. Outcomes are assessed through interactive feedback mechanisms, follow up surveys, usage tracking of AI tools, and ongoing mentorship opportunities. This scalable, transferable training opportunity positions Kentucky as a model for other Established Program to Stimulate Competitive Research (EPSCoR) targeted jurisdictions to leverage strategic investments in AI education and 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 · 2025-07

Current electronic systems that power today's intelligent digital solutions including smartphones, wearables, smart vehicles, and smart home appliances are facing significant limitations in scaling up to meet the performance demands of emerging smart applications. The goal of realizing smart cities, smart agriculture, and smart healthcare is jeopardized due to the bottlenecks facing traditional electronic platforms at the heart of these emerging applications. Silicon photonics represents an innovative paradigm that can overcome these bottlenecks, by supporting light-speed and high throughput communication and computation. However, the design of silicon photonics based smart computing platforms is still in its infancy, with many open challenges. One of the most significant of these challenges is to design integrated electro-photonic platforms that are truly sustainable while also simultaneously minimizing manufacturing costs, energy footprint, and fault susceptibility. This project will take the first steps towards realizing efficient electro-photonic computing platforms that far surpass traditional electronic platforms in terms of performance, energy-efficiency, and reliability. In doing so, this project will help establish a new ecosystem of development of emerging digital intelligence platforms, for multiple application use cases that span datacenters, scientific computing, and edge systems, where photonics is making inroads. This outcome will help reduce costs and overheads of designing future smart computing platforms, which will benefit technological innovation across U.S. consumer, industrial, and defense sectors. This will also help the U.S. outcompete global competitors to emerge as the pioneer in innovative computing design for such emerging technologies. By exposing students to diverse aspects of electro-photonic circuit design, yield analysis, lifetime evaluation, modeling and trade-offs, and computing architecture design, the proposed research will contribute towards educating an agile high-tech workforce that will maintain continued U.S. leadership in technological innovation, especially in emerging post-Moore technologies. This project will employ the principles of heterogeneity, reconfigurability, and recycling to transform performance, functional flexibility, and resource utilization of integrated electro-photonic platforms. The overarching goal will be to extend operational lifespan and co-reduce the embodied and use-phase costs of emerging heterogeneous integrated electro-photonic-based 2.5D computing platforms. To that end, this project will make three synergistic contributions: 1) Analyze variations and aging-aware footprints, by developing the first modeling framework for integrated electro-photonic platforms, quantifying sustainability costs of enabling variation resilience, and characterizing impacts of aging in these platforms, 2) Prolong carbon-minimum lifespan and co-optimize embodied and operational metrics, by developing techniques to prolong integrated electro-photonic platform lifespan with design time and runtime adaptive techniques, devising cross-layer techniques to reduce operational overheads, managing failure risk with runtime mechanisms for graceful degradation, and co-reducing use-phase and embodied sustainability metric costs, and 3) Introduce reuse and hardware polymorphism to reduce costs for multifunctional computing, communication, and storage components in integrated electro-photonic platforms. 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 · 2025-07

This award supports new research for the PI and graduate students on the development of an innovative Rare Pion Decay Spectrometer at the Paul Scherrer Institute, Switzerland. The proposed measurements will provide both important tests of the Standard Model (SM) of particle physics and importance searches for unknown particles and interactions of nature. Such measurements will have a broad impact and lasting legacy in nuclear physics, particle physics, and astrophysics. The PI and graduate students are responsible for the development of the high-performance data acquisition and real-time processing system for the spectrometer. This work includes the engineering for PCIe-over-fiber readout of FPGA devices and real-time pulse fitting for lossless compression. The PI's group will also contribute to physics simulations and physics analysis through development of rule-based and machine learning algorithms for particle tracking and vertex finding. The project provides broader impact through involvement and training of high school students, physics majors and graduate students. The group's involvement in large-scale data acquisition, analysis and simulation also expose students to new technologies and paradigms in computing. The project goals include a 15-fold improved measurement of the ratio of the pion's electronic-to-muonic decays, a 6-fold improved measurement of the pion's beta decay, as well as order-of-magnitude improved searches for exotic particles including dark matter, light axions, and heavy neutrinos. The ratio of pion electronic-to-muonic decays offers the most stringent test of lepton universality and the pion beta decay addresses tensions in the CKM matrix. The development of the silicon-strip target, low-mass tracker, and LYSO calorimeter components of the spectrometer will significantly advance experimental instrumentation for nuclear physics. 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 · 2025-07

The Research Evaluation and Analytics Capacity Hub (REACH) will provide a new national forum for the advancement of research analytics, the use of data-informed decision-making and artificial intelligence (AI), and responsible research assessment practices within the field of research administration. REACH leverages expertise from across the globe and engages institutions interested in building capacity in these areas to strengthen their research enterprise. Participants come from all types of institutions and from a variety of professional fields, including research administrators, researchers, and librarians. The goal of REACH is to foster interdisciplinary collaboration and innovation in responsible data-informed approaches in the field of research administration. REACH builds on existing empirical research and communities of practice and will expand the understanding of how we utilize data-informed approaches to build research support workforce efficiency, research strategy, and research capacity. REACH activities will include the research analytics mentoring program RAMP and an annual in-person research analytics summit. In addition, the network will maintain online resources and host a variety of webinars and training programs. The work of REACH is collaborative and involves the National Council of University Research Administrators and the Society of Research Administrators International as partners. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-07

Scientific simulations and instruments produce an unprecedented amount of data that overwhelms the network and storage systems. Due to the limited capacity in high-end parallel file systems, such data must be stored at remote sites or moved to secondary storage for archival purposes. This poses challenges to fetching the data for post hoc data analytics, as the data movement bandwidth across wide area networks or from secondary systems is very limited. This project bridges this gap by developing scalable software to realize data polymorphism, a novel paradigm that allows for variable representations of the same data under different scenarios and use cases to enable on-demand data provision with reduced data movement cost. The success of this project is expected to significantly reduce the time needed to gain scientific insights from data for a wide range of applications, thus advancing scientific discoveries in domains including climatology, cosmology, fusion energy science, and ptychography. This contributes to resolving a wide range of important societal problems, including weather forecasting, galaxy surveys, electric generation, and material design. Furthermore, an integrated education program is developed for workforce development and broadening participation in advanced cyberinfrastructure. This project aims to leverage progressive representations to realize data polymorphism and enable fast and adaptable scientific retrieval with tailored error control. The contributions are threefold. First, a generic framework is designed to abstract the generation of progressive representations for scientific data to allow for the integration of novel algorithms and flexible tuning methods with improved performance and efficiency. Second, rigorous theories and tailored implementations are developed to enable error control on the outcomes of downstream analysis during data retrieval. This significantly improves the trustability of the reconstructed data representation, as the correctness of such outcomes is of utmost importance in scientific analyses. Third, a data service library is optimized for high performance, portability, and scalability towards the diverse architectures in advanced cyberinfrastructure. Integration with the leading data management and visualization software is also planned to facilitate its use in real applications. To this end, end-to-end evaluations with the applications are anticipated to demonstrate the efficiency of the deliverables by significantly reducing the time needed for scientific discoveries. 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 · 2025-07

Carbon emissions from powering information and computing technologies (ICT) are projected to increase to 8% of worldwide carbon emissions over the next decade due to the explosion of computing required in everyday consumer electronic devices and systems. Recent studies have shown that integrated electro-photonics-based fabrics (the base layer chips are built on) for communication and computing, compared to conventional electronic fabrics, can result in substantially higher energy and carbon efficiency (up to 100× in some cases) for future computing hardware platforms. This project leverages the energy efficiency of electro-photonic hardware fabrics to investigate their embodied carbon efficiency (related to carbon emissions due to hardware manufacturing and product infrastructure-related activities) following the universally accepted sustainability tenets of Reduce, Reuse, and Recycle. The goal is to develop an infrastructure for designing sustainable computing platforms using carbon-efficient electro-photonic hardware components. The project’s novelties are (1) employing the principles of heterogeneity, reconfigurability, and recycling to design multi-functional and multi-lifespan electro-photonic transceiver and accelerator architectures, (2) transforming the sustainability, performance, resource utilization, and lifetime reliability of electro-photonics-based computing platforms using carbon-efficient cross-layer design techniques, and (3)creating novel educational materials using newly developed tutorial videos and interactive simulation modules to give students a more tangible, hands-on approach to learning the fundamentals of integrated electro-photonics and sustainable computing hardware design. The project's broader significance and importance are based on (i) creating opportunities for industrial partnerships (CMC Microsystems and GlobalFoundries), (ii) promoting outreach to the local community, (iii) engaging undergraduate students in supervised research, and (iv) promoting diversity by training STEM teachers of local middle schools that primarily serve underrepresented groups. The overarching goal of this project is to reduce the impact of embodied energy and extend the operational lifetime of electro-photonic hardware components to enhance the sustainability and carbon efficiency of computing systems. This project is expected to result in (1) a framework that factors in the critical impacts of yield, variations in fabrication-process and temperature, and aging effects for modeling the embodied and use-phase carbon footprints of electro-photonic transceivers and accelerators, (2) carbon-efficient organizations of electro-photonic transceivers and accelerators with minimal carbon footprints and maximal lifespans, (3) methods to repurpose electro-photonic hardware components for multiple functionalities to minimize resource idle time and embodied carbon emissions, (4) cross-layer techniques to extend the reliable utilization of designed electro-photonic architectures across multiple lifespans, and (5) an extensive simulation framework for evaluation, validation, and comparison of different organizations of heterogeneous computing platforms comprising electro-photonic fabrics, focusing on various important metrics for energy efficiency, carbon efficiency, performance, lifetime reliability, and resource utilization. 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

The 60th Summer Research Conference (SRC) and Statistics Undergraduate Research Experience (SURE) will be held in Jekyll Island, Georgia on June 9-11, 2025. The Southern Regional Council on Statistics (SRCOS) is a consortium of statistics and biostatistics programs from universities in 16 states in the Southern region. The SRC is an annual conference sponsored by the SRCOS. The purpose of the SRC is to encourage the interchange and mutual understanding of current research ideas in statistics and biostatistics, and to provide motivation and direction to further research progress. The SRC will give new researchers an opportunity to participate in the meeting and to interact closely with leaders in the field in a manner not possible at larger meetings. In addition to the graduate student participation, the 60th SRC will also include the 6th annual Statistical Undergraduate Research Experience (SURE) from June 9-11, 2025. SURE is a conference within a conference aimed to encourage the participation of undergraduate students to pursue graduate education and career opportunities in STEM fields. SURE will include events specifically for undergraduate students and undergraduate mentors, such as a panel about career opportunities in statistics, a real data analytics workshop, and a speed-mentoring session with current statistics and biostatistics graduate students. The SRC is particularly valuable for graduate students, isolated statisticians, and faculty from smaller regional schools in the southern region at drivable distances without the cost of travel to distant venues. Speakers will present formal research talks with adequate time allowed for clarification, amplification, and further informal discussions in small groups. Under the travel support provided by this award, graduate students will attend and present their research in posters to be reviewed by more experienced researchers. Participation in SURE will encourage undergraduate students to enter STEM fields, including statistics or biostatistics, and provide training to support this endeavor. The 60th SRC will strengthen the research of the statistics and biostatistics community as a whole and help bridge the gap for undergraduate students to pursue statistics or biostatistics, particularly in the sixteen states of the Southern Region. The SRCOS website can be found here: https://www.srcos.org; the SRC website can be found here: https://www.srcos.org/conference; the SURE website can be found here: https://www.srcos.org/sure. 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 I-Corps project focuses on the development of a centralized digital platform designed to enhance the management of transportation asset data. Transportation agencies across the United States grapple with outdated and fragmented data systems that hinder efficient asset management and strategic decision-making. These challenges result in increased operational costs, reduced infrastructure reliability, and potential risks to public safety. This digital platform addresses these significant challenges by offering a unified solution that integrates asset data across various lifecycle stages, including planning, construction, maintenance, and rehabilitation. A unified approach not only simplifies and streamlines the data management process but also facilitates greater visibility, accountability, and efficiency within state transportation agencies. By adopting this solution, state agencies can better align their practices with federal regulations, thereby reducing unnecessary costs and improving the overall effectiveness of infrastructure investments. The social and economic benefits include enhanced public safety, improved infrastructure reliability, and substantial cost savings through optimized asset management. 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. This solution is based on the development of a comprehensive digital asset management platform that incorporates an integrated four-step framework – define, organize, strategize, and execute – to streamline data governance across the transportation asset lifecycle. The platform distinguishes itself from existing solutions by providing flexibility tailored specifically to the unique operational and technological requirements of state transportation departments. Technically, this platform advances current capabilities by employing targeted data collection strategies to avoid data overproduction, and to maintain high-quality, actionable insights. The approach integrates previously siloed systems, enabling consistent data standardization, and improved interoperability. By addressing these technical and operational barriers, the technology enhances strategic planning and operational efficiency. Users benefit directly from improved data accessibility and precision, enabling more informed, proactive decision-making and resource allocation, thus enhancing overall infrastructure management effectiveness and public safety outcomes. 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 examines how financial and organizational structures shape residential and economic outcomes in rural counties in mining and farm-dependent states. The investigators develop and link global and local financial and organizational patterns in the mining and farming industries and use a variety of methods to predict the impact of these on local ecological outcomes. By linking global financial networks to the lived experiences of rural communities, the research informs concrete strategies for mitigating outcomes that negatively affect these communities. Graduate and undergraduate students are included in the project and the experience lets them gain valuable skills useful for future jobs and careers. The project benefits communities through partnerships with local organizations and public dissemination. The project uses quantitative and qualitative data to study the ownership and finance structures of four mining and farm-dependent case-study counties across two key coal producing and agricultural/farming states. The investigators compile a dataset of agricultural and energy operators and owners in these localities, tracking financial relationships and governance structures in a variety of databases. The analysis paves the way for the identification of intra- and inter-organizational network patterns such as the funding dependencies and centralized decision-making processes. Social network analysis and Autologistic Actor Attribute Models are used to examine how characteristics of networks shape social-ecological outcomes. Semi-structured interviews in study counties document social-ecological relationships unrecognized in prevailing measures of environmental, social and corporate governance. Qualitative insights, combined with quantitative network data, enable the team to construct network simulation models in which alternative arrangements that facilitate conducive ecological and environmental outcomes are modeled. Results are shared through academic dissemination, community outreach, and an information source platform. This project is jointly funded by the Sociology Program 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 2025 · 2025-05

Organic electrochemical transistors (OECTs) are amplifiers with a unique ability to translate biological signals to electron signals in flexible, skin-like electronic materials. The overarching idea behind this research is that addressing the knowledge gap on the interface between the electrode and electronic material will significantly advance OECT sensitivity, energy consumption, reproducibility, reliability and figure of merit analysis, for applications including biosensors, medical and drug delivery devices, body-machine interfaces, adaptive healthcare technologies, neuromorphic hardware and computing, chemical sensors and agricultural applications. The educational outreach goal of this project describes three activities for disseminating the research from this research, with the potential to reach over 1000 youth and 250 adults overall. The impact envisioned here is distinctively beneficial for teaching students about the challenges in producing fundamental knowledge to advance emerging technologies including, in this case, several that are highly relevant to rural Kentucky; for example, low-cost sensors for agriculture and remote healthcare, including opiate biosensors, could play an integral role in a connected healthcare internet of things for better medical treatment in remote, rural areas in Kentucky. A workshop to support math and physics classes is proposed as part of a long-term goal to train and motivate Kentucky students to be future scientists. An organic transistor course for K-12 students is proposed to increase exposure to STEM activities. Community engagement activities include a 4 H Teen Conference, supported by a new partnership with Kentucky 4-H. While transistor performance is dominated by the contact/semiconductor interface, this critical interface is overlooked in OECTs. Present state-of-knowledge is limited and contradictory. Limited knowledge is a problem because the contact/organic interface defines contact resistance, size, speed, power consumption and efficiency, in low-cost, solution-processible OECTs, whose ability to convert biological-to-electronic signals gave rise to organic bioelectronics. The fundamental roadblock for commercializing OECT technologies, and to fully realizing OECT potential, is the knowledge gap on the mechanistic role of the contact/organic interface. Specifically: (1) The inability to define contact resistance prevents OECT miniaturization, while also increasing OECT energy consumption and reducing their speed. (2) A difference between the channel resistance predicted by the existing model, versus channel resistance as measured in OECTs, means that current is not maximized. (3) Inconsistencies in understanding how device architecture impacts gm results in limited sensitivity. Therefore, there is a great need to fill the contact/organic interface knowledge gap for OECTs. Here, three objectives have been established to address the specific gaps in knowledge of the OECT contact/organic interface. The first objective is to determine the parameters affecting contact resistance. The second objective is to elucidate the physical origin of the relationship between contact and channel resistances and operating voltage. The third objective is to identify the inconsistencies in device architecture that cause differences between predicted and measured transconductance. Successful outcome of the proposed research is expected to enable the development of community wide OECT fabrication standards, and guide OECT model development for accurate organic mixed ionic electronic conductor figures of merit analysis. 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 · 2025-03

NON-TECHNICAL SUMMARY To advance the exploration of space, learning how to control liquids in the absence of gravity is essential. On Earth, the flow of liquid is mostly controlled by gravity. In the absence of gravity, capillary forces (surface tension) govern flow. In space, water in a glass would not remain conformed to the shape of the glass. In space, water’s shape is driven by capillary forces, and would cover all sides of the glass and form a blob over all surfaces. The absence of gravity also means the absence of buoyancy. On Earth, a closed bottle of water with some air would have the air on top, but in space, the air bubbles could be anywhere inside the bottle and have any size. This project focuses on molten metallic alloys, which are a liquid useful for brazing and soldering in space for repair of damaged surfaces (due to impact of micrometeoroids and space debris), as well as for construction in space. Such alloys melt and solidify gradually. The goal is to control the concurrent melting and capillary flow of a molten alloy in microgravity and predict the resulting microstructure of the bond. This project is ensuring durable bonding for repair and construction in space man-made habitats and impacting the ability to control other liquids in space and on Earth. TECHNICAL SUMMARY It is observed that near-eutectic binary alloys, subject to concurrent melting and capillary/gravitational flow, are prone to flow-induced segregation whereupon it solidifies into two very different microstructures. The extent of segregation varies, apparently depending on the processing conditions as well as on the geometry of the capillary flow. This project consists of: (i) a series of experiments performed on the U.S. International Space Station with simultaneous ground-based experiments, and, since the melting/flow solidification process cannot be observed directly – (ii) a detailed mesoscale modelling program of the process (phase field computations). The objectives of the project are to: (i) understand the detailed physical mechanisms of segregation during capillary flow under both microgravity and terrestrial conditions. Focus is being afforded to the effects of gravity, temperature gradients, peak temperature, and interaction of diffusion-controlled melting and flow of the melt. This activity also allows for the formulation of the mathematical/computational theory needed to operationalize this understanding. (ii) apply this theory to the design of methodologies for brazing and soldering in both space and terrestrial materials bonding.The new theory and predictive models being produced in this work utilize a phase field formulation of the capillary flow under conditions of void formation and is being verified through the experiments capturing the differences in microstructure of the re-solidified melts obtained in microgravity and under terrestrial conditions. In addition to brazing metals, the results will be relevant for capillary phenomena involving low temperature soldering as well as processes related to bonding of ceramics and metals at high temperatures. Furthermore, the findings of this project are leading to better understanding of capillary phenomena involved with multilayer metal deposition in advanced technologies such as additive manufacturing via selective laser melting. Educational broader impacts include the addition of new modules to the existing graduate courses at Washington State University and the University of Kentucky, research experience for undergraduate students, and outreach to high school 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 2025 · 2025-03

This award provides participant support for the 2025, 2026, and 2027 editions of the Ohio River Analysis Meetings (ORAM), the first of which will be held on March 29-30, 2025 at the University of Cincinnati. The subsequent meetings will be held in Spring 2026 at the University of Kentucky and in Spring 2027 at the University of Cincinnati. ORAM is an annual conference in mathematical analysis organized by faculty at the University of Cincinnati and the University of Kentucky. It provides a venue for mathematicians to learn about the latest developments in the field, to disseminate their own results, and to collaborate with other researchers. ORAM supports the development of early-career researchers through speaking opportunities and travel support to attend the conference. ORAM will be held for the 14th time in 2025. Each year it features a robust scientific program, with five plenary talks by distinguished mathematicians, approximately 35 contributed short talks in parallel sessions, and approximately 70 participants. The conference welcomes researchers in all areas of analysis, with a particular focus on partial differential equations, geometric analysis, and harmonic analysis. Anticipated plenary speakers for the 2025 event and additional information can be found on the conference website at https://sites.google.com/view/oram14/home. 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.