University Of Delaware

NSF Awards · FY 2026 · 2026-09

This Research Experiences for Undergraduates (REU) Site, “Dare to BE FIRST: Biomechanical Engineering Foundations in Impactful Research, Science, and Technology,” will provide undergraduate students with mentored summer research experiences in biomedical engineering at the University of Delaware. Biomedical engineering increasingly depends on quantitative skills such as modeling, computation, data analysis, and experimental design, yet many undergraduates have limited access to research environments where these skills can be developed through hands-on practice. This project addresses that need by engaging students in research at the interface of engineering, biology, and medicine, with an emphasis on how quantitative approaches can be used to understand and improve human health. The project serves the national interest by helping develop a skilled STEM workforce prepared to advance science, improve health and well-being, and contribute to U.S. prosperity through biomedical innovation. Recruitment will focus on students with limited access to research opportunities, while keeping the program open to all eligible U.S. students. Each year, the REU Site will support 9 undergraduate students in a 10-week, full-time summer research experience in the Center for Biomechanical Engineering Research (CBER)-affiliated laboratories across the University of Delaware. Students will work on interdisciplinary bioengineering projects spanning cellular and molecular mechanics, tissue biomechanics, joint and system biomechanics, and rehabilitation-related bioengineering, with a strong emphasis on quantitative analysis. This renewal builds on prior REU programming while introducing several enhancements to new programs. New learning modules include a Core Facilities Passport with structured tours of CBER laboratories and core facilities, expanded peer mentoring, and weekly cohort-building activities. The workshop series has been expanded from 3 to 9 sessions. Workshops will cover quantitative methods, experimental design, biostatistics, reproducible workflows, scientific communication, and career preparation. They will also include an alumni career session, speed mentoring, and a LinkedIn/CV clinic. Students will complete regular progress reports, prepare abstracts and posters, and present their work at the University of Delaware summer research symposium. When appropriate, students will be included as coauthors on peer-reviewed abstracts, conference proceedings, and full-length manuscripts. Recruitment will occur through the Dare to BE FIRST Alliance and other outreach channels, and program outcomes will be assessed through surveys and independent evaluation by the Center for Research in Education and Social Policy to support continuous improvement. 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 · 2026-07

Many of the important technologies of the coming decades, from ultra-precise clocks and quantum computers to sensors that may detect the elusive dark matter that fills the universe, depend on accurate knowledge of how individual atoms and molecules behave. Today, this knowledge is scattered across thousands of scientific articles, supplementary documents, and aging databases. No single trusted resource brings together the best computer-calculated values and the most accurate laboratory measurements. The result is that researchers and engineers waste considerable time searching for, checking, and re-checking information that they need to design experiments, build new instruments, and interpret their results. This project addresses that problem by expanding a free public online platform, already used by thousands of researchers worldwide, into a comprehensive scientific data resource. New features include broad coverage of atoms and molecules important for emerging quantum technologies, automated software that continuously reads scientific literature and extracts trustworthy numerical results, and a conversational interface that allows users to pose detailed scientific questions in plain language and receive properly referenced answers. The platform accelerates discovery in academic laboratories and supports the quantum technology industry. Students who participate in the project receive valuable training at the intersection of physics, computer engineering, and modern artificial intelligence. Other projects can adopt the open source software building blocks of the project. Accurate atomic data are essential for modern quantum technologies, ultracold-atom physics, precision measurements, astrophysics, and plasma modeling, yet no previous platform provided the comprehensive, high-precision atomic and molecular data needed by these communities. Under previous NSF support, the project team developed the ATOM portal, a free, open-access cyberinfrastructure that delivers rigorously curated, uncertainty-quantified atomic data from state-of-the-art calculations, augmented where possible by high-precision experimental measurements. Building on this foundation, this project transforms the ATOM portal into a next-generation, artificial intelligence (AI)-enabled data and computation platform that greatly expands the coverage of high-precision atomic data; provides AI tools for creating centralized, curated molecular bibliographies and spectroscopic datasets for molecules of highest interest to the ultracold and quantum molecular communities; and includes powerful new tools for natural-language querying, AI-assisted bibliographic discovery, and automated data extraction and verification. The project advances both domain science and cyberinfrastructure by integrating state-of-the-art atomic structure theory, artificial intelligence, and modern software engineering. Natural-language interfaces and web Application Programming Interfaces (APIs) lower the barrier for both academic and industrial users, enabling engineers and scientists who are not atomic data specialists to query and integrate high-precision data into their work. All software and data artifacts are released under open licenses and integrated with institutional data catalogs and related national efforts, ensuring long-term accessibility and reuse. The AI retrieval and validation tools, designed to be domain agnostic, provide a model for how AI can be safely and effectively applied to scientific data curation across many disciplines. This award by the Office of Advanced Cyberinfrastructure is jointly supported by the Physics at the Information Frontier Program in the Physics Section of the Directorate for Mathematical and Physical Sciences. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2026 · 2026-06

SUMMARY We discovered mutations in Tudor family RNA-binding protein (RBP) TDRD7 (OMIM: 611258) cause congenital birth defect cataract–loss of eye lens transparency–in humans. We linked TDRD7 to defects in sperm formation, leading to the recognition of a rare novel human syndrome that includes congenital cataract and azoopermia as symptoms. Cataract occurs in neonates as a rare condition, but causes permanent vision damage, with surgery being the only treatment. Even after surgery, patients face eye complications throughout life. Thus, new therapies are urgently needed. Yet, knowledge on TDRD7, especially on its function in the eye, is limited. Thus, we will address this critical knowledge-gap by identifying potential new druggable pathways linked to TDRD7. Lens differentiation upregulates select RNAs/proteins while they undergo dramatic cell-shape changes–involving ~1000-fold length-wise increase–and migration toward lens core. A long-standing question is, what mechanisms control these complex cellular differentiation events? Our data suggests the involvement of TDRD7. TDRD7 protein has OST-HTH/LOTUS and Tudor domains that may allow it to associate with RNA and methylated arginine/lysine, respectively. Our data shows Tdrd7 knockout mice (Tdrd7KO) exhibit cataract and reduced expression of genes linked to human/animal lens defects. Further, Tdrd7 loss causes severe cellular morphology defects in mature lens fibers. Our data shows that in addition to abundant Tdrd7 protein in the fiber cytoplasm, where it participates in protein-RNA complexes, Tdrd7 protein also enters fiber nucleus beginning at midembryonic stages. These exciting findings lead to a paradigm-shifting hypothesis: TDRD7 may participate in both (1) post-transcriptional control and (2) chromatin control, to facilitate proper gene expression regulation in the lens. This will be tested by pursuing the following goals: Characterize spatiotemporal chromatin and transcriptome changes in Tdrd7KO mouse lens at the single-nucleus level and use AI-based approaches to derive regulatory networks (Aim 1). Characterize the impact of Tdrd7-loss on lens proteome and identify its protein interactions in normal lens (Aim 2). This innovative proposal will fundamentally advance knowledge on TDRD7 by: (1) defining, on single nucleus level, spatiotemporal changes in lens transcriptome and (2) changes in lens chromatin, upon Tdrd7 loss, (3) defining proteins impacting Tdrd7 function and those altered in Tdrd7KO, and (4) making this regulatory information publicly available via a web-based, user-friendly resource iSyTE for continued eye gene discovery. We will collaborate with Dr. Shinichiro Chuma (Kyoto University, Japan) who is an expert on TDRD-proteins and has developed a Tdrd7 knockout (KO) mouse model that we will investigate. While the facilities in US and Japan are similar, the targeted Tdrd7KO mouse model is not commercially available in the US and Dr. Chuma’s 20 years expertise on TDRD-proteins (e.g. advise on Tdrd7 biochemical protocols) is necessary for success of the aims. This translational research will advance knowledge on an understudied protein, TDRD7, and identify potential new drug targets/pathways for novel therapies/treatments for cataract birth defect.

NSF Awards · FY 2026 · 2026-05

This Research Infrastructure Improvement (RII) EPSCoR Research Fellows project provides a fellowship to an Assistant Professor and training for a graduate student at the University of Delaware. This work is conducted in collaboration with Dr. Haihua Liu at the Center for Nanoscale Materials at Argonne National Laboratory. Through the fellowship, the PI will study how very thin materials change when exposed to light, heat, or strain. The project will use advanced imaging to observe these fast changes in real time to uncover how the internal structures of materials shift. It combines physics, materials science, and engineering to understand how light, strain, and atomic motion interact. The knowledge gained from the fellowship will help design faster and more reliable photonic technologies. Scientists can also use methods to study other materials like graphene. The project will train students, support outreach, and build a stronger research community in Delaware. It will also encourage new partnerships with industry. This project will investigate how two-dimensional phase-change materials, such as indium selenide, respond to external stimuli like light, heat, and strain. It will advance understanding of phase-transition pathways, intermediate states, and atomic-scale dynamics behind photoinduced phase transitions under mechanical strain. It will improve the reliability and performance of nanophotonic devices. The research will utilize ultrafast electron microscopy combined with strain engineering to directly observe lattice rearrangements with nanometer spatial resolution and picosecond temporal resolution. It will characterize samples with various strains to show how strain inhomogeneities influence transition kinetics. This project will expand research capacity at the University of Delaware by developing ultrafast materials characterization expertise and increasing access to world-class facilities. The graduate student will receive advanced training in fabrication and ultrafast microscopy and benefit from the collaborative environment of a national lab. The PI will gain unique skills in ultrafast electron microscopy, enhance interdisciplinary opportunities, and support future large-scale collaborations. The research project activities will integrate student training, professional development, educational and outreach efforts, and partnerships connecting academia, national labs, and industry. This project is supported by the EPSCoR Research Infrastructure Improvement Program: EPSCoR Research Fellows, which supports early- and mid-career investigators in eligible jurisdictions to develop collaborations at the nation’s private, government or academic research institutions. 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

Nontechnical description Magnetic field plays an important role in technologies ranging from electronic devices and medical diagnostics to emerging quantum technologies. Detecting magnetic signals at extremely small length scales is essential for understanding quantum materials and biological processes, yet many sensing technologies struggle to detect magnetic interactions that occur over only a few atoms. In many current solid-state quantum sensors, the sensing defects are embedded inside bulk materials and are separated from the target system by several nanometers, which reduced sensitivity to short-range magnetic interactions. This CAREER project will investigate a new approach to magnetic sensing using atomically thin semiconductor materials that contain light-emitting defects. Because these materials consist of only a single atomic layer, the defects can be positioned extremely close to nearby magnetic systems, enabling highly sensitive optical detection of nanoscale magnetic behavior. The research will establish fundamental principles for magnetic sensing at atomic length scales and will contribute to quantum sensing technologies relevant to quantum information science and quantum materials research. This project integrates research with education by training students in quantum materials and optical spectroscopy while advancing workforce development in quantum science and engineering. Research outcomes will be incorporated into the University of Delaware’s Quantum Science and Engineering curriculum through the development of virtual demonstration modules that illustrate principles of quantum sensing. Simplified versions of the modules will be adapted for outreach activities with undergraduate and K-12 students. Students will also participate in structured science communication activities and a pilot industry training activity. Technical description Quantum sensing using solid-state defects has enabled highly precise measurements of physical quantities such as temperature, pressure, strain and magnetic field. However, most existing sensing platforms rely on defects in bulk crystals that are embedded several nanometers below the material surface. This separation limits sensitivity to short-range magnetic interactions and restricts the ability to probe interfacial magnetic phenomenon. This project will investigate a fundamentally different sensing platform based on chalcogen vacancy defects in monolayer transition metal dichalcogenide semiconductors. Because these semiconductors are atomically thin, the defect states can be placed in direct proximity to target materials, enabling magnetic sensing at nanometer and sub-nanometer length scales. The research team will introduce defects into monolayer semiconductors through thermally driven processes and will systematically study their optical and spin behavior using magneto-optical spectroscopy. Measurements will be performed on both non-magnetic and magnetic substrates to isolate proximity-induced magnetic interactions. By assembling semiconductor and magnetic heterostructures using van der Waals stacking, the team will investigate tunable interfacial magnetic coupling while preserving optical addressability at practical operating temperatures. Optical spectroscopy techniques will probe changes in defect emission energy, polarization and recombination dynamics in response to magnetic interactions. These studies will establish fundamental principles governing proximity-based magnetic sensing in two-dimensional materials and will advance understanding of defect physics and interfacial magnetism in quantum materials. The research activities will be closely integrated with education and training initiatives that prepare students to work at the intersection of quantum materials, optical spectroscopy, and quantum sensing technologies. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2026 · 2026-04

Smartphones have strong video and audio capabilities. However, they are less capable when it comes to providing touch (haptic) feedback, which is limited to simple things like vibrating or simulating keyboard clicks. How to recreate the haptic richness of everyday objects, such as rubbing a cotton shirt or feeling a fine wood finish in furniture, is an open problem in building better user interfaces for phones and other devices. This project will develop methods to provide richer haptic feedback by engineering textures at multiple scales, ranging in size from microscopic to visible to the naked eye, inspired by the fact that part of why objects feel the way they do is because they have distinctive textures at these different scales. The benefits of richer haptics are broad. Beyond improving smartphones, better haptic hardware could make better medical training simulations, improve prosthetics, or make virtual reality more immersive. Further, for people who are blind, haptic devices are an important replacement for screens or pictures. This project will examine how patterns at the sizes of microns to centimeters can improve the strength of haptic feedback by modulating friction, and increase the number of distinctive feelings, also known as dimensionality. The research team will fabricate devices with patterns at multiple length scales by using microfabrication techniques; the ability to dynamically change these patterns is facilitated by a new class of polymer-based actuators. These devices will then be tested in a series of tasks with human participants to quantitatively determine the strength of stimulus and the degree of variety they can perceive. Higher level tasks will also be tested, like integration with a visual display, which also helps to disseminate the basic knowledge from this project to other applications and fields. Complementing the research activities, the project will create classroom modules on human centered design and human factors for undergraduates in physical sciences and explore opportunities for using the new haptic capabilities to better-support STEM education for blind and low-vision people. 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

Tao Li of University of Delaware is supported by an award from the Chemical Theory, Models and Computational Methods program to develop an artificial intelligence (AI) agent framework for autonomous scientific simulations in computational chemistry and related fields. Although computational simulations play a central role in modern scientific discovery, mastering each computational package requires substantial domain knowledge, which remains a major bottleneck in modern research workflows. While large-language-model-based AI agents have been reported to automate computational simulations using a single or a small set of computational packages, there is a lack of a unified AI agent framework which can handle a range of different computational packages for research-oriented simulations. To bridge this gap, this project will develop an innovative agent framework for multidomain scientific simulations. In addition to scientific advancements, the team will make this agent framework openly available to the general public. Dr. Li’s research will focus on developing a unified agent framework for research-oriented scientific simulations. The team will employ a four-layer progressive disclosure mechanism to enable efficient agent reasoning on a specific computational task, from selecting the most suitable package for the user's request to loading research-quality simulation pipelines. Additionally, the team will implement multiple computational workflows in the agent framework to facilitate autonomous computational simulations ranging from demonstrative calculations to large-scale computational tasks. The team will further extend this agent framework for simulations on both workstations and high-performance computing clusters. Overall, these efforts will not only allow researchers to explore simulation parameter spaces more efficiently, but also provide a valuable tool for NSF's Gold Standard Science policy for improving the transparency and reproducibility of scientific publications. 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 2026 · 2026-04

PROJECT SUMMARY The interactions between cell-surface ephrin proteins and their cognate Eph receptors play diverse roles in cell signaling and adhesion in nearly every tissue and organ. Disruption of these interactions may lead to various diseases, including developmental defects, autoimmunity, tumors and neurological disorders. One of the key mechanisms that regulate the ephrin-Eph interactions is the proteolysis (“shedding”) of ephrin ligands by cell-surface proteases, which is well documented for class A ephrins but understudied for class B ephrins. In this application, we present unpublished data showing that members of the disintegrin metalloproteinase (ADAM) family shed ephrinBs by responding to Eph receptor binding. We have determined the ADAM cleavage sites and generated uncleavable ephrinB mutants. Leveraging these new tools, we will assess the effects of ephrinB shedding on downstream signaling and cell adhesion in Aim 1. We have also uncovered an unexpected connection between the ADAM-ephrinB interactions and the activation of a unique branch of the beta-catenin signaling pathway, which is required for normal neuronal function. We will continue to understand this novel ADAM-ephrin-beta-catenin signaling crosstalk in Aim 2. If successful, the proposed work will not only address several fundamental questions in ephrinB signaling but also open the door to new directions of research on this ubiquitous signaling pathway.

NIH Research Projects · FY 2026 · 2026-03

PROJECT SUMMARY There is a mental health crisis among graduate students, encompassing both mental illness and broader well-being issues. These challenges don’t disappear when graduate students enter the classroom, research lab, or their mentor’s office. The pressures of academia can exacerbate these issues. As a result, faculty mentors frequently interact with students experiencing psychological distress, either acutely in the moment or more chronically over time. However, faculty mentors are ill-equipped to navigate these mental health challenges. Most faculty mentors did not receive training about mental health or effective interventions during their graduate education. Furthermore, empirically supported faculty mentorship training focused on mental health among graduate students is virtually nonexistent. Thus, faculty mentors are left to navigate complex dynamics with their students without the necessary training to do so effectively. To address this gap, we propose a series of self-paced modules for mentors of graduate students: a foundational module on interpersonal responsiveness plus 6 additional modules in the mental health toolkit. These 6 toolkit modules will cover cognitive reframing, self-affirmation, mindful self-compassion, dialectical thinking, community building, and mental health crisis support. These modules are firmly grounded in existing research in psychology and relationship science about interpersonal interactions, mental health and well-being, and coping strategies. Our team is highly interdisciplinary, with expertise in psychology, relationship science, education, mentorship, experimental design, and assessment and evaluation. In addition, there are 3 advisory boards (content expertise advisory board, interdisciplinary faculty advisory board, interdisciplinary graduate student advisory board) of 5 members each, with members representing various disciplines, institutions, and geographic areas across the U.S. Thus, the modules will be grounded in the empirical literature, utilize the expertise of the PI and Co-Is, and also be guided by input from faculty mentors and graduate students across disciplines, institutions, and geographic areas in the U.S. First, we will develop the modules using a multi-step, iterative process. Next, we will pilot the modules and further refine the module materials. Then, we will conduct a randomized controlled trial (RCT) assessing the efficacy of the modules, disseminate the results, and disseminate the modules. Evidence-based training for mentors navigating mental health challenges among their graduate students is lacking. Thus, these modules will fill an important gap in higher education training and in the scientific literature on mentorship.

NIH Research Projects · FY 2026 · 2026-03

PROJECT SUMMARY During early development, all animal embryos undergo several rounds of rapid early cleavage divisions under time constraint and exquisite regulation. It is still unclear how such exquisite regulation of mitosis is achieved. We have recently discovered that miRNA-31-mediated local translation at the mitotic spindle is important for early development and highlight the importance of post-transcriptional regulation in ensuring proper mitosis. Localization of miR-31 to the mitotic spindle is evolutionarily conserved, as we observe this localization in both sea urchin embryos and mammalian cells. This discovery has opened up key questions. Namely, how specific RNAs are transported to the mitotic spindle and what are the specific functions of proteins encoded by these localized transcripts. We will use the sea urchin embryo to address key questions in RNA regulation, and mammalian cells to test for evolutionary conservation. The relatively fast early development and experimental manipulability of the sea urchin embryo, along with its well-described gene regulatory networks and some miRNA characterizations, and conserved developmental processes are key features that make it an excellent model. Importantly, the sea urchin embryo provides an organismal-level understanding of conserved mechanism of RNA localization and post-transcriptional control. Our future work will focus on two research themes: (1) We will investigate the molecular mechanism of subcellular transport of RNAs (miRNAs and their targets) to the mitotic spindle in sea urchin embryos and mammalian cells. How RNAs are transported to the subcellular region of mitotic spindle remains unclear. We hypothesize that miRNAs and their target RNAs are co- transported by RNA Binding Proteins as a ribonucleoprotein complex to the mitotic spindle, in part by hitchhiking on organelles. The significance of this study is that by identifying evolutionarily conserved, fundamental mechanisms of subcellular transport of miRNAs and their target RNAs, we will define how spatiotemporal control is achieved. (2) We will identify the regulation and function of these RNAs during the fast dividing phase of the cleavage stage sea urchin embryos and in mammalian cells. We will test the hypothesis that the level of translated miRNA targets needs to be carefully modulated at the mitotic spindle to mediate chromosomal segregation. We will further examine how miR-31 regulates its targets en route to the mitotic spindle, as well as reveal the functions of miR-31 targets that are critical for chromosomal segregation. Our long-term goal is to obtain a comprehensive mechanistic understanding of the subcellular transport of RNA, RNA post-transcriptional regulation mediated by miRNAs, and the functions of proteins they encode during mitosis. Understanding the mechanistic basis of how RNAs are transported to specific subcellular locations and their functional significance in the spatiotemporal context will represent a transformative advance in our knowledge of how an embryo regulates its precise timing of mitosis, the myriad roles RNAs play in early development and disease, in turn impacting the development of RNA-based and RNA-targeted therapies.

NSF Awards · FY 2026 · 2026-02

Kinetic theory describes the behaviors of dynamic systems from a statistical point of view. It has wide applications in many fields, including supersonic flows, microelectromechanical systems, unconventional gas reservoirs, space vehicle re-entry problems, and nuclear fusion. Because of the high dimensionality of such models, efficient simulation is a long-standing challenge, which limits their applications to real-world problems. This research project will address this challenge by developing reduced models to approximate the kinetic equations. These models, called moment models, are expected to capture the physics and serve as good surrogates with the aid of machine learning (ML). This will provide a powerful tool in the modeling and simulation of non-equilibrium phenomena in physics and engineering. The project will provide research opportunities for graduate and undergraduate students who are interested in computational mathematics, and provide curriculum development in the PI's department. The primary objective of this research is to develop robust, accurate, and efficient ML moment models with some provable mathematical structures. The project focuses on how to preserve the hyperbolicity structure of the ML moment models. The hyperbolicity is closely related to the well-posedness of the first-order system of partial differential equations and is also vitally important for robust numerical simulations. The following ideas and methodologies will be investigated: (1) a symmetrizer-based approach and an eigenvalue-based approach that preserve the hyperbolicity of the model in multidimensional cases by exploiting the algebraic structure of the ML moment model; (2) a ML approach to learning boundary conditions that ensures necessary conditions for the well-posedness of the initial boundary value problem for the moment model; (3) a ML model with hyperbolicity enforced by generalized data-driven moments. 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 2026 · 2026-02

Parkinson’s disease (PD) is the fastest-growing neurological disorder globally, resulting in debilitating motor impairments and, in some cases, cognitive challenges that significantly disrupt daily life. While current treatments manage symptoms, they do not slow disease progression, partly due to a limited understanding of the brain circuits involved in PD-related motor deficits, especially in complex tasks that reflect real-world challenges. Although much research has concentrated on unimanual movements, everyday activities frequently involve the need for coordinated bimanual actions alongside cognitive demands, such as buttoning a shirt while simultaneously maintaining selective attention during a conversation. People with PD experience exacerbated motor symptoms in dual-task conditions - such as performing mental subtraction while engaging in complex coordinated movements like walking - yet the underlying neural mechanisms driving this interference are poorly understood, particularly in relation to bimanual coordination, which has yet to be thoroughly studied. This study aims to fill a critical knowledge gap by using advanced multimodal imaging to examine force control deficits and associated brain changes in PD during single and dual-task bimanual coordination. We hypothesize that PD will demonstrate altered brain activity during bimanual coordination tasks, with greater disruptions in brain function under dual-task conditions. These disruptions are expected to result in more pronounced motor and cognitive impairments in PD, especially when tasks involve additional cognitive demands. Using functional magnetic resonance imaging (fMRI) and functional near- infrared spectroscopy (fNIRS), we will measure brain activity during isometric bimanual force tasks at low to moderate force levels, both in isolation and while participants engage in cognitive challenges, such as selective attention tasks that require focusing on relevant stimuli while ignoring distractions. fNIRS offers distinct advantages over fMRI, including greater flexibility in participant positioning and the ability to assess brain responses in more naturalistic settings, providing additional insights into cognitive-motor interactions. Furthermore, we will investigate PD-related structural brain changes through diffusion MRI, which when combined with functional measures of neural activity and behavioral performance will allow us to identify predictors of dual- task interference. This research will deepen our understanding of the neural mechanisms underlying bimanual coordination deficits and may guide the development of targeted rehabilitation strategies aimed at improving both motor and cognitive performance.

NIH Research Projects · FY 2026 · 2026-02

ABSTRACT Although overall cardiovascular (CV)-related mortality has declined, there has been an alarming increase in CV mortality in women prior to menopause (i.e., perimenopause, PERI). There is a crucial gap in knowledge regarding physiological changes in CV function during the menopausal transition as follicle stimulating hormone (FSH) increases and estradiol (E2) declines. Heightened sympathetic nervous system activity (SNA) at rest and in response to acute stimuli (i.e., SNA reactivity) is implicated in the pathogenesis of various CV diseases. SNA is governed centrally by regions in the medulla oblongata and influenced by higher brain regions. In the periphery, efferent SNA and the subsequent release of the neurotransmitter norepinephrine (NE) is a major contributor to vascular tone. While the α-adrenergic receptor-mediated vasoconstrictor response predominates, concurrent β-adrenergic receptor-mediated vasodilation partially restrains excessive NE-induced vasoconstriction. Although this effect is lost in postmenopausal women (POST), it is unclear if the loss of β-mediated dilation occurs during the menopausal transition, and to what extent changes in E2 and FSH contribute. Thus, aberrant changes in sympathetic function either centrally or peripherally during the menopausal transition (PERI) may have physiological consequences that contribute to higher rates of CV disease. Therefore, the goal of this study is to examine the upstream (i.e., central) causes and downstream (i.e., peripheral) consequences of sympathetic activation in premenopausal, early peri-, late peri-, and postmenopausal women and to understand the mechanistic contribution of changing reproductive hormones (FSH and E2) during the menopausal transition. Our overall hypothesis is that the menopausal transition promotes SNA overactivation centrally (i.e., medullary and supramedullary) and peripherally (efferent SNA, vascular adrenergic function) and is driven by greater FSH and lower E2 concentrations. We will perform a comprehensive assessment of sympathetic function in women across various menopausal stages based on the STRAW+10 criteria of reproductive aging, as well as during a controlled hormone intervention to isolate the effects of FSH and E2. Aim 1 we will directly record efferent SNA (peroneal microneurography) to test the hypothesis that SNA at rest and SNA reactivity will be augmented across the menopausal transition due to greater FSH and lower E2 concentrations. Aim 2 will test the hypothesis that BOLD fMRI signal changes during sympatho-excitatory maneuvers will be augmented in the supramedullary and medullary sympathetic regions in women across the menopausal transition due to greater FSH and lower E2 concentrations. Aim 3 will test the hypothesis that NE-induced vasoconstriction will be augmented across the menopausal transition and mediated by a loss of β-mediated dilation due to greater FSH and lower E2 concentrations. These findings will help identify the optimal treatment timeframe and target for interventions to promote CV health in women.

NSF Awards · FY 2026 · 2026-02

This Research Infrastructure Improvement EPSCoR Research Fellows project provides a fellowship to an Assistant professor and training for a postdoctoral fellow at the University of Delaware. This work is conducted in collaboration with the University of Washington. Through the fellowship, the PI will explore how the interplay of light and controlled energy flow can be used to manipulate the properties of advanced materials at the quantum level. The project will use concepts from physics and materials sciences to understand how new forms of order can appear and be stabilized in these materials when they are exposed to light. The results could lead to new ways of engineering materials and designing functionalities, such as novel electronics with lower energy cost. This award will also promote collaboration between institutions, provide training for early-career scientists, and enriching undergraduate and graduate curricula for STEM education and workforce development. This project will develop a hybrid theoretical framework to simulate the non-equilibrium behavior of quantum materials, with a particular focus on how energy flow shapes their properties when exposed to light. The framework will integrate semi classical models for atomic motion with advanced quantum methods for electronic motion to study their complex interaction driven by light. By applying this framework to quantum electronic systems, the research will reveal how energy flow can stabilize novel states of matter that are not found under normal, equilibrium conditions. The project will foster faculty development in materials sciences and quantum theory, while providing training for both graduate students and postdoctoral researchers. Incorporating the research findings into curriculum development, outreach activities, and collaborations with other institutions will enrich STEM education, prepare a skilled future workforce, and enhance the university’s research capacity to attract talented students. This project is supported by the EPSCoR Research Infrastructure Improvement Program: EPSCoR Research Fellows, which supports early- and mid-career investigators in eligible jurisdictions to develop collaborations at the nation’s private, government or academic research institutions. 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 · 2026-01

PROJECT SUMMARY/ABSTRACT There is great interest in the early detection of amnestic mild cognitive impairment (aMCI) in older adults, as this stage often precedes dementia due to Alzheimer’s disease. Early identification provides an opportunity to intervene on modifiable risk factors to slow further cognitive and functional impairment. While behavioral and psychosocial interventions may effectively delay cognitive decline (e.g., cognitive rehabilitation, physical activity, psychotherapy), no interventions have specifically targeted the language abilities of older adults with aMCI. This is a missed opportunity because (1) language impairments may precede impairments in other cognitive domains, making language metrics potentially more sensitive for detecting aMCI. Previous research has shown measures of semantic richness (e.g., number of unique words) can differentiate individuals with MCI from cognitively unimpaired (CU) older adults. (2) Language impairments in older adults may lead to decreased participation in daily activities, including those that are cognitively protective, such as social engagement. For example, individuals with aMCI may become socially disengaged due to word-finding difficulties (e.g., they feel embarrassed about forgetting names, and attend fewer social or work functions). This proposal aims to better understand the language abilities (i.e., semantic richness) of older adults with aMCI across different contexts and its relationship with psychosocial functioning. The long-term goal of this line of research is to design interventions to improve or maintain independence in daily function and slow the onset to dementia for adults at risk. Legacy performance-based language tasks (e.g., naming line-drawn objects) are decontextualized and may not optimally capture the early semantic language changes in aMCI. In contrast, discourse (i.e., connected speech/language) tasks more closely resemble everyday language use and involve a more complex interplay between multiple cognitive domains and context demands. This study will leverage the large, open-access Delaware Corpus of DementiaBank, housed in the established TalkBank system, to address the research aims. Aim 1 will determine if discourse context (i.e., picture description; story narrative; procedural discourse; personal narrative) differentially affects semantic richness in aMCI and CU groups. Aim 2 will determine if there is a relationship between psychosocial functioning and semantic richness in older adults. Results from this proposal will offer critical insight into the semantic language abilities of individuals with aMCI across different contexts and its link to psychosocial functioning. This knowledge is essential for developing effective treatments for individuals with aMCI that target language and psychosocial function (e.g., group treatment of word-finding problems). The long-term goal of this work is to reduce the functional consequences of language impairments in aMCI, thereby supporting independence and delaying the progression to dementia.

NIH Research Projects · FY 2026 · 2026-01

Mouse models are an increasingly powerful tool in orthopaedic research, with continuous increases in the number and variety of popular methods for studying diseases such as osteoarthritis. While the use of these models is rapidly increasing, there has been little work in developing sophisticated techniques for assessing the biomechanical function of rodent joints, such as mouse knees. In this proposal, a novel force sensing universal robotic testing system will be applied to study multi-axial rodent joint biomechanics in both healthy and injured cases, allowing for clinically meaningful functional assessments of joint- and tissue-scale outcomes in powerful pre-clinical models. Specifically, the system will be used to study the immediate biomechanical effects of two common injury models: medial meniscus destabilization and anterior cruciate ligament rupture. Understanding the specific mechanical effects of these two injury models will improve experimental decision making, allowing for researchers to better select their models for future studies. Specifically, robotic testing will characterize changes in the relative motion of the knee under clinically relevant loads in both translational (anterior-posterior tibial drawers) and rotational (varus-valgus tibial rotation) loading paradigms, providing insight into the functional injury effects and potential signs of disease progression in multiple directions. The selection of physiologically relevant tests commonly performed in orthopaedic clinics will provide unique comparison points between mouse models of injury and osteoarthritis and published data from human subjects studies. Along with studies of the immediate functional effects of these injury models, in this proposal we describe a comparison of the structural and functional outcomes from three common models of orthopaedic trauma in the mouse. These models include the meniscus and ligament injuries described above, as well as a chronic overload model of cartilage damage. These three models will be implemented in animal cohorts and the overall effects of tissue structure, joint function, and individual tissue loading will be assessed. This information gained in this longer term study of three tissue damage models may be instrumental in identifying heightened injury risks in specific patient cohorts, and may lead to improved personalized medicine approaches through rehabilitation programming. Upon completion of this work, the novel robotic testing system may be applied to other joints from small animal models, including additional animals such as rats and rabbits, and additional joints such as elbows and shoulders. In summary, this work establishes a novel approach for quantifying changes in the biomechanical function of musculoskeletal joints in small animal injury models, with an initial application comparing the immediate and chronic impacts of three common models of osteoarthritis.

NSF Awards · FY 2025 · 2025-12

On April 29, 2025, scientists observed molten lava erupting on the deep seafloor at the East Pacific Rise. This RAPID project will investigate the hydrothermal vent system at the eruption site. Field work includes high-temperature fluid and temperature sampling. These measurements will extend an existing time series (6 expeditions from 2018-2025) with post-eruption (within one year) data. The project will result in a unique data set for understanding hydrothermal vent evolution. Broader impacts include training opportunities for early-career scientists and a live at-sea webinar with K-12 classrooms. The work leverages a scheduled field program at the study site in January 2026 that will include geology and biology science teams. This project benefits the US public by helping to understand volcanic eruptions and helps to build a workforce knowledgeable in cutting-edge deep-sea exploration technology. This project will advance understanding of how submarine eruptions reorganize subsurface permeability, fluid pathways, and chemical exchange between the crust and the ocean. High-temperature fluid sampling will be paired with in situ temperature measurements to provide a multidisciplinary view of post-eruptive vent evolution. These samples will constrain post-eruption conditions, including phase separation, redox, and volatile budgets, while geothermobarometric and isotopic data will link these changes to permeability shifts. Integrating these data with long-term temperature and permeability records will improve models of magmatic-hydrothermal coupling and refine the ability to predict mid-ocean ridge eruptions and their impact on the ocean. 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

Stony corals engineer complex reef ecosystems that are vital for biodiversity, food security, tourism economies, and coastal protection. Corals are complex organisms themselves, where the coral animal hosts a variety of symbiotic algae and other microbes (together, referred to as “holobiont” organisms). These mutualistic relationships are primarily driven by nutrient exchange between partners. However, environmental stress can disrupt these associations, leading to coral mortality and reef degradation. While nearly all reef-building corals rely on these symbioses, the flexibility varies across coral species. Such variation likely contributes to differences in the capacity for phenotypic plasticity, or the ability of individuals to display different characteristics depending on the environment. Phenotypic plasticity is likely adaptive for corals to thrive in variable environments without relocating. Yet, the role of each holobiont partner in mediating plastic responses is not well described, especially across coral functional groups. Environmental stress is causing declines in coral populations as well as shifting coral functional diversity. Therefore, to better predict reef resilience in a changing world, it is critical to fully understand the drivers, capacity, and role of phenotypic plasticity in acclimation across a diversity of coral species. This project will conduct a multifaceted assessment of plasticity across symbiotic partners and functional groups to advance our understanding of acclimatory mechanisms within symbiosis. Throughout two long-term thermal and photoacclimation experiments, this research will integrate physiological (metabolism and photobiology), molecular (gene expression and algal and microbial DNA metabarcoding), and organismal fitness (growth and resilience) responses of corals representing competitive, weedy, and tolerant life histories. To disentangle plastic responses across holobiont partners, transcriptomic and genomic sequencing will quantify host transcriptional plasticity and shifts in algal and microbial communities. Additionally, algal photochemical traits will be characterized by multi-spectral, single-turnover active chlorophyll a fluorescence. Such photobiological phenotypes differentiate algal populations and represent promising tools for predicting coral stress resistance. This comprehensive evaluation will resolve how symbiotic partners contribute to holobiont environmental responses. The capacity for phenotypic plasticity varies both across and within species, and emerging evidence suggests that this ability is adaptive in corals. The project will evaluate the adaptive plasticity hypothesis by assessing the fitness outcomes of environmentally-induced shifts in coral physiology and morphology. Importantly, the mechanisms and implications of plasticity will be compared across three coral functional groups. Contrasting life history strategies, including fidelity versus flexibility in symbiotic associations, are predicted to constrain acclimatization potential. This research is vital for predicting the ecological consequences of declines in functional diversity and transitions in species dominance occurring in coral reef ecosystems. Furthermore, this work will evaluate the potential for conservation interventions such as thermal acclimation and shading to increase coral resilience and survival within reef restoration. 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

Many complex and interconnected phenomena in the world - such as social media, sensor grids, and brain connectivity - can be modeled using graphs or networks. Unlike classical signal processing, which works with data regularly arranged in a homogeneous space (like audio or images), graph signal processing (GSP) analyzes signals that lie on irregular graphs or networks. Important outcomes of such analysis include detecting patterns, detecting and reducing noise, and visualizing the network. GSP has become a vibrant field of research in engineering and mathematics due to its applicability to a wide range of real-world problems, such as data analysis on sensor networks, biological networks, and neural networks. In this project, the investigator uses a blend of mathematical theories and techniques to develop the theoretical underpinning and possible new applications of GSP, especially for the case of large dynamic networks that evolve over time. The investigator plans to couple this research with graduate student mentoring, organizing scientific workshops, and outreach in the scientific community. In this project, the investigator aims to leverage a blend of techniques from harmonic analysis, functional analysis, and graph-limit theory to address challenges in information processing, particularly in the theoretical underpinning of GSP. The goal is to develop a theory that is applicable to a wide range of large dynamic networks. The relatively recent theory of graph limits and graphons provides a valuable non-parametric approach to modeling networks, particularly for stochastic networks. Indeed, graphons represent random processes that generate networks, and networks produced by the same graphon have similar large-scale features. Many large networks that arise naturally are manifestations of an underlying (hidden) spatial reality and can be efficiently modeled using Cayley graphons. In this project, the investigator develops signal processing on Cayley graphons as well as instance-independent GSP for samples of Cayley graphons. Current instance-independent graphon-based signal-processing methods apply to only dense undirected graphs. To develop a theory that applies to a large class of (possibly sparse or directed) networks, the investigator plans to address several theoretical challenges, such as convergence of spectra for sparse/directed graphs and spectral theory of nearly Cayley graphs. 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

The role of the aquatic environment is of great importance: it plays a critical role in climate; it contains the highest biodiversity on the planet; ports are among the most critical infrastructures for trade and transportation; and as much as 40% of the global population lives within 100km of the shoreline. Improving our understanding of the underwater domain is essential. Using autonomous robots to collect additional information will be safer, more cost effective, and can be extended to a larger scale than previous methods. The goal of this project is to enable autonomous operations of robotic systems in underwater environments. In order to achieve this goal, the robot needs to be aware of the environment, and of its own position inside the environment. The robot needs to develop movement strategies that would facilitate the efficient and accurate estimation of its position and the location of obstacles/objects in the environment, while taking into account the errors in the measurements. The underwater domain presents several unique challenges: there is no Global Positioning System (GPS); communication, when available, has extremely limited bandwidth; visibility conditions, even in the best-case scenarios, are limited due to particulates in the water that obstruct the view. The investigator will advance the state of the art in four areas: information from different robot sensors will be used to calculate the position of the robot as it moves through the underwater domain; then, the investigator and his graduate students will use all available information to produce a representation of the environment the robot can use to navigate; next, planning will be implemented to guide the robot through the environment taking into account the shorter distance and the areas with viewing interest; and finally, the team will investigate new strategies for exploring unknown environments efficiently. The investigator will use his research results from the underwater realm to raise interest for students and the general populace towards science, technology, engineering, and mathematics.  The goal of this project is to enable autonomous operations of robotic systems in underwater environments. In order to achieve this goal, the robot needs situational awareness. Additionally, the robot needs to develop motion strategies that would facilitate the efficient and accurate estimation of its pose and the location of points of interest in the environment, while taking into account uncertainty buildup and the effect of external forces such as wind or current. The underwater domain renders satellite-based GPS ineffective. Communications, when available, have extremely limited bandwidth; and visibility conditions are limited due to hazing and blurring, lighting variations over time, and color loss. The investigator will advance the state of the art in four areas: information from different sensors will be used to calculate the pose of the robot as it moves through the underwater domain; all available information will be utilized to produce a dense representation of the environment; next, a decision process will be implemented to guide the robot through the environment taking into account efficiency (shorter distance) and the areas with viewing interest; finally, new strategies for exploring and covering unknown environments efficiently will be investigated. More specifically, robustness measures and divergence predictors will be developed for the state estimation in order to provide early warnings of erroneous estimates. Measuring the quality of the different sensors will result in the judicious use of the subset of sensors that provide accurate information. The mapping challenge will be addressed by augmenting the feature-based map with features generated from the lighting variations, such as shadows and caustic patterns. Coverage patterns will be employed in open areas with limited obstacles, while a frontier-based strategy will guide the underwater vehicle to unexplored areas. Returning to mapped areas in a systematic manner will maintain the localization uncertainty below a user defined level. The results will be published in conferences and journals of robotics. 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

System-on-Chips (SoCs) are essential components of critical digital systems across domains such as healthcare, autonomous vehicles, and national infrastructure. As SoCs grow in complexity, they increasingly become targets for sophisticated hardware attacks that threaten privacy, safety, and national security. The project's novelties lie in developing a proactive and intelligent framework for securing SoCs against power side-channel and fault injection attacks, that not only respond to known threats but also anticipate and mitigate potential risks before they can materialize. The project's broader significance and importance are in enabling early-stage, verifiable security integration into the chip design process-shifting the research from reactive defense to built-in assurance. The project also contributes to open-source tools, student training, and the cultivation of a workforce capable of designing secure hardware systems. This project uses reinforcement learning and game theory to model evolving attacker-defender strategies and to automatically explore the SoC design space for secure hardware design configurations. It integrates advanced cryptographic primitives to enable security validation without revealing sensitive design details. The defense framework in this project is integrated into the SoC design and synthesis toolchain, allowing designers to trade-off between security, performance, power, and cost from the outset. The research advances foundational methods for secure hardware and is expected to produce new tools and practices for designing secure and high-assurance SoCs. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-10

Nontechnical Description The rapid advancement of deep neural networks (DNNs) and large language models (LLMs) is transforming many facets of modern society. These AI models are trained and deployed in data centers powered by specialized hardware such as graphics processing units (GPUs), resulting in significant energy demands and raising critical concerns around sustainability and energy security. This project aims to explore the use of light for performing neural network computations, enabling the development of energy-efficient AI hardware. Specifically, the project will leverage the integration of thin-film lithium niobate (TFLN) — a high-performance electro-optic material — with silicon photonic chip platforms to fabricate analog optical modulators that offer significantly lower loss and higher speed compared to traditional silicon-based devices. In addition, the project will design new architectures and circuit techniques to achieve high-resolution AI computation using low-precision building blocks, optimizing both efficiency and accuracy. The educational component of this project will train students in both photonic and advanced electronic chip design, equipping them with the skills essential for next-generation AI hardware development. Outreach to high-school students using AI-based projects will help build a pipeline of students to pursue engineering degrees focusing on semiconductors and AI. The industry sponsor will be actively engaged as a strategic partner to help transition the technology from research prototypes to real-world deployment. Technical Description The heterogeneously-integrated electronic-photonic AI accelerator (HIEPAA) project features cross-layer innovations from device design to integrated circuits, to wafer-scale architecture to achieve significant improvements in throughput and energy efficiency of AI accelerators. By combining co-packaged electronic-photonic ICs (EPICs) with bonded TFLN modulators promising above 50 GHz bandwidth and extremely low loss, this architecture will enable space-time multiplexed computations, delivering over 2 Tera operations per second (TOPS) per tile with 2 TOPS/W energy-efficiency and scaling to 1 ExaOPS performance at the wafer scale with 200 TOPS/W energy-efficiency. Architectural innovations will solve the long-standing challenge associated with the precision and energy consumption tradeoff of data converters and devices used in the accelerator tile by investigating residue number system (RNS)-based photonic VMM architecture. The EPIC photonic core will support coherent vector-matrix multiplication (VMM) at up to 60 GS/s symbol rates. The space-time multiplexed architecture will enable flexible VMM operations with vector lengths ranging over 1000s to perform inference on transformer-based LLM models. Fabricated PICs and EICs will be independently verified, packaged, and integrated into a system, with a packaged printed circuit board (PCB) prototype with a field-programmable gate array (FPGA)-based digital backend to validate the HIEPAA tile's performance on the state-of-the-art LLM models, which will guide wafer-scale architectural performance benchmarking. A comprehensive education and workforce development plan will focus on building expertise in electro-optic AI accelerator architecture, photonic and electronic chip design, and AI and Machine Learning. A key emphasis is to fast-track the training of students on newer FinFET CMOS nodes through a complete revamp of analog IC design courses and developing structured training material with a focus on photonics IC design. New undergraduate research opportunities will be introduced to sustain the tradition of involving undergraduates in the PIs' labs through summer scholar programs and NSF-sponsored REU initiatives. 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

Bone fractures are common injuries that impact an estimated 9.6 million Americans each year, resulting in 1.6 million years of living with disability. There is currently a lack of reliable biomarkers that can detect problems with bone healing early, which represents a major barrier to improved care. This proposal describes a translational validation study of next generation methods for fracture diagnostics. In the study, we will use ultrashort echo time magnetic resonance (UTE-MRI) sequences and finite element (FE) based virtual mechanical testing to quantify and predict bone healing. Tibial fractures will be employed as a test bed for more global applications. Our central hypothesis is that the structural quality of bone healing can be measured using MR-based virtual mechanical testing. The study will consist of 3 Aims. In Aim 1 we will establish MR assessment of callus mechanical properties. We will use an ovine tibial osteotomy model, fixed by intramedullary (IM) nailing. UTE-MRI will be used to construct FE models and in situ inverse optimization will define the mechanical properties of callus by matching the virtual mechanical tests to the postmortem physical biomechanical tests. We hypothesize that UTE-MRI measures of bone density will be negatively correlated with callus elastic modulus, and that image-based modeling from UTE-MRI can reliably replicate bench tests of healing long bones. In Aim 2, we will predict late fracture healing using early MR scanning in the same ovine model. Here, we will use MR to measure the earliest stages of repair and predict the later formation of mineralized callus in normal and delayed healing animals. We hypothesize that the sigmoid functional recovery curve of mechanical rigidity in delayed-healing fractures is significantly different from the recovery curve that characterizes normal healers. In Aim 3 we will deploy MR-based virtual mechanical testing to measure fracture healing in a clinical setting. In this observational pilot study, we will recruit 50 patients with tibial shaft fractures treated by IM nailing. Results from 6-week UTE-based virtual mechanical testing will be correlated with 12- week CT and MR measures and time to clinical union. Receiver-operator characteristic analysis will be used to identify the diagnostic cutpoint for early image-based virtual mechanical testing that best discriminates between normal healers and patients who experience delayed union (>20 weeks). We hypothesize that MR scanning can replicate the results of CT-based virtual mechanical testing for assessment of fracture healing in a clinical setting and that soft callus formed by 6 weeks predicts clinical outcomes (binary classification: union or delay). This project will demonstrate how objective measurements of bone healing using radiation-free MR imaging will fundamentally shift fracture research and clinical care paradigms by enabling an early and proactive approach to the management of high-risk fracture patients. Our findings will lead to future applications focused on femoral shaft fractures, fragility fractures, pediatric fractures, and limb salvage.

NIH Research Projects · FY 2025 · 2025-09

PROJECT SUMMARY The long-term goal of the Corbin lab is to study emergent time-dependent cellular responses through reimagining engineered microdevices. Cells engage in a continuous and dynamic interplay with their surroundings, and respond rapidly and with remarkable precision to variations in their mechanical environment including topography, stiffness, and stretching. There is a critical need to make an in-vitro system that sufficiently reflects the essential adaptive and maladaptive processes and phenotypic benchmarks to understand the vital link between biomechanics and cell structure and function. Materials with the ability to change mechanical properties have the potential to reshape our understanding of how cells interact with their micro-environments. Broadly, we seek to address two major challenges of dynamic engineering materials for mechanobiology interrogation: 1) tailoring effective and spatio-temporal realistic biomechanical cues to drive desirable interactions and responses and 2) manipulating and modeling the complex network of many signals operating on various lengths and timescales that determine cell fate. The Corbin lab has focused on developing a unifying engineered materials platform to deconstruct and decode how biological behavior can arise from a wide range of dynamic biomechanical stimuli. Major advances to date have included: (1) the evaluation of magnetorheological elastomers as a novel tunable and reversible biomechanics tool to study cardiomyocyte mechanosensitivity, (2) the discovery of pathologic mechanical increment induces rapid remodeling of cardiomyocytes, (3) the creation of local spatial mechanics and topographic mechanics with the same platform, and (4) quantify the directionality and magnitude of how spatial mechanics and chemical applications interfere or synergize with each other in time – in other terms how they “compete” or “cooperate.” In this proposal, we will dramatically enhance our efforts to engineer the elements of biological interaction with the dynamic mechanical microenvironment. We will expand our platform to include simultaneous control of stiffness gradients and adhesion gradients, local stimulation of substrate stiffness to examine the spread of mechanotransductive signaling, and cyclic variations in stiffness to mimic periodic biomechanical events. These future efforts are well suited to the research program, given the widely applicable framework to address the broader definition of invisible forces that intrinsically link cells and biomechanics.

NSF Awards · FY 2025 · 2025-09

Mathematics learning is often viewed as a sequential and cumulative process, where each stage builds upon the previous one. A crucial part of this progression is the development of formal fraction knowledge. Previous studies indicate that students develop understanding of fraction concepts as early as first grade. The project aims to help schools integrate fractions more effectively into the primary mathematics curricula, before formal fraction instruction occurs. The work aims to bridge the gap between the emphasis on whole numbers in preschool and early elementary education and the focus in later grades on fractional quantities. The research will study how fraction-related activities appear in first and second grade mathematics and how activities can be developed to support students' learning about fractional quantities alongside whole numbers. This research will enhance understanding of the nature of early mathematical cognition, particularly regarding non-integer quantities, in several significant ways. It will investigate for the first time the nature of the input in the school environment that may support early fraction knowledge. Additionally, the project will refine assessment methods for measuring this knowledge and examine how targeted fraction activities can further promote growth in this area. To study the learning environment, the approach will be to analyze textbooks and interview teachers. The next phase of the study will examine learning activities in a longitudinal study including an experiment to test the impact of the fraction learning activities on early fraction knowledge and mathematics achievement. This combined approach will contribute to describing the current state of fraction-related learning activities and develop meaningful approaches that can be used to teach fraction concepts in the early grades. This project is supported by NSF's EDU Core Research (ECR) program. The ECR program emphasizes fundamental STEM education research that generates foundational knowledge in the field. Investments are made in critical areas that are essential, broad, and enduring: STEM learning and STEM learning environments, broadening participation in STEM, and STEM workforce 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.