Regents of the University of Michigan - Ann Arbor

NSF Awards · FY 2025 · 2025-10

The aim of this project is to research a novel high-order nonlinear resonance wireless power transfer (WPT) approach for implementation in practical position-agile WPT applications ranging from dynamic and opportunistic electric vehicle (EV) charging to powering biomedical devices. Inductive coupling near-field WPT is an emerging technology with an immense potential for a wide range of applications. WPT systems use dedicated sources or transmitters for contactless electrical power transfer at different power levels ranging from milliwatts to kilowatts. Although some commercial products have adopted WPT technology, the technology remains underdeveloped because of the limitations imposed by sensitivity to alignment and position in current WPT systems. For example, in wireless EV charging, the system's sensitivity to vehicle's tire size, speed, and position could significantly degrade power transfer efficiency. This project will develop a new position-agile WPT technology based on high-order nonlinear resonance, founded on a unique topology. The new WPT technology offers an effective solution to address the sensitivity of WPT efficiency without any complex feedback or sensor circuitry requirement. Within the field of biomedical devices, when wirelessly powering implants, this approach provides a robust WPT solution that does not hinder patient's mobility and can wirelessly power a host of biomedical devices, which not only improves patients' lives but also reduces the burden on the healthcare industry. The multidisciplinary nature of the project involves nonlinear circuit analysis, mathematics, power electronics, radio frequency engineering, and applications ranging from automotive to biomedical engineering. The project will involve students of various levels in research, including graduate students, undergraduate students through Research Experience for Undergraduates (REU) program, and K-12 students from local community. The goal of this project is to demonstrate a fundamentally new "position-agile" WPT paradigm employing high-order nonlinear resonance for highly efficient power transmission that is insensitive to misalignment and transfer distance. The proposed research centers on a detailed theoretical and experimental study of the nonlinear circuits in WPT to automatically compensate for the variation in the coupling factor due to changes in distance and alignment between the transmitter and the receiver. In contrast to conventional methods, this approach neither varies the operating frequency nor must use any active matching circuitry involving feedback and control algorithms. Additionally, this approach has the advantage of providing a low-cost, low-complexity, rapid-response and highly reliable solution for practical position-agile WPT design. The first research task of this project is to design and construct a WPT system prototype for demonstration and validation of the novel approach. The second research task is to design, fabricate and test a position-agile multi-input, multi-output WPT system, where the high-order nonlinear resonance innately balances the power transfer to multiple receivers simultaneously. The third research task is to design and develop experimentation for a position-agile high-power WPT system with distributed nonlinear devices to study the capability of this approach for achieving high-power rapid charging. 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 · 2025-10

Nontechnical Description The rapid rise of generative artificial intelligence (AI) has ushered society into a new era of supercomputing-driven data exploration. This tipping point is intensifying the gap between the exploding size of AI models and the limited computing throughput available today. A major bottleneck lies in data movement, specifically, the limitations of current interconnect technologies, further constrained by the aging von Neumann architecture. To address this, this project will employ a co-designed approach spanning architecture, packaging, and device innovation to meet the demands of next-generation interconnects, including high bandwidth, low energy use, low latency, scalability, and reliability. Supported by the ASCENT program, this project introduces a novel 3.5D integrated photonic interconnect solution that combines breakthroughs in network architecture, photonic devices, and advanced packaging. By leveraging the complementary expertise of academic and industry collaborators across several ECCS clusters, this effort drives interdisciplinary innovation, trains the next generation of engineers, and enables more powerful, efficient, and scalable computing systems that benefit society. Technical Description This project advances the field of integrated photonics by introducing a co-designed solution across architecture, packaging, and devices to meet the demands of next-generation computing. It proposes a transformative photonic interconnect-switching architecture based on a novel wavelength-mode division multiplexing scheme, enabled by athermal, energy-efficient, high-speed modulation and advanced hybrid Cu-Cu bonding techniques in 2.5D/3.5D integration. The goal is to achieve terabit-per-second data transmission with dynamic AI workload optimization. Key research tasks include the design of a reconfigurable and resource-aware photonic interposer network, exploration of 2.5D/3.5D network architectures with AI workload analysis, fabrication of heterogeneous athermal capacitive modulators and switches, development of microring-based transceiver and switch testbeds, and advancement of hybrid Cu-Cu bonding technology. This tightly integrated, interdisciplinary effort will drive innovation across multiple fronts, directly aligning with the core mission and priorities of the ASCENT 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 · 2025-09

Science centers are well-positioned to support the communities that surround them through activities and programming that advance community goals. However, little is known about how science centers can act within larger networks to collectively apply innovations to meet community needs. This project will address this gap in practice by developing and researching a network, whose central hub is a science center, and whose purpose is to foster intergenerational STEM (science, technology, engineering, and mathematics) learning in the context of innovation. Specifically, a science center will coordinate with other STEM learning hubs, such as a Boys and Girls Club; family STEM workshops hosted by a youth council; and science programs located within a school. This collective network will promote innovation and STEM learning through making activities that draw from intergenerational expertise and align with community members' needs, such as making activities that address the problem of frequent local power outages through the design of low cost, solar powered handheld lights. Research will explore whether and how the proposed approach fosters intergenerational STEM learning and engagement, among other outcomes. Resulting products will be shared widely with professional networks of science centers and museums and will build national capacity to use STEM-rich making for innovation within informal learning institutions and their associated networks. In the context of a Research-Practice Partnership, a university and a science center will collaborate to strengthen an existing network with multiple hubs that promote informal STEM learning. A shared goal across the network includes using STEM-rich making for innovation and intergenerational STEM learning. This project builds from decades of innovation in makerspaces--such as design and fabrication, circuitry, desktop manufacturing, biomaking, and emerging technologies--and it combines these technical innovations with social innovations by applying existing makerspace technologies to benefit society. It further combines technical and social innovation by translating STEM learning experiences into tangible products that can improve the lives of community members. Mixed methods research will explore whether and how the approach fosters intergenerational STEM learning and engagement among participants, as well as the factors that support or inhibit this learning and engagement. Research will also explore the challenges and opportunities that emerged from the partnerships built across the network, as well as how the challenges can be addressed to foster more effective networked collaboration with the shared goal of STEM-rich making for innovation. Empirical findings will be shared widely through professional networks of STEM educational researchers and informal educators. This Integrating Research and Practice project is funded by the Advancing Informal STEM Learning (AISL) program, which seeks to advance new approaches to, and evidence-based understanding of, the design and development of STEM learning in informal environments. This includes providing everyone multiple pathways for accessing and engaging in STEM learning experiences. 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 significant portion of U.S. secondary school students participate in summer learning programs that include mathematics learning. In order to assess the effectiveness of such programs and identify their most impactful features, there is a need to understand the long-term impact of childhood summer learning on the lives of people well into adulthood. This project focuses on a site of a mathematics-centric summer program for secondary students founded in the early 1990s that has served over 3,000 students. The study focuses on former participants over the age of 25 to examine how the program influenced both mathematics and economic outcomes years and even decades after adolescence. The investigation of long-term outcomes such as participation in STEM careers and the economic measurement of life satisfaction will provide useful insights into strengthening the STEM trajectories and informing policies aimed to influence the lives of children in and beyond their participation in educational settings. Mathematics identity, or how someone thinks about themselves in relation to the practice of mathematics, has been shown to influence career decisions and mathematics course-taking trajectories, and thus is an important focus of this investigation. Studying the mathematics identities of past program participants will provide insights into the features of the program that are linked to long-lasting mathematics-related effects of the program, offering insights that support the design and redesign of mathematics learning spaces. The long-term impact of out of school mathematics learning experiences is understudied. This project extends beyond measures of program effectiveness that can be observed directly after program participation such as graduation rates, college enrollment, and test scores. This project includes long-term measures of success such as life satisfaction, sustained engagement in career pathways, and income attainment. In addition, this project introduces a new concept to STEM education literature in the form of long-term mathematics identity, providing an example for how STEM-related identities can be examined years after youth summer programming. This mixed-methods study will leverage interview and survey data of past participants. Participants will respond to the Cantril self-anchoring striving scale, a widely used scale by economists, to measure life satisfaction. Interviews will focus on participant retrospection and perceptions of the influence of their experiences in the program on their subsequent life experiences as well as their mathematics identity development trajectory since adolescence. Survey data will be analyzed using both descriptive and inferential statistics to determine patterns across variables. This approach to studying long-term mathematics identity and the consideration of economic variables will allow for a robust understanding of how lives of adult members of the nation’s workforce are impacted by STEM summer learning programs experienced in childhood. This study will provide insights on the features of the program that have long-term effects, which has implications for how both informal and formal mathematics learning environments can be intentionally designed. The project is funded by the Directorate for STEM Education (EDU) STEM Postdoctoral Research Fellowships (STEM Ed PRF) 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 · 2025-09

Ambiguous language is a common part of communication. It means using vague words or phrases that can be interpreted in multiple ways depending on the context. This project addresses how a question answering system might handle ambiguous questions about images where it is unclear which part of an image a question refers to. For example, if someone asks "What is the medicine?" while looking at an image showing several pill bottles, a system should identify all relevant parts of the image and provide answers for each so that a person receives the full picture and can resolve ambiguities later. Instead, current visual question answering (VQA) services typically provide people with one answer per question and do not explain their reasoning process for choosing the answer. This limits a person's ability to verify whether the desired interpretation was made. The possible repercussions from VQA services providing incomplete information can be grave, inflicting adverse personal, social, professional, legal, and financial consequences to VQA service users. In this project, we will develop a socio-technical solution to address the need for innovative approaches that empower people to recognize when there is question ambiguity and then resolve it. We will introduce the first back-end AI model that can specify every plausible image region that could be the focus of a question's language paired with natural language answers derived from those regions. We will also establish effective interaction designs within a user-facing tool that empowers people to recognize and resolve focus ambiguity in visual questions. Progress will be measured by evaluating the proposed AI model on our benchmark dataset and examining real users' experiences with this model when embedded within a larger VQA system. User studies will focus on blind individuals since they are the current dominant end-users for VQA services. More generally, we expect project success will benefit all VQA service users, whether visually impaired or sighted. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-09

This project develops AI technology that extracts key information from video recordings of public meetings. There is a wealth of information in video recordings of public meetings that could help all American people. To access, process, and utilize this information systematically, new artificial intelligence (AI) tools must be developed for use by individuals, decision makers and researchers. The project develops methods to identify and extract patterns in the way issues are discussed during public meetings. These methods also identify the types of issues raised by participants. The final products – a large public dataset and open-source code – support an interdisciplinary research community studying how people interact in civic contexts. Large language models have accelerated the computational social sciences, facilitating rapid conceptualizations and labeling frameworks. However, off-the-shelf methods fail to make sense of nuanced concepts and have been shown to have severe limitations in important contexts. To exploit the advances of new AI technology while overcoming its weaknesses, the project proposes a human-centered approach. The project develops technology which can leverage domain expertise to teach LLMs to make sense of the nuanced concepts at the center of this work. This project produces both data and open source methods to process it, with the data accessible to researchers and the public. The data are used to answer pressing scientific questions about the topics discussed in public meetings; the emergence of new topics of issue discussion; and topic importance. 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

Innovation in science and engineering contributes significantly to the nation’s economic growth, global competitiveness, and national security. The federal government identifies key areas of research, “Critical and Emerging Technologies (C&ET),” that are of particular importance to the nation. C&ET fields like Artificial Intelligence, Nuclear Energy, Quantum Computing, and Biotechnology are also areas of international competition. In recent years the federal government has taken steps to improve international research security to protect C&ET fields. Because C&ET research tends to be interdisciplinary, fast moving and to take place in many types of organizations – universities, companies, research institutes, national labs - it can be very difficult to identify all the research and researchers working in a particular field. This pilot project uses artificial intelligence (AI) and computational network methods and huge (100+ million) datasets of international patents, publications and grants to test new approaches to identifying critical and emerging technology researchers and research in Quantum Information Science (QIS) as a first step toward developing a validated approach that can be used for any critical and emerging technology to support research and evaluation of research security. This Early Concept Grant for Exploratory Research (EAGER) award approaches the problem of C&ET identification by treating it as a problem of community identification in complex evolving networks. This project will develop time aware multidimensional representations of an evolving international network of researchers pursuing work related to QIS based on international patents, scientific publications and research grants drawn from Digital Science’s Dimensions database. A precision-maximizing “seed set” of known QIS researchers will include authors on papers published in specialized QIS journals and inventors on patents in relevant Cooperative Patent Classification codes issued to companies participating in the US National Quantum Initiative. This group of researchers will serve as a labelled subset for the development and testing of semi-supervised models to identify additional QIS researchers in a multi-dimensional network comprised of Large Language Model (LLM) vector embeddings of patent, paper and publication abstracts, graph embeddings of co-authorship and co-inventorship, citation and journal/CPC co-publication/invention networks. Exploratory model development will progress from simple label propagation to deep learning graph neural networks with convolutional and attentional layers. Inclusion of variational autoencoder layers will test the possibility that adversaries may obscure key network ‘signals’ in pursuit of malign goals. Ablation studies across network and model layers relative to multiple validation datasets will identify the most effective mix of model accuracy and computational cost. All aspects of model and data development will be configured to allow expansion to other C&ET areas creating the potential for generalization and use across the full range of fields in future research on research security. 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

Dark matter is invisible, but its presence is detected via its gravitational effect on stars and galaxies. One powerful way to study dark matter is by looking at “tidal streams” that originate from globular clusters. These are long, thin trails of stars that are pulled away from dense clusters of old stars as they orbit the Milky Way. Tidal streams of stars can have a variety of shapes and thicknesses, and they can also have distortions in their shapes, such as “gaps”, “spurs”, or “cocoons”. These distortions contain information about the distribution of dark matter in the original dwarf galaxy where the globular clusters first formed, before the dwarf galaxy merged into the Milky Way. In this project, a team from the University of Michigan, Ann Arbor, will run a suite of computational simulations to understand how the underlying dark matter distribution affects the observed properties of globular cluster streams. The team will then use data from the Dark Energy Spectroscopic Instrument (DESI) survey to study the properties of Milky Way. As part of this project, the team will also support K-12 education by running a summer camp for high school students, where the students will learn about the forefront of astronomy and work with real astronomical data. Intriguingly, the detailed physical and velocity structure of globular cluster streams depends on the central dark matter density profile in the original dwarf galaxy in which the globular cluster was born. The goal of this project is to take advantage of this discovery by using a new spectroscopically selected sample of globular cluster tidal streams to probe the cusp/core problem in dwarf galaxies. The research team will combine advanced simulations, simulation-based inference and optimal experimental design and new observational data to explore this issue. The suite of N-body simulations to model how globular cluster streams form and evolve in different dark matter environments will be examined to determine quantitative metrics of stream “heating” and structure. These metrics will be compared with those measured for real tidal streams using new stellar velocity and chemical data from DESI. This comparison will help to constrain the dark matter density profiles of the original host galaxies of these globular clusters. 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

To grow to their incredibly large sizes, galaxies need to accumulate material from their surrounding medium. It is the gas that surrounds galaxies that provides the raw material to create stars and to fuel the growth of black holes. This program will detect the gas that surrounds galaxies and advance our understanding of how galaxies grow. The investigators will analyze data obtained with a new generation of astronomical instruments. The program will support the training of a postdoctoral scholar. This program will also enter a partnership with the Wolverine Pathways program at the University of Michigan. Wolverine Pathways is a well-established program that offers college preparation and academic development opportunities made available to every student in middle and high school in the Metro Detroit area. On long timescales, gas flows from the intergalactic and circumgalactic medium (IGM/CGM) are thought to fuel galaxy and Supermassive Black Hole (SMBH) growth. Energy and momentum released by quasars and young stellar populations couple to the interstellar medium to regulate gas cooling while expelling metals to intergalactic space. The IGM and CGM serve as reservoirs to fuel galaxy evolution while also containing a record of past feedback. The advent of wide-field integral field spectrographs (IFS) available on large telescopes revolutionized our ability to directly image IGM/CGM flows in emission, providing unique 3D (2 spatial + velocity) information along with critical spectral diagnostics of density, temperature, and even metallicity. This program will leverage wide-field IFS observations of 10 to 100 kpc scale flows around 84 archival quasars and 30 new ones at z = 0.2 to 1.5 to study galaxy and SMBH feeding and feedback during the period of “cosmic dusk,” over which star formation and quasar activity declined dramatically from their peak at z = 2 to 3. All datacubes analyzed for the program and corresponding catalogs will be made publicly available. 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 addresses the challenge of monitoring and predicting the behavior of granular materials, such as grains and pellets, that are processed in large volumes across the chemical, agricultural, and mining industries. The mechanical behavior of these materials in industrial operations is highly unpredictable due to external factors such as temperature and humidity, and internal factors such as particle degradation and fracture. Obtaining data is challenging in large industrial systems because they often span tens of meters and have strong spatial variabilities. The lack of predictive capability leads to significant economic losses and safety risks, including silo collapse and personnel entrapment during inspection procedures. The proposed research aims to develop robots with the ability to navigate through large granular systems, much like how animals burrow through soils. The robots will collect critical data and inform large-scale data-driven models that predict system responses to changes in operating conditions. The research will advance fundamental understanding of granular materials while introducing an innovative approach to enhance the energy efficiency, reduce waste, ensure safety, and benefit the welfare of the broader society. Collaboration between academia and industry will facilitate the transfer of fundamental science to industrial applications and offer hands-on experience for students at various academic levels. The technical goal of the project is to develop the mechanics for deploying self-propelling robots with sensing capabilities that continuously survey a large granular medium, infer material data representative of the entire system, and predict the behavior of unexplored systems. The team will first utilize 3D experiments, X-ray imaging, and simulations to investigate interaction dynamics between a deformable robot and various granular media. Physics-based analyses correlate the forces and deformations of the robot to the mechanical properties of the surrounding granular medium. This will allow the robot to survey key granular properties as it moves, such as material strains, pressure, and yield stress. Leveraging the data collected by the robots, a data-driven continuum mechanics model will be developed to predict the behavior of the granular system under varying operating conditions. Experimental test beds with embedded robotic sensors will validate this modeling framework. The proposed data-driven approach, combining granular intrusion physics and continuum mechanics, will be an important step for monitoring and simulating industrial-scale systems of complex materials where traditional phenomenological models face notable limitations. 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

Humans understand language rapidly and with remarkable ease. One way that humans do this is by predicting what a conversation partner might say next. But, there remain many unanswered questions about how different language backgrounds might help, or hinder, effective predictions. There is a critical need to understand proficient second-language comprehension. This project studies brain signals while bilinguals listen to an audiobook story in their first and second language. Computational modeling using artificial intelligence (AI) language systems are used to test the kinds of predictions people make, the information that guides those predictions, and how predictions are affected by differences in language background. This project offers insight into how AI can incorporate multiple languages in a realistic way and increases awareness of bilingual language with programs targeting future teachers and the public. Other benefits to society include increased transparency and reproducibility in language research by providing a large corpus of brain and behavioral data for other scientists and engineers. To meet these aims, the project collects electroencephalography (EEG) signals from three groups of bilingual participants with different levels of experience while they listen to an audiobook story. These signals reflect fast-changing brain responses and are highly sensitive to expectations in language. AI is used to capture the linguistic features of the story, such as the relationships between nouns and verbs in a sentence and the predictability of upcoming words. Statistical analyses are used to test the alignment between these features and brain activity, showing which features best capture brain activity and how this may be different for different language backgrounds. By training computational models with different amounts of exposure to one or more languages, the project further tests how the statistical properties of different languages modulate brain responses of multilingual language users. 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

Understanding planet formation requires knowledge of the chemical composition of the planet-forming material. The project will explore the chemistry of hot gas around protostars, where the first steps of planet formation are occurring. They will utilize high spatial resolution telescope observations along with chemical theory. The overall objective of this project is to identify key characteristics of the chemical composition of the material from which Earth-like planets form. The project will primarily support two graduate students and one postdoctoral researcher. The project will develop a series of nine astronomy lesson plans designed specifically for elementary school teachers, aimed to engage children with science at an early age. The lesson plans will also be developed in video format to be made available through portals accessible to homeschool teachers and those in classroom settings. The objective is to identify key characteristics of the chemical composition of terrestrial planet forming material, that is, material in the inner region of proto-stellar systems where the temperature is >300 K. The project will use extant ALMA data and accepted ALMA programs to directly characterize and contrast the content of both warm and hot gas. These results can be compared to existing models of bottom-up chemistry to quantify the abundance of hydrocarbons. The team will also use the ALMA data to search for distinct products of this chemistry via sophisticated line stacking and search techniques. The project will expand existing chemical networks for organic chemistry (UMIST and KIDA), with hot gas hydrocarbon chemistry and develop a 2D chemical proto-stellar disk calibrated model to identify the driving processes behind the chemistry in hot proto-stellar gas. 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

Dynamical systems are ubiquitous: they govern the motion of the planets, the weather, the stock market, and the ecosystems. These systems depend on different parameters, and as these parameters change, the corresponding system changes. Sometimes there are special values of the parameters for which the corresponding dynamical system is relatively simple. In complex dynamics, these are known as postcritically finite parameters. The postcritically finite parameters form a thin but sprawling collection that helps the understanding of the global structure of the parameter space. This project aims to extensively study the postcritically finite parameters and record several essential data that uniquely encode them. Broader impacts of the project are through mentoring at all levels, including a Math Corps summer camp for middle school campers and high school mentors from Ypsilanti, Michigan. In complex dynamics, one typically studies rational maps on the Riemann sphere from the point of view of iteration. A main principle in the subject asserts that in order to understand the dynamical behavior of a rational map, it is necessary to study the orbits of the critical points of the map. Those rational maps for which all critical points have finite forward orbits are quite special; they are known as postcritically finite rational maps. These maps possess only repelling and superattracting cycles, and their Julia sets are locally connected. Furthermore, (excluding well-understood exceptional cases), postcritically finite maps are inherently algebraic: every map can be conjugated by an automorphism of the Riemann sphere so that the resulting map has algebraic coefficients. In the moduli space of rational maps of a given degree, the postcritically finite locus can be used to study the geometry of this space. This project aims to investigate and explore dynamical data associated to postcritically finite maps. The projects in the proposal incorporate complex analysis, Teichmueller theory, topology, and geometric group theory to better understand these 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 · 2025-09

This grant will support research that looks to contribute to new knowledge related to the nonlinear dynamics of acoustic cavitation enhanced by vortex ultrasound. Acoustic cavitation is the process of bubble formation, oscillation, and collapse in a liquid exposed to intense ultrasound. Acoustic cavitation has been widely used in the biomedical field for tissue ablation, thrombolysis, drug delivery, etc., and in industry for metal cleaning in manufacturing processes. Vortex ultrasound is a type of acoustic wave with a helical wavefront rotating as the wave propagates. Previous studies of vortex ultrasound-induced acoustic cavitation in deionized water show that vortex ultrasound can induce acoustic cavitation at a much lower intensity threshold and with stronger cavitation activity than focused ultrasound. However, the fundamental mechanism(s) behind the lower intensity threshold and stronger cavitation activity driven by vortex ultrasound are still unknown. Without this important knowledge, it is challenging to use vortex ultrasound reliably and safely in the suggested biomedical and industrial applications. This research project seeks to understand the fundamental bubble dynamics driven by acoustic cavitation induced by vortex ultrasound and the mechanism behind the reduced cavitation threshold, as well as enhanced cavitation activities. A novel theoretical model of bubble dynamics driven by vortex ultrasound looks to be developed and verified by experimental measurements underwater with and without microbubbles or nanodroplets as ultrasound contrast agents. The results from this research seek to advance knowledge in acoustics, dynamics, fluid mechanics, as well as biomedical engineering, and can potentially lead to novel medical therapy with better efficacy and safety and industrial cleaning with less power consumption. Students at all levels, including graduate and undergraduate students as well as K-12 students, will participate in the interdisciplinary research, which will help train the next generation of leaders in science and engineering. The objective of this research is to create a new theoretical model that will provide a precise characterization of acoustic cavitation dynamics driven by vortex ultrasound and validate the model through experimental studies via high-speed camera tracking, infrared temperature mapping, and underwater acoustic measurements. This research project looks to understand the fundamental mechanism(s) that lead to the reduced cavitation threshold and enhanced cavitation activities driven by vortex ultrasound compared with conventional focused ultrasound. The central hypothesis of this research is that the strong shear effect induced by the large in-plane pressure gradient perpendicular to the propagation direction of vortex ultrasound enhances bubble formation and collapse in acoustic cavitation. This hypothesis will be tested by a newly developed theoretical model as well as experimental characterization in degassed, deionized water. The cavitation bubble dynamics driven by vortex ultrasound will be investigated using lipid-encapsulated microbubbles. The bubble formation and phase transition driven by vortex ultrasound will be studied using phase-transitioning nanodroplets. 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

Antimicrobial chemicals, called quaternary ammonium compounds (QACs), are commonly used to clean indoor spaces. QACs stay behind on surfaces for different lengths of time and are reactive. Their chemical structure, toxicity, and their fate in the indoor space may change over time. This project will measure the chemical reactions involving QACs in model indoor environments to understand the rates of reactions and the products that form. The study will also model where the newly formed chemicals might travel indoors and their toxicity to humans. The results of the project will help design safer cleaning methods to use in various indoor environments, which will support improved health for Americans. Quaternary ammonium compounds (QACs) are a class of chemicals in commercial products such as surfactants, anti-microbial agents, and emulsifiers. These chemicals have negative health impacts in the form of skin irritation and work-related asthma, and there is growing concern about the role they play in antimicrobial resistance. This project will measure the oxidation kinetics and reaction products formed for representative QAC compounds due to atmospheric aging processes indoors. The products are important because they may have different behaviors and health impacts compared to the starting compounds. The project focuses on heterogeneous oxidation of QAC-containing aerosol particles and indoor surface films. The study will evaluate the oxidation of representative QAC compounds using OH radicals and/or ozone to test the hypothesis that condensed phase radical cycling in aerosol particles and indoor surface films increases the oxidation rate of QACs except when the other chemicals in the mixture form a more viscous film that protects the QAC. The loss of the QACs and the formation of products will be measured using Liquid Chromatography/Mass Spectrometry and Gas Chromatography/Mass Spectrometry. The fate of the QACs and their products will be modeled indoors using mass balance and estimates of partitioning constants in different indoor compartments. By measuring loss rates and products in representative indoor environments, the study will provide an extensive picture of the lifecycle of QACs. The outcomes of this work could lead to recommendations for optimum QAC structures and cleaning methods that meet the needs for cleaning but minimize impacts on human health and the environment. The project will support development of learning modules in a large undergraduate class at the University of Michigan that focuses on mass balance. These will include lecture materials, a classroom demonstration, and online materials that introduce students to modeling software and guide them in implementing the tools to generate a model of pollutant persistence indoors. These educational tools will be published in an educational journal for Environmental Engineering and Environmental Chemistry undergraduate instruction. The activities will contribute to a future workforce in STEM in the United States. 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 Faculty Early Career Development (CAREER) grant supports research in data-driven robotic three-dimensional concrete printing to create materially efficient reinforced concrete building elements, enabling the rapid construction of multistory buildings and addressing urgent housing shortages. Current attempts at such printing suffer from a limitation of the printer’s depositing material only in layers parallel to the ground, restricting its application to simple forms and making it incompatible with the production of materially efficient parts that entail complex geometries. This technological limitation is exacerbated by the need for constant human monitoring and tuning of the process to eliminate defects. As a result, current applications are limited to single-family houses with simple geometries that use more concrete, not less, than if manufactured using traditional formwork methods, thus limiting widespread adoption by the industry. This project looks to develop new printing methods that reliably deposit material in complex geometries inherent to materially efficient parts. This research intends to transition its methods into education to combat the significant decline in youth interested in construction careers. The project strives to prepare future generations through courses in robotic three-dimensional printing, data modeling, and machine learning. It will engage youth through interactive puzzles and digital robotic workshops It positions the U.S. as a leader in robotic construction technologies by fostering patents and startups that drive economic growth and innovation. This CAREER project supports research that aims to develop scientific principles for waste-free, extrusion-based robotic three-dimensional concrete printing, to account for the geometric complexity of components, and the rapid time-dependent evolution of material rheology. A significant challenge lies in bridging the knowledge gap that connects three key system attributes: complex part geometries, part performance while being printed, and robotic printing process parameters. To advance the state of knowledge in robotic concrete printing, this project intends to create a novel data-driven, multi-objective optimization model incorporating machine learning that can generate and predict (1) optimal non-planar slicing and (2) robotic printing instructions for optimal part performance during printing. To drive this model, the research strives to create (i) a new slicing algorithm that is robust and generalizable for complex parts; (ii) a series of empirical models derived from real-time process data that describe process-part interactions; (iii) a framework linking empirical and established models to develop performance indexes (e.g., buildability index) as objectives optimization; and (iv) a new data-collection framework to obtain the data needed for the modeling and learning, as well as for evaluation and verification. If successful, the project will develop new frameworks and models integrating geometry, materials science, and robotics to lay the foundation for advanced data-driven, large-scale additive manufacturing. 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

Mammals evolved from a reptile-like ancestor that used its limbs and tail together for walking and running. In early mammals, however, the limbs moved independently from the tail. This allowed mammal tails to evolve entirely new functions or to disappear in species like humans and other apes. Mammal tails play essential roles in movement, social interaction, energy storage, and protection. These many functions are enabled by variation in the shape, size, and number of individual vertebrae. Yet, little is known about how such variety arose during mammal evolution, how different tails develop from embryo to adult, or how different bone-tendon-muscle connections determine how a tail is used. This interdisciplinary research program will answer these questions and provide training for students and postdoctoral fellows across three laboratories. The research will also inspire an interactive exhibit in collaboration with the University of Michigan Natural History Museum. This exhibit will include 3-D printed mammal tails, representing both real and imaginary forms, strung with cables that will allow visitors to explore how tails function. In doing so, visitors will gain an intuitive understanding of how changes in tail anatomy favor specific uses. This collaborative proposal leverages over ten years of synergy among the research team members, combining insights from phylogenetic comparative models, evolutionary developmental biology, and biomechanics. In Aim 1, a broad survey of tail morphology in extant and extinct mammals will determine how the modularity of tail shape evolved. The evolution of tail morphology across mammals will be modeled to test whether speciation rates and ecological adaptation influenced tail variations. These insights will pinpoint specific evolutionary and ecological factors that shaped tail differences. In Aim 2, studies address a gap in our understanding of the genetic mechanisms that drive this variation. Laboratory mice and bipedal jerboas will be used to elucidate the genetic controls of vertebral elongation, which will shed light on the broader evolutionary influences on tail morphology across mammals. Finally, in Aim 3, robotic models will be used to understand how inter- and intraspecific variation in vertebral proportion affect tail function. This approach seeks to understand the functional implications of varying tail designs, linking physical attributes directly to their ecological, evolutionary, and genetic origins. The work has potential for the development of new biotechnology particularly in the field of robotics. This project was co-funded by the Physiological Mechanisms and Biomechanics Program and the Developmental Systems Cluster in the Division of Integrative Organismal Systems, the Systematics and Biodiversity Science Cluster in the Division of Environmental Biology, and by 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

The prevalence of matter over antimatter is one of the most important unexplained observations in physics. As currently understood, the laws of physics predict that there should be an equal amount of matter and antimatter, which is at odds with our everyday experience and detailed astronomical observations. Such an inconsistency suggests that our current understanding of the laws of physics may be incomplete. ALPHA is an interdisciplinary antimatter experiment at the European Organization for Nuclear Research, known as CERN, that tests this notion by producing antihydrogen and sensitively measuring its properties in comparison with the hydrogen atom. These experiments are improved by the efficient conversion of collected antiprotons and positrons into antihydrogen. Trapping antimatter to produce antihydrogen is a plasma physics problem, consisting of collecting and manipulating large collections of charged particles using electric and magnetic fields. This award supports a joint effort between the University of Michigan and Marquette University, in collaboration with Brookhaven National Laboratory, that will advance the understanding of the novel plasma physics processes expected in antimatter traps and will conduct experiments using ALPHA to test the predictions. This project will also contribute to developing the next generation of the science and technology workforce by supporting the training of undergraduate and graduate students, and will contribute to local education and science engagement activities, including development of a plasma physics exhibit for the Discovery World Museum in Milwaukee, WI. Trapped antimatter is novel from a plasma physics perspective, as well as a particle physics perspective. These plasmas are so cold, and the magnetic field in the trap is so strong, that they exist in a state that is not well described by the usual models of plasma physics. Specifically, the low temperature causes the plasma to be strongly coupled, which means that it behaves more like a supercritical fluid or a liquid, than the more common dilute-gas-like behavior. The strong applied magnetic field, in combination with the low density, causes the plasma to be strongly magnetized in the sense that the circular gyromotion that charged particles make in response to the magnetic field is much smaller than the scale over which particles interact. Currently understood methods of plasma theory do not apply in either of these circumstances. This award will enable continued development of theoretical approaches that extend plasma theory into these domains, and then testing them by conducting two specific experiments on ALPHA that will measure (1) the rate of temperature relaxation between electrons and antiprotons, which is predicted to be delayed by strong magnetization of the electrons, and (2) the sympathetic cooling rate of positrons with other particles, such as Beryllium ions and protons. An expected outcome is that a better understanding of the underlying plasma physics may be used to improve ALPHA experimental operations by increasing production rate of antihydrogen atoms. 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 will develop a sub-millimeter scale robot with sensing and movement capabilities inspired by the amoeba. Like the amoeba, this robot will be able to follow biological and chemical traces in the surrounding environment by extending and contracting its body. The robot will be distinguished by its construction from uniformly soft composite materials, and by a seamlessly integrated information-processing system for converting sensed chemical signals into motion commands. The developed robot will have applications for minimally invasive medical diagnostics as well as structural inspection in confined spaces. This project will develop a micro hydrogel crawling robot with novel capabilities in selective electrochemical sensing, neuromorphic control, and thermal actuation, specifically enabled by (1) a hydrogel-MXene skin capable of detecting electrochemical changes in its surroundings; (2) 3D micro thermally activated hydrogel-nitinol actuators to power swimming and crawling gaits; (3) a functional hydrogel skin to facilitate thermal transport and interfacial friction reduction; and (4) neuromorphic circuitry incorporating MXene-hydrogel memristor elements to learn, compute, and control the sensorimotor connection. The design approach is inspired by the amoeba, whose behavior is governed by biochemical pathways linking chemical sensing and actuation mechanisms. Like the amoeba, the robot developed under this project will be capable of multimodal sensing and feedback-controlled motion in complex environments with unstructured sensing signals. The performance of the robot will be demonstrated for minimally invasive biomedical diagnostics and confined-space inspection applications. 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

Viruses experience frequent small random changes in their genetic material, their genomes. Because many of the changes in the viruses that infect one animal happen independently of those that happen in another animal, one can compare the genomes of sampled viruses to glean information about how far and fast an epidemic is spreading. This is known as phylodynamics. This project will develop new mathematical and computational tools to allow us to extract more information about how a virus is moving through a population of animals from virus genomes. Specifically, recent mathematical breakthroughs allow us to understand more precisely how aspects like virus transmission, severity of disease, and duration of immunity—and differences among animals in these aspects—leave their marks on virus genomes. The project will capitalize on these developments, along with recent advances in machine learning technology and the world’s premiere database of avian influenza virus genomes, to reduce some of the key uncertainties about how this virus spills over from wild birds into domestic animals, and potentially into humans. The project is expected to benefit public health by helping us better understand how avian influenza spreads and where the greatest risk-points are by increasing the usefulness of a very common kind of data. The mathematical and computational tools developed will also be useful in other scientific and medical fields, including cancer biology and microbiology. The project will develop a short-course for training epidemiologists and mathematical biologists in phylodynamic methods. Phylodynamics seeks to extract information from genomes of individuals to shed light on population-scale dynamic processes. Its development has largely been driven by applications in epidemiology, where pathogen genomes contain information concerning determinants of disease transmission. In this context, phylodynamics has become essential in guiding public-health response in epidemics at a variety of geographical and temporal scales. From the mathematical point of view, the aim of phylodynamics is to infer the structure and parameterization of mathematical models of demographic processes on the basis of accumulated differences among sampled genome sequences. Existing approaches rest on assumptions (large population sizes, small sample fractions, linearity of demographic processes) that are becoming increasingly dubious as the intensity and volume of genomic sampling grows and as phylodynamic methods are increasingly being applied at the leading edge of emerging outbreaks and in the face of strong nonlinearities. The project will develop accurate, scalable inference methods with minimal theoretical restrictions, based on recent mathematical advances by the project team. The first builds on mathematical breakthroughs that permit precise estimation of dynamic models from reconstructed phylogenies, while the second seeks to bypass the need for phylogenetic reconstruction altogether by applying new machine learning methods to structured genome-alignment data. Data from the world’s premiere database on avian influenza genomes will be used to resolve outstanding uncertainties regarding transmission within different host species, spillover rates, and seasonality in this system. The work will have applications beyond epidemiology in fields such as systematic biology, cancer biology, microbial ecology, and population genetics. 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

Prototyping is a crucial activity in engineering solution design and development. Engineers regularly build multiple prototypes not only to test functionality, but also to explore ideas, communicate with teammates and stakeholders, learn from failures, and guide, decision-making throughout the design and development process. However, engineering undergraduate students often adopt a narrow view of prototyping, treating it primarily as a final step to verify that their design works. This limited perspective means that students may miss out on the deeper value of prototyping as a powerful tool for creative thinking, iterative problem-solving, and collaboration. In order to prepare engineering students to be effective and innovative engineers, we must better support them in understanding and using prototypes in the many ways they can support engineering work. Thus, this project will investigate how faculty teach prototyping, how students actually use prototypes in their coursework, and what factors support or hinder more expansive, reflective prototyping practices. This work will advance the national goal of improving engineering education by better aligning classroom experiences with the realities of professional practice. By studying both educator intentions and student experiences, the project will develop practical strategies that instructors can use to help students see prototyping not only as a technical skill, but also as a mindset and process for learning, communicating, and designing with purpose. This project will investigate the goals and practices of engineering educators as well as the behaviors and experiences of students with regard to prototyping in design-focused courses. Grounded in Social Cognitive Theory and the Prototyping for ‘X’ framework, the study will be guided by the following research questions: RQ1a) What goals do engineering faculty report having for how their students use prototyping? RQ1b) How do their pedagogy and assessment align (or not) with those goals as reported? RQ2a) How do students report using prototyping in their design projects? RQ2b) What factors do students report affecting how they use prototypes? Insights from this research have the potential to understand and address the current gaps in students’ use of prototypes and how engineering faculty goals and challenges and the classroom environment and experiences they create influence the ways that students prototype. 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

Performance assessment in the workplace, whether a hospital, school, or lab setting, can be challenging. The purpose of this project is to enhance performance assessment in professional learning environments, particularly in surgical education, by developing novel, transparent, and rigorous statistical models. Medical education is increasingly using "micro" performance assessments that are collected over time and consist of one to three rating scales to evaluate a trainee's performance following a clinical activity. Despite their growing use, synthesizing multiple assessments into summary scores, integrating these summary scores with other evidence sources, and translating scores into acceleration, remediation, and certification decisions remain challenging. Identifying which trainees are struggling and why requires new ways of combining data to help the right individual at the right time and in the right ways. The approach developed in this project will help ensure that surgeons are better prepared for independent practice, ultimately leading to better patient outcomes. Overall, this project will contribute to a broader understanding of performance assessment in professional learning, enhancing public trust in newly certified professionals. This project aims to enhance performance assessment methodologies in surgical education and beyond using Bayesian inference networks. Bayesian networks are flexible and efficient probabilistic models that can be used to synthesize longitudinal data, enabling just-in-time evidentiary reasoning to drive educational decisions. In the context of surgery education, this project will summarize micro performance assessments using dynamic Bayesian networks, combine multiple sources of evidence into a quantitative professional profile using tiered Bayesian networks, and identity barriers and facilitators to collecting, analyzing, and reporting on multiple sources of assessment evidence using implementation science frameworks. Combining rigorous analytical techniques with implementation science will aid in identifying strategies to help ensure reliable assessment processes are carried out, score reports are useful to end-users, and educational interventions are tailored to meet the needs of developing professionals. This project is funded by the Research on Innovative Technologies for Enhanced Learning (RITEL) program that supports early-stage exploratory research in emerging technologies for teaching and learning. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-09

This project advances the theory and methods underlying the development of computationally lightweight control algorithms that ensure the safe and reliable operation of autonomous systems in safety-critical applications. Such methods can benefit a broad range of industrial uses, including autonomous drone delivery, robotics, manufacturing, aerial and ground transportation, autonomous driving, and precision agriculture. To fully realize their benefits, autonomous systems must be capable of making reliable decisions in real time while operating in complex environments with rapidly changing constraints. This is especially challenging because modern autonomous systems are often designed to reduce cost, weight, and energy consumption, which limits their onboard computing capabilities. To address these challenges, this project pursues a systematic and theoretically justified framework, grounded in extensions to Control Barrier Function (CBF) methods, that enables the design of control algorithms ensuring the satisfaction of safety constraints even when computational resources are severely limited. Control Barrier Functions (CBFs) hold significant promise for addressing constrained control challenges in nonlinear systems and for providing computationally lightweight solutions that ensure the safety and reliable operation of autonomous systems. At the same time, systematic design procedures for CBFs are currently limited to specific classes of systems and constraints. To provide CBF-based solutions for systems operating in environments where operating conditions and constraints can change rapidly, this research will expand the applicability of CBFs through the development of a novel class of CBFs parameterized by the reference command. The project will establish a rigorous theoretical foundation for such parametric CBFs and their onboard implementation. Methods for enhancing onboard computations to ensure real-time computational feasibility will be developed. The advances will be pursued by integrating techniques from control theory, set invariance, machine learning based on neural networks, and computational optimization algorithms grounded in robust-to-early-termination optimization. The outcomes of this research will include methods, algorithms, and theoretical guarantees that support their application. The proposed methodologies will be validated through both simulations and real-world case studies, such as drone delivery applications, to demonstrate their practical effectiveness and potential for real-world technological impact, ultimately benefiting the U.S. economy and society. 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

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

NSF Awards · FY 2025 · 2025-09

Electromagnetic waves with frequencies in the ultra-low frequency (ULF) range are known to be among the leading causes of radial diffusion and transport of energetic electrons in Earth's radiation belts. The frequencies of ULF waves overlap with the range of drift frequencies of energetic electrons as they circle the Earth, leading to resonant interactions. Numerous expressions have been derived to quantitatively describe radial diffusion so that they can be incorporated into global models of radiation belt electrons. However, most expressions of the radial diffusion rates are derived only for equatorially mirroring electrons and are based on estimates of the power of ULF waves that are obtained either from spacecraft close to the equatorial plane or from the ground. Recent studies using the Van Allen Probes and Arase have shown that the wave power in magnetic fluctuations is significantly enhanced away from the magnetic equator, consistent with models simulating the natural modes of oscillation of magnetospheric field lines. This has significant implications for the estimation of radial diffusion rates, as higher pitch angle electrons will experience considerably higher ULF wave fluctuations than equatorial electrons. This project will derive the magnetic and electric field wave powers and incorporate them into the 3D test particle simulations to estimate the diffusion coefficient. The novel, pitch-angle-dependent diffusion rates will be introduced to a global model of radiation belt electrons to evaluate the effect of the pitch-angle dependence of the diffusion coefficient on radiation belt dynamics. The result could have significant implications for the radial diffusion rates as currently estimated. It will pave the way for incorporating pitch-angle-dependent radial diffusion coefficients in global models to predict the near-Earth radiation environment better. The main goal of this project is to quantify the role of off-equatorial Ultra-Low Frequency (ULF) waves on the radial transport and diffusion of relativistic electrons (100s keV to few MeV) in the outer radiation belt (L~4 to 7), investigating the effect of pitch-angle-dependent radial transport of energetic particles on global dynamics of the radiation belts. The following science questions will be answered: How are ULF electric and magnetic field fluctuations distributed in magnetic latitude and magnetic local time under varying solar and geomagnetic conditions? What is the role of off-equatorial ULF wave fluctuations on the radial diffusion and transport of relativistic electrons in the outer radiation belt (L~4 to 7)? How are off-equatorial ULF waves expected to impact current radiation belt models, and what is their contribution to the global dynamics of the radiation belts? The team will use multiple satellite datasets (THEMIS, Van Allen Probes, Cluster, and Arase), test particle tracing simulations, and a global radiation belt model to quantify the contribution of off-equatorial ULF waves on radial diffusion in the radiation belts. 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.