West Virginia University Research Corporation

NSF Awards · FY 2026 · 2026-10

Artificial intelligence is increasingly used to support decision making in healthcare, especially in time-sensitive settings such as emergency care and intensive care units. However, people do not always rely on these systems appropriately. Some users may place too much trust in incorrect recommendations, while others may ignore useful guidance. These mismatches can affect decision quality and patient safety. This project studies how people interact with artificial intelligence in such settings and explores ways to support more appropriate use. By improving how clinicians interpret and respond to artificial intelligence, the work aims to support safer and more reliable decision making. The project also contributes to education by engaging students in simulation-based learning and providing training opportunities in human-centered artificial intelligence. This project develops a framework to study how trust in artificial intelligence changes over time during decision making. The research combines behavioral data with physiological signals, including eye movements and brain activity, to better understand user responses. First, a mathematical model is developed to represent trust as a changing internal state influenced by task conditions and system performance. Second, machine learning methods are used to estimate this state in real time using data collected from clinicians interacting with simulated clinical scenarios. Third, the project explores interface strategies that provide targeted feedback to help users better align their decisions with the reliability of the system. These approaches are evaluated through controlled simulation studies with clinician participants. The project will generate data, models, and open resources to support future research on human interaction with artificial intelligence. 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 2026 · 2026-06

This award to the North American Nanohertz Observatory for Gravitational Waves (NANOGrav) supports their goal of characterizing the low-frequency gravitational-wave (GW) Universe through radio timing observations of precise celestial clocks called millisecond pulsars. NANOGrav has already discovered the telltale spatial correlations among pulse arrival times expected from a stochastic background of GWs. This background is likely to have a significant contribution from the supermassive black hole binaries formed when galaxies merge. Future NANOGrav measurements of the properties of the stochastic background will place unique constraints on the growth and evolution of galaxies through cosmic time. There may also be background contributions from more exotic sources such as cosmic strings, phase transitions in the early universe, and relic GWs from inflation; detecting any of these would be transformative and could dramatically change our understanding of the Universe. This award will enable NANOGrav to construct a more sensitive data set that will allow constraints on the origin of this stochastic background, and searches for individual supermassive black hole binaries, whose detection will herald a new era of multi-messenger nanohertz GW astrophysics. This work requires the synergy of experts in gravitational physics, data analysis, and astrophysics coming together in a collaborative environment. In addition to monitoring a known list of millisecond pulsars, NANOGrav routinely discovers new MSPs to add to their study. NANOGrav observations also enable high-impact synergistic science, including constraining the dense matter equation of state, making dynamical tests of general relativity, further understanding the ionized interstellar medium, and discovering exotic neutron star systems and radio transients. NANOGrav reaches large numbers of the general public through exhibits and talks, as well as a growing social media presence. An associated student research program, NANOGrav STARS, reaches more than 100 undergraduate students every year, and the Pulsar Science Collaboratory involves hundreds of middle and high school students and their teachers. This award is supported by the Physics Section and the Astronomical Sciences Section within the Mathematical and Physical Sciences Directorate. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2026 · 2026-05

This award supports development of a novel quantum sensing diagnostic for studying plasma properties near solid surfaces. Plasmas are used in technologies that matter in everyday life, from computer chip manufacturing, to spacecraft propulsion systems, to potential new energy production facilities. When a plasma touches a solid surface, a very thin but complex electrical layer called a plasma sheath is formed. The properties of such sheaths and the interaction between the plasma and a surface are not yet fully understood. This project will develop new laser-based measurement methods to study how plasma sheaths form and how they may be controlled. The gained knowledge will help make important technologies more efficient, more reliable, and longer-lasting, with benefits for areas ranging from semiconductor manufacturing to fusion energy research. This award will also support the training of graduate and undergraduate researchers at West Virginia University, the development of a new advanced undergraduate laboratory course module in plasma physics, and expand outreach and opportunity to students from rural and underserved communities across West Virginia. Plasma–boundary regions will be investigated in this work to determine how electron emission from a surface modifies ion flows, electric fields, and potential structures from the plasma bulk through the plasma sheath to the surface. Particular attention will be given to strongly emitting boundaries, where transitions from classical to space-charge-limited and inverted sheath structures are predicted but have been only sparsely examined experimentally. High-fidelity, nonintrusive, spatially resolved measurements will be obtained by combining laser-induced fluorescence to measure ion velocity distributions with quantum beat spectroscopy to map electric fields and infer potential profiles without the use of intrusive probes. These measurements will be used to determine whether inverted sheath structures form under standard low-temperature plasma conditions, to identify how ion flows to a surface are influenced by tunable electron emission, and to quantify whether strongly emitting boundaries can be controlled to minimize ion flux under conditions relevant to plasma applications. The resulting data are expected to test long-standing sheath theory, validate and inform models of plasma–material interaction, electric propulsion, and low-temperature plasma processing, and establish a broadly applicable diagnostic toolkit for nonintrusive boundary measurements across a range of plasma environments. 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 2026 · 2026-04

This proposal seeks to fund US-based students to attend the 2026 IEEE International Conference on Communications (ICC), held in Glasgow, Scotland on May 24 - 28, 2026. IEEE ICC 2026, is a premier annual forum that attracts high-quality, forward-looking research contributions and provides a vibrant forum for technical and professional exchanges. IEEE ICC 2026, will expose selected students to cutting-edge developments in the field and enable interactions with world-leading researchers. The conference will feature presentations on works aligned with NSF priorities, including next-generation 6G and beyond wireless networks; security, trust, and privacy; generative AI and Large Language Model (LLM) empowered NextG networks; and quantum communications. Students will gain feedback on their ongoing work, broaden their academic perspectives, and build lasting professional connections. This project supports students from US universities to attend the IEEE ICC 2026 conference in person. Students will have the opportunity to present their work and be exposed to state-of-the-art developments in the field. They will also have the opportunity to interact with peers from institutions worldwide, meet with senior researchers, and participate in discussions that are likely to shape the future of the field. This grant supports students who will substantially benefit from attending this conference but have limited travel funds. 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 2026 · 2026-01

The NSF ECR Building Capacity of STEM Education Research (BCSER) program contributes to the NSF mission 42 U.S. Code Chapter 16 by building the US workforce undertaking STEM education research. The BCSER Individual Investigator Development in STEM Education Research (IID) track supports individual investigators who are new to STEM education research to develop foundational skills and gain practical experience to advance STEM education knowledge through mentored professional development and pilot research activities. STEM education research generates the knowledge, theories, and understandings on which viable strategies for improving STEM education and workforce outcomes are based. This project will integrate virtual reality to enhance self-directed STEM undergraduate learning for civil engineering students. The study supports ABET student outcomes, reinforcing professional responsibility in engineering education. This BCSER IID project will allow the PI to develop foundational skills and gain practical experience in designing and implementing cutting-edge STEM education research using innovative methods and tools. The project will help the PI to develop new expertise in STEM education research and technology-enhanced learning, working with a mentor and advisory board consisting of academic and industry experts on technology integration and instruction design, as well as industry practices within the construction domain. 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 2026 · 2026-01

Any system that is required to respond to stimuli from its environment is called a reactive system. The system’s response is possibly to change its status or conditions and even affect its environment. Hence, a reactive system is an event-driven system reacting endlessly to external stimuli. It maintains an ongoing interaction with its environment, changing its responses accordingly. For instance, a communications protocol is a system that must respond to each stimulus, even to a fragment of input, such as a disrupted received message. In several real-world important applications, reactive systems have to handle input that is immense. Moreover, the domain of possible inputs is unpredictable. It changes continuously due to the interplay between the environmental stimuli and the responses of the system. Reactive systems are widely used to handle and control critical procedures. Air traffic control systems, programs navigating robotic devices (e.g., trains, planes) and systems controlling nuclear reactors or chemical plants processes are typical examples of such applications. Because of the massive and unpredictable input that they must handle, and the crucial nature of the tasks they perform, the primary objective of reactive systems is to be able to respond to any input given to them at any time. In the event of failures, the results may be catastrophic, depending on the nature of the task the reactive system performs. These may include critical data or information losses, economic or environmental disasters, injury or even death. Therefore, reliability and dependability are essentially the most important qualities of reactive systems. In this project, we analyze optimization problems where the solution is guaranteed to be robust against changes and disruptions in the environment. This project is concerned with modeling uncertainty through the use of mathematical programming. The models examined in this proposal consist of a set of linear constraints, together with a quantifier string. In this string, each variable is either existentially or universally quantified. We look at both the continuous and discrete versions of this problem. The continuous version of this problem is known as Quantified Linear Programming (QLP). Whereas, the discrete version, specifically the version in which each variable must take an integer value, is known as Quantified Integer Programming (QIP). The project investigates the problem of optimization in quantified linear programming. The mathematical programs utilized in this project can also be used to model problems in the field of adversarial robotics. Problems in this field are concerned with the deployment of robots to detect threats in an adversarial environment. Two such problems are the Robotic Adversarial Coverage (RAC) problem and the Closed Perimeter Patrol (CPP) problem. In the RAC problem, robots are deployed to visit nodes in an area with the purpose of identifying the threats within the area. Since the purpose of the deployment is to identify threats, the robot must visit each possible threat. However, visiting a threat may result in the loss of the robot. In the CCP problem, robots are deployed along a fixed perimeter with the purpose of detecting threats. The goal is to determine a patrol policy for each robot that will maximize the probability that an incursion is detected. This project utilizes mathematical programs to develop solutions for both of these 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 · 2025-12

This project is funded by the Research Experiences for Undergraduates (REU) Sites program in the SBE Directorate. It has both scientific and societal benefits and integrates research and education. The REU Site for Translational Research in Behavior Science provides aspiring behavior scientists with high-quality research experience and mentorship. This project supports the development of a skilled workforce by fostering interest and participation in translational research in experimental psychology and preparing students for careers in science, education, and healthcare. REU participants conduct laboratory research in behavioral psychology and neuroscience. Research conducted for this project addresses questions relevant to NSF priority areas, including translational research and the science of public safety. Specifically, REU participants will conduct translational research in behavioral pharmacology, behavioral neuroscience, relapse prevention, prosocial behavior, behavioral approaches to injury rehabilitation, crime prevention and investigation, and improving the efficacy of behavioral interventions. Students present their work at public research symposia and are trained to communicate findings to scientific and general audiences. Their studies advance knowledge of behavioral processes and their application to socially significant challenges. Recruitment is open to NSF-eligible undergraduate students focusing on reaching students from institutions with limited research opportunities. This project aims to foster interest and participation in experimental psychology and expand the pool of skilled researchers in behavior science. The objectives include: (1) Providing research opportunities to students from primarily undergraduate-serving institutions, (2) Involving students in impactful research that advances behavior science, (3) Improving students’ technical research skills (4) Developing students’ scientific and broader communication skills, (5) Retaining students in behavior science education and careers. Each year, a cohort of 8 REU participants works with faculty research mentors. Students attend a 3-day training, conduct a research project with their mentor, participate in weekly professional development seminars, and disseminate their work. Student projects contribute to refining behavioral theories through translational research, with implications for both understanding basic processes and informing applied outcomes. 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 · 2025-11

This project aligns with NSF's priorities related to economic competitiveness, public welfare, STEM education improvement, and academia-industry partnerships in the United States. It seeks to examine how sustainability education aligned with the Engineering for One Planet (EOP) framework can support the ethical formation of globally competitive engineers—particularly their development of environmental and social responsibility—for the enduring economic competitiveness of the United States. The ethical dimensions of sustainability that enable long-lasting economic competitiveness and public welfare advancement are often underemphasized in engineering curricula. This project addresses this gap by leveraging embodied carbon (EC)—a measurable and design-relevant concept—as an entry point for students to critically engage with the environmental and societal impacts of engineering practice. Guided by the EOP framework developed by the Lemelson Foundation with input from industry, this project integrates EOP-aligned and EC-focused ethics modules into five engineering courses total across West Virginia University (WVU) and Purdue University, directly promoting the ethical formation of approximately 800 engineering students across two institutions. The findings will contribute to the scholarship of engineering education and inform national efforts to strengthen undergraduate STEM instruction by forging partnerships between academia and industry in the United States for a sustainable economic competitive future. This project directly supports the NSF Research in the Formation of Engineers (RFE) program by advancing research on how educational experiences shape students' professional and ethical formation. By situating EC within engineering education, this work contributes to knowledge regarding best practices in developing engineers who are not only equipped to design sustainable solutions but are also motivated to do so with a deep sense of responsibility to people and the planet. The project is guided by the EOP framework, which emphasizes comprehensive design thinking, material selection, professional responsibility, and teamwork in engineering education. It will (1) investigate how exposure to EC education affects students' understanding of environmental and social responsibility; (2) examine how students' ethical reasoning evolves in response to EC-focused learning experiences; and (3) explore how the impacts of EC modules differ across three implementation modalities (i.e., lecture-based, case-based, and project-based). Through specially designed modules in five engineering courses across WVU and Purdue, students will engage with real-world sustainability challenges delivered in varied formats to assess instructional effectiveness. A mixed-methods research design will combine pre/post surveys, group case study reports, and reflective writing to capture changes in students' knowledge, ethical awareness, and reasoning. Comparative analyses will identify patterns across modalities and institutions. This project will produce openly shared instructional toolkits and assessment instruments for use by many instructors across the U.S. Furthermore, by explicitly aligning the modules with the EOP framework and implementing them in multiple engineering disciplines, the project will generate empirical evidence and scalable models that support the broader goals of the Lemelson Foundation's initiative—to develop responsible engineers for a sustainable future through improved engineering education. In doing so, this project will help further operationalize the EOP competencies in real classroom settings and inform national efforts to prepare engineers for the future. 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

A major goal of ecosystem science is to understand complex processes occurring belowground. These hidden processes involve microbial communities made up of bacteria and fungi. It has been difficult to correctly identify and measure the mechanisms operating belowground. Specifically, the balance between saprotrophs (organisms that feed on decaying organic matter) and mycrorrhizae (fungi that have a symbiotic relationship with the roots of many plants) has been difficult to understand. It is commonly assumed that saprotrophs are pretty much all alike with respect to their traits and function. However, some recent data demonstrate that saprotrophic community composition, and interactions between mycorrhiza and saprotrophs, have a significant impact on ecosystem carbon cycling. This project advances our fundamental understanding of the belowground interactions that regulate ecosystem function. The project conducts experiments in a forest in West Virginia to develop, parameterize, and validate representations of these interactions in an ecosystem model. The project also provides hands-on learning activities for K-12 students, undergraduate biology students, graduate students, and postdoctoral scholars. The goal of the project is to determine how cooperative and competitive soil microbial interactions shape ecosystem level carbon and nitrogen cycling. This knowledge is used to improve the sophistication and performance of an ecosystem model. The Carbon Acquisition Ecological Strategies (CAES) framework has been developed to facilitate the incorporation of soil saprotrophic microorganisms into a leading model of below ground ecosystem function (the Carbon, Organisms, Rhizosphere, and Protection in the Soil Environment (CORPSE) model). In CAES, soil saprotrophs are classified into groups that are similar to the carbon pools in CORPSE. This project provides the empirical data necessary for parameterization of this model. The project uses a field experiment, a greenhouse experiment, and different techniques like quantitative stable isotope probing to identify the taxonomic composition of saprotrophic functional groups in different soil environments. Ultimately, the research links cross-scale experiments with a novel modeling platform (CAES-CORPSE) to transform our predictive understanding of belowground processes. 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

Clean water resources play an important role in the life of rural communities. Set in the context of low-income communities in West Virginia and Delaware, this project aims to foster elementary school students' understanding of local watershed ecosystems, addressing critical environmental challenges while sparking interest in environmental-STEM (E-STEM) careers. To achieve these aims, the project enhances an existing "Shared Waters" curriculum by integrating local outdoor learning with new tools including augmented reality, mixed reality, simulations, and computer modeling technologies. Through this initiative, students and teachers working in schools in West Virginia and Delaware will gain access to innovative, interactive tools that empower students to become informed environmental stewards and engaged learners in STEM fields. The project will investigate the impact of spatial computing technologies on student learning outcomes, engagement, and interest in E-STEM careers. Research questions compare the proven curriculum (control) with the enhanced spatial computing curriculum (experimental) and evaluate the impact of local partnerships and selectable mixed reality avatars on student interest in E-STEM careers. Using a quasi-experimental design, researchers will explore how interactive instructional technologies and career avatars enhance elementary school students' comprehension of environmental science content and foster interest in E-STEM careers. Mixed methods, including pre/post-assessments, focus groups, and spatial computing platform analytics, will evaluate the project's effectiveness. Planned deliverables include an updated "Shared Waters" curriculum, professional development modules for teachers that enable them to implement the curriculum, and web-based dissemination of curriculum resources. By providing a scalable model for integrating spatial computing into elementary grade-level environmental education, this project contributes to educational excellence and workforce development. This project is funded by the Innovative Technology Experiences for Students and Teachers (ITEST) program, which supports projects that build understandings of practices, program elements, contexts and processes contributing to increasing students' knowledge and interest in science, technology, engineering, and mathematics (STEM) and information and communication technology (ICT) careers. 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

Wave-particle interactions are a fundamental process underlying phenomena across the plasma universe, from laboratory plasmas to the magnetosphere. Understanding how energetic particles interact with waves in space and laboratory plasmas has the potential to improve our ability to protect satellites, design cleaner energy sources, and develop technologies that rely on controlling high-temperature plasmas. This award supports a collaboration between Columbia University, West Virginia University, and New York University to study how modulations of the background magnetic fields can impact the interactions between energetic particles and plasma waves. Machine learning techniques will be leveraged to discover simplified models that capture the relevant dynamics. In addition to advancing science, this project will support the training of students and early-career researchers, develop interactive classroom tools for K-12 and graduate education, and promote open, accessible science through videos, software, and tutorials. This project will bring together expertise from energetic particle dynamics in magnetic confinement fusion, radiation belt electron transport, and data-driven reduced models to address two fundamental questions: How are resonant wave-particle interactions (WPI) modified by three-dimensional (3D) structure of magnetic fields? and How do 3D magnetic fields modify wave-induced particle transport? These questions will be addressed using two model problems: resonant interaction of energetic particles with Alfvén waves and transport of radiation belt electrons by ultra low frequency (ULF) waves. The project will develop a reduced particle-based simulation framework to address these questions, taking advantage of the separation of timescales between the background evolution and resonant population evolution. This analysis will be complemented by data-based development of reduced-order models of WPI. An interpretable machine learning paradigm, sparse identification of nonlinear dynamics (SINDy), will be used to discover reduced models for particle transport due to WPI and 3D fields. These reduced transport models will fill the gap between quasilinear diffusion coefficients and particle tracing simulations, while also informing global magnetospheric modeling, where a neural network with an autoencoder architecture will be used to identify a nonlinear low-dimensional latent space where the nonlinear behavior of WPI can be mapped. 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 an open-source computational platform to study the magnetic and electronic properties of strongly correlated materials (SCMs), which are essential for emerging technologies and devices related to quantum computing, spintronics, and magnetic storage. SCMs exhibit complex behaviors that are difficult to capture through conventional static theories or based on pure experimental measurements. By incorporating dynamical fluctuation effects into conventional first-principles methods, this project offers a new way to accurately simulate magnetic phenomena at multiple scales, from atomic spins to macroscopic magnetism. The developed software automates the modeling workflow and enables users to compute critical properties such as magnetic ground states, exchange interactions, spin excitations, and temperature-dependent magnetic transitions. By integrating this tool with community-developed packages and offering training through virtual workshops and Research Experiences for Undergraduates (REU) programs, the project fosters inclusive education and expands access to cutting-edge materials in science research. This work promotes the progress of science by enabling high-precision simulations, supporting national efforts in technology innovation, and training a broad range of students in computational materials research. The project develops a unified, extensible software framework that integrates Density Functional Theory (DFT), DFT+U, and Dynamical Mean Field Theory (DMFT) calculations to compute magnetic properties of SCMs with high fidelity, under the basis of two different electronic structure implementations, VASP and SIESTA. It builds on the existing DMFTwDFT codebase, expanding it to support collinear and noncollinear magnetic configurations and to interface with spin analysis tools including TB2J, PyProcar, Multibinit, and Vampire. Key technical advances include a spin-dependent Green’s function implementation, magnetic susceptibility calculations using the Bethe-Salpeter equation, and perturbative method implementation to extract spin-exchange interactions from the localized spin Hamiltonian. This framework is embedded in AiiDA for high-throughput studies and allows for direct comparison of DFT+U and DMFT-derived magnetic parameters. Additional modules capture ligand corrections, multispin interactions, and spin anisotropy contributions. Results are validated against experimental data such as neutron scattering and ARPES, ensuring robust predictions across diverse material classes. The platform supports automated U and J calculations and aims for integration with public repositories such as Materials Cloud. The project also includes extensive outreach, user documentation, and education efforts to ensure sustainable and impactful adoption by the research community. This award by the Office of Advanced Cyberinfrastructure is jointly supported by the Division of Material Research within the Directorate of Mathematical and Physical Science. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-09

This award will provide travel supplements to US-based undergraduate and graduate students to attend the 2026 International Congress on Membranes and Membrane Processes (ICOM 2026), held in San Antonio, TX from July 18-25, 2026. The conference will also offer a student workshop which will involve students learning and practicing various communication skills for both technical/non-technical audiences. ICOM traditionally has participants from academia, national labs, federal agencies and industries. This will provide the student attendees with networking and career development opportunities. Including undergraduate students in these opportunities will integrate them into the membranes community early in their training and career. This meeting will cover a broad range of membrane-related topics membrane materials, new manufacturing techniques, a wide range of membrane applications (water treatment, crude oil separation, gas separation, organic solvent separation and bioprocessing) and membrane modeling. The presentations will also include emerging separation areas such as rare earths and critical minerals recovery. The membrane community is focusing on several approaches to improve membrane properties, including new material discovery and design, new and sustainable membrane manufacturing processes and combination of artificial intelligence (AI) /machine learning (ML) and process modeling with experimental approaches. Presentations in this conference will also include “membrane adjacent” topics such as polymer synthesis, advanced manufacturing, and computational material development. 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 REU program addresses critical national challenges related to the aging highway infrastructure and limited maintenance funding by preparing undergraduate students to apply digital technologies to infrastructure engineering. Focusing on digital twins—virtual models that mirror physical road assets—the program equips students with the knowledge, skills, and tools to improve infrastructure monitoring, decision-making, and long-term resilience. It supports NSF’s mission by advancing science, promoting national welfare, and developing a skilled STEM workforce capable of leading digital innovation in infrastructure. Through hands-on research, mentorship, and international collaboration, students gain interdisciplinary experience that blends engineering, computing, and data science. The program also broadens access to emerging research areas and prepares participants for graduate study and future careers in infrastructure systems. The objective of this REU site is to engage U.S. undergraduate students in interdisciplinary research on digital twins for road infrastructure. Over three summers, 24 students from West Virginia University, the University of Wisconsin–Madison, and nearby institutions will participate in a 10-week program—eight weeks at U.S. host institutions, followed by two weeks at the University of Cambridge’s Laing O’Rourke Center. Students will conduct research on data acquisition, modeling, simulation, and decision-support tools for digital replicas of road assets. Activities will address challenges such as creating scalable models, integrating sensor data, and validating digital twin outputs for infrastructure monitoring and maintenance planning. The program combines civil engineering, computing, and data science to expose students to real-world infrastructure systems and emerging digital technologies, while fostering transatlantic collaboration and broadening their academic and professional perspectives. This project is jointly funded by the Division of Engineering Education and Centers (EEC) and the Division of Civil, Mechanical and Manufacturing Innovation (CMMI). This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-09

With support from the Chemical Measurement and Imaging (CMI) Program in the Division of Chemistry, Professors Lisa Holland and Hacer Karatas Bristow of West Virginia University are working to improve our ability to perform chemical analyses important for health studies, monitoring industrial processes, and many other elements of research and discovery. Specifically, a new separation strategy is being developed to resolve heterogeneous protein samples that are glycosylated. The technique will adapt to each protein sample to be separated, making it widely applicable to limited amounts of complex biomolecules. To meet this challenge, a small library of ligands will be developed. In solution, these special molecules will form a unique template of the protein samples being analyzed. In contrast to liquid chromatography, the approach enables researchers to create phases on-demand and significantly reduces solvent consumption. Students engaged in these novel studies gain key interdisciplinary research skills. Dr. Holland is devising innovative ways to integrate concepts relevant to this research into undergraduate courses, greatly enhancing student career preparation. A low-cost handheld instrument and accompanying curricular materials are being developed for use in the teaching laboratory to enhance the workforce development and training of scientists within and beyond the field of separations. The template driven separation method being developed through the collaboration with the Holland and Karatas Bristow labs focuses on self-assembly of ligands and glycosylated proteins. By combining bio-inspired self-assembly and click chemistry, each separation develops unique three-dimensional porous networks. The adaptive phases create ligand interactions tailored specifically for the protein sample to be resolved. The customized capillary separations can be used with limited amounts of protein sample. Moreover, because the approach is based on templating and self-assembly, each separation is unique based on the combination of a small ligand set developed through this project. A rapid, selective and efficient modality for glycoprotein separations will be demonstrated. 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

Measurements of the spatial clustering of matter in the universe can reveal clues about the nature of the mysterious “dark energy” that is causing the universe’s expansion to accelerate. A new powerful way to measure this clustering at early epochs in the universe’s history is to use radio telescopes to detect the hydrogen gas that is ubiquitous in all galaxies. A team of scientists from Arizona State University, Massachusetts Institute of Technology, Yale University, and West Virginia University, is using a custom-built radio telescope, the Canadian Hydrogen Intensity Mapping Experiment (CHIME), to make one of the first measurements of dark energy from observations of radio waves emitted by hydrogen in the universe. This project will develop several new techniques to leverage the latest developments in signal processing, detector technology, and theoretical modeling. In parallel, this project will expand several outreach programs that teach high school and college-aged students about astronomy and scientific thinking. The goal of this project is to solve critical calibration and analysis challenges for CHIME that will reduce residual foreground contamination by an order of magnitude and enable the detection of the large-scale structure of the universe with the 21cm line, independent of other probes. CHIME’s recent measurements of cross-correlations between 21cm intensity maps and eBOSS galaxies up to redshift 1.4, and the Lyman-alpha forest up to redshift 2.3, have demonstrated CHIME’s potential for high-precision large-scale structure measurements. In this project, the team will develop new modeling frameworks and analysis pipelines for 21cm cross-correlations; generate improved beam models and integrate them into the analysis; implement new radio-frequency interference excision algorithms based on cyclostationary signal processing; and deploy innovative foreground filtering techniques that are robust to the dominant systematic errors in the data. These advances should lead to a CHIME-only auto-correlation detection of large-scale structure and ultimately the baryon acoustic oscillation signal. 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

Enormous black holes a billion times more massive than the Sun orbit and merge with each other in the hearts of distant galaxies. These mergers produce gravitational waves, ripples in the fabric of space-time itself, with periods of years. Recently, the North American Nanohertz Observatory for Gravitational Waves (NANOGrav) collaboration confirmed the existence of these ripples. The collaboration has been observing cosmic clocks, called "millisecond pulsars", for 15 years. Gravitational waves stretch and squeeze space-time, making these clocks appear to speed up and slow down. The observation that these clocks vary in concert, not independently, reveals the existence of these gravitational waves. Making these observations requires radio telescopes of enormous sensitivity. The NANOGrav collaboration has been partnering with the DSA-2000 project to build a telescope that can continue these observations. Graduate and undergraduate students will receivehands-on training on the development of hardware and algorithms. The results will be presented widely to the scientific community as well as the broader public. This award contributes to the goals of NSF's "Windows on the Universe: The Era of Multi-Messenger Astrophysics" meta-program by including the development of metrics to evaluate millisecond pulsars for their timing suitability, and the selection of an expanded sample for timing observations. It will support the development and deployment of pulsar timing instrumentation and pipelines, and its commissioning on prototype hardware and the DSA-2000 telescope as construction proceeds. As a result, the infrastructure will be in place to accurately characterize the low-frequency gravitational wave background, and thus to characterize the astrophysics of supermassive black holes, as well as to potentially identify individual black hole binary systems. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-08

This three-year REU Site: Rural Appalachia Research in biosensing Technology (RAREST) is hosted by West Virginia University. The project features research investigations in health disparities in rural Appalachian areas, such as tick-borne infections, cancer, and cardiovascular disease. Nine REU students per year will engage in research opportunities with significant local and national impact, focusing on biosensor-related projects in health. Undergraduates will work in small groups state-of-the-art sensor research labs, guided by interdisciplinary faculty across engineering and chemistry. Students will engage in hands-on research, weekly presentations, workshops, entrepreneurship training, and a research symposium, promoting collaboration and innovation in biosensing technology. The REU Site will directly address the educational and economic challenges faced by rural Appalachian students, and result in equipping them with the skills and guidance needed to pursue high-skill, tech-focused careers. Enabling rural sensor and automation technology will require new approaches in several interdisciplinary research areas. These projects feature topics including developments in miniaturization and microfabrication technologies (such as neuroprosthetics), the use of novel bio-recognition molecules, and low-energy strategies for deploying as point-of-care tools (e.g., wearable sensors). Students from socio-economically disadvantaged regions will be recruited to engage in this program and grow into more independent-thinking researchers. Professional development activities will focus on pursuing graduate education, entrepreneurship training to enable new start-ups in rural settings, and regional outreach activities. 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

In this project, funded by the Chemical Mechanism, Function, and Properties Program of the Chemistry Division, Professor Fabien Goulay from the Department of Chemistry at West Virginia University investigates combustion and atmospheric reaction mechanisms. The specific focus is on the role of stable free radicals, which can accumulate in these environments and are known to play a major role in the formation of pollutants and toxic compounds. Gas-phase and high-temperature reactions are investigated both experimentally and computationally. This research sheds light on reaction mechanisms that will be used by the combustion and atmospheric community to develop new and more efficient strategies for transportation and power generation. It also provides training opportunities for undergraduate and graduate students. The project investigates the formation and reactivity of radicals derived from the vinoxy radical. These radicals exhibit enhanced stability and lower reactivity, potentially slowing down the chemical oxidation scheme. Kinetic and mechanistic information about the formation of substituted vinoxy-type radicals and their reactions with abundant species are required to reduce uncertainties in atmospheric and combustion models. Professor Goulay and his group will investigate the chemistry of vinoxy-type radicals during the initiation steps of unsaturated carbonyl compounds. Kinetic and product data obtained using a combination of laser spectroscopy and mass spectrometry experiments will be used to benchmark ab initio computational results and infer general mechanisms. The mechanisms, reaction rate coefficients, and product branching ratios for the formation and reaction of vinoxy-type molecules will be available for inclusion in reaction mechanism generators and atmospheric 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

Measurements of neutral hydrogen in the Universe with radio telescopes can inform our understanding of dark energy, a mysterious component in our Universe causing its expansion to accelerate today. To make these measurements, astronomers must understand the radio telescopes very precisely. In a previous grant, researchers from Yale University and West Virginia University, in collaboration with Canadian astronomers, developed a radio calibrator source using a new, fast chip that can be flown on a drone with signal to noise good enough for this precise calibration. Previously, the researchers focused mainly on development and testing in the lab. They will now upgrade the source, expand its capabilities, and focus on using it to calibrate radio telescopes by deploying it on the telescopes and drones. They will also develop a more robust lab version of a radio receiver outreach lab, used primarily for high school teachers and students. New 21cm interferometers targeting measurements of dark energy, reionization, and the dark ages require precise calibration, particularly of the instrument beam and gain, to remove bright foregrounds and extract the cosmological signal of interest. Currently, the incoherent (power only) calibrators developed to address this challenge limit the dynamic range of the measurement and also have no direct sensitivity to instrument phase. This research team has developed, tested, and validated a digital calibration source that addresses these critical gaps to make full-sky, high signal-to-noise measurements of the instrument beam. The researchers propose to use newly available versions of a Xilinx RFSoC board to update this source for wider bandwidth, improved stability, develop the ability to use multiple such sources simultaneously, deploy these sources on drones to calibrate new telescope arrays particularly well suited to drone beam mapping, and explore gain stabilization with this source. They also propose to leverage efforts already underway in an NSF-funded radio instrumentation outreach program (DSPIRA program) to continue developing education-oriented radio receivers, which can be used in STEM programs at WVU and Yale. Graduate students supported by this ATI grant will use the DSPIRA receivers as a prototype to make a more robust lab version appropriate for outreach activities, based on their experiences building a week-long outreach module for the Pathways to Science program at Yale. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-07

The National Science Foundation (NSF) EPSCoR Graduate Fellowship Program (EGFP) supports EGFP designated institutions and programs in EPSCoR jurisdictions by providing funding for graduate fellowships for new or continuing EGFP-eligible applicants. EGFP awards provide funding for a total of three years of stipend and an associated cost-of-education (COE) allowance for each NSF EPSCoR Graduate Fellow. This award to West Virginia University (WVU) provides support for thirteen Fellows for a three-year period. Fellows will focus on interdisciplinary studies required to enhance WV’s economic development in support of evolving energy options. Graduate students will work with mentors from across nine departments on research that includes topics such as the bioeconomy, ecosystem resilience, environmental chemistry, photophysics within photocatalytic materials, and techno-economic analysis. By building a cohort of students that will enhance interdisciplinary research, the program will seek to transform the graduate education environment of WVU and enhance WVU’s leadership in future economic transitions. This award supports Fellows conducting research on specific topics or areas that align with interests identified by the Directorate for Biological Sciences (BIO), Directorate for Engineering (ENG), Directorate for Geosciences (GEO), Directorate for Mathematical and Physical Sciences (MPS), and Directorate for Social, Behavioral, and Economic Sciences (SBE) at the National Science Foundation. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-06

This award supports an experimental study of space-like plasmas in a laboratory setting. Space plasma phenomena impact human infrastructure in space, such as satellites and manned space vehicles, and technological systems on Earth, such as power grids, global positioning systems (GPS), and long-distance radio communication. One of the challenges in understanding naturally occurring phenomena in outer space is the difficulty and the expense of performing scientific measurements in space. Another challenge is that nature rarely produces the exact same event multiple times to allow for distinguishing random measurement fluctuations from the phenomena of interest. Yet, the characteristics of the plasma in the space environment are key to understanding and predicting such space plasma phenomena. This project will study how energy is transferred from waves to particles in space-like plasmas contained in the PHASMA facility at West Virginia University. The broader impacts of the project include training of graduate students in a research environment that emphasizes the synergy between basic and applied plasma physics; recruiting and retaining undergraduates into physics through involvement in cutting-edge research activities; and support of an educational initiative that provides hands-on STEM activities to K-12 students. The in-situ measurement of ion and electron velocity distribution functions in space has revolutionized the field of space physics by providing the space physics community the ability to study and understand kinetic-scale plasma processes; test theories and computational models; and to discover new plasma phenomena. The PHASMA facility has the capability to non-perturbatively measure the three-dimensional (3D) ion and electron velocity distribution functions (VDFs), magnetic fields, and turbulence in laboratory plasmas created at space-relevant conditions. This project addresses three open scientific questions in plasma physics and supports the development of a new diagnostic method, quantum beat spectroscopy (QBS), for measuring weak magnetic and electric fields in plasmas. The first study focuses on particle energization during magnetic reconnection by exploring the correlation between perturbations to the 3D electron velocity distribution function (EVDF) and spontaneously appearing electrostatic lower hybrid drift waves. The second study involves measurements of EVDF perturbations when the waves are externally driven rather than spontaneously excited by local instabilities, as in the edge of a helicon plasma source. A third study will explore the effects of flow shear on the rate of reconnection and the effects of changing the guide field strength on the direction of propagation of the reconnection site between two tilted merging flux ropes. All three of these studies will be compared to numerical and theoretical predictions. The QBS development effort will provide an exciting new method of non-perturbatively measuring weak magnetic and electric fields in low-pressure laboratory plasmas. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-04

This project aims to serve the national interest by developing STEM courses that will prepare students to apply knowledge from mathematics courses to other disciplines. Undergraduate students are increasingly asked to make connections and articulate problems from disciplines outside of mathematics in quantitative terms. College faculty also need to understand and meet the changing educational needs of students, and most institutions have limited resources to address this critical challenge. The National Consortium for Synergistic Undergraduate Mathematics via Multi-institutional Interdisciplinary Teaching Partnerships (SUMMIT-P) has been working since 2016 to address these issues by forming and strengthening faculty partnerships across disciplines and institutions. These partnerships have reduced the separation between disciplines, resulting in undergraduate courses that make explicit connections between mathematics and numerous other disciplines. This Level 2 IUSE Institutional and Community Transformation project, led by West Virginia University, Virginia Commonwealth University, and Western Michigan University, includes approximately 40 new institutions that will adapt the SUMMIT-P model to form local faculty teams. The SUMMIT-P team plans to study the experiences of students in these revised courses while also collaborating with national professional societies to ensure the long-term sustainability of the consortium’s work, significantly expanding the scope and positive impact of SUMMIT-P to thousands of additional college students. The goal of this project is to assist institutions with the adaptation of a known model for developing and implementing cross-disciplinary STEM courses. The project will also work to broadly disseminate the SUMMIT-P model to institutions beyond the intended 40 project participants. The project will support the development of sustainable collaborations that aim to minimize traditional disciplinary silos. Additionally, the proposed work will advance understanding of the student experience in these interdisciplinary courses, using previous student outcomes in SUMMIT-P courses combined with new data focused on long-term student impact. The project’s research plan is designed to advance understanding of how choices made by institution-based teams affect positive impact of the change process, as well as the institutional qualities and resources that are critical to lasting change. Additionally, the research team plans to study the effectiveness of faculty partnerships formed using SUMMIT-P change processes in creating significant and lasting curricular change that results in positive long-term student impact. The NSF IUSE: EDU Program supports research and development projects to improve the effectiveness of STEM education for all students. Through its Institutional and Community Transformation track, the program supports efforts to transform and improve STEM education across institutions of higher education and disciplinary communities. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-02

Perfluorooctanoic acids (PFOAs) are chemicals used in many products because they repel water and oil. They can be found in non-stick cookware, water-resistant clothes, stain-resistant carpets, firefighting foam, and even some food packaging. These chemicals don’t break down easily in the environment or inside our bodies, so they are called “forever chemicals.” Over time, they can build up in drinking water and organs like the liver and kidneys, which has raised concerns about possible health risks. This project focuses on finding a way to remove PFOA from water using a safe and low-energy method. It combines the natural ability of enzymes, which help break down substances, with special membranes that can separate PFOA and its breakdown products, all while keeping clean water flowing easily. The project will train students and teachers at graduate, undergraduate, and K-12 levels. They’ll learn about creating materials for membranes, enzyme reactions, and using advanced techniques to study these processes. The goal is to help the public understand important environmental issues and expand the science and engineering workforce through educational programs in schools and outreach activities. The proposed work describes the formation and testing of an environmentally benign hybrid membrane platform which facilitates the degradation of perfluorooctanoic acid (PFOA), in addition to the removal of its degradation by-products and residual PFOA to thus produce clean drinking water. The approach is based on immobilizing a natural biocatalyst, Laccase, onto a polyelectrolyte membrane developed in lab settings using various surface-anchoring techniques. Pore size, porosity and other morphological properties of the underlying membrane will be tailored to serve the dual purpose of both providing a sustainable platform for enzyme immobilization and resulting degradation abilities, as well as to reject the by-products of such PFOA degradation. Toxicity analysis of the degradation by-products via a metabolism assay technique will be implemented to demonstrate the feasibility of the approach to be used for eliminating potential deleterious health effects. To advance understanding and thus ensure project transferability and sustainability in real settings and for other fluorochemicals, the synergistic kinetics of the membrane separation and the specificity, efficiency and operational stability of the enzymatic breakdown processes will also be evaluated. The research component of this work will be complemented by educational, mentoring and outreach activities to contribute to the development of the next generation workforce capable to understand both the implications of water contamination processes, how they can result in environmental and human health effects, as well as capable to develop and implement next generation technologies for low energy, benign decontamination. This project is jointly funded by the Process Systems, Reaction Engineering and Molecular Thermodynamics (PRM) program, and the Established Program to Stimulate Competitive Research (EPSCoR). 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 · 2025-02

Industry 4.0 has introduced a transformative era in manufacturing, driven by the integration of advanced digital technologies into smart manufacturing systems. These advances require human operators to adapt and learn new skills to interact with complex machinery. However, many workers face challenges in understanding these complex tasks, leading to potential safety and health risks, labor shortages, and declining productivity in U.S. manufacturing. This project aims to address these challenges by developing immersive training environments that enhance skill acquisition, knowledge transfer, and workplace safety. By integrating Mixed Reality (MR) with Digital Twin (DT) technologies, the project will transform traditional training methods and provide workers with practical, self-guided modules to acquire essential skills. Collaboration with the University of South Carolina’s Future Factories Laboratory will help develop scalable training models that can be used across a wide range of industries, improving worker adaptability and safety. This initiative will not only enhance U.S. manufacturing competitiveness but also expand STEM education opportunities, particularly for underrepresented groups in the workforce. By promoting a diverse, highly skilled workforce, the project aligns with NSF’s mission to advance the nation's economic prosperity, public health, and scientific progress. The technical goal of this project is to optimize workforce training in smart manufacturing through the integration of MR and DT technologies. This will be achieved through three key objectives: (1) enhancing learning and skill transfer via MR-enabled environments, (2) evaluating instructional design factors that influence learning outcomes, and (3) synthesizing DTs to optimize ergonomics and work processes. Using MR head-mounted displays and wearable motion capture systems, expert techniques and ergonomic practices will be overlaid onto real-world manufacturing environments, enabling workers to interact with virtual elements that guide task performance. The project will focus on machine assembly tasks, which pose high risks for musculoskeletal disorders (MSDs) due to repetitive movements—one of the leading causes of worker injuries and lost productivity. Controlled experiments will assess how different instructional designs—such as point of view, augmented paths, and real-time feedback—impact worker performance, safety, and learning efficiency. A combined productivity-biomechanical analysis will quantitatively assess these factors using statistical techniques, including ANOVA and factorial experimental designs, to isolate the effects of each instructional factor. Integration of DTs will provide real-time data analytics to optimize ergonomic practices and predict task performance, resulting in safer, more efficient manufacturing processes. This research will significantly advance workforce training by developing highly effective, scalable, and ergonomic training systems that address the need for a skilled and adaptable workforce in Industry 4.0 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.