University of North Carolina at Chapel Hill

NSF Awards · FY 2025 · 2025-09

With the support of the Macromolecular, Supramolecular and Nanochemistry Program in the Division of Chemistry, Professors Jillian Dempsey and Yosuke Kanai of the University of North Carolina at Chapel Hill are studying how defects on the surface of nanoscale materials impact energy flows through these materials. They are also examining how different types of defects on the surface can be repaired through selective chemical reactions. Their work will combine experimental work with advanced theory that probes these materials on the atomic level. Through this work, they will learn how to repair defect-rich materials to access high performing materials that can be used in sensing applications, display technologies, solid state lighting, and photon energy harvesting. Through this project, Professors Dempsey and Kanai will help prepare students for the STEM workforce, providing them with comprehensive training in materials science, chemistry, and theory. They will also develop and deploy hands on activities at science festivals, introducing K–12 students and their families to the applications of nanomaterials. With the support of the Macromolecular, Supramolecular and Nanochemistry Program in the Division of Chemistry, Professors Jillian Dempsey and Yosuke Kanai of the University of North Carolina at Chapel Hill are using the tools of molecular chemistry and atomistic theory to probe the structural identity and energetics of surface-based defects which trap charge carriers on semiconductor nanocrystals. They will apply selective ligand addition and exchange reactions to passivate or expose specific defect sites. Subsequently, they will combine spectroscopy with simulation to learn how these surface-based states influence charge carrier dynamics. This work will ultimately provide molecular-level details and energetics of charge carrier trap states on semiconductor nanocrystal surfaces. Explicit principles by which these states can be chemically and electronically passivated will be obtained, providing an enhanced understanding of how to rationally mitigate surface-based defects. This knowledge will enable enhanced performance of nanocrystal-based optical devices with applications in sensing, display technologies, solid state lighting, and solar energy harvesting. 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

Accurate statistical inference is essential for making reliable decisions in various fields, such as forensic science, medicine, economics, and machine learning. This project develops and advances generalized fiducial inference (GFI), an innovative statistical method that quantifies uncertainty without requiring subjective assumptions. By addressing complex real-world problems, such as evaluating evidence in criminal cases, understanding causal relationships in economics and health, and improving reliability in machine learning, the project will significantly enhance decision-making processes. Additionally, the project provides valuable research training opportunities for graduate students in science, technology, engineering, and mathematics (STEM), thereby contributing directly to national goals of promoting scientific advancement, health, prosperity, and welfare. This collaborative research aims to advance generalized fiducial inference (GFI), building upon Fisher’s original fiducial argument and recent developments in modern statistics. The primary objectives include extending GFI methods to causal inference models, particularly instrumental variable models, and redefining GFI through normalizing flows to manage computational complexity in non-analytic scenarios. The project will also apply these methodological innovations to pressing real-world problems in forensic science, specifically addressing the accurate calibration of likelihood ratios from machine learning models, as well as, resources permitting, investigations into uncertainty quantification for social network learning and sports analytics. 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

Computational wave imaging, vital for uncovering hidden properties in diverse fields of science and engineering, such as materials science, medicine, and geoscience, faces significant challenges. Traditional methods struggle with the inherent complexity and computational demands of such problems. Although deep learning offers promise for these scientific inverse problems, its efficacy is hindered by the scarcity of labeled data, often due to costly experiments and expertise requirements. This underscores the need for innovative approaches that circumvent data limitations in wave imaging. This project seeks to optimize the potential of deep learning in computational wave imaging by introducing techniques to address data scarcity and improve generalizability, aiming to drastically lessen deep learning's dependence on extensive labeled datasets, efficiently generate high-quality training data, and greatly improve deep learning's capacity to solve real-world problems. It also emphasizes educational integration and interdisciplinary collaboration, and promotes the sharing of open-source computer codes and datasets, enhancing the broader scientific community’s ability to conduct research and providing educators with valuable tools for teaching computational and data-enabled science, engineering, and mathematics. Physical principles will be integrated with advanced deep learning models in hybrid learning strategies. Hybrid strategies involve efficient wave simulations results which can address the challenges of data and label scarcity, and the weak generalizability in computational wave imaging. A novel self-supervised learning method will be introduced, which can uncover hidden physical principles within the latent space. Preliminary investigations have revealed an “Auto-Linear” phenomenon, where features from different physical domains automatically correlate linearly. This discovery allows for simultaneous forward and inverse modeling, significantly enhancing performance in imaging tasks that lack paired data. Efficient wave simulations will also be developed. They will involve high-order methods for effective forward propagation and backpropagation, with explicit Runge-Kutta time stepping for non-stiff problems and A-stable implicit Runge-Kutta time stepping for stiff problems, combined with Fourier or spectral element spatial approximations. Furthermore, integral-based methods with asymptotic short-time Green's function will be developed for problems with point-source-like source functions. This configuration is designed to simulate wave propagation with high accuracy and minimal sampling requirements in both time and space, thus avoiding the pollution effect and promising a leap in simulation efficiency and quality. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-09

With the support of the Macromolecular, Supramolecular, and Nanochemistry program in the Division of Chemistry, Dr. Aleksandr V. Zhukhovitskiy of the University of North Carolina at Chapel Hill is developing methods to prepare polymeric materials with a high content of nitrogen atoms embedded within the polymer chains. Polymers form key components of plastics and rubber materials that are, in turn, at the heart of innumerable modern technologies: from vehicle parts to food packaging. Among existing synthetic commodity polymers, most are exclusively composed of carbon, oxygen, and hydrogen. In contrast, Nature’s polymers like proteins have a high content of nitrogen, which is crucial to their remarkable versatility. While some examples of nitrogen-rich synthetic polymers do exist and are even produced industrially (for example, nylons and polyurethanes) the scope of these materials is relatively small, and most lack structural precision, which limits their applications. Previous research led by Dr. Zhukhovitskiy and supported by this program demonstrated that iridium-based catalysts can enable the ring-opening polymerization of cyclic carbodiimides—moieties, wherein a carbon atom is linked to two nitrogen atoms via double bonds—to produce poly(carbodiimide)s that can be readily transformed into an assortment of other valuable polymer classes. This research seeks to improve the precision of this process, as well as to develop catalysts based on alternative, more abundant transition metals like iron. Lastly, the lessons learned from the carbodiimide polymerization will be extended to new nitrogen-containing molecules to ultimately access structurally precise photo-responsive plastics. This research will feature both experimental and computational components at the interface of polymer, organometallic, and physical organic chemistry; as such, this work will train students to be multidisciplinary experts capable of tackling complex modern scientific challenges. The research will transform how industrial and academic scientists access nitrogen-rich polymers. Dr. Zhukhovitskiy and his team will also grow their educational polymer-focused program that targets K-12 students and science teachers in their community. This program will engage students to think about both plastics and catalysis and provide high school STEM teachers with research training that will help them design engaging hands-on projects for their classes, with safety as a top priority. This research will focus on advancing carbodiimide ring-opening metathesis polymerization (CDI ROMP) and enabling diazene ROMP through augmenting our understanding of critical aspects of these transformations. The first objective of this research is to study the process of chain transfer in CDI ROMP, which must be limited to develop living CDI ROMP, and can potentially be exploited to enable catalytic CDI ROMP and precise control of end-group installation. The chemistry of iridium imido and guanidinate complexes, which serve as initiators and catalysts in this context, will be advanced as part of this objective. The second objective is to explore the use of late first-row transition metal imido and guanidinate complexes to initiate and catalyze CDI ROMP. The last objective is to extend the mechanistic insights of CDI ROMP to enable catalytic metathesis of nitrogen-nitrogen double bonds. The latter will, in turn, enable the development of precise synthetic methods toward main-chain poly(diazene)s. Thus, this research simultaneously advances the fundamental chemistry of late transition metal imido and guanidinate complexes, mechanistic design applied to polymer synthesis, and our ability to access precise nitrogen-rich polymer backbones with unprecedented compositions and architectures. With the latter materials in hand, this proposal will also begin to illuminate the effects of different structural aspects of the nitrogen-rich polymer backbones on their thermomechanical properties. 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 fundamental nature of dark matter, the largest and most mysterious component of galaxies, remains one of the key questions of modern physics. Wide-field surveys such as those planned by the Rubin Observatory, the Euclid Mission, and the Roman Space Telescope will fundamentally change our understanding of the nature of dark matter. One such change will come from the ability of these surveys to discover large numbers of stellar streams: delicate trails of stars created when star clusters are pulled apart by the gravity of their parent galaxy. These streams are extremely sensitive tracers of the parent’s gravitational potential. Since galaxies’ gravity comes mostly from their dark matter, streams present a unique opportunity to probe dark matter’s properties. A team of scientists from the University of North Carolina, the University of Pennsylvania, and Northwestern University, will be the first to combine supercomputer simulations of both the galaxies and the star clusters themselves to study how these clusters form, live, and create stellar streams in galaxies with different quantities and forms of dark matter. The project’s main goal is to create a dark matter “spotters guide” for stellar streams, which can be used to understand the deluge of data coming from next-generation telescopic surveys. As part of this project, the team will lead the development of an interactive virtual reality (VR) program for middle school students, based on the simulated star clusters and streams, designed to educate the public on the nature of dark matter, its relationship to the evolution of galaxies and their star clusters, and the exciting science potential of these upcoming survey instruments. The goal of this project is to develop a self-consistent model of globular cluster formation and evolution in a cosmologically evolving galaxy and use it to predict the properties of globular clusters (GCs) and their streams for next-generation surveys. The project will build upon an existing model for the formation of globular clusters based on zoomed cosmological-hydrodynamical simulations, combining galaxy simulations with star-by-star N-body simulations of clusters to fully resolve this multi-scale problem. The team will implement this model for a suite of cosmological simulations with consistent baryonic physics and alternative dark matter models, e.g. self-interacting dark matter and atomic dark matter. The team will also produce full synthetic observations of GCs and thin stellar streams in external galaxies, their morphology, and their stellar populations, and the cosmologically motivated host stellar halos. The plan is to connect the detectable set of clusters and streams to the origin and evolution of their host environment and the underlying dark matter model. The result will, for the first time, predict self-consistent cluster and stream populations for varying dark matter models, providing a crucial dataset for the interpretation of next generation astronomical surveys such as Rubin, Roman and Euclid. 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 researches the translation of surveillance capacities of AI systems into higher education for university management, public safety, and research. It investigates how institutions of higher education are using and adapting AI systems. Results of this study inform the understanding of scholars, stakeholders, decision makers, and the public, and contextualize how universities position themselves with respect to AI developments. This project uses mixed methods to study surveillance functions of AI systems in learning management, monitoring, productivity, campus safety, health and wellness, and research. The study involves analysis of research documents and interviews with university stakeholders and investigates the societal context of this emerging AI system for higher education. 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 Chemical Catalysis program in the Division of Chemistry, Professors Erik Alexanian and Aleksandr Zhukhovitskiy of the University of North Carolina at Chapel Hill are studying fundamental catalytic reactions of raw materials and high-volume commodity polymers using earth-abundant metals. The classes of reactions being studied—including some of the highest-volume catalytic processes globally— currently rely on precious metals, which limits their sustainability. The work herein will develop new catalytic processes using Earth-abundant metals to access both valuable small molecule building blocks and new plastics and rubber materials. This research is expected to break new ground in Earth-abundant metal catalysis and will provide students an excellent training spanning the areas of organic chemistry, polymer chemistry, and catalysis. Through this proposal, the Alexanian and Zhukhovitskiy groups will develop demonstrations that increase students’ understanding of the relationship between the chemistry, properties, utilization, and sustainability of rubber materials, and will initiate new outreach programs relating synthetic chemistry to everyday life. With the support of the Chemical Catalysis program in the Division of Chemistry, Professors Erik Alexanian and Aleksandr Zhukhovitskiy of the University of North Carolina at Chapel Hill will develop a photochemical carbonylation platform using earth-abundant cobalt addressing fundamental catalytic transformations of small molecules and commodity polymers. This work will enhance the sustainability of carbonylations that currently rely on precious metals, while introducing new reaction modes that are inaccessible using established methods. The studies involving small molecules will extend the capabilities of the cobalt-catalyzed platform to encompass fundamental carbonylation processes using alkenes and aryl halides as substrates. The entire suite of hydrocarbonylation reactions will also be applied to the functionalization of diene polymers for the synthesis of new rubber materials. The proposed studies are expected to deliver several valuable cobalt-catalyzed carbonylations for applications across chemical synthesis. 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

Estuaries, where rivers meet the sea, are dynamic environments that help move and transform carbon in the natural world. However, we still do not fully understand how single carbon (C1) compounds like methane and methanol are cycled in these systems, or the role that microscopic organisms play in controlling these processes. In this project the investigators are determining how microbes in the Neuse River Estuary and Pamlico Sound in North Carolina influence the movement and breakdown of C1 compounds, improving understanding of how estuaries contribute to the broader carbon cycle. In addition, the investigators are providing hands-on research opportunities for graduate, undergraduate, and high school students, training the next generation of environmental microbiologists and biogeochemists. This project investigates the role of one-carbon (C1) metabolism, focusing on methane and methanol, in estuarine carbon cycling within the Neuse River Estuary and Pamlico Sound. The overarching goal is to quantify the stocks and oxidation rates of C1 compounds, identify the microbial communities responsible for their turnover, and characterize the physiological traits that influence methanotroph and methylotroph distributions across estuarine gradients. The investigators hypothesize that methane and methanol, despite low ambient concentrations, act as high-flux currencies within the estuarine carbon cycle, exerting underappreciated influence on microbial food webs. They are integrating spatiotemporally resolved field surveys, laboratory experiments on growth kinetics and metabolic versatility, and the development of a cell-scale reaction-transport model to link microbial physiology with biogeochemical processes. Through this comprehensive approach they are improving predictive models of estuarine responses to environmental change and advance understanding of microbial controls on carbon cycling in estuarine 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 · 2025-09

This project examines the combined effects of stressors on the livelihoods of populations. In settings where instability disrupts infrastructure and transportation, there are potentially detrimental impacts on the food security of rural residents, and these effects can be compounded by extreme weather, such as droughts and intense rainfall. Using an array of methods, including agricultural mapping, survey research, analysis of satellite imagery, and spatial data, the researchers evaluate the effects of weather extremes and other stressors on the food security of rural residents. Also, the researchers document evidence of abandoned agricultural plots and concomitant attempts by farmers to adapt to novel challenges. The project informs understandings of the precursors to migration, which has implications for border security. The project also contributes to the education and training of a graduate student. In addition to contributing to the priority areas of security and public safety, the project's use-inspired approach and pathways for disseminating its findings align with the priority area of translational research. This study combines novel sources of data to elucidate the effects of weather extremes and stressors on the food security and subsistence strategies of rural residents. Data on food security stem from a longitudinal multi-sited survey of geolocated households, which is combined spatially and temporally with measures of temperature, precipitation, and conflicts. Similarly, these predictor variables are combined with remote sensing data that show geolocated processes of agricultural abandonment over time. In addition to the quantitative analyses, the researchers use methods to examine the adaptive strategies that residents employ in response to the livelihood risks. The study expands insights about the multifactorial determinants of food insecurity and land management decisions. In turn, this project enhances theoretical understandings about the proximate determinants of human mobility. 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

All living things, including humans, experience trade-offs between directing energy to their own cells and organs, versus directing energy to the reproductive system cells that will make the next generation. The trade-off becomes more complex when animals face cellular stresses caused by short-term difficulty (like starvation), or long-term damage due to aging. These questions are hard (and expensive) to address in long-lived animals; for example, lab mice in aging studies live about two years. This project uses a small short-lived worm (lifespan of weeks to months) that shares many genes and cellular properties with humans, to ask questions about how animal cells and organs respond to starvation at different ages, and how those responses affect the animal’s ability to reproduce later in life. Broader Impacts include creating hands-on and lecture-based courses in developmental biology for undergraduates, and coordinating with a clinical professor at the UNC School of Nursing to teach seminars to nursing students about the biology of pregnancy and human development. C. elegans nematode worms have an alternative developmental stage called “dauer” that is induced by early larval starvation and crowding. Dauer larvae can live for several times longer than the normal lifespan of a worm without eating or noticeably aging. After conditions improve, the worms rapidly resume feeding and growing, and their subsequent lifespan is unaffected by the time spent as dauers. For that reason, dauers have long been considered to be “non-aging”. Recent discoveries in support of this work demonstrate that dauers lose well over half of their germline cells during their first month in the dauer state. Germline shrinking also occurs in starved adults. In adults, differentiation contributes to germ cell loss, but in dauers it does not. After conditions improve, both dauers and starved adults rapidly regenerate the germline and commence producing healthy offspring. This work will investigate the molecular and cellular basis of starvation-induced germline shrinkage and recovery at a genetic level at these two ages. To integrate teaching into this research, a new undergraduate teaching lab will screen for genes required for germline recovery by RNAi knockdown. Candidate pathways include Wnt, Netrin, Notch, insulin-like signaling, programmed cell death, autophagy, and DNA repair, all of which are conserved with humans. This research is funded by the Cellular Dynamics and Function Program of the Division of Molecular and Cellular Biosciences. 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

Small (2-20 km) scale horizontal features in the southern Mid-Atlantic Bight region are biologically important due to their role in moving nutrients and larvae across the continental shelf. This work will resolve the physical oceanography components of these exchanges and provide benchmark datasets for verifying numerical models. Surface current observations from this experiment will be integrated into K-12 programming to foster STEM education. Two students and a post-doctoral investigator will be trained. The curated surface current dataset will be shared widely, allowing a broad range of science and societal uses of the combined moored and remotely sensed observations. The investigators will incorporate the methods and results of this project in their classrooms. The scientific objective of this project is to examine the role of sub-mesoscale eddies and incoherent horizontal stirring of water masses across the southern Mid-Atlantic Bight. The experimental work will include installation of the array of coastal high-frequency radars that will provide surface currents with 2-km horizontal resolution and 30-minute temporal resolution for a period of 3 years. This array will complement existing observations from the Ocean Observatories Initiative's Pioneer Array, in-situ observations from gliders, and satellite observations of sea surface height (SWOT) and of sea surface temperature (AVHRR). 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 investigates the behavior of large systems of interacting particles over time and space, with a focus on how such systems evolve, stabilize, and occasionally deviate from typical patterns. Such models are foundational to understanding complex dynamics in areas including cloud computing, financial markets, biological synchronization, robotics, and social competition. By developing new mathematical tools to study convergence toward stable configurations and atypical behaviors, this research contributes to core knowledge in probability, statistical physics, and dynamical systems. The project also offers extensive opportunities for research training and workforce development. Together, these efforts contribute to both scientific progress and societal benefits through a deeper understanding of systems central to today’s data-driven and networked environments. Technically, the investigator studies the long-term behavior and scaling limits of several classes of interacting particle systems using mathematical tools such as hydrodynamic limits, fluctuation theory, ergodic analysis, and large deviation principles. The first class of models involves rank-based diffusions arising in stochastic portfolio theory, with emphasis on the infinite Atlas model. The goal is to characterize hydrodynamic limits through Stefan-type free boundary problems, particularly in cases with dense initial configurations. The second set of models originates from load balancing in large-scale service systems, focusing on a regime where server count and traffic intensity are both large. The limiting object in this case is an infinite-dimensional reflected Atlas model, and the study explores its stationary behavior, fluctuation limits, and connection to stochastic partial differential equations. The third model family includes pure jump processes where lagging particles move more frequently, modeling dynamics in distributed computing and financial markets. This research develops hydrodynamic limit theory, studies ergodicity and stability, and investigates the emergence and properties of traveling wave solutions in the associated partial differential equations. Collectively, these studies aim to deepen mathematical understanding of complex, large-scale systems governed by intricate particle interactions. 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 improve understanding of the impact of cyanobacterial harmful algal blooms (cHABs) on the atmosphere. cHABs have been observed in freshwater lakes in all 50 US states and are increasing in frequency, severity, and spatial extent due to anthropogenic factors. This study addresses the knowledge gap regarding biogenic volatile organic compound (BVOC) emissions to the atmosphere from cHABs and the potential for cHABs to cause secondary organic aerosol (SOA) formation. A series of laboratory and field measurements, along with chemical modeling, will be performed. Advancing the understanding of SOA production due to cHABs addresses societally important issues of air quality and radiative forcing. The project includes education and research opportunities for high school teachers and students. To test the hypothesis that cHAB-SOA is a significant contributor to total SOA in regions with substantial cHABs, three scientific objectives will be targeted: (1) Determine SOA formation from hydroxyl radical oxidation of cHAB-BVOC in the absence and presence of pre-existing inorganic aerosols in the laboratory. (2) Measure cHAB-BVOC species and SOA production adjacent to a cHAB through field measurements. (3) Box model SOA production and species evolution using gas and particle data from lab and field measurements to evaluate potential contributions to atmospheric SOA. In the laboratory, a series of oxidation experiments investigating key BVOCs will be conducted using an oxidative flow reactor or chamber. In the field, using a variety of instrumentation, sampling will take place at Grand Lake St. Mary’s in Ohio, which has a predictable and intense cHAB each year. 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

Approximately 3000 km beneath Earth’s surface lies the boundary between the liquid iron core and the rocky mantle above. The nature of this region, the deep mantle and the shape of the boundary, remains mysterious, but it is important in that the heat emanating from this region powers mantle convection, the process that drives plate tectonics. If the properties of this region can be constrained, much progress in understanding the evolution of our mantle and the flow of the liquid outer core can be made. This project involves combining observations of global scale Earth processes—including large scale vibrations of the Earth triggered after an earthquake, the warping of Earth crust due to the unloading of ice sheets and its associated changes in earth’s gravitational field— to constrain the density, viscosity and shape of this boundary. With this knowledge, the team will make interpretations that will have implications for Earth’s mantle thermal and chemical evolution. The project will contribute to the education and professional development of high school students, undergraduate students, and graduate students. New tools developed through this project will be shared with the research community through workshops. The geodynamic landscape of the deep mantle holds implications for the evolution of chemical and thermal heterogeneity in Earth’s mantle, the heat escaping the outer core and its flow driving the geodynamo, and the large-scale nature of plate tectonics. However, its key characteristics – namely core-mantle boundary topography, its density and viscosity distribution – remain unconstrained and are often considered in isolation. These properties dictate the driving (buoyancy) and resistive (viscous) forces of convection. The difficulty in constraining these properties stems from several issues: for buoyancy variations, seismic waves are largely insensitive to density and therefore few density tomography models exist. Those models that use seismic normal modes apply consequential approximations that are avoided in this project. Because viscosity variations are expected to be several orders of magnitude, constraining 3D variations is computationally challenging. This project takes a holistic view in considering 3D fields self-consistently, using observations that span the seismic (∼seconds) to convection bands (more than 100 million years). The project begins with a 3D density and core-mantle-boundary field produced by a state-of-the-art inversion methodology for seismic normal mode, which enables exploration of the most dynamically consistent 3D viscosity fields using convection data. Finally, results may be confirmed by comparing consistency with variations of Earth’s rotation (∼1-year timescale) and gravitational changes due to adjustment from the last glacial maximum (∼1000-year timescale) – both of which are sensitive to deep mantle viscosity structure. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-08

The Large Enriched Germanium Experiment for Neutrinoless Double Beta Decay (LEGEND) uses an isotope of germanium, Ge-76, to search for a postulated rare decay process known as neutrinoless double beta decay (NLDBD). The observation of NLDBD would reveal the quantum nature of the neutrino, demonstrate matter creation, reveal that neutrinos and antineutrinos are indistinguishable, and offer a potential explanation of the mystery of why we see the predominance of matter over antimatter in the universe. LEGEND-1000, an international experiment with participation of over 60 institutions in the U.S. and Europe, aims to answer these high priority questions in fundamental physics. Once constructed, it will achieve world leading discovery sensitivity for NLDBD. LEGEND-1000 will be built with support from the NSF, the U.S. Department of Energy, the Laboratori Nazionali del Gran Sasso, and science agencies in Italy, Germany, Poland, Switzerland, and the United Kingdom. This NSF grant supports the final design of the NSF portion of the LEGEND-1000 project. Potential benefits of this research include fundamentally changing our understanding of the nature and origin of matter, should the decay be observed. Additionally, the technology of large, low-background Ge radiation detectors will enable a new generation of highly-efficient, ultra-low-background gamma spectroscopy measurements. Among the fields that stand to benefit from this technology are: quantum computation and sensors; direct dark matter searches; nuclear structure; nuclear astrophysics; environmental monitoring; atmospheric, ocean, and groundwater environmental transport; methods of radioactive dating; reactor monitoring; bioassay for determining very low occupational exposures to radiation; and biological studies involving radiotracers at very low activities. Likewise many of the same fields will benefit from LEGEND’s production of ultra radio-pure materials, with natural U and Th reduced to ultra-low levels. These technology advances will also likely impact non-low-background applications such as nuclear medicine and Homeland Security. In designing LEGEND-1000, students and postdoctoral fellows will receive training in experimental design, low-background methods, detector technology, cryogenics, nuclear physics and neutrino physics. Neutrinos have been at the forefront of discovery in nuclear and particle physics for decades. The study of their properties drove the conception of the weak interaction and modern quantum field theories. With the realization that neutrinos have small, non-zero masses there is intense interest in understanding their mass generation mechanism and determining the absolute neutrino mass scale and spectrum. Intriguingly there is no fundamental symmetry that would preclude each neutrino mass eigenstate being identical to its anti-particle, that is: a “Majorana” particle. There is also another central question – is lepton number conserved? Experimental evidence of NLDBD decay would demonstrate lepton number violation, definitively establish the Majorana nature of neutrinos, and provide information about the absolute neutrino mass. It would also hint at mechanism for generating the observed matter-antimatter asymmetry in the universe. LEGEND-1000 builds on its predecessor, LEGEND-200, in using novel, large high-purity Germanium radiation detectors with an intrinsic energy resolution of 0.1% that are surrounded by low-Z shielding (water and argon). The instrumentation of the liquid argon provides an active veto through the detection of argon scintillation light. This proposal will complete the final design phase for the NSF portion of the LEGEND-1000 experiment, which includes providing over 400 kg of the planned 1000 kg of detectors. LEGEND-1000 is designed to achieve a discovery potential that covers the inverted-ordering neutrino mass scale region. It will have world leading discovery potential and a half-life sensitivity of > 10^28 yr for a 10-ton yr exposure. 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 Pathways to Enable Open-Source Ecosystems (POSE) project focuses on building a collaborative and transparent ecosystem to strengthen the reliability of artificial intelligence (AI) applications in healthcare. As AI tools increasingly support clinical decision-making, advancing their safety, robustness, and interpretability becomes critical. This project creates a national open-source community centered on the evaluation and continuous improvement of medical AI systems. By developing shared infrastructure, governance structures, and community engagement strategies, the project empowers researchers, clinicians, and technologists to collaboratively enhance these systems. The system-wide benefits include more consistent clinical decision-making, improved patient safety, and greater transparency in AI-assisted healthcare workflows. The project also seeks to educate a wide range of stakeholders on best practices for developing and deploying reliable AI, thereby strengthening the nation's leadership in responsible medical innovation. Healthcare providers and patients stand to benefit most directly from the outcomes of this initiative. The project will also provide educational resources and technical guidance on evaluating and applying reliable AI systems in practice. The outcomes of this initiative are designed to support a wide range of users involved in the evaluation, deployment, and oversight of medical AI systems. This POSE project develops the technical and organizational foundations necessary to sustain an open-source ecosystem focused on evaluating the trustworthiness of medical AI systems. Building upon an existing benchmarking framework, the project enhances its modularity, scalability, and security. The team will integrate additional evaluation dimensions, such as interpretability and develop tools to facilitate ongoing community-driven improvements. A transparent governance framework, including a steering committee and safety operating guidelines, will guide ecosystem development. Through workshops, comprehensive documentation, and collaborative events, the project will engage stakeholders from AI research, clinical practice, and open-source ecosystem design to co-develop evaluation protocols and tools. By enabling reliable assessment of medical AI systems, this project aims to advance scientific understanding of reliable machine learning and support the safe and effective integration of AI into healthcare. 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

With the support of the Macromolecular, Supramolecular, and Nanochemistry Program in the Division of Chemistry, Dr. Frank A. Leibfarth of the University of North Carolina at Chapel Hill will develop approaches to enhance the properties of materials accessible by 3D printing. Photochemistry-based 3D printing, where a liquid resin is cured into a solid material using light, is an enabling polymer processing technology in applications where high degrees of precision and customization are required. Currently, the properties available from photochemistry-based 3D printing are limited to relatively brittle plastics. By developing an understanding of new catalysts that can be activated by both heat and light, the Leibfarth lab aims to expand the properties available from photochemistry 3D printing to tough plastics, degradable materials, and stretchy elastomers. Access to these valuable properties using inexpensive and widely available 3D printing technology could allow the on-demand, personalized production of materials for automotive parts, medical devices, and performance sportswear when and where they are needed. The ability to expand access to fabrication of these materials will help encourage advanced manufacturing in the US that can be deployed in rural or underserved regions, and it will promote the development of a distributed, highly trained technical workforce. The Leibfarth group will also use hands-on experiences with 3D printing as a platform through which to educate undergraduate and K-12 students about how the chemical structure of plastics influence their properties and recyclability. The Leibfarth group aims to develop a catalyst-driven approach to expand the suite of properties accessible from vat photopolymerization (VP) 3D printing. Thermally and photochemically latent catalysts will be developed that are dormant during printing but can subsequently be revealed to initiate the synthesis of interpenetrating networks whose properties are a synergistic combination of the two networks. Specific objectives of the work include the development of thermally and photochemically latent catalysts, the study of how reactivity is influenced by additives commonly found in 3D printing formulations, and the systematic evaluation of how polymer network architecture influences material properties. The expected outcomes of these studies are a fundamental understanding of catalyst reactivity and selectivity in complex environments relevant to advanced applications, such as VP 3D printing, as well as quantitative structure–property relationships in polymer networks. The focus on synthetic approaches that work in widely available photopolymerization 3D printers means that the broader impacts of these synthetic advances can immediately be translated into finished polymeric parts in modern manufacturing infrastructure. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-08

This project develops mathematical foundations and statistical methods for data when each data object or the set of all possible data points has complex geometric structure. Data of this sort arises in an increasing multitude of societally important contexts. In forensic science, for example, fingerprints are not described by single numbers or even lists of numbers. In geoscience, patterns in thin slices of rock or ice serve as signatures of chemical composition or history. In medicine, imaging commonly produces high-resolution 3D scans that capture shapes, or even 4D scans (videos) that capture shapes evolving in time. The shapes themselves can be complicated geometric objects: branching trees of blood vessels, airways, or nerve cell dendrites; folded or kinked surfaces of brains or teeth; segmented or irregular blobs that vary subtly from patient to patient. Often, the geometry is not smooth: even from very close up, a branch point or a kink, for instance, does not look like a flat line, plane, or Euclidean space of higher dimension. These sorts of resolutely non-Euclidean phenomena pose fundamental challenges for data analysis, whose goal is to identify trends, search for anomalies, or classify. Even making these tasks precise in non-Euclidean geometric settings requires the mathematical foundations and statistical techniques targeted by this project. These scientific pursuits serve as a platform to mentor a group of interdisciplinary junior researchers, from high school to postdoctoral, in a vertically integrated scientific research lab environment, and enhance their professional development through direct research funding and travel support. The supported research will develop statistical methods to handle data sampled from non-Euclidean geometric spaces such as manifolds, algebraic varieties, simplices, and more general singular spaces, drawing on methods from probability, notably surrounding geometric central limit theorems on stratified spaces, as well as from algebraic, differential, and convex geometry. The project aims to produce specific contributions to (i) fundamental geometric statistics in non-smooth settings, including confidence regions and hypothesis testing for singularities as well as (ii) exploratory data analysis by deformation and slicing to find modes of variation in geometric sample spaces such as polyspheres, probability simplices (for compositional data), and more general semialgebraic varieties. This research meets the critical need for new methods in the emerging and important area of statistics and data science of complex data. 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 three-year mixed-methods project investigates how college students in science, technology, engineering, and mathematics (STEM) disciplines engage with generative artificial intelligence (AI) tools for academic help-seeking. As AI technologies become increasingly embedded in higher education, students encounter growing uncertainty about how to use these tools. Through surveys, interviews, and case studies, the project will generate empirical data to better understand how students use AI tools, the challenges they face in doing so, and the kinds of guidance or support they find most helpful. These findings will inform the development of institutional strategies for the use of AI. Project goals include supporting student learning, strengthening AI literacy, and upholding academic integrity. The central research questions guiding this project are: how do STEM students use generative AI for academic support, and how can institutions apply this knowledge to develop policies and support services? The project addresses a critical knowledge gap regarding how students are incorporating AI into their academic routines and how universities can respond with policy. The study integrates qualitative and quantitative methods, combining grounded theory analysis of interview and focus group transcripts with descriptive and inferential statistical analysis of survey data. The findings will inform the creation of policy briefs, a comprehensive policy recommendation, and a series of librarian-led workshops aimed at strengthening institutional guidance and support for generative AI use. 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 project supports the development of a program that aims to broaden participation of undergraduate students in research and education in the field of advanced network and computing technologies. Hosted at the Renaissance Computing Institute (RENCI) at the University of North Carolina at Chapel Hill (UNC), the program leverages the NSF-funded FABRIC testbed, a cutting-edge network research instrument that spans 33 U.S. and international sites and integrates high-performance computing, storage, and networking resources. The program is designed to provide students a world-class, immersive, hands-on research experience in many important emerging technology areas of national importance, including computer networking, cybersecurity, and artificial intelligence applications. By providing research training and professional development opportunities that are often inaccessible to students at less-resourced institutions, this project seeks to foster a more robust STEM research workforce. Students are introduced to the broader scientific community and mentored in research communication, collaboration, and career planning, helping to inspire future scientists and engineers while expanding the impact and accessibility of NSF’s research infrastructure investments. The REU Site program is divided into two phases: a 3-week “Core” curriculum and a 6-week mentored research experience. The Core phase builds foundational knowledge in networking, programming, systems, and cyberinfrastructure, with hands-on tutorials using the FABRIC platform. During the Research phase, students are paired with mentors from UNC and partnering institutions to conduct independent research on advanced topics such as federated learning, in-band network telemetry, network security using P4 and FPGAs, AI-assisted medical imaging, and performance evaluation of congestion control algorithms. Students benefit from a layered mentoring structure that includes faculty, research staff, graduate students, and peer cohorts, creating a supportive and collaborative learning environment. Long-term engagement is supported through alumni tracking and community-building activities. By combining rigorous research training with sustained mentorship, this REU Site offers a scalable model for preparing students from all backgrounds to pursue graduate education and contribute to the future of multidisciplinary cyberinfrastructure 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 · 2025-08

Biological cells need to sense their own shape and size for a variety of their functions, such as deciding when to divide and how to fit in narrow spaces. Studies over the past decade have shown that the filament-forming protein septin can sense the cell membrane’s shape and curvature by preferentially binding and assembling to areas of cell membrane curvature. This is surprising, since septin protein is only several billionths of a meter in length but can sense broad curvature over 100 times that length. This would be akin to using your foot to measure the curvature of a sphere the width of a football field. Recent studies by this team show that septin curvature sensing is determined through septin polymerization and assembly on the membrane, rather than by single molecules of septin. However, little is known about the interplay between the molecular makeup of these septin bundles, filament formation and the whole-cell scale organization of septins on the membrane, and how the assembly process relates to membrane curvature. This project will study this question in three scales of septin assembly, namely the scale of a few septin moelcules bound together, larger septin filaments, and the whole-cell scale. In order to tell the story of this research to the broader public, the team will design and conduct activities around the theme of “Self-Organization in Biology'' for middle school and high school students and their teachers. Lastly, integral to this research plan is the training of multidisciplinary undergraduate and graduate student scientists to work at the interface of physics, mathematics, and biology. Cellular surfaces often adopt shallow micron-scale curvatures, such as in fungal branches and cytokinetic furrows, whereas the proteins that sense these shapes are only several nanometers in size. This project will determine how cells sense geometry on the micron-scale with nano-scale proteins. The researchers have discovered that septin, a highly conserved filament-forming cytoskeletal protein, is localized to areas of micron-scale curvature on the membrane, and that septin curvature sensing is determined through its multistep, multiscale assembly on membranes. Yet, the relationships between septin’s molecular structure, its packing and organization, and curvature sensing ability remain poorly understood. This project will develop a multiscale mechanical model of septin assembly and curvature sensing in three aims, that correspond to three scales of septin assembly. In Aim 1 (molecular scale), the researchers will determine the effect of septin molecular structure on the binding, unbinding, and polymerization rates of a single oligomer. Aim 2 (filament scale) focuses on determining how this molecular-scale information influences filament-scale structure and transport. Aim 3 (system scale) focuses on analyzing the processes that determine the system-scale assembly - the density, packing, and layering of septin filaments. In order to measure and model these couplings, the researchers will combine multiple simulation and experimental tools across scales, including atomistic and coarse-grain particle simulations and kinetic modeling, with experimentally derived parameters from single-molecule imaging, confocal microscopy, and scanning electron microscopy. This project is funded by the Cellular Dynamics and Function Program of the Division of Molecular and Cellular Biology. 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

One of the most persistent challenges in education is the gap between research and classroom practice, meaning that research-informed recommendations and practices that could support students’ mathematics learning do not always reach the classroom. Students struggling with mathematics in upper elementary grades experience cascading difficulties and limited access to science, technology, engineering, and mathematics (STEM) fields. Improving how mathematics-focused education research is communicated to a teacher audience—using strategies that are useful and valuable from the teacher perspective—is one key avenue for mitigating consequences of the research-practice gap. This project will develop, assess, and refine innovative key abstracts (i.e., concise, infographic-type resources) for communicating mathematics-focused practitioner articles with a teacher audience. Teacher perspectives will be embedded throughout the project to inform key abstract design. The project also involves a collaboration with the university disability center to provide funded research opportunities in STEM education to university students with disabilities. This project builds on a sequence from a systematic journal analysis that will develop a database of practitioner articles for subsequent project studies; to a qualitative exploration of teachers’ perceptions of key abstracts; to a mixed methods study; and finally, to an experimental pretest-posttest control group design to investigate the impact of abstract types on teachers’ perceptions and behavioral intentions. The context for the research is communication of mathematics-focused practitioner articles for supporting upper elementary students with or at risk for mathematics difficulty. As noted, promoting students’ mathematics and overall STEM achievement is a critical need in the United States. The focus of the research aligns with and advances in the PI’s early-career research program. Project outcomes hold potential for empowering researchers, teachers, and students. Researchers will have strategies grounded in evidence for improving their communication which may, in turn, support use of effective mathematics practices in the classroom that bolster student outcomes. This is a Faculty Early Career Development Program project responsive to a National Science Foundation-wide activity that offers the most prestigious awards in support of early-career faculty who have the potential to serve as academic role models in research and education. The proposal was submitted to the Discovery Research K-12 program, which seeks to significantly enhance the learning and teaching of science, technology, engineering and mathematics by preK-12 students and teachers. 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 will investigate open problems at the intersection of probability theory and physics and develop new techniques for the study of the Anderson transition, a concept in condensed matter physics describing a sharp change from conducting to insulating behavior in certain materials as the density of impurities is increased. This transition plays a pivotal role in the physics of semiconductors and quantum materials. While the Anderson transition has been studied for decades, many foundational theoretical questions remain unresolved. Further, recent advances have revealed a tight connection between these questions and current research in many-body quantum systems. The project aims to provide a rigorous mathematical framework for the Anderson transition and its applications to contemporary quantum physics. The project also incorporates training of graduate students, research opportunities for undergraduate students, and the creation of new expository materials. A variety of mathematical models of disordered systems will be studied using tools from random matrix theory. The project will consider random matrices with heavy-tailed and sparse entries, as well as random operators on tree-like structures. The research aims to rigorously establish the existence of Anderson transitions in such models. It will also explore closely related phenomena, including multifractal eigenstates and anomalous quantum dynamics, which are believed to occur alongside Anderson transitions. The planned approaches will leverage recent insights in random matrix theory related to the resolvent formalism and local spectral laws. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-07

The use of modern computing and data infrastructure is critical to harnessing the full potential of instruments, data, and tools offered by state-of-the-art laboratory facilities, but many scientists do not have the necessary knowledge of data management, scientific programming skills, or the ability to use computing resources to bring them to bear for data analysis, leading to new discoveries. This project - CITEAM - addresses the gap by developing an innovative training program targeting the materials science research community that relies on advanced microscopes for research and needs to process and manage large data volumes to make fundamental advances in materials science. CITEAM provides training for microscope data processing, the use of Artificial Intelligence methods in data analysis, and effective data management, thereby reducing time-to-science. The project helps researchers in overcoming challenges in handling large-scale datasets and utilizing novel computing methods and resources. The project increases computing skills, awareness, and literacy for researchers with limited computing expertise, thereby accelerating the scientific innovations in materials science. The CITEAM project brings together a team of researchers with expertise in cyberinfrastructure (CI) as well as in imaging-enabled materials science to develop an innovative training program targeting the materials science community that relies on advanced microscopes (e.g. Transmission Electron Microscopes (TEM)) for research. This project aims at optimizing return on a state-of-the-art investment in physical infrastructure - a new aberration-corrected Transmission/Scanning Electron Microscope (AC TEM/STEM) at UMD. The training program covers several relevant thematic areas - TEM instrument software, image analysis, scientific computing, application of AI in TEM image and data analysis, diffraction and spectroscopy data analysis, distributed computing for microscope data processing, data curation, and FAIR principles. The training program includes an additional element of "training the trainers" by exposing the research facilitators and laboratory staff scientists to advanced CI topics, empowering them to guide others and innovate in the use of CI for materials science. Training is offered for both users and trainers in a multitude of modalities to promote efficient learning - self-paced modules, video lectures, templates and catalogs, office hours, training sessions at annual CITEAM Users' workshop, and tutorials at domain science conferences. CITEAM promotes community building by developing a coordination network comprising similar imaging laboratories, different domain science communities that use advanced microscopes, and experts from national CI resource providers. The CITEAM coordination network helps in adapting and disseminating training materials beyond the participating institutions, ensuring both scalability and sustainability of the program. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-07

This I-Corps project focuses on the development of a specialized disconnection device designed to address the challenges faced by nurses, patients, and family caregivers in safely disconnecting luer connections from various catheters. The project aims to improve patient safety, patient outcomes, nurses’ efficiency, while also reducing healthcare costs by providing a reliable solution to a widespread problem in healthcare settings. Luer connections are essential for managing catheters, but their disconnection often leads to complications, including catheter damage and increased risk of medical errors. This device securely grips luer connections and catheters of varying sizes, allowing for safe and efficient disconnection without causing damage. The adoption of this solution is expected to positively impact patient care, minimize the use of off-label instruments and workarounds, and lower overall healthcare costs by reducing the need for catheter repairs and replacements. This I-Corps project utilizes experiential learning coupled with a first-hand investigation of the industry ecosystem to assess the translation potential of the technology. This solution is based on the development of a symmetric, U-shaped disconnection device with two arms connected at one end and open at the other. The device's design includes ribbed areas for firmly grasping and two main grip sizes to accommodate different luer connections and varied catheter types. The technical advancements of this device include its ability to securely surround luer connections and catheters, providing the necessary leverage for safe disconnection, and ultimately contributing to better patient safety, patient outcomes, nurses’ efficiency, and reduced healthcare costs. 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.