University Of Tennessee Knoxville

NSF Awards · FY 2025 · 2025-09

Additive manufacturing (AM) with lasers is a method of creating 3-dimensional structures by fusing layers of material together. However, building with tungsten using traditional continuous lasers often leads to cracking because these lasers generate too much heat over a large area. Initial experiments show that using ultrashort pulsed lasers, which release energy in tiny bursts, can prevent cracking by limiting the heat to a very small area. While this approach looks promising, more research is needed to fully understand how it works. This project will conduct experiments and develop strategies to eliminate cracking when working with high-temperature materials. These improvements are vital for advancing technologies in aerospace, automotive, energy, and healthcare industries. In addition, the team plans to create educational programs and outreach activities to train future engineers, equipping them with the skills needed to lead in advanced manufacturing. The overarching goal of this project is to achieve crack-free additive manufacturing (AM) of tungsten using a femtosecond (FS) laser. The high ductile-to-brittle transition temperature of tungsten makes the metal vulnerable to cracking, particularly in AM processes. Based on the hypothesis that the thermal response to FS laser can induce tungsten conditions favorable for crack-free AM, the team will conduct a combination of experiments and physics-based simulations to identify such conditions for crack-free AM. This project will clarify the key factors, such as laser scanning velocity, layer thickness, and hatching spacing, focusing on the thermal and mass transfer processes induced by nonequilibrium photonic sources, and identify the optimal FS laser processing conditions of laser powder-bed fusion for achieving desirable thermal and mechanical profiles. The findings will enable the team to develop a mechanistic understanding of the thermal, metallurgical, and mechanical responses of tungsten to the localized heating of FS laser that can eliminate tungsten cracking during fusion-based processing. The project activities also provide learning opportunities to diverse populations. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-09

With support from the Chemical Structure and Dynamics (CSD) program in the Division of Chemistry, Professor Thanh D. Do at the University of Tennessee, Knoxville is combining sophisticated gas-phase and condensed-phase experimental approaches with density functional theory calculations to investigate the complexation of cyclic depsipeptides with transition metals and lanthanides, resulting in sandwich-like structures that govern metal selectivity and transport. Cyclic depsipeptides are naturally derived macrocycles featuring N-methylated amides, ester-amide linkages, and ion-binding motifs that enable selective interactions with biological targets. Yet, the structures and reactivities of their metal complexes remain poorly understood, despite their relevance to biological activity. Professor Do and his students will investigate how metal size and coordination geometry influence the formation of these complexes, enabling unusual reactivities such as C–H activation. To achieve this, they will employ ion mobility spectrometry–mass spectrometry (IMS-MS), synchrotron-based X-ray spectroscopy (APS, Chicago), and the Free-Electron Lasers for Infrared experiments (FELIX, Netherlands). Their discoveries could advance the fundamental understanding of metal–ligand recognition and reactivity, enabling the design of new macrocyclic scaffolds for metal separation and catalysis. The project supports broader impacts through interdisciplinary training, outreach to high school students, and internships that foster participation in chemical research. This research investigates how metal complexation alters the conformational preferences and reactivity of cyclic depsipeptides. The research team will use IMS-MS to separate and structurally characterize distinct conformers of metal–ligand complexes, followed by ion activation experiments to explore transitions between low-energy conformers and those with reshaped coordination geometries. Particular emphasis is placed on lanthanide ions, which offer high and tunable coordination numbers, enabling the study of structural selectivity driven by excess donor groups or flexible ligand architectures. These experiments will be complemented by infrared multiple-photon dissociation (IRMPD) spectroscopy, nuclear magnetic resonance (NMR), X-ray crystallography (XRC), and X-ray absorption spectroscopy (XAS), along with density functional theory (DFT) calculations to support structural assignments and mechanistic insight. In addition, the research examines how lanthanide-bound cyclic depsipeptides can induce C–H bond deprotonation and promote reactivity such as enolate formation and aldol condensation. Together, these studies will provide a fundamental understanding of metal-mediated structure and function in macrocyclic ligands. The project will also train students in cutting-edge experimental techniques and computational modeling, preparing them for careers across academia, industry, and national laboratories. 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

Anatolia is an important epicenter for documenting the timing of major faunal dispersion on Earth following establishment of the “Gomphotherium landbridge” that occurred millions of years ago. Faunal migration included emigration of hominoids from Africa to Eurasia. However, there is a delay of at least 2 Million Years (Myr) and possibly up to 6 Myr between establishment of the landbridge and dispersal of hominoids to central Anatolia. High elevations in western Anatolia may explain this delay in faunal dispersion, as mountain ranges, like the oceanic passages that precede landbridges, pose formidable barriers to faunal dispersion. In this study, scientists will combine datasets of Earth's crust (structural analysis, thermochronology, crustal thickness) with surface expression (basin evolution and surface elevation) to document how the formation of mountains may have potentially impacted faunal migration. This project endeavors to establish and strengthen international partnerships among four institutions in the USA, Canada, and Türkiye where field work will be conducted. The project will collectively contribute to the academic growth and training of several graduate and undergraduate students including through engagement in workshops and joint field trips that combine expertise from each institution. Results from this multidisciplinary research will cover a wide range of spatial and temporal scales necessary to link crustal and basin processes to their surficial, environmental, and biological impacts. Western Anatolia is considered to have been at low elevations since the Eocene. However, previous research places the pre-extensional crustal thickness at 55–60 km predicting elevations of 3.5–4.1 km (if in isostatic equilibrium). This has direct implications for the timing and location of faunal migration corridors. Tectonic reconstruction of this region is hampered by multiple factors: (1) basins are characterized by widely dispersed and incomplete stratigraphic sections and lack detailed chronostratigraphic control, (2) existing structural reconstructions are in conflict on the geometry of Oligocene–early Miocene extension, (3) paleo-crustal thickness estimates are limited, and (4) no proxy-based paleoelevation estimates exist. This project will investigate the Oligocene–Pleistocene evolution of sedimentary basins, extensional structures, crustal thickness, and paleoelevation in this highly extended region. Refined age control and stratigraphic correlations, as well as new crustal thickness estimates from whole-rock and detrital zircon geochemistry and proxy-based paleoaltimetry, will provide context to test models explaining single- or doubly-vergent extension using thermochronometric analysis and thermokinematic modeling. Collectively, this project will assess general models of crustal evolution during subduction to determine whether Airy isostasy was the primary control on elevation or if alternative models such as Pratt isostasy or dynamic, mantle support are permissible. 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

The long-term pattern in the frequency and severity of multi-year drought in the Caribbean is poorly understood. While there is progress in the reconstruction of long-term drought history and dynamical drivers of drought in the continental regions surrounding the Caribbean, there is a gap in data for the Caribbean islands. For Mesoamerica, there is evidence that the mechanisms the drive multi-year drought include changes in moisture transported by winds associated with patterns in sea surface temperature in the tropical Atlantic and Pacific oceans. However, it is unknown whether these patterns are associated with drought in the Caribbean, or if droughts occur in Mesoamerica and the Caribbean at the same time. This project will reconstruct the long-term history of drought in the Caribbean using measurements of blue intensity of Pinus occidentalis Swartz (Hispaniolan pine), an endemic tree species on Hispaniola Island which is known to form annual rings. The project will also conduct climate model experiments to investigate the mechanisms that drive multi-year drought in the circum-Caribbean, and estimate the risk of future multi-year drought. The Broader Impacts include support for undergraduate and graduate student participation in the project and a 5-day workshop on dendrochronology, data science, and modeling at Universidad Autónoma de Santo Domingo. The goal of the project is to collect samples from and measure blue intensity in Pinus occidentalis Swartz from the Dominican Republic to reconstruct past drought. The frequency, duration and magnitude of the past interannual and decadal droughts will be assessed from the new data and the existing drought atlases from North America and Mexico, Caribbean and tropical Americas. Mechanisms for past drought will be investigated with existing climate model ensembles and new simulations with Community Atmospheric Model 6 (CAM6). A hybrid statistical-dynamic approach will be used to estimate future drought risk in the region. The Broader Impacts include support for undergraduate and graduate student participation in the project and a 5-day workshop on dendrochronology, data science, and modeling at Universidad Autónoma de Santo Domingo. 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

Cyanobacteria are photosynthetic microbes and keystone members of aquatic systems, yet in some cases they produce compounds that poison water supplies. The metabolic landscape for freshwater cyanobacteria is complicated. Cyanobacteria persist in environments where short-term changes in temperature, light, and nutrient availability vary during the summer as well as during episodic events (i.e., large storms). This variability leads to many issues for biology, including oxidative stress in photosynthetic organisms. We describe a multidisciplinary research program to quantify the mechanistic role of oxidative stress responses in a model cyanobacterium (Microcystis) to episodic events. Living cells cope with oxidative stress using multiple mechanisms: e.g., all use enzymes to degrade reactive oxygen, while some also employ biologically expensive secondary metabolites (for example, microcystins, which are dangerous liver toxins) for protection. Our goal is to quantitatively demonstrate how more biologically expensive but longer-term coping mechanisms (in this case microcystin) vs. short-term and cheaper strategies (peroxiredoxin enzymes) influence cyanobacteria strain succession and composition in lake communities. Using this data, we will update an existing mathematical model that predicts microcystin production from cell conditions and add a module that predicts growth response, microcystin production, and strain competition due to episodic events. The resulting model has implications for the bioeconomy through the improvement of in-season predictions of toxin production that will help resource managers better protect the health of lake-dependent residents. We will broadly communicate results to both the public and scientific community. We will train students and stakeholders, all with the goal of better informing the public while advancing science. We hypothesize that increased episodic weather events will select for microcystin producers. To test this, we will directly compete strains with and without the ability to make microcystin (M. aeruginosa PCC7806 and a microcystin-free mutant, ΔmcyB) in continuous cultures under conditions that mimic current and projected seasonal conditions for Lake Erie (June, when microcystin producers dominate, and August, when non-microcystin producers dominate). We will evaluate this in the context of increased episodic events (storms) that may alter Microcystis community composition through selection of one coping strategy over another. Strain composition will be monitored by established qPCR methods and changes in physiology quantified with flow cytometry, fluid imaging (FlowCam), pigment content, and photosynthetic analyses. We will track cellular microcystin concentrations and glutathione-dependent peroxiredoxin activity to determine how this enzyme performs in the presence or absence of microcystin across conditions of episodic change (i.e., temperature drops consistent with large storms). We will conduct mesocosm studies with natural communities to validate in-lab observations on short-term temperature effects, collecting metatranscriptomes to monitor both the target cyanobacteria as well as the entire community. Lab studies will also help us reinterpret existing metatranscriptomic datasets from Lake Erie (300+ samples collected from 2012-2024) for indications of similar responses under natural field conditions. All this information will be used to redesign an existing agent-based, mechanistic model that predicts toxin production on a per cell basis in response to environmental 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 · 2025-09

Fire investigation training programs aim to equip investigators with the skills to identify fire origins and causes, but the chaotic nature of post-fire scenes presents substantial challenges. Investigators must connect evidence and scene features to the fire dynamics that shaped a scene, which requires strong spatial-temporal reasoning skills. Immersive training in realistic environments is essential to help investigators piece together evidence, analyze fire progression, and accurately trace fire origins. However, most training programs in the U.S. rely on lectures and 2D visuals, lacking the immersive experience needed to develop these crucial reasoning skills. Further, many investigators lack formal education in fire science, which is essential for understanding fire behavior. This project seeks to create a multimodal embodied training platform that advances fire investigation training through adaptive deliberate practice and learning analytics, focusing on the spatial-temporal reasoning skills needed to reconstruct fire development from observed fire damage and scene features. This new training approach will improve the quality and effectiveness of fire investigation practices, benefiting public safety by enabling more accurate identification of fire origins and causes. Many of the ideas can be extended to related fields such as crime scene investigation and other STEM areas requiring advanced spatial-temporal reasoning skills. To achieve these goals, the training platform will incorporate an AI-driven, physics-informed 3D fire modeling system that dynamically generates and visualizes fire scenarios based on learner-selected fire origins. Learners will identify and analyze scattered evidence, reconstruct fire progression, test interpretations, and explore variations in fire dynamics relative to observed damage patterns. Multimodal sensors will track learner interactions, enabling adaptive instructional approaches, enhancing engagement, and fostering seamless interactions between learners, instructors, and virtual fire scenarios. A deliberate practice pedagogical model will integrate structured skill-building exercises, multimodal analytics for performance assessment, and personalized adaptive training tailored to individual learner profiles. The platform's effectiveness will be evaluated in three phases: iterative expert reviews, student prototype assessments, and nationwide testing by early-career fire investigators, ensuring robust skill development in spatial-temporal reasoning for fire investigation. This project is funded by the Research on Innovative Technologies for Enhanced Learning (RITEL) program that supports early-stage exploratory research in emerging technologies for teaching and learning. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2025 · 2025-09

Appalachia, spanning 13 states from Alabama to New York with over 25 million people, 42% of whom live in a rural community (Appalachian Regional Commission [ARC], 2017) is a medically underserved area whose residents experience significant healthcare challenges. Rural workers also are underrepresented in jobs resulting from STEM majors and rural high school students aspire to pursue STEM fields at lower rates and are less academically prepared in STEM topics than their peers (Saw & Agger, 2021). We know that rural Appalachians often distrust community outsiders (Keller & Helton, 2010), so we must attract residents from within these areas to enter the biomedical and behavioral research workforce to reduce these health challenges. To accomplish this, we propose Picturing Possibilities and Envisioning Selves (PiPES3), which aims to increase the number of rural Appalachian high school students choosing to pursue post-secondary education and enter a STEMM (science, technology, engineering, math, medical) field. We will use a strengths-based lens that highlights cultural values to inform our framework, emphasizing ways in which the students’ internal strengths and external familial and community resources provide concrete supports for students’ future selves. Importantly, we cannot assume that increasing STEMM interest and skills will be sufficient to attract students from this region to these careers, given barriers to post-secondary education in general. Therefore, our long-range goal is to develop efficacious interventions that reduce contextual barriers and increase supports for and interest in both post-secondary education in general and STEMM in particular among rural Appalachian youth. Our hypotheses are that addressing both and doing so over time, will increase interest in both college-going and STEMM in rural Appalachian students. We have three specific aims in meeting these goals: 1) Increase science identity, as well as self-efficacy, outcome expectation beliefs, and interests related to college-going and STEMM; 2) Teach skills to help students navigate barriers and increase supports for pursuing post-secondary education and STEMM careers; and 3) Determine the additive effects of our new program experiences on college-going and STEMM beliefs. Achieving these aims will provide concrete tools for schools across rural Appalachia, and perhaps other regions, to increase the number of their students equipped to join the high-growth biomedical and behavioral research workforce.

NSF Awards · FY 2025 · 2025-09

Accumulation of snowpack and the timing of the melt in the mountains that feed the Columbia River Basin change both the timing of spring flood risks and the risk of drought in summer and fall. These changes in streamflow impact ecosystems, including salmon migration and reproduction, and challenge water management for agriculture and the property and livelihoods of populations in the Basin. Information about the range of streamflow variability and changes through time are valuable for water management. There are existing reconstructions of annual streamflow for the Columbia River Basin based on tree rings. However these annual records do not contain information about seasonal-scale or shorter variations in streamflow. This project will use a novel combination of existing statistical and modeling techniques and measurements of previously-collected archives of tree rings to reconstruct daily streamflow from the past. The project will support one postdoc and two graduate students. In partnership with Futurum Careers, the project will create teaching materials for educators, create other educational materials K-12 and college students, and conduct other public outreach efforts. The goal of the project is to use existing tree-ring collections from the Pacific Northwest to measure blue intensity in order to reconstruct spring and summer precipitation and temperature for the Columbia River Basin for the last 500 years. The project will statistically disaggregate the seasonal precipitation and temperature reconstructions into daily values then use a distributed water balance model to produce daily streamflow reconstruction. The project will support one postdoc and two graduate students. In partnership with Futurum Careers, the project will create teaching materials for educators, create other educational materials K-12 and college students, and conduct other public outreach efforts. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-09

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

NSF Awards · FY 2025 · 2025-09

This project aims to serve the national interest by developing and implementing a model professional development (PD) initiative for natural science graduate students that emphasizes the alignment of skills related to excellence in undergraduate instruction and scientific research. Graduate students are important instructors of undergraduate science courses, especially classes critical to the success of first-year students. For this Level 1 IUSE Institutional and Community Transformation project, the Division of Natural Sciences and Mathematics (NSM) and Teaching and Learning Innovation (TLI) at the University of Tennessee, Knoxville (UTK) aim to create PD for first-year NSM graduate students focused on the synergy between teaching and research skills. This "holistic" PD will be based on UTK's institutional principles of excellence in teaching and will focus on skills that promote excellence in both research and teaching. Faculty and graduate students from each NSM department, along with the project PIs, will work as a collaborative multi-disciplinary team to create, implement, and assess the holistic PD. Assessment of the project will investigate the extent to which graduate students and undergraduate students are positively impacted, and the level of departmental support for the PD. The significance of this project is that it will advance national understanding of how to develop graduate student teaching and research skills in parallel, which could be a significant shift in PD approaches. Science graduate students who teach undergraduates need to develop excellence in both research and teaching. Although the skill sets for these tasks are often seen as different, this project plans to use the UTK's institutional principles of teaching excellence to highlight the synergy between teaching and research skills. The project goal is to create a sustained, departmentally supported 'holistic' professional development initiative for first-year NSM graduate student instructors. The project team includes the NSM Dean, the leader of UTK's TLI, and NSM faculty education researchers, who will guide a cross-departmental collaborative model across the seven NSM departments at UTK. This project will implement two strategies: 1. Create a cross-departmental change community (CCC) to design, revise, and advocate for holistic PD. In year 1 of the project, one faculty and one graduate student from each NSM department will design the year-long holistic PD for first-year NSM graduate students. Annual surveys will assess faculty and graduate student perceptions of and commitment to the PD. The CCC will use these results to engage departmental faculty in discussions about impacts and gain continued support for the PD. 2. Engage Graduate Student Instructors in holistic PD with NSM peers and departmental faculty. In years 2 and 3, first-year NSM graduate student instructors (GSIs) will participate in the year-long holistic PD. A January workshop will engage GSIs and departmental faculty in activities to reflect on the synergy between teaching and research skills. Annual pre- and post-survey data will assess GSI teaching and research identity and self-efficacy; undergraduate perceptions of their course learning experience will be compared between GSIs who received and did not receive the holistic PD. In terms of outcomes, this project will advance knowledge about how to design holistic PD for science GSIs and the potential to sustain departmental commitment for PD by emphasizing synergies between teaching and research. A strong advisory team and project evaluation will support the rigorous assessment plan to generate data useful to researchers and practitioners. The project team will work with the departments and the graduate school to sustain the PD efforts. 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.

NIH Research Projects · FY 2025 · 2025-09

The ability to accurately measure biomembrane structure is critical for understanding its role in membrane functionality, but the complexity of the cellular environment makes this an exceedingly difficult task at present. This study aims to provide proof-of-principle for using cryogenic electron microscopy to quantify key membrane properties such as thickness and average molecular area, even in heterogeneous samples with multiple types of membrane domains. The knowledge gained from this project will help open a new window into the nanoscale architecture of the cell.

NSF Awards · FY 2025 · 2025-09

This award provides funding for early career scientists to take part in a symposium at the annual Society for Integrative and Comparative Biology (SICB) meeting in January 2026. SICB is the leading society for interdisciplinary biology research from molecules to whole organisms. The symposium speakers will present cutting-edge research that integrates perspectives from ecology, physiology, and evolution to understand behavioral responses to the environment. In addition to presenting research, early career scientists funded through this award will participate in workshop discussions, develop their professional networks, and exchange ideas that advance research and move the field forward. This award supports the participation of early career scientists in a symposium examining behavioral responses of organisms to the environment. The symposium brings together researchers studying behavior from ecological, physiological, morphological, and evolutionary perspectives in order to identify and synthesize data and build an integrative, comprehensive understanding of behavioral responses. 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

Non-technical Abstract: Elemental tin (Sn) presents a rare opportunity to explore quantum phenomena using a clean, single element platform. In its two distinct structural forms—alpha-Sn with topological properties and beta-Sn as a conventional superconductor—Sn offers a natural setting to study the interplay between topology and superconductivity. However, existing synthesis techniques often produce mixed-phase films, limiting both fundamental discovery and future applications in quantum technology. This project seeks to overcome those limitations by developing precise, scalable methods to selectively grow phase-pure Sn films and construct heterostructures with atomically sharp interfaces between topological and superconducting regions. The research is closely integrated with educational goals that include using these high-quality materials in undergraduate laboratories and outreach demonstrations. These efforts aim to inspire a new generation of scientists by making cutting-edge quantum materials research accessible and engaging to students from all backgrounds and educational levels. Technical Abstract: This research focuses on understanding and controlling the structural phase formation in epitaxial Sn films by tuning lattice strain and film thickness. The principal investigator hypothesizes that such control enables the selective growth of alpha-Sn and beta-Sn phases, allowing the creation of single-elemental heterostructures with clean interfaces. The project includes four interrelated thrusts: (1) synthesizing phase-pure Sn films by strain engineering, (2) uncovering their intrinsic quantum properties such as Luttinger semimetal behavior and superconductivity, (3) constructing alpha-Sn/beta-Sn junctions through engineered lattice transitions, and (4) probing emergent quantum phenomena in Sn-based Josephson junctions to reveal the interplay between topology and superconductivity. The research builds on preliminary results and leverages state-of-the-art molecular beam epitaxy and transport measurement techniques. Educational activities are interwoven with the research plan, using the developed materials and devices in classroom experiments and public outreach. 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 aims to develop mathematical techniques for analyzing nonlocal models, which have proven effective in modeling complex natural, scientific, and engineering phenomena such as material fractures and anomalous diffusion processes. In mechanics, understanding material behavior, failure, and strength under deformation is crucial for their proper application and the design of new materials, with significant implications for manufacturing, materials engineering, and related technologies. This project provides a robust analytical foundation for the peridynamic model, widely used in the engineering community, as well as other nonlocal models employed in scientific research. The research activities will enhance the effectiveness of these models and ensure that future modeling and simulation efforts based on nonlocal theories are more quantitative and reliable. Additionally, the project offers valuable training opportunities for students and young researchers. The research topics provide a rich training ground and research experience that draw on ideas from various interdisciplinary areas. The project research activities aim to establish the necessary mathematical foundation for the recently proposed continuum-kinematics-inspired peridynamics model, which addresses many limitations of the well-known bond-based peridynamics. The principal investigator (PI) demonstrates the well-posedness of both nonlinear and linear models, provide rigorous derivations of simpler linearized models, and establish connections with local models. This research presents significant technical challenges not encountered in traditional local approaches. To address these challenges, the PI employs various methodologies and utilizes analytical tools from the theory of integral and differential equations, linear and nonlinear functional analysis, and calculus of variations. Implementing these approaches requires novel insights to adapt standard classical tools to the nonstandard nonlocal setting, thereby advancing knowledge in this area. The rigorous variational analysis and mathematical frameworks developed by the PI will significantly impact the creation of effective and reliable finite element methods and other numerical schemes for solving complex engineering problems using enhanced peridynamics models. 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

Non-technical summary: Polymer based products penetrate all aspects of our life, from food packaging to energy and medical technologies. This wide use of polymeric materials and their poor recyclability (caused by strong covalent bonds) led to the explosive growth of plastic waste and raises the urgent need for creating recyclable polymers. One of the potential solutions is introducing a few reversible (dynamic) bonds. Polymers with reversible bonds traditionally called Dynamic Covalent Networks (DCNs) are not only easily recyclable but have many unique properties not achievable in traditional polymers, including self-healing and shape memory aspects. However, detailed quantitative understanding of mechanisms controlling properties of DCNs remains limited. This hinders development of novel functional materials with desired properties. The main goal of the research proposed here is to unravel the fundamental mechanisms controlling properties of DCNs. Several experimental techniques will be employed to study dynamics and structure of DCNs on different time and length scales. The proposed research will deepen fundamental understanding of mechanisms controlling properties of polymers with reversible bonds. This will be instrumental in a rational design of easily recyclable polymeric materials with unique viscoelastic and self-healing properties not achievable in traditional polymers. The proposed program will significantly impact the education of specialists for future science and engineering critical for the US competitiveness. Technical Summary: Dynamic Covalent Networks (DCNs) containing reversible (dynamic) bonds provide a solution for creating polymers recyclable by design. Moreover, DCNs are not only easily recyclable but have many unique macroscopic properties not achievable in traditional polymers, including self-healing, shape memory, and time programmable functions. However, detailed quantitative understanding of microscopic mechanisms controlling dynamics and viscoelastic properties of DCNs remains limited, due to additional complexity introduced by the reversible bonds. The goal of the proposed here research is to decode the fundamental mechanisms controlling dynamics and viscoelasticity of DCNs. The project focuses on three main objectives: (i) Bridge quantitatively the segmental and chain dynamics with bond rearrangement processes and viscoelasticity of DCNs with both dissociative and associative bonds; (ii) Develop a predictive understanding of the role of steric factor in bond rearrangement processes for both dissociative and associative mechanisms; and (iii) Unravel the mechanism of viscoelasticity in DCNs with microphase separation of reversible bonds. In the proposed work, the chain and segmental dynamics, bond dissociation and rearrangement, and viscoelastic properties of polymers will be studied by a combination of rheology, dielectric and light scattering spectroscopy, and differential scanning calorimetry. It will be complemented by analysis of structure using small angle X-ray and neutron scattering. The proposed experimental research will deepen fundamental understanding of microscopic parameters and mechanisms controlling macroscopic properties of polymers with reversible bonds. This will be instrumental for a rational design of easily recyclable polymeric materials with unique viscoelastic and self-healing properties which are critical for the sustainability. From a broader perspective, it might also have a strong impact on understanding of many biological materials where reversible bonding and interactions play a critical role. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2025 · 2025-08

PROJECT SUMMARY Polymicrobial communities are ubiquitous and the interactions between microbes are critical drivers of overall community function. The commensal, pathogenic, and mutualistic organisms in host-associated microbial communities display synergies as a direct result of the interplay between their metabolic activities and spatial arrangement relative to each other, leading to sustained cooperative and competitive interactions. However, the fundamental biological principles of how microbes interact and the spatial constraints to these interactions, from the macro to micron to chemical scale, remain largely unknown. To fill this knowledge gap, we propose interdisciplinary approaches to map the nutritional landscape and determine how the nutritional landscape impacts microbial community assembly, spatial organization, and interactions within host-associated microbial communities. Here, we use chronic wounds as a model system, as they have features conducive to addressing our goal: 1) sustained interactions in their native environment; 2) access to in situ sampling and visualization; and 3) measurable outcomes of interactions. Our objective is to combine top-down and bottom-up approaches to define the processes by which structured microbial communities form, the molecular mechanisms governing interactions between microbes, and the spatial parameters that dictate microbe-microbe interactions. First, we will use high-resolution confocal imaging, imaging mass spectrometry, and spatial metatranscriptomics to quantify the metabolic landscape of polymicrobial communities, in three-dimensions at the micron scale with an innovative computational pipeline we developed. This will yield an unprecedented view of microbes in their natural environment and provide a platform for visualizing polymicrobial communities. Further, we will evaluate how this landscape shifts in response to changes in community composition and host environment. Next, we will use -omic and genetic approaches to define how shifts in nutrient availability due to changes in community composition and host nutritional status impacts cellular metabolism, cooperation, and competition within microbial communities. Collectively, this proposal will provide a framework for understanding the ecological factors that contribute to community composition, stability, and interactions in host-associated polymicrobial environments. While this project is centered on chronic wounds, the long-term goal is to apply this platform broadly across microbial communities. This work will advance our fundamental knowledge of microbe-microbe interactions and identify new strategies that leverage these interactions to improve human health.

NSF Awards · FY 2025 · 2025-08

The majority of elements in the Periodic Table of Elements are synthesized either in massive stars and their explosive deaths in core collapse supernovae or in the merger of neutron stars born in such supernovae. Supernovae and neutron star mergers are complex, explosive, turbulent, and energetic events. They involve strong-field gravity, turbulent fluid flow, magnetic fields, radiation in the form of subatomic particles known as neutrinos, thermonuclear reactions, and high-density nuclear physics and exotic particle physics not seen in terrestrial experiments. The solution of the complex equations governing the dynamics of these catastrophic events requires the development of high-fidelity methods and simulation frameworks to execute these solution methods on the world’s leading supercomputing platforms. These are arduous tasks requiring the multidisciplinary expertise of astrophysicists, applied mathematicians, and computer scientists. The completion of these tasks typically requires years of development. The development of high-fidelity methods and well-engineered and high-performance open-source software then enables the broader astrophysical modeling community and facilitates the reproducibility of results, critical when complex systems are involved. Such development provides the astrophysics community with a set of optimized tools to explore some of the Universe’s most important phenomena, utilizing the National Science Foundation’s major investments in the Laser Interferometer Gravitational Observatory, designed to detect gravitational waves from core collapse supernovae and neutron star mergers, which encode key information about the dynamics in such events. The project offers a multidisciplinary research environment that contributes to training the next generation of computational scientists, bridging physics, mathematics, and high-performance computing. This project implements novel software elements for relativistic, spectral two-moment neutrino kinetics in the open source toolkit for high-order neutrino-radiation hydrodynamics (thornado). The methods are based on discontinuous Galerkin (DG) discretization and implicit-explicit (IMEX) time stepping.The project designs robust methods that capture key continuum properties of the phase-space flow (i.e., respecting physical bounds and conservation laws). thornado kinetics solvers, together with existing thornado solvers for hydrodynamics and gravity, and recognized open-source computational science software technologies, are coupled for deployment on high-performance computing systems to enable large-scale multi-physics models of core-collapse supernovae and related problems in nuclear astrophysics. Distributed parallelism and adaptive mesh refinement is enabled by AMReX, and time accurate multi-physics coupling is enabled by the SUNDIALS library, which offers advanced multi-rate IMEX integration and adaptive step-size control. For deployment on heterogenous computing systems, the project instantiates implementations that target accelerators (GPUs). The project joins expertise in physics, mathematics, and computational science to develop novel computational tools to improve understanding of core collapse supernovae — among the most energetic events in the Universe — and provide better predictions of their neutrino and gravitational wave signatures. It leverages long-standing research collaborations to train next-generation scientists in a multidisciplinary research environment. This award by the Office of Advanced Cyberinfrastructure is jointly supported by the Division of Physics in the Mathematics and Physical Sciences Directorate. 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

Non-Technical Summary As laboratory tools become increasingly automated and machine learning (ML) continues to transform scientific research, a new challenge has emerged: how can ML agents control and coordinate multiple instruments at different labs, share information between them, and make meaningful decisions to accelerate discovery? This project tackles that challenge by developing strategies for building smart experimental systems that allow different tools - such as microscopes, structural characterization, and synthesis instruments - to work together autonomously, and develop metrics that allow evaluation of the return on the investment. These systems are designed to not just automate tasks, but also to “learn” which experiments to run next based on previous results, optimizing both speed and insight. The research is focused on discovering new materials for energy storage and information technologies, such as batteries and next-generation electronics, where even small improvements in materials can have large technological and economic impacts. In addition to scientific breakthroughs, the project shares tools and training with students and researchers from a broad range of institutions, helping to build an innovation-driven workforce for deep tech industries and manufacturing. In doing so, this work supports NSF’s mission to promote the progress of science, support national prosperity and security, and prepare a skilled workforce. Technical Summary This research develops an autonomous experimental framework for materials discovery based on multi-instrument coordination, active learning, and reward-driven optimization. The central objective is to create machine learning agents that can operate multiple experimental tools - such as scanning probe microscopes, structural probes, and synthesis platforms - in parallel, sharing information and prioritizing experiments in real time. These systems are applied to the exploration of combinatorial materials libraries, particularly targeting ferroelectric and electrochemical functionalities relevant to energy storage and electronics. The proposed methods include the use of structured and deep Gaussian Processes, causal learning, and symbolic regression, all within a framework of reward function design - where experimental strategies are guided not only by accuracy but by their expected contribution to downstream functionality. In addition to combinatorial libraries, the same strategies can be applied to multiple identical synthesis tools exploring the same parameter space, enabling high-throughput autonomous experimentation across facilities. Emphasis is placed on multi-objective and multi-fidelity optimization, where low-cost proxy measurements are combined with high-resolution tools, and on building decision-making logic that can generalize concurrent decisions across dissimilar experimental platforms. The project contributes to reproducible AI-driven experimentation and provides shared tools and training to advance materials research infrastructure, aligning with NSF goals in innovation and national competitiveness. 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

Phishing attacks represent a major cybersecurity threat affecting billions of Internet users worldwide. These attacks involve cybercriminals fabricating websites to look like legitimate ones, to trick users into revealing sensitive information such as login credentials. In response to escalating phishing threats, researchers have intensified their efforts to understand and counter these attacks. Researchers have also turned to machine learning and deep learning for detection solutions, with recent focus on "reference-based visual similarity models" that analyze visual elements such as logos and login forms. This approach compares potential phishing sites to legitimate ones. However, this method has two critical flaws. First, it lacks resilience against evolving evasion tactics which can effectively bypass phishing detectors. Second, countering these evolving tactics necessitates substantial human effort, including identifying vulnerabilities, curating ground-truth datasets, retraining models, and evaluating the updated models. This results in vulnerable time periods between the advent of new attack tactics and the deployment of updated models. This project is enhancing Internet user protection against phishing attacks by developing novel detection approaches that enhance resilience and minimize human efforts using forced execution of unexposed program elements and large language models (LLMs). The research aims to strengthen protection against phishing attacks by addressing two critical weaknesses in current detection systems: their vulnerability to evasion and their dependency on extensive human intervention. The project team conducts this through a dual approach. First, the project team conducts a systematic evaluation of state-of-the-art detection models to identify exploitable weaknesses and fundamental flaws. Second, based on these insights, the project team develops two novel detection mechanisms: a JavaScript forced execution technique that reduces dependency on reference-based visual elements, and an advanced system utilizing large language models (LLMs) that both narrows critical temporal gaps between new attack vectors and defensive updates and provides users with contextual semantic information for better threat assessment. This project extends beyond incremental security improvements by establishing more resilient, autonomous systems that minimize human oversight requirements while enhancing protective capabilities -- simultaneously advancing the theoretical understanding of phishing methodologies within the research community and delivering practical solutions that protect users against increasingly sophisticated social engineering threats. 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 Pathways to Enable Open-Source Ecosystems (POSE) project establishes a sustainable open-source ecosystem for the PArallel Runtime Scheduler and Execution Controller (PaRSEC) software framework. PaRSEC is an execution environment designed for scientific applications running on advanced, large-scale high-performance computers. It orchestrates the execution of computing tasks, manages data movement, and allocates the resources needed to efficiently run scientific applications on supercomputers. PaRSEC improves the performance and scaling of demanding, large-scale applications in areas that include advanced materials modeling, seismology, and artificial intelligence. It also supports research into novel mathematical algorithms that underlie a wide array of scientific computing problems. This project cultivates a sustainable, openly governed, open-source ecosystem around the PaRSEC software so that it can be supported by a broad community of contributors and users. Ultimately, widespread adoption of the PaRSEC runtime system promotes more efficient use of national computational resources and accelerates scientific and technological progress for the benefit of society. This POSE project will establish a pathway for the PaRSEC project to transition from its current centralized development model to an open-source, distributed community ecosystem. PaRSEC works by describing applications as parameterized task graphs. At runtime, a directed acyclic graph (DAG) of execution tasks is generated, where nodes represent computing tasks and edges represent the flow of data between them. This approach eliminates unnecessary synchronization points and enables full utilization of computing resources, freeing application developers from the burden of explicitly managing communication. The PaRSEC runtime system has been successfully applied in domains ranging from quantum chemistry to seismology. Although PaRSEC was originally developed in a centralized manner at a single institution, its developer and user bases have since expanded to multiple institutions. This project will support further community growth by establishing development standards, best practices, and onboarding materials to better support open community development and governance models and to build a sustainable open-source ecosystem for PaRSEC. 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

Computer-aided drug design (CADD), including structure-based virtual screening of a large number of available compounds (ligands) for a given drug target, has become an essential component of modern drug discovery. The actual value of the virtual screening relies on the accuracy of the target-ligand binding affinity prediction. It is recognized as a grand challenge for the virtual screening to accurately predict the target-ligand binding structures (molecular geometries) and binding affinities associated with diverse and massive datasets. This project aims to address the grand challenge in development of machine-learning (ML)-CADD models by introducing new, more effective mathematical representations of molecular geometries with the ability to track molecular geometry changes via Ricci curvatures and their associated spectral information. The outcomes of this project will furnish novel, more reliable computational approaches in essential areas of computational drug design, biomolecular modeling, data analysis, dimensionality reduction, and mathematical biology. Moreover, this project will provide graduate and undergraduate students with training in data analysis, biological modeling, algorithm development, and computational drug design. The enhancement of curricula from this project is planned as a continuation of the investigators' teaching-research practice. The new mathematical framework and deep learning architectures are directly integrated into computer software packages to ensure extensive usage by the community of researchers in drug design, biology, computer science, and mathematics. Additionally, the project will help train the next generation of researchers in advanced mathematics, data science, and molecular biology. This project will develop novel low-dimensional representations for biomolecular data analysis from mathematics-based approaches and robustness training data to revolutionize the current practice in structure-based virtual screening. The main objectives are: 1) to introduce molecular shape guided persistent Ricci curvature and, at the same time, to provide local geometry and spectral information to reduce the structural complexity while still maintaining an adequate description of biomolecular interactions; 2) to develop a target-ligand adaptive deep learning protocol for post-docking pose selection, binding affinity prediction, ranking, and estimation of other molecular properties; 3) to extensively validate the proposed methods on a variety of datasets to optimize the mathematical representations and learning networks. Specifically, this project will focus on the development of the proposed models for the virtual screening of phosphodiesterase-2 (PDE2) inhibitors, providing valuable hits of a promising therapeutic strategy for the treatment of various human diseases. A close loop integrating computational-experimental models will further strengthen the robustness and accuracy of the proposed models.; 4) to develop user-friendly software packages and web servers using parallel and GPU architectures for researchers who are not formally trained in advanced mathematics or sophisticated machine learning. 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

Tennessee Technological University, the University of Tennessee Knoxville, Meharry Medical College, and Vanderbilt University have joined in this project to address health challenges via Artificial Intelligence (AI) and Machine Learning (ML) infused workshops and training. At present, Tennessee ranks 44th among the 50 states in national health outcomes. This project will advance the use of modern, AI/ML-enabled computer technology in medical research and healthcare delivery. At the heart of the project is a three-part workshop series, powered by National AI Research Resource (NAIRR) Pilot resources aimed at accelerating interdisciplinary research at the intersection of advanced cyberinfrastructure, AI/ML, and health outcomes. These workshops train participants in high-performance computing, cloud-based AI applications, and open data tools, while fostering sustained collaboration among medical professionals, engineers, scientists, and students who participate. Workshop course content and outcomes will be shared with the NSF NAIRR program and broadly with the public. This project brings together leaders in medical AI/ML research at Vanderbilt University and the University of Tennessee, along with emerging research cyberinfrastructure centers such as Meharry Medical College. It builds upon collaborative frameworks previously advanced by the AI Tennessee Initiative, a statewide initiative led by UT Knoxville and TN Tech's AI Center---structures that have demonstrated success in enabling cross-institutional efforts. The workshops are linked to the usage of NAIRR Pilot AI resources, and will train participants to use NAIRR resources through hands-on training. Significant training on NAIRR resources---both HPC and Cloud---for AI applications, methods, and practice is included in all three workshops. Relevant methods, applications, and techniques working on open data will provide participants with significant training and scaffolding to engage in further AI/ML use-inspired research and to use NSF NAIRR resources in the future. Overall, this workshop series will engage and train a significant group of medical professionals, scientists, engineers, and pre-professional students on NAIRR Pilot resources and AI/ML concepts, advancing the careers of medical professionals, scientists, and engineers. 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 Pathways to Enable Open-Source Ecosystems (POSE) project creates a software ecosystem to support terrain parameter analysis. Terrain parameter analysis provides numerical descriptors of the shape and form of land surfaces, which are indispensable data used in the study of agriculture, wildfires, irrigation, and other geosciences-related domains. Terrain parameters provide significant insights into soil moisture distribution, fire behavior, and hydrological processes. This project directly addresses the need for robust, open-source terrain parameter analysis tools through the development of an open-source software ecosystem centered around the GeoTiled software. This software empowers users to engage in swift computation of terrain parameters, thereby making data analysis both accessible and practical. The development of an open-science ecosystem to support GeoTiled will lower technical and computational barriers faced by the scientific community, and will facilitate data-driven decision-making that can enhance agricultural productivity and reduce environmental risks. The open-source nature of the project fosters innovation and transparency, providing the United States with a long-lasting edge in geospatial science and environmental modeling. This project establishes a sustainable open-source software ecosystem centered on high-performance tools for computing terrain parameters. The project is based on a software platform that partitions digital elevation models into manageable sections to compute landscape features at scale while efficiently preserving accuracy. Project activities include identifying community needs through surveys and research discussions and enhancing reproducibility through improved data management and documentation. The team builds and supports a community of developers, scientists, and educators through governance structures, automated testing, user support, and training workshops. These outcomes build on a robust user-driven software platform and increase participation from contributors as well as broader adoption in Earth science workflows, supporting scientific discovery in areas such as wildfire prediction and agricultural planning. 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

With support of the Chemical Synthesis Program in the Division of Chemistry, Joseph Clark of Marquette University is studying processes for the preparation of small molecules incorporating tritium (and/or deuterium) atomic labels and complementary analytical techniques to characterize and probe these materials. Tritium is widely used as a radioactive tracer element in biological and chemical research and molecules labeled with this rare isotope of hydrogen are useful during the development of pharmaceuticals, agrochemicals, and other kinds of important substances. The findings of the funded research are anticipated to permit the incorporation of tritium (/deuterium) into small molecules with a high level of precision, such that the quantity and location of the hydrogen isotope of choice can be finely controlled. The broader impacts of the funded project extend to the benefits accrued to society as Dr. Clark and his research team members engage in a variety of educational and outreach activities. Foremost among these efforts is a holistic undergraduate career development and training program that combines undergraduate summer research experiences with a collaborative pharmaceutical industry and academic workshop jointly facilitated by the University of Puerto Rico Cayey and Marquette University. The program is designed to provide students with critical training to prepare them for successful careers in science, technology, engineering, and mathematics (STEM) and it places an emphasis on attracting participation from individuals belonging to groups traditionally underrepresented in science. Tritium is an ideal tracer nuclide because of its sufficiently long half-life (12.3 years) and the ability to prepare tritiated compounds with high specific activity for a variety of applications in chemical and biological research. For example, tritium-labeled small molecules are often used in radioligand binding assays and also for absorption, distribution, metabolism, and excretion (ADME) studies of drug candidates. Despite the many important roles for tritium-containing molecules, there are a limited number of effective synthetic methods for the selective incorporation of precise quantities of tritium into organic compounds. Furthermore, spectroscopic techniques to characterize and quantify tritiated small molecules, especially enantioisotopomers that are chiral by virtue of isotopic substitution, are either underdeveloped or do not currently exist. To address the first challenge, a suite of copper-catalyzed transfer tritiation and hydrotritiation reactions are being investigated that permit the regio- and stereo-controlled incorporation of tritium-atom labels into various types of alkene and alkyne substrates (including: allenes, 1,3-dienes, and enynes). The development of enantioselective variants of these transformations is also being pursued to allow for access to enantioisotopomers that are chiral by virtue of tritium substitution and/or by virtue of deuterium and tritium substitution. To support reaction development, and in regard to the second challenge identified above, molecular rotational resonance (MRR) spectroscopy is being established as a general analytical technique to quantify enantiomeric excess of enantioisotopomeric materials and to determine their absolute configurations. It is anticipated that the findings from this research will expand access to selectively tritiated small molecules as useful tools for understanding how metabolites and/or pharmaceutical agents are processed in biological systems. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-06

The conference "Hilbert Function Spaces 2025" will be held June 30-July 4, 2025 in Frascati, Italy, at a facility that belongs to the University “Tor Vergata” of Rome. The purpose of the meeting is for distinguished researchers in the field of Operator Theoretic Function Theory to share their recent progress during the plenary talks, for participants as a collective to work out the “state of the art” of the field, and to establish new collaborations among the participants. Funds from this grant will primarily be used to support the conference travel of US based early career mathematicians. Participation in this conference will facilitate the integration of these individuals into the international research community in analysis, and it will inform and inspire their research and teaching efforts as they return to their home institutions in the US. The conference will be an interdisciplinary workshop on Operator Theory, Function theory, and Harmonic Analysis, and their applications and interactions with other fields. These topics in pure mathematics are important parts of the foundations of the mathematics necessary for our modern digital devices such as cell phones and imaging devices, but they are also important in many other areas such as optimization and quantum physics. Information of the conference can be found at https://sites.google.com/view/hfs2025/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.