University of Utah

NSF Awards · FY 2025 · 2025-09

This I-Corps project investigates the commercial potential of a portable sensor that can detect specific chemical markers, called volatile organic biomarkers (VOBs) that are associated with colorectal cancer. Colorectal cancer (CRC) is the second leading cause of cancer-related deaths in the United States, yet many people avoid screening due to discomfort, inconvenience, or limited access to traditional tests like colonoscopies. This project aims to improve early detection of CRC using a simple, low-cost, non-invasive test that analyzes breath or urine instead of requiring stool samples or invasive procedures. This new approach could significantly improve access to screening, by offering a test that is fast, painless, and easy to use. 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 colorectal cancer screening system that employs screen-printed electrodes combined with metal salts in a custom-designed electrolyte, optimized via molecular modeling to selectively bind CRC-associated volatile organic biomarkers (VOBs). This platform builds on electrochemical sensing architectures developed in prior tuberculosis VOB detection studies, re-engineered here for CRC-specific biomarkers. While the application and biomarkers differ, the underlying sensor design and signal processing pipeline provide a validated technical foundation for CRC-specific adaptation. 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

A Digital Twin (DT) is a representation of a real-world system that continuously exchanges data between digital models and their physical counterparts, allowing them to simulate, monitor, and predict the behavior of real-world systems in real-time. As such, DTs hold transformative potential across critical sectors including manufacturing, infrastructure, energy, and defense. However, existing methods for updating the digital models with real-world data are often too slow for real-time use. To overcome this barrier, this research introduces a novel mathematical and computational framework to dramatically accelerate digital model calibration, enabling faster and more accurate digital twin applications. The potential benefits of this work are far-reaching, advancing capabilities in predictive maintenance, process optimization, and risk mitigation, directly supporting the US economic productivity, public safety, technological innovation, and competitiveness. The project also fosters the next generation of scientists and engineers through interdisciplinary training and hands-on research experiences for graduate and undergraduate students. Together, these contributions lay the groundwork for a new generation of scalable, real-time Digital Twin systems with wide-reaching impact across science, industry, and education. Digital Twins require continuous two-way communication between physical systems and high-fidelity digital models. However, the cost in time and resources to update the digital models with real-world data is often prohibitive. To address this technical challenge, this project explores a fundamentally new approach for DT model updating centered on the efficient computation and exploitation of high-order derivatives obtained via a new class of hypercomplex algebras. These derivatives will serve as the foundation for a new derivative-informed Bayesian updating method that dramatically reduces the number of required model evaluations while preserving accuracy. The project is structured around three interconnected aims. Aim 1 develops hypercomplex algebras specifically formulated to compute arbitrary-order derivatives efficiently and accurately, even in high-dimensional settings. Aim 2 computes and applies the new hypercomplex algebras to accurately and efficiently obtain sensitivities of high-fidelity digital models. Aim 3 develops a derivative-informed Bayesian updating strategy that utilizes the derivatives to reduce the cost of model updating while maintaining high accuracy. The anticipated outcomes include faster and more accurate model calibration, improved uncertainty quantification, and reduced operational costs, enabling scalable, real-time DT systems across high-impact domains such as aerospace, defense, infrastructure, and 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 · 2025-09

How can we explain the enormous diversity of life on our planet? For example, there are over 7,000 species of frogs and toads alone. What is the origin of this diversity? Biologists tend to agree that species differ in their basic biology, but how species differences arise is often difficult to study unless one can see catch species just as they form. Social communication in frogs, where individuals produce sounds heard by others, is a key aspect of what makes a species. This project will explore the idea that processes in the brain that influence the choice of mates play a pivotal role in promoting formation of new species (speciation). The work will investigate how neuronal circuits in the brain change in Upland chorus frogs when they encounter other frogs that also produce sounds that are needed for females to choose mates. A primary objective is to better understand what aspects of brain function are particularly prone to change among frog populations and how this divergence promotes the formation of new species. The populations of Upland chorus frogs to be studied are presently undergoing speciation and, therefore, are ideal for this investigation. This project will also train postdoctoral researchers and graduate students to understand brain physiology, animal behavior, and evolution. This project will investigate how ultimate evolutionary forces drive diversification of proximate neural mechanisms of speciation, and how neural divergence, in turn, feeds back to accelerate the engine of speciation. Specifically, the objective is to investigate how the relationship between auditory neural circuits and mating behaviors facilitates reproductive isolation (RI) during speciation. The overarching hypothesis is that divergent selection, acting directly on mating behaviors used in species recognition, can drive differential changes in auditory neuronal circuits, thereby promoting the evolution of RI and the radiation of new species. This project will focus on a species (the Upland chorus frog, Pseudacris feriarum) in which RI has evolved among populations, driven by independent reinforcement of mating behaviors in multiple lineages. Given knowledge of mating behavior and the auditory neurons mediating these behaviors, a series of complementary experiments will characterize the neural architecture of behavioral phenotypes. An empirically informed auditory neural circuit model will be used to generate predictions about the mechanistic neural changes underlying behavioral diversification. These models will then be validated through directed neurophysiological experiments. Finally, integrative modeling will test the evolutionary consequences of this neurodiversity in nature and how this variation contributes to the origin of species. This project is jointly funded by the Evolutionary Processes Program in the Division of Environmental Biology, the Neural Systems Activation Program in the Division of Integrative Organismal Systems, and the Division of Emerging Frontiers, all in the Directorate for Biological Sciences. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-09

Computers, be it those in mobile phones or servers, are increasingly being designed with heterogeneous processors and memory that is shared across all of these processors. The heterogeneity enables greater performance, with different processors tailored for specific purposes (e.g., graphics), and the shared memory facilitates easier programming. These processors are already being used to support critical computational tasks, including artificial intelligence (AI), robotics, and medical research. While these processors offer great potential, they pose two problems. First, it is difficult to understand how to compose them. Specifically, different types of processors use different communication protocols, and composing these protocols is complicated. Second, it is also challenging to verify that the processors will behave correctly in all situations. The composed protocols have a vast number of possible interactions, and verification techniques do not scale up to meet this challenge. This project addresses both challenges by developing a systematic way to compose processor protocols and a new, scalable technique for verification of these processors. These contributions can offer many benefits, including shorter time to market, confidence that processors will behave as expected, and a lower barrier to entry for startups and researchers seeking to create new processors. By providing a foundation for the correct design of heterogeneous shared memory processors, the project will help to enable the coming generation of high-performance computing systems. These systems will sustain American economic competitiveness, supporting breakthroughs in AI, medicine, science, defense, and many other fields that will enhance the lives of all Americans. This project will make three important contributions to the theory and practice of processor design and verification. First, it will provide, for the first time, a mathematical foundation for defining and reasoning about the interaction of programs sharing memory in a heterogeneous system. This understanding will be crucial for designing the coming wave of heterogeneous systems-on-chip that will drive system performance for consumers and industry in the era of AI. Second, the work will provide an understanding of the large design space of heterogeneous coherence protocols and the first automated tools for correctly synthesizing the protocol converters needed to connect diverse local and global protocols. Third, the project will develop the first compositional approach for verifying heterogeneous coherence protocols and the first application of translation validation to cache coherence protocols. It will integrate verification as part of the protocol design flow, enabling designers to realize cost-effective proofs, and provide an exemplar for making formal methods practical in systems design. 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

Soft robots, constructed from flexible and deformable materials, are opening new frontiers in robotics by enabling safe and adaptable operation in delicate and complex environments. This project focuses on developing microscale soft robots inspired by the shapes and motions of bacteria. These robots are specifically designed to navigate biological fluids that are highly viscous or elastic. The miniature robots will have the ability to change shape, adapt to tight or irregular spaces, and move with precision through external magnetic and light-based control. Unlike conventional rigid robots, these soft robots can bend, twist, roll, and swim with ease, allowing them to access regions that are otherwise unreachable. This capability makes them especially promising for medical applications, including targeted drug delivery and minimally invasive procedures in anatomical areas such as the ear, nose, and throat. In addition to advancing robotic technology, the project includes a strong educational and outreach component. At Southern Methodist University and the University of Utah, undergraduate and graduate students will participate directly in research activities, gaining valuable experience in robotics, materials science, and biomedical engineering. Through these efforts, the project aims not only to develop transformative biomedical tools but also to cultivate a skilled future workforce in science and engineering. Technically, the research involves the design, fabrication, modeling, and control of rod-shaped soft robots built from adaptive polymer scaffolds. These scaffolds contain paramagnetic disks for magnetic actuation, polydiacetylene vesicles for thermal sensing, and gold and silver nanoparticles for light-responsive shape modulation. The robots will be actuated using different magnetic field configurations to produce a variety of locomotion modes, such as swimming, crawling, wiggling, and slithering, both on surfaces and within three-dimensional environments. To better understand and optimize these behaviors, the project will develop and validate computational models based on Kirchhoff rod theory and reduced-order representations. These models will incorporate factors such as environmental forces, magnetic interactions, and spatially controlled changes in stiffness. Experiments will be conducted in synthetic biological fluids and biomimetic environments, including mucus analogs and tissue-like gels, to simulate real-world biomedical conditions. The robots will also be capable of modular assembly and disassembly, allowing them to work together in swarms for more effective manipulation and enhanced adaptability. By integrating computational modeling, materials science, and control methodologies, this research will advance the state of the art in microscale soft robotics and open new possibilities for biomedical applications and other areas that demand highly adaptive, minimally invasive technologies. 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

Modern scientific challenges—from predicting complex fluid flows to modeling plasma behavior in fusion reactors—demand computationally efficient, trustworthy surrogates that can rival traditional numerical solvers while harnessing the power of artificial intelligence. Scientific machine learning (SciML), identified as a core technology for AI, offers immense potential for surrogate modeling in both data‑rich and data‑scarce situations; of particular interest is the field of operator learning. However, current operator learning frameworks lack unified theoretical foundations, robustness guarantees, and scalable training methods, which limit their adoption in high-stakes applications. The Unified Neural Operator (UNO) considered in this project will fill this gap by embedding all operator learning techniques into a unifying framework, marrying the mathematical rigor of traditional methods with the expressivity of modern AI. By delivering certifiable, interpretable AI‑driven surrogates, UNO advances Presidential priorities in artificial intelligence and nuclear energy—supporting both next‑generation AI capabilities and efficient modeling of magnetohydrodynamic systems critical for fusion energy—while fulfilling NSF’s mission “to promote the progress of science; to advance the national health, prosperity, and welfare; and to secure the national defense” Within SciML, operator learning has shown tremendous potential as a powerful tool for creating surrogate models, leading to a bevy of deep machine learning (ML)-based operator learning techniques known as “neural operators”. However, poorly-understood robustness characteristics, lack of explainability and interpretability, and the sheer variety of such approaches make it challenging for practitioners to choose the appropriate methods for different tasks, especially in the context of scientific applications. This project tackles these urgent challenges through the inception of a new computational framework: the Unified Neural Operator (UNO). UNO distills neural operators down into three essential components: an input encoder, a set of basis functions for the output space, and a projection operator. The work will (1) provide a mathematical formalism that both encompasses existing neural operators and allows us to generate novel architectures that target specific tasks and problems; (2) provide algorithms for scalable and adaptive training and inference, allowing UNO to adapt to local solution features and to tackle high-dimensional data efficiently in data-rich regimes; (3) provide a robust theoretical foundation in the form of universal approximation theorems, error estimates, and a guiding theoretical framework for robust sampling and adaptivity. The UNO framework also allows for automatic and natural uncertainty quantification capabilities of existing and new neural operators. In data-poor situations, the UNO framework preserves accuracy by analytically preserving physics, thereby making it well-suited to both in situ and ex situ surrogate modeling in scientific applications. The challenging applications targeted by this project include turbulent, multiscale, and multiphysics fluid flow 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 · 2025-09

With the support of the Macromolecular, Supramolecular and Nanochemistry Program in the Division of Chemistry, Professor Tang of the University of Utah is studying building blocks for quantum computing comprised of silicon nanocrystals, also called quantum dots (QDs), and organic radical compounds. These novel hybrid nanostructures have great potential for quantum information science (QIS) for two key reasons. Firstly, these quantum bits (qubits) can be initiated with light; and secondly, they can support long-lived, coherent spin-active states. The latter is important for information storage at the quantum level and is enabled by the fact that carbon and silicon are light elements with low spin-orbit coupling. Professor Tang and her team will vary the electronic coupling between the silicon QD and organic radical by molecularly engineering their covalent bridge, as well as the conjugated framework of the radical. This synthetic flexibility may allow experimental access to new physics and advance the field by establishing the physical parameters affecting the exchange coupling between the nanocrystal and radical needed to create higher order excited states useful as qubits. Importantly, students working on this project will be exposed to a broad swath of experiments with state-of-the-art equipment conducting spectroscopic and structural characterization in an interdisciplinary environment. This rigorous training is valuable for a career in science and engineering, critical for boosting domestic manufacturing in the United States. All this excitement about qubits for QIS will be shared with students from Whittier elementary school in Salt Lake City school district via a series of chemical demonstrations. With the support of the Macromolecular, Supramolecular and Nanochemistry Program in the Division of Chemistry, Professor Tang of the University of Utah is studying the synthesis and characterization of silicon nanocrystals covalently functionalized with molecular radicals which are designed to systematically tune the resultant exchange coupling all the way from the weak, to the intermediate and finally to the strong coupling regimes. Here, graduate students will systematically toggle between saturated, aliphatic and unsaturated, conjugated bridges, swapping between meta- and para- linkages, varying the distance between both the nanocrystal and radicals like 1,3-bisdiphenylene-2-phenylallyl (BDPA) and 2,2,6,6-tetramethyl-piperidin-1-oxyl (TEMPO). This approach will vary both the state energies and frontier molecular orbital levels of the radicals anchored on the silicon QD. Optical and electron paramagnetic resonance (EPR) spectroscopy will be used to chart the trajectory of photogenerated spin-states of triplets initially created in the Si QDs, subsequent coupling to the doublet states on the radicals, and evolution to the strongly coupled triplet-doublet and triplet-quartet states. Steady-state and time-resolved optical measurements will establish the rate and yields of photogenerated species. Time-resolved and pulsed EPR will reveal the strength of the exchange coupling and identity of the spin-active species. The data obtained will allow theoreticians to benchmark their predictions to experimental measurements of dipolar and exchange interactions in photogenerated triplet-quartet and triplet-doublet states. If successful, this system will fulfill the DiVincenzo criterion for qubits, i.e addressable, spin-active states with long coherence times. 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 development of a comprehensive framework that ensures the principled use of artificial intelligence (AI) technologies in data-intensive education research. The framework will benefit society by enabling more trustworthy education research, fostering public confidence in AI applications, and ensuring that technological advances serve all students. The project provides insights on responsible AI practices in education research and supports education by creating publicly available training materials for researchers and others with varying technical backgrounds. The project aims to bridge the gap between high-level principles and practical implementation by developing a responsible and principled framework for data-intensive education research in the AI era. The framework targets three key stakeholder groups: data administrators who manage access to education data; researchers who use education data to perform analysis; and individuals who are deciding whether to participate in research studies. The project uses a mixed method study with all three stakeholders to understand their current practices, concerns, and decision-making processes regarding education data usage. Based on these insights, the project team will develop and deploy a novel web-based assessment tool that leverages state-of-the-art responsible AI techniques to detect potential risks within datasets, helping stakeholders make informed decisions about data sharing and usage. Additionally, the project team will create a toolkit that identifies bottlenecks from the community and translates complex AI risks and benefits into accessible formats, utilizing interactive visualizations to facilitate understanding among non-technical stakeholders. Finally, the team will develop comprehensive educational support materials, including video tutorials, interactive modules, and real-world case studies that demonstrate principled AI practices in education 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 · 2025-09

Current systems used for weather observations have a difficult time discriminating between precipitation types in cold weather. This uncertainty affects societally-relevant decisions about road maintenance, risk to electrical infrastructure, and even avalanche safety. Scientifically, the lack of observations of the density of frozen precipitation impacts research on how winter precipitation is viewed by weather radars and how best to simulate winter precipitation in weather models. This award will allow for the continued development and testing of a new instrument that can measure individual particles and assess their size and mass. The Differential Emissivity Imaging Disdrometer (DEID) consists of a heated aluminum plate and an infrared camera. The plate is maintained at a temperature just below the boiling point of water and the camera images precipitation particles that fall on the plate. By measuring the temperature, area, melt time, and evaporation rate of each particle, the mass of the particle can be determined and density relationships calculated. The new research under this award will add and improve techniques to distinguish precipitation phase and measure the mass of the particles. Laboratory measurements will be conducted using well-characterized spherical and non-spherical ice particles to ensure a successful comparison between the DEID and known masses of frozen, partially melted, and liquid precipitation. The DEID will also be deployed to an instrumented site near the University of Utah campus for field observations. The DEID measurements will be compared to a particle tracking velocimetry laser-camera system that can measure the size and phase (but not mass) of falling precipitation and a Multi-Angle Snowflake Camera to validate the DEID’s ability to quantity partially melted snowflakes. 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 (MSN) program in the Division of Chemistry, Professor Valerie Pierre of the University of Minnesota will be developing polymer-supported receptors that capture and release phosphate reversibly as needed for remediation and purification of waste water and for recycling phosphate—a critical resource needed to secure our food supply. Professor Pierre and her research team aim to synthesize and study polymers and receptors that function as sponges, capturing the phosphate from polluted water selectively and then releasing it upon addition of stimuli such as light or pressure. This controlled reversibility is designed to enable separation and recovery of phosphate and reagent-less recovery of smart receptors. This interdisciplinary research project will provide training opportunities to undergraduate and graduate students across a range of useful skills, including chemical synthesis, polymer characterization, and analytical studies of the receptor-phosphate binding. Broader impacts of the project also include the development and evaluation of new hands-on activities for middle schools, with the goal of increasing interest in science and the research enterprise in students early in their education. The Pierre research team seeks to develop a general strategy based on allosteric electrostatic interactions to render receptors for ions responsive to external stimuli. A unique aspect of these receptors is their ability to release their guests on demand upon addition of an external physical or chemical stimulus. The catch-and-release properties of such designed receptors rely on reversible chemical reactions, such as those governed by light, redox potential, temperature, and pressure. This controlled reversibility is expected to enable separation followed by controlled recovery of phosphate and reagent-less recovery of receptors supported on smart polymers. This approach broadens the capabilities of supramolecular ion receptors by enabling them to release the guests up a concentration gradient. The Minnesota research team further seeks to combine the strength of supramolecular chemistry with that of polymer science to enable solid support control of the behavior of the supported receptors. 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

Mountainous regions are the primary source of water for much of the western United States. Many mountain streams are sustained by groundwater, but conceptual and hydrologic models often oversimplify groundwater processes. As a result, it is challenging to predict how streamflow responds to changes in groundwater recharge and storage caused by extreme wet and dry conditions. This project is evaluating how groundwater regulates stream responses to hydrologic extremes by integrating high-resolution stream and groundwater observations with hydrologic models. The knowledge generated from this work will improve understanding of how stored groundwater impacts mountain streamflow generation, thereby enhancing streamflow predictions. Broader impact activities include an early-career workshop on data-model integration in Earth surface processes, with the goal of fostering cross-disciplinary collaboration. Additionally, the project will integrate field infrastructure and models into undergraduate coursework at three institutions to expose more students to hydrologic science. This project aims to determine the role of groundwater in regulating streamflow response to hydrologic extremes across a groundwater storage gradient using a data-model integration approach. Field observations of stream discharge, source, and age in two mountain watersheds will be integrated with an iteratively calibrated process-based hydrologic model capable of simulating groundwater-surface water interactions under future long-term and short-term hydrologic extremes and with variable subsurface structure. Study sites include two mountain watersheds with high- and low-groundwater storage settings. The project will address how the structure of the subsurface influences the source, age, and magnitude of streamflow, as well as the extent to which upstream heterogeneity affect conditions at the watershed outlet. The project will improve understanding of how groundwater storage modulates streamflow during hydrologic extremes. The project will develop a transferable data-model integration framework to address critical zone science questions. The framework will be the focus of a broader impacts workshop that will provide early-career scientists the opportunity to learn field data or modeling techniques from peers, as well as foster new collaborations and cross-disciplinary learning within the critical zone community. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-09

With the support of the Chemistry of Life Processes program in the Division of Chemistry, Professor Amy Barrios from the University of Utah and Professor Brian Popp from West Virginia University are investigating phosphatase enzymes involved in cellular signaling. Protein phosphorylation and dephosphorylation controls many of the pathways of cellular communication. While the enzymes that add a phosphate group to protein targets (the kinases) are relatively well understood, much less is known about the enzymes that remove this phosphate signal (protein phosphatases). This project aims to develop a set of chemical tools that can be used to answer questions about the cellular locations of specific phosphatases, the impacts that signaling molecules outside the cell have on the activity of individual phosphatases inside the cell, and the roles that key phosphatases play in important signaling pathways. For example, this work centers on study of a family of phosphatase enzymes that are involved in neurological development. Chemical tools will be developed to monitor the activity of these phosphatases in cells, identify signaling molecules that activate them, and validate inhibitors that will block the activity of the phosphatases. The tools developed in this project will provide new biological insights that cannot be obtained readily in any other way. The project is also integrated into a larger effort to build a strong culture of excellence in mentoring for scientific research trainees by incorporating regularly cross-campus mentor training workshops. The long-term objectives of this work are to provide chemical approaches to studying receptor tyrosine phosphatase activity that can be used to answer key questions about the roles of these critical enzymes in cellular signaling pathways. The investigators have a strong track record of tailoring fluorogenic substrates and targeted inhibitors to individual tyrosine phosphatases and utilizing these tools both in vitro and in cells. This project is focused on the Leukocyte common Antigen-Related (LAR) subfamily of receptor tyrosine phosphatases. Fluorogenic peptide substrates with selectivity for the LAR phosphatases will be developed based upon the sequences of known biological substrates and validated for cellular applications. Potent and selective inhibitors for the LAR subfamily will be optimized and utilized to probe the roles of these enzymes in cellular signaling. Signaling molecules that act by binding to the extracellular portion of these receptor phosphatases to modulate intracellular phosphatase activity will be identified and investigated. The targeted chemical probes optimized and validated through this project will provide novel insights into the biological roles of LAR phosphatases. 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

When some stars die, they can create jets moving almost at the speed of light. These jets probe extreme conditions near black holes and neutron stars and can even be seen from the farthest parts of the universe. This project will use sensitive radio telescopes to study how these jets form, what they are made of, and how they are shaped. This study will address open questions about how these dramatic cosmic explosions work. This research program also will provide research and training opportunities for undergraduate students at the University of Utah. The program will communicate results to the public via outreach efforts to improve scientific literacy. This project will advance our understanding of transient relativistic jets launched during the explosive deaths of stars. The investigators will combine new radio and millimeter photometry, polarimetry, and astrometry with multi-wavelength (optical, ultraviolet, X-ray) data to characterize the structure and magnetization of jets in gamma-ray bursts, tidal disruption events, and compact binary mergers. By applying analytical models and leveraging existing numerical simulations, the investigators will constrain key jet properties such as energy, opening angle, and magnetic structure, testing the "universal jet structure" hypothesis across diverse transients. The project will also probe the physics of jet launching and acceleration by connecting observed jet properties to central engine models (black holes or magnetars). Much of the required data is already secured, and the investigator is positioned to capitalize on new multi-wavelength and multi-messenger detections from upcoming surveys (e.g., VRO/LSST, LVK) and next-generation facilities (e.g., SKA, ngVLA, CTA, JWST, Roman). 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

Water-based (aqueous) iron batteries are a promising technology for low-cost, grid-scale stationary energy storage. This technology could store energy for renewable electricity generation such as solar or wind. In addition, electrolytic ironmaking is a promising technology for the ironmaking industry. At the core of these technologies is the reduction of iron ions and oxides to metallic iron using electrical current. This project will generate fundamental knowledge on the mechanism and reactivity of these chemistries. The knowledge will enable the rational design of iron metal batteries, which have the potential to reduce energy storage costs. The new knowledge will also enable the design and development of novel low-temperature electrolytic ironmaking processes, which could strengthen the competitiveness of the nation’s iron and steel industry. Additionally, the project will develop an education program that integrates outreach, research, and teaching which will create systematic opportunities for attracting and retaining engineering students into energy research and industry. The primary goal of this project is to obtain fundamental understanding of the electro-reduction of Fe ions and oxides in acidic aqueous electrolytes, and how the reaction mechanism and reactivity depends on the chemical environment. Experimental (e.g. electrochemical, microscopic and spectroscopic) and modeling methods will be combined to understand the electro-reduction thermodynamics and kinetics of Fe ions, the pathway of hematite electro-reduction, and the correlations of electro-reduction reactivity and the chemical environment. The fundamental knowledge can guide the design of electrolytes to accelerate the development of Fe metal batteries and guide the design of lower emission ironmaking processes. The established models can be generalized to understand the electro-reduction of other transition metal ions. The knowledge on hematite electro-reduction can be extended to the electro-reduction of other Fe oxide ores (magnetite, goethite, siderite) and electro-metallurgy of other transition metal oxides. 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, Valerie Pierre of the University of Minnesota will be developing a new class of three-dimensional precision polymers. The unique wireframe topology of these hollow polymers opens up new possibilities for a number of applications including their potential use as solubilizing agents, as molecular and imaging probes, and as encapsulating and delivery agents. Broader impacts of the project include the development and evaluation of new hands-on activities to engage grade six students in science with the goal of improving their attitude and increasing their interest toward science early in their education. In this project, Dr. Valerie Pierre and her team will engage in a focused study directed ad the development of a class of three-dimensional wireframe polymers. A unique aspect of the wireframe polymers is their templated synthesis. The approach employs predictive supramolecular strategies to template the position of each monomer on a DNA nanostructure. Subsequent coupling of the monomers yields the final desired macromolecule in one pot. The modularity of the templated approach will facilitate tuning of the polymer’s size, topology, and functional groups to match the needs of the intended applications. Orthogonal conjugation chemistry will enable functionalization of a large yet precise number of groups on the wireframe polymer. The modularity of the supramolecular approach will enable the facile introduction of a set number of a second and distinct monomers at precise and predetermined locations on the macromolecules. The ability to pinpoint and efficiently incorporate single monomer polymorphism opens the door to precise bifunctional polymers. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-08

This project will develop new techniques in the study of harmonics, along with applications to fundamental questions about the structure of prime numbers. More precisely, the mathematical theory of automorphic forms builds from the study of harmonics, or the fundamental tones of musical instruments, to a general theory of the vibrational modes of highly symmetric shapes in arbitrary dimensions. Because of a surprising connection between automorphic forms and prime numbers, it has also proven important to study these highly symmetric shapes with an alternative theory of geometry built up from an unusual notion of size and distance that detects divisibility by a fixed prime number. This is called p-adic geometry. The basic shapes in p-adic geometry look more like fractals such as the Cantor set than the shapes we encounter in our day-to-day life, thus much of our usual physical intuition about the real world cannot be applied in this setting. This project will improve our ability to reason intuitively in p-adic geometry by developing ideas from calculus and geometry, like derivatives and curvature, so that they can be applied also in p-adic geometry, and then use these tools to answer fundamental questions about automorphic forms and their connections to prime numbers. The mathematics of harmonics plays a fundamental role in signal processing, while questions about prime numbers are essential to the modern cryptography schemes that allow us to communicate and make purchases securely online, so that the mathematics to be developed in this project will have ties to areas of importance across the modern economy. The project also provides training opportunities for the next generation of researchers through research supervision and mentoring for undergraduate and doctoral students in mathematics. At a more technical level, this project will develop new tools for studying p-adic geometry using the language of inscribed v-sheaves, which adds a differential layer on top of the theories of diamonds and perfectoid spaces that make up the modern foundations of p-adic geometry. This theory is akin to equipping a topological space with the additional structure of a differential manifold. The project will connect these tools with other recent advances in the theory of p-adic geometry and analytic structures, and use them to study the relation between different spaces of p-adic automorphic forms and to study the geometry of moduli spaces in p-adic Hodge theory. 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

Cardiovascular diseases are the leading cause of death in the United States, with arrhythmic heart disease and heart failure as common final pathways. The engineering and design of therapies, devices, and drugs to combat these diseases is challenging due to a limited understanding of both patient-specific and population-level characteristics of cardiac anatomy and physiology. A promising approach for supporting the development of precision medicine in addressing these challenges is the simulation-based, data-driven paradigm of cardiac digital twins (CDTs). This project makes core advances in the science and application of CDTs by leveraging mathematical and statistical foundations to enhance the trustworthiness of CDT simulations. This project will then demonstrate the potential of CDTs in clinical settings by deploying them on state-of-the-art cardiac simulation models and utilizing real-world clinical data. The project aims will be achieved through three technical tasks. The first task builds a framework for assessing and constructing CDTs through statistical inference of virtual heart populations (VHPs) using novel data sources, optimization-based calibration of simulation models, and the introduction of customized methods for surrogate modeling and quantification of aleatoric and epistemic uncertainty. The second task involves developing new exploration-exploitation meta-algorithms to enhance the predictive capabilities of CDTs through innovative paradigms for ensemble learning, model management, and computational budget allocation. The foundational algorithmic advances from the first two tasks will be applied to establish a new holistic CDT framework in the third task. This new framework integrates models across cellular, tissue, and organ-level scales with multimodal and multifaceted clinical data. Exploratory scientific tasks using this new CDT and VHP framework include the development of VHPs for populations affected by specific classes of diseases and the investigation of population-level progression mechanisms that lead to cardiac disease. 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 break the low latency performance barrier in today’s fifth generation (5G) networks that hinders progress and adoption of remote driving industry (the “vertical” application). It advances an innovative “vertical-aware” framework to optimize both 5G networks and the vertical application. Despite tremendous progress, today’s “self-driving” cars may encounter many situations where they cannot drive themselves safely. Examples include construction zones and traffic accidents on the road. By ensuring low latency needed for remote driving, the developed solutions will allow a human teleoperator to remotely steer a “connected and autonomous” vehicle (CAV) through complex situations as if sitting in the driver seat. Technological advances enabled by this project will help (re-)establish the U.S.’s leadership in next-generation (NextG) wireless telecommunications and major vertical industries such as automotive and robotic automation. This project also provides a unique educational platform to train students and expand the STEM (Science, Technology, Engineering & Mathematics) workforce. Two major hurdles in ensuring low latency over 5G networks are i) high mobility of vehicles leads to poor radio channel conditions, causing data delivery errors; ii) frequent handovers among radio base stations further prolong data delivery. The project will develop a novel Open Radio Access Network (O-RAN) enabled, vertical-driven framework with mobility-aware, proactive mechanisms to reduce impacts of high mobility and handovers on the tail latency performance of the target vertical application. This is achieved by enabling 5G networks to utilize information (e.g., vehicle trajectory and speed) provided by remote driving applications to make intelligent decisions to speed up the delivery of sensor and command-and-control data that are critical to remote driving, whereas CAVs can also take advantage of vertical-aware predictions made by 5G networks to decide when and how to transmit data. Additional innovations include incorporation of integrated 5G and cellular vehicle-to-everything (C-V2X) technologies for cooperative situation awareness to further ensure safe remote driving operations. The phased approach to developing the proposed solutions and demonstrating their capabilities will ensure a high chance of successful execution, truly moving the needle with transformative impacts on relevant industrial sectors. The project represents close collaboration across three academic institutions and two industry leaders in key relevant sectors providing an accelerated pathway to technology transition. By demonstrating the value of vertical-aware advanced 5G/NextG networks in support of remote and cooperative driving and other industrial use cases, this project will help create new opportunities and business models for both mobile network operators and network equipment vendors for sustained investments in network innovations. It will also help accelerate adoption of autonomous driving with teleoperation capabilities. 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 ability to measure which genes are expressed in cells has revolutionized our understanding of biological systems. Discoveries range from pinpointing what makes different cell types unique (e.g., a skin vs. brain cell) to how diseases emerge from genetic mutations. This gene expression data is now a ubiquitously used tool in every cell biologist’s toolbox. However, the mathematical theories for reliably extracting insight from this data have lagged behind the amazing progress of the techniques for harvesting it. This CAREER project will develop key theoretical foundations for analyzing imaging data of gene expression. The advances span theory to practice, including developing mathematical models and machine-learning approaches that will be used with data from experimental collaborators. Altogether, the project aims to create a new gold standard of techniques in studying spatial imaging data of gene expression and enable revelation of new biological and biomedical insights. In addition, this proposed research will incorporate interdisciplinary graduate students and local community college undergraduates to train the next generation of scientists in the ever-evolving intersection of data science, biology, and mathematics. Alongside research activities, the project will create mentorship networks for supporting first-generation student scientists in pursuit of diversifying the STEM workforce. The supported research is a comprehensive program for studying single-molecule gene expression spatial patterns through the lens of stochastic reaction-diffusion models. The key aim is to generalize mathematical connections between these models and their observation as spatial point processes. The new theory will incorporate factors necessary to describe spatial gene expression at subcellular and multicellular scales including various reactions, spatial movements, and geometric effects. This project will also establish the statistical theory of inference on the resulting inverse problem of inferring stochastic rates from only snapshots of individual particle positions. Investigations into parameter identifiability, optimal experimental design, and model selection will ensure robust and reliable inference. In complement to the developed theory, this project will implement and benchmark cutting-edge approaches for efficiently performing large-scale statistical inference, including variational Bayesian Monte Carlo and physics-informed neural networks. The culmination of this work will be packaged into open-source software that infers interpretable biophysical parameters from multi-gene tissue-scale datasets. This CAREER Award is co-funded by the Mathematical Biology and Statistics Programs at the Division of Mathematical Sciences and the Cellular Dynamics & Function Cluster in the Division of Molecular & Cellular Biosciences, BIO Directorate. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-08

The human brain is fascinating as it can form multiple memories that allow us to remember different types of information like facts and events (e.g., Paris is the capital of France) and procedures (e.g., how to ride a bike). These types of memories are often studied independently, even though memories that are formed everyday combine information from different domains. For example, when one learns to play the piano, one not only learns the sequences of movements on the piano keyboard, but also the melody associated with the movements and the sequence of notes written on the music score. Although the content of these memories differs, their structure is similar. This research examines how learning different material with similar structures is carried out in the brain, whether it is more likely to form lasting memories, and also tests whether this property can be used to improve memory. If successful, this work has translational potential for developing new ways to enhance the formation of lasting memories. In addition, this project supports public engagement with science and includes workshops for K-12 students, as well as mentored research experiences in cognitive neuroscience and advanced data analysis for undergraduate trainees. This project utilizes sequence learning as a study model as it underlies several daily activities in both memory domains (e.g., memory for sequences of events and actions). Magnetic Resonance Imaging (MRI) is employed to delineate the brain responses supporting sequence learning across memory domains (motor sequence vs. object sequence learning), with a focus on a brain region critical for memory, namely the hippocampus. The project examines how these brain responses can be strengthened across domains to improve memory consolidation, which is the process by which memories become more stable and robust for the long-term. This research also uses electroencephalography (EEG) to measure brain activity during an important memory consolidation period, namely sleep. The goal is to experimentally reactivate memories from different domains during specific sleep periods and ultimately investigate whether memories from different domains that are learned together consolidate together. In addition to the potential translation to methods for improving memory consolidation, this project will increase our understanding of the principles underlying learning and memory consolidation in the human brain which may inspire advancements in artificial intelligence models. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-08

The Earth’s mantle melts as it rises beneath mid-ocean ridges and hotspot volcanoes. This process generates ocean crust and is responsible for most of the volcanic eruptions on Earth. The presence of even small amounts of water in the mantle is known to have large effects on the amount of magma produced. Likewise, small changes in chemical composition of the mantle rocks will also change the amount of magma produced. This project will be the first to study both of these factors together. The project involves both laboratory experiments that replicate the conditions in the mantle and computational models that predict melting. Two graduate students will conduct each aspect of the project, respectively, but also will be cross-trained under the guidance of principle investigators. Several first-year undergraduates will also be involved in the research, leveraging an existing program at the University of Utah. The anticipated outcome of this research will be the first models that accurately predict the combined effect of water and composition variations on mantle melting. The project will also support making video tutorials to help with the adoption of the software developed in this project. The presence of even small amounts of water and other volatiles can significantly alter the way that mantle rocks melt and how much magma is produced to form new crust and create volcanoes. Understanding the mantle melting processes from the composition of basalts produced is a very difficult problem, so laboratory experiments and computational modeling provide the best ways to address these questions. This project integrates high pressure-high temperature laboratory experiments and geochemical numerical models to investigate the impacts of variable water content on melting of a realistic mantle composition containing both peridotites and pyroxenites as they coexist in a decompressing, rising mantle plume. The project will develop the first model parameterization that can accurately predict the effects of water on melting a lithologically realistic mantle containing pyroxenites, and make publicly available open-source software to enable other researchers to run these models. The central hypothesis is that increasing both the abundance of pyroxenite and the concentration of water in the decompressing mantle will generate measurably deeper melting than either factor alone. Experimental data generated during the project will add significantly to the public Library of Experimental Phase Relations and the EarthChem national data repository. This project will enable two graduate students to continue their studies and will provide first-hand research experiences for freshmen students at University of Utah. 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

Commutative algebra studies mathematical structures such as the integers and polynomials, and has broad applications in computer science, engineering, and other areas of mathematics. Execution of the planned research will deepen the theoretical foundations of the field and address problems in related disciplines such as algebraic geometry, which studies the geometric objects associated to polynomials. A central focus of this research is on singularities, or points where the geometric objects behave irregularly (for example, a curve crossing over itself), using techniques from modular arithmetic (also known as clock arithmetic or prime characteristic algebra) and mixed characteristic settings (where a prime is treated as a variable). These approaches, including the use of perfectoid algebras, help bridge distinct mathematical worlds and enhance our understanding of both. In addition, the principal investigators are dedicated to promoting mathematics education, developing future generations of researchers, and assisting in building a strong STEM workforce in the US. Towards these goals, the principal investigators will supervise, train, and mentor graduate students and postdoctoral fellows. The principal investigators will also facilitate seminars and workshops for undergraduate and graduate students. Recent advances, including past efforts of the principal investigators, have inspired a rapidly-emerging theory of singularities in mixed characteristic, bridging the existing notions from classical complex geometry defined using resolutions of singularities with those in positive characteristic commutative algebra that utilize Frobenius splittings and tight closure theory. Establishing finiteness properties is a crucial component in the study of singularities across characteristics. In prime characteristic, the principal investigators will study finite generation of the anticanonical algebra for certain singularities defined by Frobenius, and the existence and properties of boundary divisors in both prime and mixed characteristics, which in turn can be used to prove strong finiteness conditions. The investigators will also research log canonical singularities and ideal closure operations in the complex setting, better definitions of F-pure pairs in prime characteristic, and the theory of singularities outside the F-finite setting in prime characteristic. 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 proposal is primarily in the area of geometric group theory, which connects two foundational fields of mathematics, namely group theory and geometry/topology. A group can be thought of as the set of symmetries of an object or a space such as a water molecule or a fractal. The geometric properties of the space can often reveal algebraic properties of the group. This project aims to explore these connections between groups and the spaces on which they act. Broader impacts of the proposal include mentoring students and organizing conferences and special sessions at meetings. The focus of this project is on groups which arise as symmetry groups of Cantor sets. Cantor sets have the unique property that, when divided in half, give two copies of the original Cantor set. Many examples and counterexamples in group theory arise as symmetry groups of Cantor sets, including Grigorchuk's group and the family of Thompson's groups. Although these groups have been around for many years, they are still driving examples in modern group theory. The first goal of the project is to better understand Thompson's groups themselves via the Poisson boundary of F and the maximal subgroups of V. The second goal is to use related constructions to gain a better understanding of groups having some form, or acting on a space with some form, of nonpositive curvature. The final goal is to obtain applications in model theory and dynamics. 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 Faculty Early Career Development (CAREER) award funds research that intends to intends to identify and quantify factors contributing to the instability of jointed rock slopes. Rock slope failures pose significant risks to lives, infrastructure, and economies. These slopes are made of structured rock masses, which include intact rock blocks (rock matrix) intersected by natural fractures. Predicting the stability of such slopes is difficult due to the complex interactions between the rock matrix and natural fractures. Traditional models often oversimplify these interactions, averaging properties of the rock mass, failing to account for the distinct roles of the rock matrix and natural fractures in instability, and thus overestimating the safety margins and leading to inadequate risk assessments. This project seeks to advance fundamental knowledge of how the properties and degradation rates of the jointed rock components ‒ i.e., natural fractures and the rock matrix ‒ influence the instability of jointed rock slopes to improve predictive models and contribute to the safety and sustainability of infrastructure in mountainous regions. Other broader impacts include engaging students of different ages and the general public in innovative STEM outreach activities, such as inquiry-based problem-solving techniques for high school students, the Youth-in-Custody program participants, and visitors of natural history museums and mountainous national parks, to inspire public appreciation for civil and geotechnical engineering. Graduate and undergraduate students look to develop science communication skills through the design and delivery of educational and outreach activities. Each activity will be evaluated for its effectiveness in increasing enrollment in the regional civil engineering schools and the effects of inquiry-based activities on the public’s perception of engineering. The project is expected to expand participation in STEM fields and foster partnerships between universities, national parks, and local communities. The research looks to integrate laboratory experiments and numerical simulations to reveal the mechanisms driving fracture evolution and progressive failure processes in jointed rock slopes. The results seek to quantify the consequences of jointed rock failure, such as failing rock volume, runout distance, and velocity. The distinct contributions of the rock matrix and natural fractures to slope stability and the factor of safety look to be characterized based on the contrast between the hydraulic and mechanical properties of these components and the geometric characteristics of the slope relative to the natural fracture network. Experimental studies seek to simulate fracture-matrix interactions at various scales by monitoring the evolution of strain and fractures on the surface and through rock blocks and joints using high-spatiotemporal process monitoring techniques, such as digital image correlation and acoustic emission measurements. Scaled jointed rock slopes will be constructed in the laboratory using metal-ceramic 3D-printed rock blocks, with elevated gravity achieved by applying a strong magnetic field. Hydromechanically coupled, hybrid continuum-discontinuous numerical models will be calibrated and validated with experimental results with the intent of extending scenarios of the effects of contrasting hydromechanical properties and strength degradation rates on the instability of jointed rock slopes. These models seek to provide insights into strain and water pressure distribution effects on failure progression in full-scale jointed rock slopes. This project is expected to advance the state of the art in rock mechanics and slope stability analysis, improve infrastructure safety, and foster an informed STEM workforce through cutting-edge research and 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 · 2025-08

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.