University Of Delaware

NIH Research Projects · FY 2025 · 2025-09

PROJECT SUMMARY Temporomandibular joint osteoarthritis (TMJ-OA) is a degenerative joint disease of the jaw and a major driver of temporomandibular disorders (TMDs) that affects approximately 11 million individuals in the United States alone. TMDs are commonly characterized by severe pain and functional defects that lead to TMJ dysfunction and negatively affect the quality-of-life in patients. However, the developmental and pathophysiological mechanisms in TMJ development and disease are poorly understood, thus creating significant barriers to identifying novel non-invasive treatments for TMDs. Here, we aim to examine the cell lineage heterogeneity and molecular mechanisms that contribute to TMJ development and assess how TMJ cell subpopulations are dysregulated in disease. We hypothesize that the fibrous surface layers of the mandibular condyle are composed of unique progenitor cell populations that regulate TMJ development and may contribute to TMJ-OA pathophysiology. Further, we will examine how alterations in the structural organization of the progenitor niche can impact cell lineage plasticity. In Aim 1, we will use single-cell transcriptomic analysis, lineage tracing, and TMJ-OA preclinical modeling to define unique cell subpopulations in the TMJ and their role in TMJ-OA pathogenesis. In Aim 2, we will determine how impaired organization of the fibrous superficial layers of the mandibular condyle alters TMJ progenitor cell differentiation using single-cell multi-omics approaches and investigate the underlying molecular mechanisms that cause TMJ dysfunction.

NSF Awards · FY 2025 · 2025-09

This award funds the research activities of Professor Harikrishnan Ramani at the University of Delaware. This project seeks to advance our understanding of the fundamental laws of nature by developing innovative methods to detect physics beyond the Standard Model of particle physics. A major focus is on exploring the possibility that new stable particles --- such as millicharged particles and gluinos --- exist but have eluded detection by conventional experiments. This project repurposes existing detectors, with only minor modifications, to enable the detection of these elusive particles. It also addresses a particularly challenging scenario in which dark matter interacts solely through gravity, rendering it extremely difficult to study in laboratory settings. By employing sensitive electric field sensors, devising novel search strategies at the Large Hadron Collider, and leveraging astrophysical observations of subtle gravitational effects on star clusters, this research opens new pathways for discovering previously inaccessible forms of new physics. The project addresses foundational open questions in particle physics and cosmology, while serving the national interest by strengthening the scientific infrastructure essential for transformative discoveries. A detection of new particles through these methods would mark the first such discovery on American soil in the 21st century. The project also contributes to public education and scientific literacy through outreach efforts and classroom instruction, including a particle physics course designed specifically for non-science majors. Technically, the project follows a three-pronged approach to probing physics beyond the Standard Model. It includes: (1) searches for fractionally charged particles produced in cosmic ray showers using precision electric field detectors, which offer sensitivity in the MeV–GeV mass range; (2) novel techniques for identifying heavy, stable gluinos --- hypothetical supersymmetric partners of gluons --- that may become trapped in Large Hadron Collider detector material, with a mass reach exceeding 2.5 TeV; and (3) gravitational probes of dark matter via its energy exchange with star clusters, which can reveal evidence for self-interacting or dissipative dark matter. These methods will also shed light on astrophysical substructures far smaller than what is currently observable. Collectively, these efforts leverage precision technologies and astrophysical insight to explore longstanding mysteries about the fundamental constituents of the universe. 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 sizes and shapes of leaves determine plant growth and function in both natural and agricultural environments. At present we do not understand how strong mechanical forces in plant tissues (roots, leaves) simultaneously enable tissue growth and maintain physical connection between cells in tissue. These knowledge gaps preclude rational engineering of plant tissues with desired architecture. This project combines expertise from three different labs in the disciplines of plant biology, soft-matter physics, and computational mechanics. The team will analyze the types of forces that are important, and how they are sensed by individual cell wall and cell signaling proteins within the cells. Experiments on plant tissues will measure tissue mechanical properties to be used in computational mechanics simulations to determine how plant cells sense and respond to different types of forces in the cell wall during growth. This project will also train the next generation of biologists and provide new ways to discover fundamental control mechanisms of plant growth and function. This knowledge is needed to enable future strategies to engineer crops with specified architectures and material properties to maximize efficient production. The forces that drive growth in plants also create mechanical interactions between cells that destabilize connectivity. At present, knowledge about the source, type, and sensing of these intercellular forces is lacking. Adhesion between adjacent cells in the plant epidermis exerts strong controls over organ-scale growth dynamics. This project tests the central hypothesis that the Actin-Related Protein (ARP2/3) complex-dependent actin-cytoskeleton control module mediates focal secretion of proteins and/or polysaccharides that maintain cell-cell adhesion in response to destabilizing wall stresses. Three specific aims are pursued: (1) The bioadhesive properties and biomechanical conditions that govern adhesion of cell-cell interfaces in the leaf epidermis will be analyzed quantitatively; (2) The manner by which localized ARP2/3 activation promotes cell-cell contact and maintains tissue integrity in the growing cotyledon epidermis will be determined; (3) The interactions among cytoskeletal, transport, and cell wall biogenesis genes that mediate tissue integrity will be defined. The research team will integrate live-cell imaging, quantitative micro-mechanical manipulations, and state-of-the-art finite element (FE) mechanics analyses to analyze how the growing epidermis manages enormous tensile forces and avoids mechanical failure. This project is jointly funded by the BIO Extended Frontiers program and the Established Program to Stimulate Competitive Research (EPSCoR), and managed by the Cellular Dynamics and Function 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

Use and reuse of long-term ecological data is needed for understanding how biological communities are responding to a changing world. In marine environments, key data includes large-scale patterns such as El Niño, ocean conditions such as sea surface temperature and winds, and biological data such as the distribution and abundance of food resources and marine wildlife. These core data are often collected in non-standardized ways, which makes it a challenge to compare patterns of biological response across different regions or marine ecosystems. The Long-Term Ecological Research (LTER) network provides an opportunity to make comparisons between sites as they share similarities in conceptual design and data collection procedures. In this ULTRA-Data project, a team of scientists is harmonizing ecological data from three different LTER sites representing temperate (California Current), subpolar (Northern Gulf of Alaska) and polar (Antarctic Peninsula) marine ecosystems. These three sites are influenced by global-scale processes and each provides comparable local data on ocean conditions, lower trophic level planktonic food resources (euphausiid crustaceans, also known as “krill”), and upper trophic level consumers (seabirds). The investigators are testing the idea that seabird populations and community structure are affected by local ocean conditions (habitat quality) and food resource availability, affected by larger-scale processes as observed during El Niño. Results from this study are helping scientists and marine stakeholders understand how changing ocean conditions and food availability affect marine biological communities. This study is revealing whether large-scale environmental variability is affecting disparate marine ecosystems similarly or if response mechanisms differ between regions. This research is supplying cross-ecosystem knowledge to help inform management and conservation. The scientists are also training younger researchers, including early-career scientists, graduate students, and an undergraduate intern. This project addresses a gap in our understanding of how marine biological communities respond to environmental change by conducting cross-ecosystem syntheses on the climate responses of geographically disparate but functionally analogous prey and predator communities. Seabird communities are an ideal metric for regional comparisons, as their local distribution and abundance can reflect both short-term and long-term ecosystem dynamics, and geographically unrelated seabird communities retain similar functional compositions (e.g. divers vs. fliers, planktivores vs. piscivores, etc.). By leveraging data available from three different Long-Term Ecological Research (LTER) sites, this project is testing how local ocean conditions affect seabird abundance, diversity, and community composition across the California Current Ecosystem (CCE), Northern Gulf of Alaska (NGA), and Antarctic Peninsula (PAL). These three regions represent temperate, subpolar and polar ecosystems, yet are structurally linked by large-scale Pacific climate modes including the El Niño Southern Oscillation, Pacific Decadal Oscillation, and Southern Annular Mode. The three LTER sites have collected similar long-term datasets on oceanography (hydrographic casts), prey (net-sampled euphausiids), and seabirds (at-sea visual observation surveys). Data are thus being harmonized into 30+ year datasets to investigate bottom-up linkages between climate modes, local oceanographic patterns, and local prey/predator variability. Generalized Additive Mixed Models (GAMMs) and Hierarchical Modeling of Species Communities (HMSC) are being used to test biophysical relationships, including the evaluation of temporal effects such as direct and lagged effects of climate mode variability. The integrative and cross-ecosystem framework utilizes valuable data provided by LTER sites, identifies unknown dynamics underlying ecosystem synchrony and divergence, and provides mechanistic perspectives on how regional biophysical processes contribute to productive and globally important ecosystems. 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

Per- and polyfluoroalkyl substances (PFAS) are synthetic chemicals, and certain legacy PFAS may pose health and environmental risks due to the strong chemical bonds. This project addresses the challenge of PFAS removal and conversion by integrating expertise in materials science, separations, reaction engineering, electrochemistry, process systems, multiscale modeling, artificial intelligence, and social science. Spanning Delaware, Alabama, and South Carolina, the project aims to build regional research capacity and infrastructure to support PFAS mitigation within a circular economy framework. Led by the University of Delaware, in collaboration with Delaware State University, University of Alabama at Huntsville, Alabama A&M University, University of South Carolina, Clemson University, and Benedict College, the project has the potential to revolutionize defluorination technologies across water, air, and soil, impacting medical, agricultural, and industrial sectors. Education and outreach efforts will train skilled educators, scientists, and engineers to tackle PFAS challenges and advance national health, prosperity, and economic growth. The project will employ a multi-scale research framework, integrating experiments and modeling, to create innovative knowledge and robust technologies for PFAS separation and conversion, aiming for near-zero fluoro-pollution. It will address critical knowledge gaps in PFAS concentration and defluorination within a circular economy context, while tackling engineering challenges, such as complex water matrices, pilot-scale testing, and environmental and cost analyses. The major research goals include: (i) advancing PFAS adsorption and electrochemical separation across diverse water sources; (ii) uncovering mechanisms for selective electrochemical and plasma-assisted PFAS reduction; and (iii) designing energy-efficient, modular systems that couple up-concentration with reduction processes. The project will strengthen STEM capacity and research infrastructure across three EPSCoR jurisdictions by building PFAS expertise, launching sustainable STEM education and training programs across seven partner institutions, and fostering long-term collaboration with national labs, industry, and communities to cultivate a diverse new generation of innovators and educators. This project is supported by the EPSCoR Research Infrastructure Improvement Program: Focused EPSCoR Collaborations (FEC), which supports interjurisdictional teams of EPSCoR investigators to perform research in topics that align with NSF priorities, with the goals of driving discovery and building sustainable STEM capacity. 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 defining characteristic of human motor behavior is the ability to effectively control movements during physical interactions with the environment. This remarkable capacity depends on the nervous system’s ability to integrate multiple control policies, including force control, impedance control, and feedback control. However, the underlying neuromuscular mechanisms that facilitate such robust and skillful movement control in new environments are still not well understood. This Collaborative Research in Computational Neuroscience (CRCNS) project seeks to elucidate how the human brain and body coordinate movement strategies that combine force, impedance, and feedback control during physical interactions. Gaining insight into these processes will yield significant benefits by advancing foundational neuroscience, enhancing the design of wearable robotics and assistive technologies, and informing innovative approaches to neurorehabilitation for individuals with motor impairments. Furthermore, the project promotes international collaboration between research groups in the United States and France while providing hands-on training opportunities for graduate students and postdoctoral researchers in experimental neuroscience, robotics, and computational modeling. By deepening knowledge at the intersection of neuroscience and engineering, this work aligns with national priorities around automation, health, scientific innovation, and education. This research develops and tests a novel theory of neuromuscular control that predicts how humans coordinate force, impedance, and feedback responses during physically interactive tasks. The approach integrates three key elements: (1) an optimal control framework capable of modeling coordinated force and stiffness control; (2) the incorporation of a computationally efficient muscle model that captures the mechanical properties of muscle force and impedance within the optimal control framework; and (3) the experimental validation of model predictions through experiments that merge robotics with functional MRI to investigate the neuromuscular control involved in tasks requiring physical interaction. Model predictions will inform the design of behavioral experiments where participants are subjected to controlled dynamic perturbations while performing movements primarily involving the wrist joint, both inside and outside the scanner. These experiments aim to test hypotheses regarding the differential expression of specific neuromuscular strategies and identify their neural correlates. By linking computational predictions to both muscular and brain activity, this project will elucidate how the nervous system flexibly deploys distinct control policies in response to varying task demands. 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

In this information-driven world, discrete mathematics forms the backbone of many essential technologies and systems. At the heart of this field are objects with strong regularity, which are central to the branch of mathematics known as combinatorics. The current project studies difference sets and partial difference sets, both of which can be neatly described as subsets exhibiting remarkable regularity. These are the underlying objects behind many elegant configurations in a wide range of areas including, on the theory side: finite geometry, coding theory, combinatorial design theory, number theory, graph theory, and on the application side: sequence design, signal processing, information security. Despite their wide-reaching significance, finding explicit constructions of these sets has remained a long-standing challenge. This project embraces an ongoing paradigm shift in the approach to constructing such objects, with the goal of uncovering novel constructions that will substantially advance the constructive landscape of difference sets and partial difference sets. The PI plans to involve graduate students. This project investigates the central challenge in the study of difference sets and partial difference sets: their explicit constructions. Inspired by the recent breakthrough of Applebaum et al., which determined exactly which of the 56,092 non-isomorphic groups of order 256 contain a difference set, the project will pursue three key directions: (1) constructing difference sets in abelian groups with high exponents, (2) generalizing the classical Denniston partial difference sets to a wider range of parameters and groups, (3) advancing construction techniques beyond abelian settings to the comparatively unexplored territory of nonabelian groups. 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.

NIH Research Projects · FY 2025 · 2025-09

Project Summary Hypertension (HTN) affects over 90% of older adults with Alzheimer's disease and related dementias (ADRD). Randomized trials have shown that an intensive HTN treatment strategy (increasing the dose or adding medications when the systolic blood pressure [SBP] is >130 mmHg) reduces the risk of cardiovascular events, mortality, incident dementia, and cognitive decline among healthy older adults. Harms of intensive HTN treatment included syncope, fall-related injuries, electrolyte abnormalities, and acute renal injury. However, trials excluded persons with ADRD and those residing in nursing homes (NH). Little data exist to provide direct evidence on the benefits and harms of an intensive HTN treatment strategy for older adults with ADRD, half of whom reside in NH. Intensive HTN treatment may offer critical benefits for older adults with ADRD, as they are at high risk of CV events and cognitive decline. Conversely, intensive HTN treatment may cause disproportionate harms in this population because of a high prevalence of risk factors for falls and adverse events, like polypharmacy, frailty, and cognitive impairment. Given the diverse range of patients with ADRD residing in NH, these benefits and harms are also likely modified by clinical (e.g., prior stroke, heart failure) and demographic (e.g., sex) characteristics. Evidence-based HTN treatment strategies are needed to avoid harms while maximizing benefits for older adults with ADRD. Without additional evidence, providers may continue to either over- or undertreat HTN in people with ADRD. The overall objective of this proposal is to compare the safety and effectiveness of different HTN treatment strategies for older adults with ADRD residing in NH. This proposal has three aims: (1) describe HTN treatment strategies among NH residents with ADRD, including BP measures and changes to medications; 2) estimate the benefits and harms of an intensive HTN treatment strategy for NH residents with ADRD; and (3) identify optimal HTN treatment strategies for clinically relevant subgroups of NH residents. Our central hypothesis is intensive HTN treatment provides limited benefits and increased harms for many NH residents with ADRD, yet select subgroups can still derive benefits. Our rigorous pharmacoepidemiologic studies will combine novel NH resident electronic health record (EHR) data from up to 10,000 NHs linked with several other rich data sources including: (1) Medicare Part A and Part B claims data; (2) Medicare Part D drug claims; (3) Minimum Data Set version 3.0 clinical assessments; (4) the Certification and Survey Provider Enhanced Reports and LTCFocus databases; and (5) several other databases, including U.S. Census data. Studies will leverage innovative causal inference methods including dynamic treatment strategies, g-computation, and target trial emulation. This project will produce critical, generalizable evidence to optimize HTN treatment strategies for older adults with and without ADRD and inform deprescribing interventions. This research project will address Strategic Goals C and D of the National Institute on Aging and NOT-AG-21-045: “Opportunities for Research in Epidemiology of AD/ADRD and Cognitive Resilience.”

NSF Awards · FY 2025 · 2025-09

The rapid adoption of large language models (LLMs), which use deep learning to process natural language, in aspects of healthcare has the potential to revolutionize critical processes, including clinical decision support and administrative tasks. However, these applications pose significant challenges in ensuring scalability and effectiveness of use. Addressing these challenges is essential to prevent modern AI tools from perpetuating or exacerbating ongoing issues. This project seeks to improve the effectiveness of healthcare-focused LLMs by developing scalable methods for evaluating and mitigating incomplete data. The research will empower healthcare providers and decision-makers with more robust AI systems, fostering trust and improving outcomes for all. Additionally, the project integrates educational initiatives to promote responsible AI principles among students, professionals, and the public, contributing to the national interest by advancing healthcare delivery and technological progress. This award focuses on two complementary research objectives: evaluating effectiveness in LLMs and mitigating identified data issues The evaluation framework will incorporate evidence-based datasets and novel techniques to address factual and faithfulness hallucinations, ensuring truthful and transparent LLM outputs. On the mitigation front, the project will introduce innovative reinforcement learning methods to align LLM outputs with effective principles. The work includes developing preference optimization techniques and flexible inference-time approaches, providing practical tools for responsible AI deployment in high-stakes environments. Furthermore, the project will implement a comprehensive educational strategy, offering an interdisciplinary course on human-centered AI, engaging a wide range of students, and creating accessible public educational resources on AI's societal impact. By bridging research and education, this project advances the responsible use of LLMs in healthcare and beyond, addressing critical fairness issues at the intersection of technology 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

The Vera C. Rubin Observatory will take high-cadence observations of the entire southern hemisphere sky over a 10-year period as part of its Legacy Survey of Space and Time (LSST), providing an unprecedented decade-long “movie” of the Universe. One of the most exciting promises of LSST is the discovery of entirely new astrophysical phenomena that have not been previously observed or predicted. The PI, from the University of Delaware, will develop software using machine learning and artificial intelligence (AI) methods to detect anomalous phenomena in the LSST data. The project will include new methodologies specifically designed for LSST data to unveil new objects and phenomena, a software package, and a catalog that includes full characterization of each detected source and its time evolution, as well as tools to aid the interpretation. The products of this research will be publicly available on the Rubin Science Platform. The project will include research and training opportunities a graduate student researcher. Production of the anomalous phenomena detection software package involves three key milestones. The first is feature extraction; methods for light curve characterization will be developed using artificial neural networks and generative AI. The second is development of the open software package for anomaly detection in LSST; an ensemble method for the detection of anomalous light curves mostly using existing methods grounded in the literature and of demonstrated effectiveness will be assembled. Third is application of distance-based methods to enhance interpretability of the anomalous light curves; an existing light curve classification package will be modified to detect anomalous light curves and aid the interpretability of anomalous detections. The project will deliver novel methods for the encoding of LSST data and anomaly detection, along with open-source software for the identification of anomalous light curves. 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

Climate risks impact food supply chains. As the geographic separation grows between food production and consumption, food supply chains are increasingly exposed to local and distant climatic disruptions. Current research has focused mostly on the production of a few staple crops, leaving substantial gaps in understanding domestic and international climate risks to an array of U.S. food supply chains. This project will pioneer new approaches to systematically detect, attribute, and track individual climate disruptions to food production along the entire food supply chain to locations of consumption in the U,S. Research findings will increase national capabilities to identify and track climate-associated food supply chain disruptions, thereby supporting investments and policies to enhance the resilience and competitiveness of the U.S. economy and protecting national food security against climate-related disruptions. The project will support a comprehensive education plan to achieve widespread reach at K-12, undergraduate, and graduate levels and local, state, and national scales. The overarching question addressed by this project is: How, where, and to what extent do climate-related disruptions to food production travel through domestic and international supply chains to affect U.S. food supply? This project aims to create a quantitative framework to detect, track, and attribute climate disruptions throughout the entire food supply chain and the full food basket. The central hypothesis is that specific combinations of food items, climate extremes, and supply chain structures can collectively undermine the resilience of food supply chains. This research will systematically quantify climate risks and identify opportunities to enhance resilience through three objectives: i) measure climate vulnerability of U.S. crop production, ii) characterize national and global food trade networks, and iii) identify opportunities to reduce climate vulnerabilities. These activities will integrate with the project’s education goal to enhance understanding of sustainability and resilience through systems thinking. This plan includes developing science modules for middle and high school students and creating summer research opportunities for high school and undergraduate students, aiming to increase interest in STEM careers and interdisciplinary collaboration. This project is jointly funded by the ENG/CBET Environmental Sustainability 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 · 2025-09

This grant provides funding to organize and execute the 2026 National Science Foundation Mind, Machine, and Motor Nexus (M3X) Principal Investigators' Meeting. This meeting will convene investigators of active NSF M3X Program awards for the first time since the program began in 2019. This meeting serves as a conference bringing together a community of interdisciplinary researchers whose work is specifically relevant to advancing seamless bi-directional interaction between intelligent engineered systems and humans. The two-day event will feature poster sessions, M3X program updates, keynote addresses, and interactive panel discussions, creating a rich environment for knowledge exchange and the identification of new research avenues. This meeting will also support participation by a cohort of potential future researchers in the M3X program. The inaugural M3X Principal Investigator meeting will take place at NSF headquarters. This event will gather researchers from a wide array of fields, including but not limited to robotics, cognitive neuroscience, human factors, and artificial intelligence, to showcase innovative projects and prioritize collaboration. Through workshops, discussions, and networking opportunities, attendees will collectively explore how these interactions could revolutionize various industries, from healthcare to transportation. From a scientific perspective, the M3X meeting represents a pivotal convergence of disciplines that address the complexities of bidirectional human-machine interaction. Researchers who have received M3X funding, or those considering future applications, will have the platform to present ongoing projects and theoretical insights that contribute to this field. By fostering interdisciplinary dialogue among scientists specializing in motor control, human factors, cognitive science, behavioral science, AI, engineering and robotic systems, the M3X initiative aims to generate transformative insights and applications that address significant challenges in areas such as surgical robotics and autonomous driving technology. Ultimately, the meeting seeks not only to highlight the program's achievements but also to inspire future research that enhances the synergy between humans and intelligent machines. 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 provides funding for U.S. researchers to attend the Northeastern Analysis Meeting, which will be held November 1-2, 2025, at the University of Delaware, Newark, Delaware. We plan to host six plenary speakers and expect about 75 total participants, roughly 60 of whom will be non-local. The funds will be used to cover some transportation and accommodation expenses for some plenary speakers (in particular, the early career mathematicians), as well as graduate students, postdocs, and people without external funding. Contributed talks in parallel sessions will provide a venue for the early career researchers to share their work, form professional networks, and get feedback. The conference brings together junior and established researchers in different flavors of analysis. It will foster new collaborations, particularly among experts in adjacent areas of analysis, and feature new developments in the following three areas: 1. Operator algebras is a vibrant area focusing on important subalgebras of bounded operators on Hilbert spaces. Witnessed by the negative resolution of Connes’ embedding problem, there has been a surge of interactions between operator algebras and quantum information theory (mainly surrounding understanding quantum entanglement, quantum channels and nonlocal games). 2. The area of non-commutative (nc) function theory of several complex variables has experienced explosive growth in the past decade. In the one-variable setting, function theory is intimately connected with operator theory. While questions about holomorphic functions of several complex matrices have proven more difficult, recent progress in the area seems promising. This area links to nc probability and nc harmonic analysis. 3. The field of harmonic analysis is a classical branch of analysis that has important applications and connections to a variety of other fields including analysis of PDEs, geometric combinatorics, geometric measure theory, scattering theory, and complex analysis. Extensions to topological locally compact groups require Banach algebra/operator algebra theory to develop a theory of nc harmonic analysis. The conference webpage is https://sites.google.com/view/neam2025/home. 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

Macrophages are immune cells that play critical roles in fighting disease, healing tissues, and maintaining overall health. Their behavior can shift in ways that either help or harm the body, depending on their environment. Histone lactylation, in which lactate, a small molecule metabolite produced in the body, changes how genes are turned on or off, may help explain how these cells change roles. This project will use synthetic biology to create precision tools that can add or remove lactylation marks on DNA-packaging proteins. These tools will help scientists understand how lactylation affects macrophage behavior and could lead to new ways to control immune responses in diseases like cancer. Course module development and support of undergraduate researchers will help to grow the biomanufacturing workforce. This project investigates the functional role of histone lactylation in macrophage polarization. Lactylation has been linked to macrophage transitions from inflammatory (M1) to anti-inflammatory (M2) phenotypes, particularly within the immunosuppressive tumor microenvironment. The central hypothesis is that modulating histone lactylation can selectively control macrophage phenotype, independent of external stimuli. In Phase I, lactylation writers and erasers will be created. These are enzymes capable of adding or removing lactylation at specific sites on histones. First, a detailed map of histone lactylation across macrophage polarization states and external stimuli will be established. Site-specific lactylation tools will be generated by engineering a dCas9-p300 fusion system to promote lactylation at H3K18 near the Arg1 gene and evolved using a yeast-based screening platform for increased lactylation selectivity. Histone deacetylase complexes using CRISPR-Cas9 strategies to remove lactylation will be designed and implemented, and the impact on macrophage phenotype assessed. Key milestones for Phase 1 include validation of lactylation writers and erasers with confirmed selectivity over acetylation and demonstration that these tools can meaningfully regulate macrophage phenotype. In Phase 2, the functional impact of these new lactylation tools in preclinical cancer models will be evaluated. Studies will employ lactylation readers and writers to quantitate how lactylation influences tumor-associated macrophage repolarization and how these tools can improve outcomes of chimeric antigen receptor macrophages, using both in vitro and in vivo tumor environments. In total, this project will advance fundamental understanding of macrophage immunometabolism, provide versatile tools for lactylation engineering, and lay the groundwork for durable macrophage-based therapies for cancer and other immune-mediated diseases. 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.

NIH Research Projects · FY 2025 · 2025-09

Project Summary The goal of this Phase III COBRE to continue and sustain our work to discover new chemical probes and lead molecules for biological research and therapeutic discovery. Our approach is broad-based and interdisciplinary, with a focus on using chemical biology and molecular design to investigate and understand disease while leveraging our core capabilities in synthesis, biochemistry, engineering, and biology. Building on the successes of Phase I and II, our center will continue to build center momentum with the continued recruitment and development of new investigators through faculty hiring and through pilot project funding. During Phase III, we will continue to support our successful and broadly utilized cores, and we outline a plan for sustaining the cores beyond COBRE funding with a broad base of extramurally funded user groups and an outstanding University commitment to support the cores through a significant match commitment and hard-funded Ph.D. level staff. We will sponsor an annual seminar series to be hosted by COBRE investigators and the UD Allies for a Culture of Inclusive Diversity (ACID) committee. The center will develop resources for mentoring and professional development including Grant Development Awards, UD ACID activities, MIRA workshops, NIH Diversity Supplement workshops, and Career Long Collaborative workshops. Center progress will be evaluated by an Advisory Committee that will meet twice per year. Our Center will collaborate with other centers in the Delaware IDeA network, UD centers and the NIH funded Chemical-Biology Interface (CBI) Training grant centered at UD in order to maximize our research capacity. Discovery COBRE values the importance of diverse teams working together to maximize innovation. In Phase III, our center will create infrastructure for biomedical research teams who bring diverse backgrounds and expertise to our center.

NSF Awards · FY 2025 · 2025-09

This project aims to improve infrastructure system resilience through investigating how best to minimize the negative consequences of infrastructure system service interruptions. Interruptions to infrastructure system services, such as electric power, water supply, and telecommunications, can have serious consequences for the economy, public health, and national security. While substantial infrastructure systems resilience research has been conducted in recent years, there remains a need for improved understanding of (1) the relationship between system impact (service outages) and societal impact (interruption in daily activities), (2) the complex, dynamic interactions that occur during the restoration period between the functioning of the system, restoration actions taken by the system operators, and adaptations implemented by consumers, and (3) the way the risk and best strategies vary depending on the characteristics of the specific network, users, disruptive event, and larger context. This project investigates these issues, and provides corresponding tools for researchers and, as a translational benefit, for infrastructure service providers. The project offers a leap forward in infrastructure system resilience through four objectives: (1) Develop and validate an outage impact scale (OIS) that describes the impacts of outages on users of different types (residential, commercial, etc.); (2) Develop quantitative models to predict adaptation implementation and user impacts in terms of the new OIS; (3) Develop the computational model-based Interdependent Resilient Infrastructure Simulation tool (IRIS) to describe infrastructure service restoration, using the OIS and models from Obj. 1 and 2; and (4) Generate a large suite of analysis scenarios representing a wide range of synthetic systems, communities, and disruptive events; and use IRIS to analyze them to better understand system behavior under different circumstances and support the design and implementation of best risk management strategies. The research focuses on electric power and water supply but aims to be extensible to other infrastructure types in the future. This project advances understanding of the resilience of infrastructure as sociotechnical systems and the best strategies to manage them in different circumstances so as to minimize negative societal impacts. IRIS will be integrated into a simulation platform that provides next-generation computational modeling and simulation software tools. 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

Flocculation, a dynamic process that binds fine muddy sediments with organic material in saltwater to form larger porous aggregates, is a fundamental process in estuarine and coastal zones that controls particle settling velocity and the vertical distribution of sediment; hence, it plays an important role in sediment deposition/erosion patterns, light attenuation in the water column, nutrient and carbon cycling, and water quality. To advance the general understanding and predictive capability of coupled flocculation dynamics and sediment transport, this project will integrate field, laboratory, and modeling approaches to address the knowledge gaps in 1) understanding the control of floc size and settling velocity in the estuarine boundary layer and their relationship to bottom shear stress and suspension and deposition; 2) evidence-based model coefficients for a flocculation model that reflects natural mud properties; 3) the relationship between floc size and settling velocity, especially for high organic content environments and muds with varying amounts of silt; 4) computationally efficient yet reliable coupling of flocculation dynamics in coastal models. This study has the potential to transform our ability to understand and include flocculation dynamics in coastal modeling under different levels of primary productivity due to seasonal and spring-neap variability. As such, it will impact broader research communities in biogeochemistry, carbon cycling, ecosystems and water quality. Field and laboratory data will inform the development of flocculation models to be effectively coupled with the existing open-source coastal models COAWST and OpenFOAM, already widely used by researchers from different disciplines. The project will support two PhD students for their research and three undergraduate students for their research experience in field experiments and sensors. The project also utilizes two international collaborations on FLOCMOD model development and quantifying transparent exopolymer particles (TEP) which the flocculation aggregates are made of. The investigators leading the project and the graduate students involved will participate in outreach efforts organized in their respective institutions. All the codes, numerical models are open-source, and all field and laboratory data will be made publicly available. This collaborative study will integrate four focused field campaigns (spring/fall during neap and spring tide), uniquely designed laboratory experiments, and numerical and data-driven modeling to address the knowledge gaps with the objectives to 1) quantify the importance of flocculation and its seasonal variabilities on sediment transport in the estuarine boundary layer via field observations that integrate several in-situ techniques to concurrently measure profiles of floc size, settling velocity, sediment concentration, turbulence, as well as characterization of organic content and concentration such as chlorophyll-a and TEP; 2) carry out extensive laboratory experiments to characterize floc size distributions and settling velocities over a large range of environmental conditions with an emphasis on varying organic and silt content in natural muds and measuring the transient response of the flocs to inform flocculation models solving the size-class population balance equations (PBE) and other reduce-complexity models; 3) provide an enhanced size-class PBE flocculation model for settling velocity coupled with existing hydrodynamic and sediment transport models COAWST and OpenFOAM to simulate cohesive sediment transport in the estuarine boundary layer, including validation with field observations; 4) implement machine learning methods to tackle upscaling challenges in flocculation, including the development of evidence-based model coefficients for the PBE flocculation model and surrogate models for solving PBEs. Novel aspects of this work include the concurrent deployment of unique instrumentation (e.g., PICS, LISSTS, and FlocARAZI) to provide unprecedented details of in-situ data to reveal the interplay of turbulent shear, resuspension/deposition, and floc properties in the water column; the design of new laboratory experiment focusing on the transient response of flocs beyond equilibrium state to provide the largest dataset of floc sizes under different conditions produced to date; and rigorous specification of flocculation model coefficients informed by lab data and a data-driven approach for tackling the upscaling challenge of including flocculation effects in coupled sediment transport and hydrodynamic modeling. 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

Hundreds of millions of dollars every year are spent by states and school districts on providing preK-12 teachers with professional development to improve their mathematics and science instruction, but these efforts are not always successful in resulting in better instruction for the nation's students. Research on teacher professional learning over the last several decades has expanded the field's understanding of how and under what conditions teachers learn to teach mathematics and science, and this body of work can inform these efforts. Findings and insights about teacher professional learning across this body of work, when understood collectively, can be powerful towards supporting teacher change. This project synthesizes research on teacher learning to distill ideas and develop a new, deeper understanding of how preK-12 teacher professional learning in mathematics and science influences teacher beliefs, knowledge, and practice. This study will provide information that enables states, districts, and schools to elevate the quality of teacher professional learning in STEM to lead to more effective instruction that fosters more and better STEM student engagement and learning and motivates more students to choose STEM careers. This project's potential benefit to society is to increase the field's capacity to improve STEM teacher professional learning in ways that transform and strengthen teacher effectiveness, which in turn improve STEM engagement and learning for all students. This three-year synthesis project involves a mixed method systematic review of research on teachers' professional learning to distill ideas and develop a new, next generation framework (theory of change) that reflects a deeper, improved understanding of how teacher professional learning in mathematics and science influences teacher beliefs, knowledge, and practice. The long-term goal of the project is to elevate the quality of teacher professional learning in STEM to lead to more effective instruction that fosters more and better STEM student engagement, learning, and choice of STEM career pathways. To support this goal, the project will (1) develop and utilize an initial professional learning framework benefiting from insights in STEM and other fields, (2) use the initial framework to guide a mixed-method systematic review that leverages existing teacher professional learning literature in mathematics and science, and (3) produce a next generation theory of teacher learning in mathematics and science that reflects complexity, context, and interactions, to guide the design of professional learning to make it more effective in fostering teacher growth. The project's central hypothesis is that a consideration of theoretical ideas from STEM and other fields and the comprehensive review of both quantitative and qualitative mathematics and science professional learning literature can result in new important understandings that can advance the field's ability to design and implement effective STEM professional learning in service of improved teaching and learning. The project will result in a professional learning framework that will guide the field in its pursuit to support teachers in providing more engaging and impactful mathematics and science instruction. This project is funded by the Discovery Research preK-12 program (DRK-12), an applied research program that seeks to significantly enhance the learning and teaching of science, technology, engineering, and mathematics (STEM) by preK-12 students and teachers. Projects in the DRK-12 program build on fundamental research in STEM education and prior research and development efforts that provide theoretical and empirical justification for funded projects. 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.

NIH Research Projects · FY 2025 · 2025-09

Project Summary Achilles tendinopathy is a prevalent and painful condition that hinders physical activity, with a significant incidence in the general population. Current treatments primarily involve calf-strengthening exercises, but success rates vary, leading to persistent symptoms in many patients. The Achilles tendon is composed of three subtendons twisted into a bundle and each linked to one of three individual muscles in the triceps surae, with each subtendon-muscle unit serving a distinct function. This intricate anatomy – which has yet to be considered in the development of treatment options for tendinopathy – may explain the varied responses to treatment. This study aims to explore the complex structure of the Achilles tendon to understand why some patients respond to generalized exercise treatments while others do not. This proposal integrates advanced in vivo techniques, including novel biomechanical sensors and cutting-edge medical imaging, with state-of-the-art modeling approaches to assess the structure and function of Achilles subtendons in patients with Achilles tendinopathy. The long-term goal of this work is to develop diagnostic and therapeutic strategies tailored to the individual structures of muscle-subtendon units. As a critical first step, this proposal will advance our understanding of the relationship between muscle-subtendon unit anatomy and Achilles tendinopathy and employs a three-stage approach. In Specific Aim 1, we will complete a longitudinal assessment of specific muscle responses to standard-of-care exercise treatments. To do this, we will track changes in muscle-subtendon units in 50 patients with tendinopathy over 12 weeks of exercise treatment. Using MRI, ultrasound, elastography, and biomechanical assessments, the study will analyze how different muscle-subtendon units respond to standard treatment. In Specific Aim 2, we will perform a high-field MRI study to assess the subtendon-specific damage incurred in Achilles tendinopathy. Using a research-grade MRI scanner and our newly-developed techniques, we will image tendons in patients with Achilles tendinopathy in high-resolution, allowing us to create previously inaccessible 3D subtendon reconstructions and region-specific effects of tendinopathy, providing detailed anatomical maps of tendon damage. Finally, in Specific Aim 3, we will develop and interrogate subject-specific computational models to simulate therapeutic loading via targeted muscle activation intervention. These models will compare the mechanical effects of generalized versus subtendon-specific treatment, predicting mechanical effects and potential failure modes based on individualized anatomy. Our interdisciplinary team aims to create a new paradigm for understanding the muscle-subtendon unit specific responses to Achilles tendinopathy treatment, exploiting the unique structure of each patient's Achilles tendon. This research will enhance understanding of anatomic variability in Achilles tendinopathy and lead to more effective, personalized clinical interventions.

NSF Awards · FY 2025 · 2025-09

Saltwater intrusion is an often-invisible process that is challenging to identify until it has already caused substantial harm to coastal lands. Salty waters seep inland – above and below ground – salinizing soils and waters, devastating crop harvests, and burning forests from the inside out. In the low-lying Mid-Atlantic region, large areas of coastal farmland and forest have converted to marsh, causing substantial economic losses and damage to ecosystems. To address these pressing challenges, an assembled coalition of farmers, landowners, researchers, government, non-profits, and the private sector will work together to develop, evaluate, and implement science-based solutions, focused on two important coastal economic sectors: farming and forestry. By developing and implementing a portfolio of practical solutions, such as novel agricultural easements, web applications to map saltwater intrusion, market development for salt-tolerant crops, and alternative timber harvest strategies, the project will improve the resilience and well-being of rural coastal communities impacted by saltwater intrusion, now and in the future. Thus, the project will translate research into practical solutions to promote regional resilience through community-engaged team science. The project goal is to improve regional resilience across rural coastal lands affected by saltwater intrusion by extending the life of farms and forest tracts, reducing storm surge damage and revenue losses, and supporting regional terrestrial and aquatic biodiversity. We will achieve this by developing and implementing coordinated, community-engaged solutions, focusing on agricultural and forest lands in Maryland, Delaware and New Jersey. To co-develop solutions, the project will bring together leaders from academia, government (local to federal), non-profits, and the private sector—who often have worked to face these challenges in isolation. Building on recent advances in earth system science at the land-sea interface, knowledge of the region’s complex hydrological, ecological, geomorphological, biogeochemical, and human systems will be synthesized to develop and evaluate a portfolio of social, technological, and nature-based resilience strategies. Selection of solutions will be informed by both research and community input and assessed for feasibility, risk, cost, and benefit through approaches such as techno-economic analysis. A Guide to Coastal Resilience will be developed that details the coalition’s shared vision of resilience and coordinated implementation solutions and will be disseminated broadly to guide policy, investment, and advocacy. This coordinated effort will bridge the gap between basic science and practical, community-aligned resilience strategies to meet the region’s evolving challenges. 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

Bovine Respiratory Disease (BRD) is an infection of the respiratory tract in cattle that compromises the welfare of the calf. It is estimated that BRD costs the industry $800-$900 million dollars a year. Precision technologies have the potential to monitor calves’ behavioral information to help detect diseases, such as BRD. However, the science of inferring BRD using these technologies is just at its infancy. Existing works often rely on expensive precision technologies, preventing widespread adoption. Furthermore, these approaches adopt simplistic inference solutions, are trained and tested on individual farms, and cannot provide explainability of model predictions. To address this gap, this project develops CalfHealth, a comprehensive framework that adopts innovative sensing technologies to enable the cost-effective and explainable detection of BRD in dairy calves. This can have profound implications for improving the profitability of farmers and calf welfare. In addition, this project will have a significant impact on the community through innovative education and outreach activities. These include: (i) field experiments and participatory workshops with relevant stakeholders, including farmers, veterinarians, companies, and consumers; (ii) interdisciplinary research experience for undergraduate and graduate students; (iii) wide dissemination of the project outcomes through high-quality publications; and (iv) demonstrations to future students at the E-Day of the College of Engineering of the University of Kentucky. CalfHealth is based on a novel multimodal learning framework that exploits accelerometer sensors to model calves' behavior using a fine-grained attention mechanism and fuses it with data regarding respiration rate, acquired by a Wi-Fi sensing system, through cross-attention mechanisms. To effectively adapt the detection framework to diverse farms and environmental conditions, the project adopts zero-shot and few-shot active learning approaches. Furthermore, CalfHealth enhances explainability, interaction with technology, and the ability to explore what-if scenarios. To this purpose, CalfHealth exploits language models combined with a feature attribution approach to develop an interactive chatbot for farmers. This project also accelerates the adoption of CalfHealth by using state-of-the-art economic experiments and qualitative methods to assess the behavioral and technological factors influencing farmers’ acceptance of precision technologies aimed at detecting BRD in calves. Additionally, comprehensive behavioral interventions are tested to enhance the farmer-chatbot interaction and increase farmers’ trust in CalfHealth. Finally, extensive validation on several farms is performed, including closing the loop by testing the benefit of early intervention for cattle identified by CalfHealth. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-09

With the support of the Chemical Synthesis Program of the Division of Chemistry, Professor William Chain of the Department of Chemistry and Biochemistry at the University of Delaware is developing new classes of reactions with aromatic compounds. The goal of this research is to exploit weak bonds and temporarily elevate the reactivity of normally inert chemical compounds. This temporary inversion of reactivity of aromatic compounds facilitates formation of a wide array of new carbon–carbon, carbon–oxygen, carbon–nitrogen, and carbon–sulfur bonds, forging new cyclic frameworks that lie at the heart of many chemical structures and serve myriad purposes in industrial, chemical, and medical applications, including novel therapeutic agents. The project provides a high level of education and training for students starting as early as high school. The PI is also active in outreach to local elementary, middle, and high schools to promote science education and engagement of students in science disciplines. This project applies umpolung methodology, in which normal reactivity patterns are inverted, to functionalization of electron-rich aromatic systems, starting with oxidation to introduce relatively weak nitrogen–oxygen bonds and to activate the system for functionalization, including by electrochemical means. The manipulation of latent aromatic compounds and/or the temporary engagement of heteroatom-bound electron pairs, and the harnessing of these energetic structures, offer opportunity for new bond formations and the construction of polycyclic arrays. This project leverages three unconventional reactivity manifolds facilitated by amines utilizing both traditional and electrochemical reaction processes. In the first reactivity manifold, N,N-diarylamines and carbazoles undergo a wide array of bond formations via group transfers that take advantage of the excision of the weak N–O bond within amine N-oxides and N-hydroxylamines. In the second reactivity manifold, a variety of amine N-oxides undergo elimination reactions to afford N-aryl iminium ions, facilitating Povarov cyclizations. In the third reactivity manifold, N-acylanilines, N-arylamino acids, and N,N-diaryl-N-alkyl-amines are converted to iminium ions via electrochemical oxidation sequences, thus constituting more efficient, less waste-producing pathways toward Povarov cyclizations. Broader impacts include workforce development starting with high school students and outreach to local elementary, middle, and high schools. 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

Healthy soil microbiome is essential for productive agriculture, influencing plant nutrition, growth, and resilience. This project seeks to understand the molecular and ecological principles that govern soil microbiome composition and function, with the goal of developing strategies to reintroduce and maintain beneficial microbes in agricultural systems. The insights gained from this study will enable precision engineering of native and synthetic microbial communities that improve nutrient uptake and drought tolerance in crops and promote sustainable agriculture. Educational activities will bring microbiome science to the broader community through workshops, public events, and student-led programming, helping train the next generation of scientists while addressing the US national priorities in food security. This research combines microbial ecology, synthetic biology, and plant-microbe interaction studies to investigate how carbon metabolism shapes microbial community structure and function in soil. Despite the importance of soil microbial communities, the mechanisms governing their assembly, stability, and persistence remain poorly understood. Using a well-defined synthetic microbial community, the project will identify carbon substrate preferences among plant-beneficial bacteria, engineer bacterial strains to enhance nutrient solubilization and plant drought resilience, and develop novel biocontainment strategies using selective carbon auxotrophy. By integrating high-throughput phenotyping, metagenomics, and microbial engineering, this work will generate new tools and knowledge for precision microbiome engineering in agriculture, with broad relevance to other ecosystems and biotechnological 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 · 2025-08

Quantum computing is advancing rapidly, but even with future improvements in hardware and noise reduction, quantum devices alone will not be able to solve many large-scale real-world problems due to fundamental limitations in the number of logical qubits. To address this, it is essential to develop hybrid quantum-classical systems that combine quantum computing with high-performance computing (HPC). This project is focused on the application-aware co-design of hybrid algorithms to enable practical quantum advantage in optimization and machine learning tasks. These tasks are common in domains such as logistics, medical signal analysis, and materials science, all of which require real-time and large-scale processing. A large project is necessary to integrate expertise across quantum algorithms, high-performance computing systems, and domain-specific applications. This planning grant represents a critical step in building the collaborative infrastructure and technical foundation necessary for a successful large-scale NSF proposal in this emerging interdisciplinary area. This planning grant will support the conceptualization and design of new quantum-classical algorithmic pipelines specifically tailored for optimization and machine learning models. It will catalyze new collaborations among EPSCoR researchers and industry partners through joint research activities and a series of workshops. The team will evaluate the scalability and feasibility of variational quantum algorithms in the application contexts and develop methods for optimizing circuit structure to make them realistically applicable on the quantum devices. These efforts will inform future hybrid HPC-quantum frameworks, guided by insights from real-world use cases. The planning activities also aim to expand quantum computing educational programs and workforce development initiatives, particularly learning from the University of Delaware’s graduate program model at partnering institutions. Ultimately, this grant lays the groundwork for a transformative CISE Large proposal that addresses the technical challenges of scalable quantum-classical computation. 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

An award is made to the University of Delaware to acquire a 600 MHz solid-state dynamic nuclear polarization (DNP) system to enable research across various scientific disciplines. This new instrument will significantly enhance the scientific infrastructure in the Mid-Atlantic, Northeast, and “Rust Belt” regions by providing access to unique DNP nuclear magnetic resonance (NMR) capabilities for cutting-edge scientific discoveries. The instrument will serve as a vital regional resource for numerous academic and industrial partners, facilitating the development of new collaborations and driving innovative projects. Access to this state-of-the-art technology will provide exceptional training opportunities for graduate, undergraduate, and postdoctoral students. The outreach program includes a rich array of societally beneficial activities aimed at sharing advanced NMR capabilities with the broader community, increasing public awareness of NSF-funded research, and contributing to the development of an effective and highly skilled STEM workforce. The science enabled by the new spectrometer will impact multiple fields, including chemistry, chemical and biomolecular engineering, fundamental biophysics, NMR spectroscopy, structural biochemistry, molecular cell biology, material sciences, biotechnology, and marine sciences. The significant sensitivity enhancements achieved in DNP experiments will facilitate the investigations of a wide variety of systems previously inaccessible with current instrumentation, such as intact cells or organisms, macromolecular assemblies, polymers, biologics, highly complex organic mixtures, surfaces, and engineered materials. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.