Grants

Capoeira and Arts Association of New England

Caporira Cultural Exchange Batizado and Workshops

Seaport Museum New York

Captive Passage Programming

Chicago Department of Transportation

FFY 2025 STATE/LAKE LOOP ELEVATED

National Institutes of Health

Career Transition Award for NINDS Intramural Clinician-Scientists (K22 Clinical Trial Not Allowed)

National Institutes of Health

Career Transition Award for NINDS Intramural Clinician-Scientists (K22 Clinical Trial Required)

NSF

This Faculty Early Career Development Program (CAREER) grant funds research that enables general purpose robotic systems that can change their forms to optimally achieve a range of functions. This research introduces a systematic framework for synthesizing form and function within a dynamical system, replacing manual motion design with a scalable, tractable, and data-efficient approach. Unlike traditional fixed-form systems, whose shapes are determined at design time and tailored to specific tasks, this research enables next-generation platforms that can continuously morph to select shapes for solving complex multi-stage tasks in an optimal manner, thereby promoting the progress of science, advancing national prosperity and welfare, and securing the national defense. Tightly integrated with the research activities, this grant also funds a comprehensive outreach strategy to engage participants across various educational levels, including K-12 students, schoolteachers, undergraduate students, and graduate students, and to establish a foundation for lasting contributions to robotics theory, system design, and STEM education through layered mentorship and interdisciplinary learning in the United States. Mobile robotic systems involve complex dynamics with high degrees of freedom, hybrid transitions, and sensitivity to contact and the environment. These challenges are magnified in morphable systems, where the configuration space is combinatorially large and time-varying. Overcoming them requires new representations, numerical methods, and control strategies that generalize across shapes and tasks. This research aims to develop a systematic framework for modeling, analyzing, and controlling hybrid dynamical systems with structured morphological variability and to provide theoretical and algorithmic tools that enable scalable co-design of physical form and control across diverse tasks. The research encompasses three thrusts: (1) constructing a unified framework for modeling morphology using symbolic representations of form and symmetry-aware model reduction; (2) characterizing the form-function relationship and constructing a task-based motion library through trajectory optimization, sensitivity-guided continuation, and bifurcation analysis; and (3) developing a novel data-driven hierarchical control strategy that enables rapid adaptation to changes in morphology and tasks, which will be validated on physical robot platforms with diverse morphologies. Beyond robotic systems, this research has potential applications in reconfigurable and automated manufacturing lines, space exploration missions, and senior and medical care centers, where multitasking capability and versatile operation are strongly required. 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

NON-TECHNICAL SUMMARY Plastic waste is a growing environmental and societal challenge, with millions of tons accumulating in landfills and oceans each year. These plastics persist for centuries, harming ecosystems and public health. This research addresses this problem by creating sustainable plastics that can naturally decompose, using plant-based materials called cellulose nanofibers. These nanoscale fibers are abundant, break down safely, and derived from the non-edible parts of plants, making them a promising alternative to current commercial plastics. However, due to limited methods to modify plant nanofibers at scale, plastics made from these materials have constrained properties and functionality, preventing their widespread use. This research tackles these limitations combining eco-friendly chemistry with artificial intelligence and machine learning (AI/ML) to develop plant nanofiber-based plastics with “programmable” properties, meaning their resistance to breakage, water barrier abilities, and adhesiveness to other surfaces, etc., can be customized for applications in electronics, packaging, and biomedical devices. The approach uses water-based chemical processes instead of harmful solvents, while AI/ML accelerates innovation by predicting how chemical changes affect material performance. This research supports national interests by reducing plastic pollution, advancing U.S. leadership in sustainable manufacturing building on domestic feedstocks, and promoting biotechnology for a circular bioeconomy. Beyond environmental benefits, it invests in education and workforce development. Students will gain hands-on experience in sustainable materials and data-driven design in relevant courses, participate in industry internships, and create short educational videos for platforms like TikTok and YouTube to engage K–12 learners and the public. These efforts will enhance STEM education and prepare future leaders in biotechnology and advanced materials. TECHNICAL SUMMARY Global plastic production exceeds 400 million tons annually, with less than 10% recycled, creating severe environmental challenges. Cellulose nanofibers (CNFs), derived from non-edible plant residues, are abundant, biodegradable, and mechanically robust, making them promising candidates for sustainable plastics. However, their adoption is constrained by limited surface chemistry and the absence of scalable functionalization strategies, limiting performance tuning for diverse applications. This research develops a scalable, aqueous phase “click” chemistry platform for modular CNFs functionalization, enabling bioplastics with programmable properties. The research integrates green chemistry, polymer engineering, and machine learning through three objectives: (1) Establish catalyst-free reaction in water for selective attachment of diverse functional groups to CNFs, (2) Fabricate and characterize CNF-based bioplastics with tunable mechanical, interfacial, and physical properties, mapping structure–property relationships. (3) Implement an invertible machine learning (AI/ML) framework for bidirectional design—predicting material properties from functionalization parameters and generating functionalization “codecs” for target performance. The experimental framework includes systematic variation of functional group type and density, combined with advanced characterization to build a comprehensive dataset for predictive modeling. Educational components include a research-integrated undergraduate course, student internships, and datasets generated by student projects that feed into the machine learning model, creating a closed-loop system linking education and research. The results of this research are aimed at establishing fundamental design rules for property-programmable biotechnology-based polymers and enabling multifunctional bioplastics for advanced applications such as flexible electronics, soft robotics, and smart packaging. These outcomes are expected to advance predictive materials engineering, biotechnology and sustainable manufacturing. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF

Modern technologies like lithium-ion batteries, electric vehicles, and advanced manufacturing depend on critical minerals. These minerals are often found in seawater, brines, and wastewater. The minerals of interest mix with other minerals of similar size and chemical behavior. Separating nearly identical ions is very difficult. Membrane-based separation methods are energy-efficient, but designing membranes that can tell similar ions apart is a challenge. This project will improve membrane design by analyzing how the membrane’s chemistry and internal structure control the motion of ions through membranes. The project will use quantum calculations and other simulations to produce models that predict the motion of ions. By identifying chemical features that help certain ions pass more easily than others, the project will support better membrane design. The results will improve U.S. critical mineral recovery, water treatment, and energy technologies, while also training students through coursework and K–12 outreach activities. This project focuses on water-stable metal–organic framework (MOF) membranes as a model platform to investigate how membrane chemistry, structural heterogeneities, and ion–ion interactions control selective transport among chemically and physically similar ions. The study targets technologically important separations relevant to critical mineral recovery, including lithium and sodium ions, as well as selected heavy metal ions. An integrated multiscale modeling framework is employed, combining ab initio quantum calculations, molecular dynamics simulations, and transport models grounded in statistical thermodynamics. Atomistic simulations are used to characterize interactions among ions, membrane atoms, and the local chemical environment that govern ion selectivity. Insights from these simulations inform physically based transport models that link molecular-scale mechanisms to membrane-level selectivity and permeability. In addition, machine-learning models are incorporated in a mechanism-aware manner as surrogate representations of transport behavior learned from physics-based simulations, enabling efficient exploration of chemically and structurally defined parameter spaces. Model accuracy and predictive trends are validated through close collaboration with experimental partners. The expected outcomes include mechanistic insight into selective ion transport in chemically heterogeneous nanoporous membranes, predictive modeling frameworks that bridge atomistic and membrane scales, and broadly applicable computational tools for separation, energy, and water 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

The human body has a protective shield between the bloodstream and the central nervous system (CNS). This shield includes the blood-spinal cord barrier (BSCB). The BSCB strictly controls which molecules enter the spinal cord. A damaged or compromised BSCB can contribute to neurological disorders, including spinal cord injuries and Amyotrophic Lateral Sclerosis (ALS). Studying these diseases or testing new treatments is challenging, because researchers do not have laboratory models that accurately mimic the BSCB. This CAREER project will bioengineer a "BSCB-on-a-chip." The project will use advanced stem cell technology and micro-engineering tools to create a three-dimensional (3D) model that replicates complex interactions between human spinal cord tissue and blood vessels. The BSCB-on-a-chip will help researchers understand how the spinal cord barrier develops and screen new drugs without extensive animal testing. The project will also provide interdisciplinary training for college students and early-career researchers in bioengineering and neuroscience. Additionally, the project team will lead outreach programs to spark interest in STEM fields among elementary and middle school students, as well as pediatric patients and their families. The blood-spinal cord barrier (BSCB) is distinct from the blood-brain barrier (BBB) in its molecular composition, permeability, and susceptibility to pathological insults. Despite its critical role in the progression of neurodegenerative disorders, physiologically relevant in vitro models that capture the regional diversity of the BSCB remain undeveloped. This project will integrate microfluidic engineering with stem cell-based tissue assembly to develop a human BSCB assembloid-on-a-chip. The project will: (1) leverage microfluidics to generate a 3D spinal cord model with physiological rostral-caudal and dorsal-ventral organization and engineer a functional BSCB assembloid-on-a-chip that integrates vascular and neural tissues to faithfully recapitulate molecular, cellular, and transcriptomic signatures; and (2) elucidate the signaling mechanisms by which spinal cord tissues instruct endothelial cells to acquire BSCB identity. Additionally, the project will establish a bio-inspired, chemically defined approach for generating scalable BSCB organoids to facilitate high-throughput drug screening. This research will provide the first biomimetic platform to study the distinct regulatory mechanisms of the spinal cord barrier, offering transformative tools for neurovascular research, precision medicine, and the development of therapeutics for CNS disorders. 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

Hydrogen is used as transportation fuel, to produce ammonia for fertilizers, and to upgrade oil for numerous products. This project seeks to lower the cost of producing hydrogen by improving water electrolysis technology. Electrolyzers use ion-conducting membranes as separators. The project will identify relationships between membrane polymer structure of the membrane and its performance in an electrolyzer. These relationships are not well understood. The goal is to design efficient ion-conducting membranes that remain stable over time. The results of the project will help guide membrane design. The project will provide hands-on learning activities that connect to the research. It will create a training environment that prepares students for careers in polymer science and electrochemical engineering. Training activities will emphasize laboratory work, mentorship, teamwork, and clear scientific communication. The research will support manufacturing and the domestic energy industry. The goal of this research is to establish structure-property relationships in anionic ion-conducting membranes. This will be achieved through modular polymer synthesis and high-throughput characterizations. The research will develop design rules that link polymer chemistries and polymer structures to polymer performance. Polymers will be prepared using controlled synthesis methods that allow careful changes in molecular structure. High-throughput measurements will be used to study chemical stability, water uptake, ion transport, and swelling. A key innovation of this project is the development of a new block copolymer platform. The platform has the potential to decouple and optimize conflicting properties within a single material family. The knowledge and methodologies gained will advance polymer science and support the development of electrochemical energy 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

Engineers need to create robust solutions for society through technological, infrastructural, and medical advancements. To accomplish such tasks requires that they effectively work on teams with people from a variety of backgrounds. Effective teamwork involves skills to promote positive collaboration such as addressing challenging interpersonal situations. This project will identify the ways engineering undergraduate peers interact with each other in the classroom and whether or not they have self-awareness about their behaviors toward others. The project also aims to determine whether certain teaching practices positively influence peer interactions in engineering. The findings of the study will help instructors across engineering disciplines, through their teaching practices, help their students learn how to prevent negative peer interactions so they can effectively work on teams, a key skill needed for productivity in the engineering workforce. By focusing on peer interaction during undergraduate education, the domestic engineering workforce will be better equipped with the relational skills, including collaboration and cooperation, necessary to promote advancements in engineering at a more efficient pace. Overall, this study will improve teaching and learning practices and foster positive peer interactions in engineering classrooms, which will contribute to a more effective engineering workforce, which will in turn support the United States in achieving global competitiveness. This study aligns with the goals of NSF (1) to develop an innovative and inclusive technical workforce and (2) to improve inclusion and participation in engineering by addressing structural issues within educational systems. The goal of this CAREER grant is to advance research on peer interaction in engineering classrooms and how instructors may mitigate negative interactions to enhance student’s abilities to work on teams to improve classroom learning experiences. This study examines (1) why and how peers develop their cultural beliefs about engineering; (2) whether or not peers are aware of negative behaviors they witness or enact themselves; and (3) whether faculty instructional practices focused on positive peer behaviors can mitigate negative peer behaviors. In Year 1, an intake survey will be sent to a broad array of engineering networks to determine eligibility for participating in the preliminary research. The initial part of the study will involve instructor interviews, undergraduate peer interviews, and a peer survey. In Year 2 & 3, 2-3 courses will be selected from four case study sites, representative of R1s and R2 universities, to conduct observations of labs and/or classrooms and interview both instructors and students. During Years 3 & 4, data will be analyzed and developed into scenario-based learning videos and a toolkit for instructors that would include resources and strategies to help facilitate positive peer cooperation. In Year 5, integration of research and education activities will include a video campaign utilizing various STEM and engineering networks and the case study institutions. Podcasts as well as workshops, online and in-person trainings, webinars, and presentations at local and national engineering conferences will promote the research and educational activities. This research will advance knowledge in engineering education by contributing to the scholarship on teaching and learning (SoTL) and will provide practical tools and resources to support instructors in enhancing the classroom learning experience of all engineering students. The research overall will contribute to societal well-being, improvements in STEM education and the STEM workforce, and foster the inclusive participation of all students in engineering. 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

Remanufacturing restores worn or damaged products to like-new performance, extending the life of high-value assets while reducing dependency on costly replacements and lowering supply chain vulnerability. Many repairs still depend on technician judgment that is difficult to document and is increasingly at risk as experienced workers retire faster than replacements can be trained. Although robots offer the potential to alleviate workforce shortages, today’s programmed automation is largely limited to repetitive operations and cannot replicate human-level reasoning and adaptability required to manage the unique geometries, uncertain damage states, and evolving conditions inherent to repair workflows. This Faculty Early Career Development (CAREER) project aims to create scientific and educational foundations for an integrated digital thread framework that enables autonomous, self-evolving cooperative robotic systems capable of additive repair. This project advances remanufacturing by moving from programmed automation toward cognitive automation, creating intelligent systems that leverage expert knowledge and continuously adapt to perform unique, customized operations across all remanufacturing steps. Further, this project will broaden participation through curriculum modules at the University of Connecticut, hands-on research and mentoring, summer programs with local schools and community colleges, and workforce development activities for manufacturers and small businesses. The overall research goal is to establish a mind-body-environment loop that integrates knowledge-based reasoning, physics-informed embodied interaction, and continuous environment-loop adaptation, to support adaptive repair actions and scalable deployment across emerging remanufacturing applications. Specific objectives include: (1) Develop a self-evolving, memory-augmented planning module to sense, diagnose, identify, and learn what processes are needed for the repair task, enabling generalizable context-aware reasoning. (2) Develop an embodied engine to decompose tasks, allocate subtasks to individual arms, optimize high degree of freedom motion plans, and execute non-planar slicing, ensure morphology-driven reconfiguration, and (3) Develop an adaptive digital twin for decision making based on multi-fidelity process data and physics-based simulation within a continuous environment loop, completing the mind-body-environment framework. Driven by the neuro-vector-symbolic architecture, this research integrates distributed sensory embeddings with structured symbolic knowledge, embodiment constraints and physics-based dynamics, and multi-fidelity simulation with experience-driven refinement. The resulting unified representation enables knowledge-driven reasoning, morphology-configured planning, and simulation-augmented adaptation. The system will be validated on multi-arm laboratory experiments and industrial case studies that include both reconstruction of damaged parts and modification to new specifications. This research advances foundational knowledge at the convergence of cognitive intelligence, embodied robotics, and advanced remanufacturing. 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

NONTECHNICAL Summary This award supports computational research and education to advance understanding of physical polymer aging, the process where the configuration of atoms relaxes over time leading to changes the properties of polymers. Polymers are long chain molecules made of several repeating units. When these chains are tangled and randomly arranged without any order, the polymer is said to be ‘amorphous’. Below the glass transition temperature (the point at which the polymer changes from soft and flexible to hard and glass-like), amorphous polymers are widely used in applications ranging from storage and packaging to separation technologies. Owing to their low cost and ease of manufacturing, polymer membranes have the potential to dramatically reduce the energy required for industrial separations, which currently account for a substantial fraction of global energy consumption. Polymers of intrinsic microporosity are especially promising membrane materials because their loosely packed molecular structure creates extra internal space (also known as free volume), which increases how quickly and efficiently gas molecules can move through the material. However, these polymers are not widely used in industrial applications, because they undergo physical aging, a slow and irreversible process in which the polymer relaxes and densifies over time. This relaxation leads to reduced separation efficiency, loss of mechanical integrity, and ultimately diminished membrane performance. At present, physical aging in high free volume polymers like polymers of intrinsic microporosity is captured indirectly through changes in membrane performance, and the underlying structural changes at the molecular level remain poorly understood. This CAREER award addresses this critical knowledge gap by uncovering how polymer chains rearrange during aging and how these rearrangements affect membrane performance. Molecular simulations will allow the PI to directly observe these small-scale rearrangements over time, revealing how the material’s internal structure and movement change in ways that cannot be observed experimentally. By developing a predictive framework for aging behavior, this research will enable the rational design of polymer membranes that maintain their performance over long times, facilitating the transition of energy-efficient membrane technologies from the laboratory to industrial use. In addition to advancing membrane science, the project integrates education and workforce development. While molecular simulations are used across many science and engineering disciplines, access to simulation-based training at the K-12 and undergraduate levels remains limited. To address this gap, the project includes mentoring and training activities that are open to all students at the K–12 and undergraduate levels. In addition, the project will increase the accessibility of molecular simulations for blind and visually impaired students, enabling their early engagement in STEM research. Successful completion of these educational goals will broaden access to molecular simulation tools across multiple educational stages while also encouraging blind and visually impaired students to pursue STEM degrees and enter the STEM workforce. TECHNICAL Summary This award supports computational research and education to advance understanding of physical polymer aging with potential to help guide polymer design and discovery. Physical aging in amorphous polymers arises from the gradual relaxation of a non-equilibrium glassy structure toward a thermodynamically favorable state. In polymer membranes, these microscopic relaxation processes manifest as macroscopic declines in permeability and mechanical properties, limiting the long-term viability of state-of-the-art high free volume materials such as polymers of intrinsic microporosity. Despite their technological importance, the molecular mechanisms governing aging in these materials, including the roles of chain rigidity, cooperative dynamics, and free volume redistribution, remain poorly understood. This project uses atomistic molecular simulations in conjunction with glassy aging theories to elucidate the molecular origins of physical aging in high free volume polymer membranes. The research focuses on quantifying the interplay between monomer rigidity, segmental mobility, and free volume distribution, and on identifying the dominant relaxation modes that control aging behavior. Temperature jump protocols are employed in simulations to accelerate and probe aging dynamics, enabling systematic investigation of relaxation pathways that are inaccessible to conventional experimental techniques. The project further develops a computational protocol to predict aging behavior in high free volume polymers based on molecular-level descriptors. The outcomes of this research will bridge polymer physics and membrane science by providing a mechanistic framework for understanding aging in glassy polymers. The resulting insights will guide the design of rigid, solvent-cast polymer membranes with improved resistance to aging, advancing the fundamental understanding of non-equilibrium polymer dynamics while enabling the development of durable, energy-efficient separation 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

This Faculty Early Career Development Program (CAREER) grant will advance the national prosperity and economic welfare by enhancing the analytical capabilities of organizations in sectors such as healthcare, finance, construction, and national defense that leverage stochastic computer simulation models to make critical decisions in the face of uncertainty. This award supports a fundamental reinvention of how such models are paired with optimization methods to inform decision makers of risks and tradeoffs in stochastic system performance. This research will make simulation optimization approaches more systematic, productive, and aligned with user needs and facilitate more holistic decision making than conventional approaches. Close collaboration with industry partners will ensure the methods created are intuitive, informative, and practicable. The educational component of the project will create high school outreach activities and teaching modules that explore analysis techniques for simulation data and improve programming proficiency and statistical literacy. This project will also produce software, including open-source implementations of the methods, a prototype of an interactive dashboard, add-ins for commercial simulation software, and versions that are compatible with an open-source simulation optimization testbed used by researchers and educators. The research is motivated by shortcomings of existing simulation optimization (SO) approaches, which generally require decision makers to specify summary performance measures to serve as objectives or constraints in an optimization problem. By beginning with a narrow problem formulation, SO practitioners often fail to think about their simulation model in the broadest stochastic sense. This research shifts the initial focus of SO from summary performance measures to distributions of performance measures, exposing the user to inherent risks and tradeoffs. The research invents a transformative framework that couples exploratory simulation analysis with powerful optimization technologies to facilitate more holistic decision making. This project will create new search methods for discovering solutions with differing output distributions, incorporate user input in pursuit of optimization goals, exploit parallel computing resources to accelerate the search and optimization processes, and extend the framework to handle simulation trace data. The research will require the invention of new methods for dynamically clustering multivariate distributions and stochastic processes and metamodeling simulation outputs and output distributions that will be rigorously analyzed from both a theoretical and computational perspective. 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

NONTECHNICAL SUMMARY A central challenge for quantum materials is understanding how large numbers of electrons interact and organize themselves into collective quantum states. These interactions often lead to emergent behavior, where the system exhibits new properties that are more than those of the sum of its parts. Well-known examples include superconductivity, which enables electric current to flow without resistance, and magnetism, which enables materials to generate magnetic fields. These phenomena have led to mature applications in technologies such as medical imaging and magnetic storage. Even more exotic is the phenomenon of charge fractionalization, where the material behaves as if its electrons have split up and carry a fraction of the electron charge. Such effects could potentially be harnessed to enable noise-resilient quantum information processing. Recent advances in experimental techniques have made it possible to fabricate and measure mesoscopic-scale quantum devices that serve as simplified yet powerful platforms for studying these interactions. This project seeks to deepen our understanding of theoretical models and to bridge the gap between theory and experiment by making testable predictions using advanced analytical and numerical techniques. By predicting signatures of complex electron interactions in relatively simple quantum devices, the research will propose new approaches to create and probe electronic states that go beyond conventional theories. The project also emphasizes education and workforce development. Graduate and undergraduate students will receive training in advanced theoretical and computational methods and will participate in research at the frontiers of quantum science. Outreach and classroom activities will include hands-on demonstrations of quantum entanglement and the development of a modern course on superconductivity, both available to the general public. These efforts aim to broaden public understanding of quantum physics and prepare a highly skilled quantum workforce, strengthening U.S. leadership in quantum information science. TECHNICAL SUMMARY This project investigates strongly correlated quantum systems through the theoretical study of quantum impurity models where both the impurity and its environment can be topologically nontrivial. These systems are perhaps the simplest examples that exhibit rich physics including non-Fermi liquid behavior, emergent anyonic excitations, and unconventional symmetry structures beyond SU(2). Recent advances in mesoscopic device fabrication and materials development have made it possible to probe these paradigmatic models experimentally, while theoretical progress has revealed new opportunities to explore the interplay of topology, symmetry, and electron correlations. The project aims to bridge the gap between theory and experiment by making testable predictions using analytical and numerical techniques. The research will focus on elucidating electron correlations and topology through quantum transport in mesoscopic devices, probing topological boundary excitations using quantum impurities, and developing new tools to study correlated multi-impurity states. The work combines quantum many-body and linear response theory, conformal field theory techniques, as well as numerical methods such as density matrix renormalization group. A key goal is to clarify the properties of emergent anyons in quantum impurity models, including Kondo anyons that arise in gapless systems and exhibit nontrivial impurity entropy, and to determine how their behavior compared to anyons in gapped topologically ordered phases. The project also emphasizes education and workforce development. Graduate and undergraduate students will receive training in advanced theoretical and computational methods and will participate in research at the frontiers of quantum science. Outreach and classroom activities will include hands-on demonstrations of quantum entanglement and the development of a modern course on superconductivity, both available to the general public. These efforts aim to broaden public understanding of quantum physics and prepare a highly skilled quantum workforce, strengthening U.S. leadership in quantum information science. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF

Sulfur is an attractive material for energy storage because it is abundant, inexpensive, and can store more energy than materials used in batteries today. Still, sulfur-based batteries have not reached their full potential. The various forms sulfur takes during operations are not well understood. This project focuses on a newly discovered liquid sulfur phase. This liquid form of sulfur has not been explored even though it may be a transformative energy material. The project will investigate how different sulfur phases form, transform, and interact with electrodes during charging and discharging. The team will design sulfur-based electrochemical cells that deliver high energy density quickly and efficiently. The project will lead to a better understanding of sulfur phases and pave the way for low-cost, high-performance sulfur-based energy storage systems. The project will establish a Microscopy, Spectroscopy, and Electrochemical Characterizations (MSEC) program for OU students and the local automotive industry. This project will provide undergraduate research opportunities in the Engineering Chemistry program, introduce K–12 students and teachers to energy science and engineering concepts, and inspire the next generation of scientists and engineers. Sulfur is a highly promising cathode material due to its abundance, low cost, and exceptionally high theoretical capacity. However, the behavior of sulfur during battery operations — especially its phases and transitions, which govern electrochemical performance — is poorly understood. This project highlights a liquid sulfur phase at room temperature, a newly discovered, largely unexplored, and potentially transformative energy material system. The project comprises four objectives: (1) Understand intermediate sulfur phases using in situ and operando platforms; (2) Chemically generate liquid sulfur on carbon electrodes using redox mediators; (3) Electrochemically generate liquid sulfur on carbon electrodes via fast and pulse charging; and (4) Design liquid-sulfur electrochemical cells with high capacity and fast kinetics. The project will employ in situ and operando platforms that integrate customized electrochemical cells, as well as optical, Raman, X-ray, and electrochemical microscopy and spectroscopy, along with a high-speed camera and microelectrodes. The research will close key knowledge gaps in sulfur phases and transitions, leading to the design of high-performance, low-cost sulfur-based electrochemical systems. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF

This Faculty Early Career Development Program (CAREER) award supports a research and education program that improves the understanding of how head impacts and traumatic events cause brain injury in humans and animals. Traumatic brain injury is a major cause of long-term disability and economic burden in the United States. However, brain injuries often occur at microscopic scales that cannot be seen in living humans, limiting efforts to understand and prevent them. This project will develop detailed digital models of human and animal brains to examine how features such as brain shape, folding patterns, and nerve fiber pathways influence where injuries occur and how they spread through the brain. The project will advance scientific understanding of brain injury and help inform the development of more effective protective strategies for the general population. It will also support the design of animal studies that better reflect human injury, reducing the need for new animal testing in alignment with current efforts by United States science agencies and regulators. The project includes educational activities that use brain models, public exhibits, and teacher training to engage learners, support workforce development in science and engineering, and promote broad access to scientific knowledge. The goal of this project is to determine how structural differences in brains across species, influence mechanical responses and injury thresholds under head loading. The research advances fundamental biomechanics and mechanobiology by explicitly linking brain structure to tissue-level deformation and injury mechanisms. The project will develop high-resolution, species-specific computational brain models that capture regional anatomy, cortical folding, and white-matter fiber architecture derived from medical imaging data. These models will be validated and used to simulate head impacts from controlled animal experiments as well as reconstructed or recorded human head impacts. The simulations will resolve brain deformation patterns, enabling direct comparison of mechanical responses across species. By relating predicted tissue deformation metrics to observed injury patterns, the research will establish mechanically grounded injury criteria comparable across species. The project will develop a predictive framework that integrates machine learning with biomechanical modeling to map head motion to brain deformation and injury risk, supporting translational studies while contributing to fundamental advances in biomechanics and mechanobiology. 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.

NYC Department of Cultural Affairs

Caribbean American Repertory Theatre, Inc.

Tropicalfete, Inc.

Cultural Immigrant Initiative

Tropicalfete, Inc.

Local

Center for Linguistic and Cultural Democracy

Caribbean Sea Fest 2020 will be hosted virtually on Saturday, July 26, 2020 at the Royal Esquire Club, 5016 Rainier Avenue South, from 11:00 am-7:00 pm. The all-day watch party will be a combination of pre-recorded performances and live-streaming. Social distancing and appropriate sanitation measures will be observed.

NYC Department of Cultural Affairs

caribBEING

City of Gainesville

Caring & Sharing Learning School

City of Gainesville

Caring and Sharing Learning School