University of Utah

NSF Awards · FY 2025 · 2025-08

Industrial turbines that burn natural gas currently generate about 40 percent of all electricity and nearly 15 percent of all CO2 in the US. A transition from natural gas to clean hydrogen could significantly reduce CO2 production. While gas turbine manufacturers anticipate 100 percent hydrogen utilization in the future, current industrial turbines operate closer to 50 percent hydrogen by volume. One of the main obstacles slowing the transition to hydrogen is flame flashback, a phenomenon wherein the flame proceeds upstream of the combustor with catastrophic consequences. When flashback happens near the wall of a combustor, it is called boundary layer flashback. The addition of hydrogen as a fuel significantly increases the likelihood of such boundary layer flashback phenomena. The goals of this project are to better understand the physical and chemical mechanisms responsible for flashback and to develop general, accurate and affordable models capable of predicting its occurrence. The project also aims to provide rural communities affected by hydrogen projects with a source of information on the impact of the fuel on their lives and to encourage students to participate in STEM starting from high school and continuing through their undergraduate careers. The goal of this project is to enable a new modeling paradigm for boundary layer flashback of turbulent lean hydrogen flames that allows for general, accurate, and affordable prediction of its occurrence. The primary hypothesis guiding this work is that there is a fundamental relationship between the onset of boundary layer flashback in turbulent premixed lean hydrogen flames and strained premixed flames at the extinction limit, and that this relationship can be leveraged to develop and extend a new class of boundary layer flashback models. As part of this project, direct numerical simulations of boundary layer flashback will be performed to understand the fundamental physical relationship between flashback and extinction limit flames. Insights from these simulations will be used to build engineering models capable of predicting the onset of flashback in a wide range of practical combustors. Finally, wall models will be developed to capture boundary layer flashback in computational fluid dynamics simulations even without fully resolving the flow at the wall. The models developed as part of this project will ultimately aid in the design of next generation low-to-no carbon combustors capable of enabling carbon-free power. 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

Wildfires are growing more frequent and severe in the western United States, threatening lives, infrastructure, health, and economic stability. This project brings together scientists, insurers, government officials, and community members to co-develop solutions for reducing wildfire risks and protecting insurability in Utah, where wildfire risk is rising and insurers are withdrawing coverage or raising premiums. The team will use a community-based approach to produce wildfire risk models tailored to local areas, test the effectiveness of mitigation strategies, and design new insurance approaches that reward resilience. A set of strategies and tools will be developed to guide local decision-makers, particularly in high-risk and high-vulnerability areas of multiple Utah counties with extensive wildland-urban interface. Educational activities—including a new undergraduate research stream, course materials, and a speaker series—will help train future leaders. By aligning science, public engagement, and policy innovation, this project will strengthen the ability of communities to prepare for and recover from wildfires, supporting the national interest in public welfare, economic security, and scientific advancement. This interdisciplinary project integrates Earth system modeling, fire and ecosystem modeling, machine learning, insurance analytics, and community planning to develop actionable strategies for wildfire resilience and financial protection. Three high-resolution wildfire models will be tested, compared, and validated using historical burn data for the entire state of Utah, with more detailed maps for Summit, Salt Lake, and Utah counties. These models will be linked to detailed exposure maps of homes, infrastructure, and economic assets, enabling precise risk assessments and cost-benefit analyses of mitigation actions vetted through community engagement. Machine learning techniques will be used to identify at-risk assets and simulate the impacts of mitigation strategies such as defensible space, infrastructure hardening, and utility risk reduction. Concurrently, the project will assess insurance coverage gaps and test innovative solutions—including parametric products, community-scale covers, and public-private partnerships—to improve affordability and coverage. Through iterative co-design with community partners, the research will produce scalable tools and strategies for use across Utah and the Southwest region. This work will advance the scientific foundations of environmental risk modeling, bottom-up socio-technical adaptation, and adaptive insurance frameworks for wildfire and other hazards. 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

One of the myriad functions of mathematics is to provide language for the formulation of equations that describe the physical world, and tools to solve them. Often the equations that arise are algebraic in nature, like those describing lines, circles, parabolas, and the like, in contrast with, say, equations involving the trigonometric functions, or logarithms, or derivatives. Typically, the equations have infinitely many solutions--think about the equation defining a circle--and it is usually not possible to write down a complete list of solutions. Rather, the objective is to find ways to study the structure of the collection of the solution set. A fruitful approach has been to consider the (algebraic) functions on these solution sets. These functions form a mathematical structure called a commutative ring, and the principal investigator's research over the past two decades has been dedicated to understanding these structures; not in the abstract, but in their various manifestations, which are galore, for they arise in various contexts across mathematics--e.g., in the representation theory of groups, in topology, and in number theory. This might be seen as an instance of what Venkatesh in his Ahlfors Lectures calls “the unreasonable effectiveness of mathematics in mathematics”. In conjunction with the principal investigator's research plan, this project also provides research opportunities for, and training of, graduate students and postdoctoral scholars. Andrew Wiles deduced Fermat’s Last Theorem as a byproduct of his proof of the Shimura-Taniyama-Weil (or modularity) conjecture that postulates that every elliptic curve, an object from the world of algebra, arises in a natural way from a modular form, an object from the world of analysis that is endowed with a lot of symmetries. Wiles’ proof introduced many new techniques; central among them is the method of patching (in collaboration with Richard Taylor) and a numerical criterion for detecting when a map between certain types of commutative rings is an isomorphism. The Taylor-Wiles patching method has been developed extensively, leading to many new modularity results. However, the numerical criterion had resisted attempts of generalization to a broader context. A few years ago, the principal investigator, in collaboration with Chandrashekhar Khare and Jeff Manning, found a way to do just that. In combination with the patching method, they have used this to establish more refined modularity results than possible with pure patching alone. The principal investigator plans to develop new and more sophisticated commutative algebraic tools, driven by major open problems around modularity lifting, and algebraic number theory more generally. He and his PhD student will also implement some of these tools in the proof assistant Lean. 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 Research Experiences for Undergraduates (REU) site award to the University of Utah, located in Salt Lake City, UT, supports the training of 10 students for 10 weeks during the summers of 2025-2027. In this program, funded by the Division of Chemistry and the Department of Defense in partnership with the NSF REU program, participants pursue cutting-edge research projects under the guidance of experienced and committed mentors, addressing a range of challenging chemistry-related problems in energy, health, and the environment. Participants also will take part in professional development activities to enhance skills in data analysis and computation, develop scientific communication skills, and prepare for graduate schools and modern STEM careers. By providing research experiences enriched by cohort building, mentor training, workshops, a graduate school mini expo, and a research conference, this site trains undergraduates for careers in STEM and strengthens connections between the University of Utah and primarily undergraduate institutions (PUIs) from around the nation. This site also has a focus on serving regional students in Utah and the Mountain West region. Participants will advance research projects with an emphasis on model building, data analysis, and computation from all areas of chemistry. These projects include studying organic mixed ionic-electronic conductors; modeling the self-assembly of nanoparticles; discovering chemical ligands of bacterial sensory domains; developing new materials and polymers to separate and recover phosphate; and studies of organometallic catalysts combining experiments and data science. To support the data analysis and modeling aspects of their projects, students will participate in a series of summer workshops run by members of the Henry Eyring Center for Theoretical Chemistry. Students will also receive opportunities for career exploration and networking through local industry tours. 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

Many bacteria swim using slender, spiral-shaped filaments called flagella, which are rotated by molecular motors to generate propulsion. This movement allows bacteria to navigate complex environments, seek nutrients, and interact with host organisms. The mechanical properties of these flagella, especially their stiffness, play a crucial role in how efficiently bacteria can swim and reorient. This project will investigate how the stiffness of bacterial flagella influences swimming behaviors and turning maneuvers. Beyond biology, these insights are relevant to the development of nanorobots that use bacterial flagella for propulsion. Improved understanding of flagellar mechanics could enable better design and control of nanorobots for applications such as targeted drug delivery, microsurgery, and microscale fabrication. This research supports the progress of science and national health by contributing fundamental knowledge with wide-reaching impact, from microbiology and ecological function to biomedical innovation and soft robotics. It will also provide training opportunities in biophysics, computational modeling, and microscopy for students at multiple levels, helping to build a skilled scientific workforce. This study will advance fundamental understanding of flagellar deformability by overcoming limitations in existing models. Prior research often relied on simplified force equations, inadequate representations of the surrounding fluid flow, and low-resolution image-based geometry extractions. This project will integrate high-precision experiments on tethered flagella with advanced image processing algorithms to reconstruct the three-dimensional geometry of flagella in motion with subpixel accuracy. Using the method of regularized Stokeslets, the study will incorporate hydrodynamic interactions with nearby surfaces to better model bending mechanics. These approaches will allow for improved characterization of bending stiffness, including nonlinearities. The results will clarify how mechanical properties affect motility in bacteria and propulsion efficiency in flagella-powered nanorobots. Together, this project will deliver new theoretical and experimental tools to the biomechanics community and contribute critical insights into the design principles of biologically inspired locomotion 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 · 2025-07

In a variety of real-world applications, eigenvalues of linear partial differential operators describe physical phenomena of interest, e.g., light propagation, mechanical vibrations, and liquid sloshing. It is of practical and fundamental interest to study the dependence of an eigenvalue on a control variable, such as the material coefficient or the domain shape, and to engineer/design/optimize control variables to enhance relevant spectral properties. This project will develop and analyze new computational methods for solving extremal eigenvalue problems, especially involving challenging geometric constraints. The research activities will advance discovery and understanding in computational mathematics and mathematical physics, as well as more general areas of science and engineering through applications. Educational activities are integrated with research activities in four specific ways: (i) training of students (including K-12, undergraduate, and graduate students across different schools) and junior researchers at different levels, (ii) encouraging participation of researchers in the area (iii) dissemination and sharing of research results publicly, and (iv) organization of international workshops on proposed research topics. Due to the collaborative nature of this proposal, students will engage in activities across R1 and primarily undergraduate institutions. The aim of this project is to tackle two canonical extremal eigenvalue problems from the mathematical and engineering communities: (1) study the Steklov eigenvalue problem on a compact Riemannian surface with boundary and seek to maximize of an eigenvalue over the class of smooth metrics; (2) address a key challenge in the design of topological photonic crystals (TPCs): find materials that have large shared spectral bandgaps where the adjacent dispersion surfaces have prescribed topological invariants (e.g., the Chern number, a topological invariant obtained from Berry curvature). A technical challenge in these problems is to handle geometric constraints - either stemming from topological constraints on Riemannian surfaces or topological invariants of dispersion surfaces. The proposed research activities will develop analytical and computational tools to tackle this challenge. 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 a research program to study the biomechanical origins of bone fragility in aging and type-2 diabetic populations. The most basic building blocks of bone are collagen and mineral. Aging and diabetes change the microstructure of bone through increasing the number of interconnections (crosslinks) of bone’s collagen network. This increase in crosslinks is hypothesized to reduce bone resistance to fracture. The mechanisms that are responsible for the observed increase in bone fragility will be investigated during the project. The findings are likely to have significant implications for public health, particularly as diabetes prevalence rises. The project supports NSF's mission by promoting scientific progress, advancing national health, and potentially leading to targeted therapeutic treatments that could improve quality of life for all Americans. This work will look to reveal the role of advanced glycation end product cross-links on fracture behavior at quasi-static and dynamic loading rates representative of physiological and fall-event strain rates. Fracture experiments will be supported by elasto-plastic fracture theory, anisotropic stiffness tensors derived from non-destructive acoustic elastography, and high resolution in-situ imaging to provide new insights of active crack growth mechanisms. Because this study uses human tibia and fibula specimens obtained from healthy and diabetic individuals, for the first time fracture behavior will be assessed against bone specific quantities of 15 different advanced glycation end product cross-link types. Statistical analysis will aid in pinpointing the specific mechanisms and advanced glycation end products responsible for bone embrittlement and therefore look to support targeted therapeutic treatments to recover bone’s fracture resistance. 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

Plastics pollution can impact ecosystems and human health, which makes the search for sustainable alternatives especially urgent. Global reliance on traditional plastics has contributed to greenhouse gas emissions and proliferation of microplastics in terrestrial and aquatic ecosystems. Biodegradable plastics derived from polyhydroxyalkanoates, which are produced by microorganisms using renewable resources, offer a promising alternative to conventional plastics. This project will develop new biopolymers with enhanced functionality and controlled biodegradability. It will evaluate the environmental impact of these new materials, including their potential to reduce microplastic pollution and mitigate the spread of antimicrobial resistance. The project will support the field of sustainable materials by fostering collaborations between academia, industry. It will support educational opportunities and public engagement initiatives to help create a future science and engineering workforce. This project was submitted under the United States-Ireland-Northern Ireland R&D Partnership and is a collaboration between researchers at the University of Utah, University College Dublin, and Queen's College Belfast. The project will help reduce the impact of plastics pollution by enhancing the performance of chemically modifying polyhydroxyalkanoate-based plastics, evaluating the breakdown of these materials across various environmental conditions, and assessing the impact of biodegradable microplastics on the spread of antimicrobial resistance. The chemical modifications will be made by increasing unsaturation and grafting with other polymers, including polylactic acid, polystyrene, and polypropylene. The mechanical and thermal properties as well as degradation rates of this next generation of bioplastics will be evaluated in various environments including soil, compost, and wastewater biosolids. The project will use advanced microcosm experiments, molecular biology, and high-throughput sequencing to assess the impact of both the biopolymers and their degradation products and the prevalence of antimicrobial resistance genes. This comprehensive approach will provide critical insights into the environmental fate of biodegradable plastics and inform the design of materials that minimize ecological risks. The expected outcomes include novel environmentally neutral bioplastics, new methodologies for assessing their impact, and new knowledge to guide future innovations in sustainable polymer science. The project will also generate new knowledge about the environmental fate of biopolymers, ultimately supporting a shift toward safer and more responsible use of plastic materials. 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 work is focused on advancing understanding of the Quasi-Biennial Oscillation (QBO), a prominent and persistent mode of variability in the tropical stratospheric winds with a period of approximately 28 months. The QBO influences higher latitude phenomena such as the stability of the stratospheric polar vortex (the “Holton-Tan effect”) that subsequently impacts the tropospheric mid-latitude jet stream. As the QBO vertically propagates from the middle stratosphere to the tropopause, it also influences the position of the North Pacific jet as well as the variability in the tropical troposphere through its interactions with deep convection and the Madden-Julian oscillation. The persistence and quasi regular nature of the QBO offers the potential for predictability out to several years if well represented in global models. This study aims to provide insight into the longstanding model bias in QBO strength in the lower stratosphere, with the QBO being too weak in the models, and the inability of models to correctly capture the QBO modulation of the stratospheric polar vortex. The project includes the training of two graduate students in climate dynamics and provides research opportunities for undergraduate students in a STEM field. It additionally includes creating outreach materials for the University of Utah and the Natural History Museum of Utah on the QBO and its role in the climate system. The main goals of the project, to better understand the weak QBO bias in the lower stratosphere in fully coupled models and the inability of these models to capture the Holtan-Tan effect, will be addressed using a simplified model of the stratosphere with a self-generating QBO. To test the leading theories regarding the weak QBO bias in the lower stratosphere, experiments isolating the effects of vertical resolution, varying tropical upwelling speeds, varying imposed tropical latent heating and gravity wave drag, and adjusting gravity wave sources will be performed. To better understand the interactions of the QBO with the stratospheric polar vortex additional experiments will be conducted focusing on the sensitivity of the Holton-Tan effect to polar vortex strength and the sensitivity of the polar vortex strength to the mean equatorial zonal wind vertical structure. The use of the simplified model offers the ability more clearly interpret the results compared to a fully coupled climate model and to perform a greater number of experiments at low computation cost. The graduate students will lead these experiments, with guidance from the lead investigator, offering training for the next generation of atmospheric scientists capable of addressing societally relevant issues. 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 project explores how complex and unpredictable growth patterns occur in nature—for example, how infections spread, how crystals form, or how traffic jams develop. These systems can look chaotic, but they often follow deep mathematical rules. By studying the mathematics behind these random and irregular processes, the project aims to uncover the hidden patterns that govern them. Although the work is rooted in abstract mathematics, it has real-world applications in areas like medicine, environmental science, and engineering. Better understanding how random growth and movement work can help us manage diseases, improve materials, and design smarter technologies. This research not only advances our knowledge of mathematics but also contributes to solving real problems that affect people’s lives. This project focuses on the mathematical study of random motion in random environments, with particular emphasis on models of random growth. The principal investigator has developed and applied a key analytical tool--Busemann functions--to a broad class of stochastic models, including directed and undirected percolation, random polymers, and random walks in random environments, across discrete, semi-discrete, and continuous frameworks. Notably, this includes work on the Kardar-Parisi-Zhang (KPZ) equation, a central object in the study of stochastic growth. Busemann functions provide deep structural insights: they solve energy-entropy variational formulas, describe infinite-volume Gibbs measures in positive temperature regimes, and characterize geodesic rays in zero-temperature settings. They also yield eternal solutions and stationary distributions in associated random dynamical systems (RDS), playing a central role in understanding their stability properties and in identifying shock locations in inviscid or zero-temperature models. This project aims to extend this framework to a wider class of stochastic Hamilton-Jacobi equations--both viscous and inviscid, with time-dependent or time-independent Hamiltonians--and to resolve several open problems that emerged from prior work. Chief among these is a longstanding question in the theory of lattice percolation: the differentiability of the shape function. 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 POWDER-RDZ project investigates ways to share the electromagnetic (radio-frequency) spectrum between experimental or test systems and existing spectrum users, and between multiple experimental systems. This research team will deploy and evaluate a prototype automatic spectrum sharing management system for the POWDER testbed in Salt Lake City, Utah (part of the NSF’s “Platforms for Advanced Wireless Research” program). Spectrum access challenges currently create significant constraints on experimentation and testing at wireless testbeds. Automatic spectrum sharing for safe access to additional frequencies – beyond the frequencies reserved exclusively for testing – will relax these constraints and thus increase the nation’s capacity to conduct wireless research and development. Increasing this capacity will help accelerate growth and global leadership of the US communications industry, strengthen academic research into wireless systems, and benefit other spectrum-dependent sectors such as radar, public safety, and national defense. As a pathfinder for the National Radio Dynamic Zone concept, the project will help future federal/non-federal spectrum sharing arrangements assure that spectrum sharing does not negatively impact government users. The POWDER-RDZ team will design, develop, and prototype an end-to-end radio dynamic zone (RDZ) for advanced wireless communication. They will validate its functionality by performing spectrum sharing experiments and field studies on the resulting artifact. The project uses the existing POWDER mobile and wireless testbed as the physical infrastructure of the RDZ. POWDER’s existing radios and other equipment supports the spectrum sharing experiments and provides part of the RF sensing functionality needed by the RDZ. The project will design and develop a modular zone management system (ZMS) to manage, control and monitor all aspects of the RDZ. The project plans to conduct experiments on spectrum sharing with users outside of POWDER. Experiments potentially include RDZ shared access to federal, non-federal, and commercial spectrum, such as coarse- and fine-grained spectrum sharing with a commercial mobile operator and spectrum sharing with a weather radar. 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

Powder metallurgy is widely applied in the manufacturing economy, directly impacting economic welfare and national security. This award supports student participation in the International Conference on Powder Metallurgy and Particulate Materials (PowderMet2025) and the co-located Additive Manufacturing with Powder Metallurgy (AMPM2025) conference, to be held 15-18 June 2025, in Phoenix, Arizona. The support provides an opportunity for outstanding student researchers to attend the advanced manufacturing conferences, present their work, learn from leading experts in the field, and interact with students from other institutions, fostering a sense of worldwide community of scientific inquiry. The supported students will present research posters and participate in the conference poster award competition, among other activities. The relationships with conference participants will help the students in determining their specific areas of interest in this broad field and create colleagues for future interactions and potential collaboration. This will not only stimulate learning and training for the students, but also allow them to be exposed to new research concepts in powder metallurgy and additive manufacturing. The objectives of the student participation in PowderMet2025 and AMPM2025 are to (1) expand career-enhancing learning opportunities for a broader knowledge of powder metallurgy and metal additive manufacturing, (2) promote technology transfer and provide a forum to introduce new research and development activities, (3) promote collaborative partnerships between organizations in advanced manufacturing and in emerging technologies to increase workforce development, and (4) develop a workforce aligned with regional economies based on emerging technologies across the Nation. The conference organizers will support up to 35 successful student applicants, who study at US institutions. An emphasis will be placed on students who would otherwise not be able to attend the conference and students presenting their research results at the conference. The organizers will invite applications through conference announcements and other means. The awards will provide support toward registration and accommodation. The availability of this support will encourage the participation of graduate and undergraduate students who otherwise would be unable to afford attending highly specialized advanced manufacturing conferences. 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

Dust storms rarely happen in the Northern Great Plains today, but a dust storm in 2022 could illustrate future storms under intense drought conditions. The last time extensive drought and dust storms plagued the Northern Great Plains was during the Dust Bowl of the 1930s. Previous research on the Dust Bowl focused primarily on the Southern Great Plains, while this research seeks to explore if the types of dust sources and dust emission processes in the Northern Great Plains are similar to those of the Southern Great Plains. For example, in the 1930s cultivated cropland may have been the primary dust source in the north, whereas cultivated cropland and drought-degraded grasslands were sources in the south. The team will collect weather data and drought indicators from state archives and analyze early aerial photographs to document drought locations in the Northern Great Plains during the 1930s. Using a portable wind tunnel, they will measure the potential of agricultural soils to erode by the wind, such as what occurred during drought conditions in 2022. The dust emission data will be used with land use and agricultural crop data to model present and future dust emissions in the Northern Great Plains. Understanding drought and wind erosion potential in a changing climate is critically important in the Great Plains where agriculture is an economic driver. Extensive drought and dust storms, like those during the 1930s Dust Bowl, can have local, regional, and global effects. The broader impacts of this work will include development of a curriculum that underscores the importance of drought, climate change, and wind erosion on soil health. These teaching materials will be implemented in rural schools and institutions serving Native American students. This collaborative research will also provide research opportunities to two early-career researchers, an undergraduate and two graduate students. This research will evaluate drought and dust emission potential in the Northern Great Plains, an area that was devastated by the 1930s Dust Bowl, and predict landscape response to future extensive drought. Dust emission processes and soil characteristics, and meteorological variables will be compared to areas equally devastated by drought in the Southern Great Plains. A new quantitative framework for accessing drought and dust storms in the Northern Great Plains will be established through analysis of state meteorological records from the 1930s, coupled with geospatial analysis of the aerial photographic record. The 1930s will be compared to the modern-day agricultural landscape by geospatial analysis and soil properties. This database, which leverages dust fluxes measured by a portable wind erosion device (PI-SWERL), will identify critical dust source areas, dust emission processes, and yield the key information needed for dust emissivity estimates from potential drought-stricken areas. Coupled atmosphere-land dust emissivity modeling using the Weather Research and Forecasting (WRF) model will provide insights on dust storm formation, trajectory, duration, and the regional and global fate of dust loading events. This framework will also be able to account for the extreme drought variability observed across the Great Plains. These results will provide needed insights on how extreme climate variability projected for the 21st century will impact atmosphere-land interactions for drought prone areas in the agriculturally important Great Plains. 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

Electricity production from wind energy is increasing rapidly in the U.S. and U.K. When more wind plants are added in areas of high wind resource availability, the distance between neighboring plants becomes shorter. The wake from a wind plant can extend miles downstream of the plant, which means that it may affect the efficiency of a downstream plant within the wake. Most research regarding wakes from wind turbines has focused on the effects from a single turbine. Results from this research has been used to optimize the design and performance of a wind plant. However, interactions between neighboring plants have not been studied extensively. This project will use a combination of experiments and numerical analysis to develop relationships between wind farms under conditions when one wind plant affects downstream ones. The models will be implemented to current wind farm engineering flow-tools, which will increase their accuracy and efficiency, and contribute to more accurate planning of wind farms. The proposed research will investigate the issue of successive wind farm wake interactions and develop reduced-order model representations for use by industry and national laboratories. Specifically, the research will quantify and develop models for (i) the production and recovery of momentum deficit that a single wind farm generates at its downstream outflow, and (ii) this outflow interaction with downstream farms. Detailed large- and small-scale laboratory experiments and high-fidelity numerical simulations will be used to consider a multitude of real-world conditions, including changes in ground surface roughness, the Coriolis force, thermal stratification, global blockage and entrainment between the atmospheric boundary layer (ABL) and the wind farm wakes. This collaborative U.S.-U.K. project is supported by the U.S. National Science Foundation (NSF) Division of Chemical, Bioengineering, Environmental and Transport Systems (CBET) and the Engineering and Physical Sciences Research Council (EPSRC) of United Kingdom Research and Innovation, where NSF funds the U.S. investigator and EPSRC funds the partners in the United Kingdom. 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 aims to advance machine learning techniques by developing new mathematical tools to better analyze and visualize complex, high-dimensional data. Many real-world data sets, such as genetic information or molecular images, contain hidden structures that can be uncovered using geometric methods. Leveraging these hidden structures can decrease the computational resources needed to analyze large data sets and can also provide scientific insight regarding various biological and physical processes. The goal of this project is to harness the full power of geometric methods for modern machine learning by the design of innovative solutions to the critical challenges facing the field. The project will focus on developing methods that can handle noisy data and tools for data visualization that preserve important geometric details. In addition to its scientific goals, the project will have a broad impact by offering new educational programs to support student mental health, increase diversity in data science, and encourage underrepresented groups to engage in this field. By addressing both technical and social challenges, the project aims to create more reliable, scalable tools for data analysis while promoting inclusion and innovation in science and education. In many applications, real data often concentrates around low-dimensional structures or manifolds, and manifold learning algorithms are a powerful tool for uncovering this low-dimensional structure. The project is on manifold learning algorithms, tackling the key challenges facing the field, including how noise affects algorithm performance, how to preserve both local and global geometric details, and how to effectively handle complex collections of manifolds. This research will investigate best practices for denoising, analyze how noise impacts spectral manifold learning algorithms, and clarify the regime in which algorithms can reliably recover manifold structure. The project will develop novel algorithms that utilize diffusion to faithfully encode geometric information at various scales and also hybrid methods for geometric regularization of manifold learning algorithms. Since real data often fails to concentrate around a single manifold, this work will contribute a robust and scalable solution for clustering collections of manifolds via a novel angle-based path metric on simplices, so that manifold learning algorithms can be applied under less restrictive assumptions. In addition, since data often contains numerous irrelevant features and features measured on very different scales, the project aims to develop a probabilistic framework for learning and adjusting features according to their actual relevance. Overall, the goal is to design a comprehensive data analysis pipeline that is noise-robust and capable of balancing local and global geometric information to represent data in just a few dimensions. 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

With support from the the Chemical Structure and Dynamics (CSD) program in the Division of Chemistry, Professors Ilya Zharov and John Conboy of the University of Utah, along with their students, are combining their expertise in materials synthesis, characterization, and spectroscopy to investigate the molecular-level interactions between polymer-grafted silica nanoparticles during self-assembly. This process offers a promising pathway to design materials with unique properties and applications. However, achieving self-assembly remains challenging due to the intricate interactions among the polymer chains. To understand these interactions, the researchers will employ sum-frequency generation (SFG) vibrational spectroscopy, a technique that uses a combination of infrared and visible light to excite polymer vibrations, in order to determine their relative orientations. This approach provides access to the polymer structure at buried interfaces, a notoriously difficult area to probe. In addition, the team will create polymer nanoparticle films to further study the dynamics of polymer interdigitation. The insights gained from these studies are expected to lead to improved design principles for novel porous materials that can respond to temperature and pH changes. Furthermore, the project contributes to science education through student mentorship and outreach programs in local schools, inspiring future generations of researchers. This research aims to elucidate the molecular interactions that govern the self-assembly of polymer-grafted nanoparticles, focusing on the degree of polymer interdigitation and its dependence on polymer structure and environmental conditions, such as solvent polarity, pH and temperature. By systematically varying nanoparticle size, polymer composition, chain length and grafting density, the researchers will investigate how these parameters influence particle-particle interactions and assembly. Complementary deuterated polymer brushes will be prepared on fused silica substrates to facilitate surface-specific vibrational analysis. The team will utilize sum-frequency generation (SFG) spectroscopy to probe the orientation and conformation of the polymer chains on the nanoparticles, while in situ TEM and Langmuir-Blodgett techniques will provide direct visualization of the self-assembly process. These studies will generate critical insights into the effects of chain conformation, nanoparticle curvature, and solvent polarity on the self-assembly and formation of porous nanoparticle assemblies. The knowledge gained will have broad implications for nanomaterial design, offering principles for engineering new classes of nanoparticle-based materials with well-defined porosity and which are responsiveness to external stimuli. This work will also advance the application of SFG spectroscopy for the study of macromolecular and supramolecular chemical interactions, reinforcing its role as a powerful tool for studying complex interfacial phenomena. 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

This I-Corps project focuses on the development of an assistive robotic system that enables object retrieval and manipulation through voice commands. The technology addresses challenges in healthcare, assisted living, and service environments where individuals may have limited mobility or require assistance with daily tasks. The solution combines accessibility with automation to enhance operational efficiency while promoting user independence. When deployed in medical facilities, the system allows staff to focus on specialized care tasks rather than routine object retrieval. In residential settings, it gives users greater autonomy in their daily activities. The technology also has potential applications in industrial and commercial environments where human-robot collaboration can streamline workflows and reduce physical strain on workers. Beyond its immediate applications in healthcare and assistance, the system's ability to reliably grasp and manipulate objects opens possibilities in manufacturing, logistics, and service industries where object handling automation can increase productivity and workplace safety. 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 vision-guided robotic grasping systems that can pick up objects using input from a single camera. The underlying technology leverages physics simulation for data collection and learning, enabling successful transfer of grasping capabilities from simulated to real-world environments. The system demonstrates robust performance in grasping previously unseen objects, with even higher success rates achieved using simplified robotic hands. Research results show that the technology performs particularly well when handling familiar objects, making it suitable for deployment in structured environments where the set of target objects remains relatively constant. The technical approach combines computer vision, machine learning, and robotic control to create a reliable and adaptable object manipulation system. 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 · 2025-04

An in-person workshop of leading experts across government, industry, and academia will be convened, to identify challenges and opportunities for developing a National Strategic Computing Reserve (NSCR). As envisioned, the NSCR would ensure critical computational resources are available during a national or international crisis. The U.S. government, industry, and academia lead the world in high performance computing and scientific critical infrastructure. The NSCR aims to ensure resources such as access to high performance computing systems, AI data and resources, and scientific critical infrastructure are available to address significant crisis events. A crucial objective of the workshop is to identify and assess both challenges and opportunities that would arise at a National Strategic Computing Reserve. The workshop has identified four key topic areas that will be addressed by distinct breakout groups that focus on challenges that would be essential to a National Strategic Computing Reserve. The first topic is a multidisciplinary and computational integration that would be essential in a multi-organization computation reserve. Second, a computing reserve is anticipated to require applied mathematics in Artificial Intelligence and will be explored. Third, the reserve would be activated during a crisis and uncertainty management will be an operational challenge. Finally, consensus building and trust would be a key aspect of a computing reserve that spans different government agencies, industry partners, and academic experts. The outcome of the workshop will advance science and understanding across all four topic areas and serve as a critical next step in directions for a National Strategic Computing Reserve. 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 · 2025-04

Computer simulations are central to science and engineering activities across tasks as varied as weather prediction and electrical grid optimization. Simulation relies on numerical solvers whose continued improvements in terms of speed and accuracy are of pivotal national importance. Unfortunately, the progress of solvers is hampered by two emerging challenges: (1) the problems being modeled and solved, including their data captured through matrices, are becoming numerically harder, causing many solvers to fail; (2) newer arithmetic hardware, increasingly second-purposed from those developed to support artificial intelligence (AI), ends up having suboptimal precision while also deviating from established numerical standards. The project's novelties are: (1) the development of practical formal methods that are capable of capturing the correctness expectations of numerical algorithm designers as formal requirements; (2) the development of formal models capable of modeling non-standard hardware while bridging their behavioral differences to present uniform higher level formal abstractions; and (3) methods to carry out end-to-end correctness verification that help establish that the formal models of the underlying hardware meet the numerical algorithm correctness requirements. The project's contributions will help advance the nation's simulation-based scientific exploration capabilities. It will also help recoup the investments already made in today's numerical solvers, allowing them to be easily and reliably adapted to new problems and hardware. Without these capabilities, scientific computing and data-enabled discoveries can experience multiple productivity gaps, negatively impacting scientific research and engineering advances. The project will also train students to have the debugging skills necessary to solve numerical issues arising in the context of future solver design and deployment. This project handles data hardness using iterative refinement algorithms that are followed by the linear algebra routines underlying linear solvers. These algorithms can be verifiably guarded by novel formal properties that stem from how the problem eigenvalues appear in the problem’s data matrices. This project's techniques adapt the numerical hardware to pre-existing solvers (and their assumptions) and help develop new solvers that employ mixed numerical precision schemes during iterative refinement. These adaptations will be aided by novel emulation schemes that help match and formally verify numerical precision, rounding rules, and floating-point exception handling rules. The goals of these techniques are to resolve the "Data/Numerics Tug of War" so that each solver developer obtains their preferred starting point: from algorithms down to hardware or vice-versa. This project will contribute key scientific principles and algorithms to support future research and development activities in adapting solvers to newer hardware. It will have a broad impact, including (1) sustain established solvers across new generations of hardware and (2) solve numerical issues that arise when solvers and optimizers used in AI are enhanced to handle larger scale and newer problems. 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 · 2025-04

The fossil record informs evolutionary history, but current knowledge has been drawn from finds located in a small number of sites from geographically restricted areas. This situation partially accounts for existing gaps in the fossil record, limiting knowledge regarding fundamental questions about our species. This study aims to expand the evolutionary record by (i) surveying for new fossil-rich cave sites, (ii) conducting exploratory excavations to evaluate the fossil record preserved within them, and (iii) dating the cave sites to establish the age of the fossiliferous deposits. The project enhances US research capacity by providing student training opportunities and strengthening research links across a broad network of scholars. This project examines the paleoanthropological and paleontological research potential of fossiliferous caves through a combination of drone-assisted and pedestrian surveys. At five caves known or suspected to preserve rich fossil deposits, paleontological test excavations document the fossil record preserved within them. Samples generated through excavations are used for radiometric dating, utilizing a combination of radiocarbon and U-series approaches. This project expands paleontological and paleoanthropological research. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-04

Non-technical Abstract: Manipulation of materials properties at the nanoscale offers transformative potential for energy-efficient information processing and storage. For example, manipulation of a property known as spin-orbit torque allows one to change magnetic properties using an electrical current. This process can be ultrafast and energy efficient. Developing new methods and materials allowing such manipulation of magnetic properties is essential to advancing next-generation spintronic devices. This project seeks to establish precise causal links between ferromagnetic material properties and universal torque behavior. The outcomes are expected to have a significant positive impact on the knowledge and understanding of novel magnetic materials. This project engages students across educational stages, modernizes an advanced lab course, and provides hands-on physics learning to underrepresented middle and high school students. By providing participants with essential skills for developing next-generation semiconductor technologies, this project seeks to strengthen the domestic talent pool and enhance the economic competitiveness of the U.S. Technical Abstract: Spin-transfer torque and spin-orbit torques have enabled control of the magnetization vector in magnetic materials using electric fields or currents. Recent breakthroughs in self-generated planar and anomalous Hall torques have expanded the range of materials capable of generating giant spin-orbit torques. These torques are self-generated as they act on the same ferromagnet where the spin current is produced. Alongside spin Hall torque, planar Hall and anomalous Hall torques form a triad of universal Hall torques, which are expected to arise in all magnetic conductors. However, the fundamental mechanisms driving these torques remain poorly understood. This project aims to achieve a deeper understanding of the interplay between charge and spin transport in the triad of Hall effects and how it induces self-torque on the magnetization of magnetic materials. Spin-orbit torque characterization within this project entails spin-torque ferromagnetic resonance measurements in nanowire structures fabricated by magnetron sputtering and e-beam lithography. This project seeks to establish precise causal links between ferromagnetic material properties, in particular electronic band structure and material interfaces, and universal Hall torque behavior to uncover the microscopic mechanisms underlying these torques. The anticipated results are expected to advance the fundamental understanding of coupled charge-and-spin transport and coherent spin manipulation in ferromagnetic conductors. This knowledge has the potential to transform the design and development of next-generation spintronic devices. 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

With support from the Chemical Measurement and Imaging Program in the Division of Chemistry, Professor Nagy's group at the University of Utah is developing new methods to characterize isomers, molecules which share the same molecular weight, based on their isotopic shifts using advanced ion mobility separations and mass spectrometry. Specifically, the Nagy group is working to introduce new ways in which molecules across varying classes can be modified to enhance their isotopic shifts based on changes in their moments of inertia and center of mass. Through interactions with both industry and other academic groups, research results will be disseminated and enable interlaboratory validation. Individuals and their families in the Salt Lake City area will benefit from the deployment of the education plan and thus increase their exposure to STEM. This research focuses on developing new methods to transform mass distribution-based isotopic shifts as a new dimension for the characterization of isomers with high-resolution ion mobility spectrometry-mass spectrometry. This proposed research will focus on assessing and implementing the use of isotopic shifts for the delineation of complete sets of small molecule isomers, developing theoretical models to better understand the physical basis of these shifts, and also introduce new derivatization strategies to expand isotopic shifts to larger molecular classes such as proteins. This research also includes an education plan to increase STEM involvement and expose our next generation to STEM at a younger age. The overall focus of this plan is to design a simple ion mobility separations experiment and implement/deploy it at the Discovery Gateway Children's Museum in downtown Salt Lake City, Utah. This education plan will enable the Nagy group to provide younger children and their parents/guardians access to STEM. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-03

Traditional air conditioning cooling is a human necessity and essential for society. However, it consumes a significant amount of energy and releases undesirable greenhouse gases. Passive daytime radiative cooling with engineered photonic structures is a promising sustainable and energy-efficient cooling technique. However, current photonic structures either require sophisticated, expensive, and hard-to-scale manufacturing processes to achieve precise material and structural control or have poor material and structural control with inexpensive, scalable manufacturing processes leading to inefficient and costly passive cooling. This award advances the fundamental understanding and development of self-assembled architectures for new sustainable cooling technologies. In addition, this integrated design and manufacturing approach is universally applicable to other photonic applications in broad wavelength ranges, such as structural coloration in the visible wavelength. The project’s multidisciplinary research activities offer new perspectives in broad technology domains, including advanced manufacturing, optics, optoelectronics, semiconductors, heat transfer, and machine learning. Furthermore, this project creates new education and workforce development opportunities including transferable modular curricular and online lectures that are integrated into graduate and undergraduate courses across different disciplines, and new hands-on demonstrations for a variety of outreach activities. The project employs a holistic, paradigm-shifting, and closed-loop approach of designing, manufacturing, and deploying multilayer self-assembled monolayer microsphere (MSMS) array architectures for passive daytime radiative cooling applications. In the MSMS structure, individual microspheres are accurately self-assembled into a monolayer film of an ordered array, instead of random distribution. The particles can be a large variety of materials, such as SiO2, TiO2, polymethyl methacrylate (PMMA), and others. Further, these self-assembled films are stacked together with polymer films to form a multilayer structure for engineering optical response. The research team integrates expertise in experimental self-assembly, photonic design and manufacturing optimization to achieve its objectives. Specific tasks are: (1) Universal self-assembly of particles based on a salt-assisted and acoustic self-limiting mechanism, whose manufacturing processes are automated with in-situ characterization systems and scalable through roll-to-roll systems; (2) Experiment-aware inverse design of structures based on a graphics-processing-unit-accelerated high-throughput semi-analytical rigorous-coupled-wave-analysis solver, which is further integrated with a machine learning-driven optimization of the manufacturing processes; and (3) Experimental demonstrations of passive daytime radiative cooling with the engineered photonic structures. In addition, the project studies processing-structure-property relationships and detection and mitigation of imperfections. 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

With the support of the Chemical Catalysis and Chemical Synthesis programs in the Division of Chemistry, Dr. Long Luo of Wayne State University and Dr. Jingwei Li of Merck & Co are developing new methods for hydrogen isotope labeling of pharmaceuticals. With the use of heterogeneous photocatalysis and electrochemistry, the Luo team seeks to establish improved chemical methods for labeling pharmaceuticals and their precursors with either multiple hydrogen isotope atoms or selective labeling at specific sites. Hydrogen isotope incorporation at multiple sites will improve the diagnostic signals for detecting and quantifying drugs and drug metabolites in preclinical and clinical studies. In addition, the site-selective labeling will provide new tools for synthesizing drugs containing deuterium. This integrated university/industry research program will provide unique learning and training experience for graduate and undergraduate students, preparing this “next generation workforce” for a future in which multi- and inter-disciplinary research is the norm. Under this GOALI award, Dr. Long Luo of Wayne State University and Dr. Jingwei Li of Merck & Co will engage in synthetic methodology research toward new approaches to hydrogen isotope labeling of pharmaceuticals. The team will focus on: (1) developing heterogeneous photocatalysts to enable additional hydrogen isotope exchange sites relative to the conventional molecular photocatalysts utilizing their unique catalyst-substrate interface (For example, efforts to simultaneously activate benzylic and α-amine sites using metal chalcogenide quantum dot photocatalysts) and (2) developing electrochemical methods to achieve the hydrogen isotope exchange site selectivity among sites with similar chemical reactivity such as α-amino C-H bonds, utilizing the time-resolved redox environment provided by electrochemistry to control the reaction kinetics at different sites. The team will work to answer the following important scientific questions: (1) how can one achieve efficient hydrogen atom transfer via the heterogenous photocatalyst-substrate interface during a hydrogen isotope exchange reaction? (2) how can one control the reaction pathways of hydrogen isotope exchange reactions by tuning the electrochemical redox environment? The impact of this project will not be limited to the field of hydrogen isotope labeling of pharmaceuticals but will likely also provide new strategies for controlling reaction pathways in general for photocatalytic and electrochemical reactions. 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 support for US-based mathematicians and graduate students to participate in a school and two workshops associated to a trimester program on “Higher Rank Geometric Structures” organized by the Institute Henri Poincaré (IHP), Paris, from April to July 2025. The school will be held the at the CIRM-Centre International de Rencontres Mathématiques near Marseille, France, while the two workshops will take place at the IHP. This school will offer an opportunity for US based graduate students and post-doctoral fellows to learn a variety of topics on higher rank geometric structures from international experts. The two workshops will bring together researchers throughout the world and give the US based attendees a venue to exchange ideas and make connections with their European and international colleagues. The primary focus of the award is to support travel for early career US-based mathematicians. The study of discrete subgroups of Lie groups has a long history with connections to many different areas of mathematics including geometry, dynamics and number theory. Some of the earliest advances in the field (of Mostow, Margulis, Ratner) were about characterizing rigidity. For example, showing that a group is isomorphic to a unique lattice in a Lie group. On the other hand, Teichmüller theory and Thurston’s theory of hyperbolic 3-manifolds involved studying the deformation theory of hyperbolic 2 and 3-manifolds. While Thurston developed this theory in the 70’s (depending heavily on the earlier work of the Ahlfors-Bers school) this deformation theory of flexible groups was largely undeveloped outside of these two examples. This changed two decades ago when Labourie introduced the notion of an Anosov subgroup and Fock- Goncharov gave coordinates for studying certain discrete representations of surface groups in semi-simple Lie groups. Since then, the subject has grown immensely and attracted a multitude of researchers. This award will give the opportunity to early career mathematicians from the US to learn from and interact with experts from all over the world who are leading the field. Further information about the school and workshops can be found at the website: https://indico.math.cnrs.fr/event/11551/ 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.