Rutgers University New Brunswick

NSF Awards · FY 2025 · 2025-09

The main objective of the project is to study geometric flows, more precisely equations in which one evolves a geometric object (for example a metric or a surface in the Euclidean space) in time, expecting it will improve its properties in time, for example become more symmetric, starting resembling familiar objects, such as spheres or cylinders. These geometric equations usually develop singularities in a finite amount of time, after which one cannot expect to have a nice solution to the considered geometric flow. One would like to understand more closely what happens at those singular times, and what the singularities look like. This should help one find a way to define a solution past the singularities. After repeating this finitely many times, one should get in the end very familiar geometric objects, and this could help, for example, to understand and classify all possible topologies of the initial geometric object. The PI expects that new students and postdocs, besides current ones will be trained, and that they will benefit from the research activity. The PI also plans to co-organize workshops in various topics in Geometric Analysis. This project is to study singularity formation in asymptotic sense, and to classify singularities in nonlinear parabolic equations which come from differential geometry problems, such as the evolution of a hypersurface in the Euclidean space by functions of its principal curvatures, and the Ricci flow. The first part of the project is to understand the formation of cylindrical singularities which are expected to be generis and their stability. This includes understanding the dynamics of nondegenerate and degenerate neckpinch singularities, more precisely the denseness and stability of nondegenerate neckpinch singularities in the Ricci flow and the mean curvature flow. The PI plans to understand the formation of cylindrical singularities in the asymptotic sense in both, the Ricci flow and the mean curvature flow. The second part of the project is the classification of ancient solutions to nonlinear geometric flows, such as, the Ricci flow and the mean curvature flow, since ancient solutions appear as singularity models in both flows. The PI will combine the PDE techniques and geometric estimates to study ancient solutions of such flows. The PI plans to classify ancient closed noncollapsed solutions to higher dimensional Ricci flow (cases n= 2,3 have been solved), under suitable conditions. More precisely, the PI would like to classify four dimensional noncollapsed Ricci flow ancient solutions whose asymptotic shrinker is a round cylinder or a bubble sheet. One motivation for this classification comes from showing an analogue of the Mean Convex Neighborhood Theorem for the Ricci flow. This could potentially help one to perform surgery in the Ricci flow in dimension four without assuming global curvature conditions initially. The PI will also try to understand mean curvature flow ancient solutions whose tangent flows at negative infinity are generalized cylinders. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-09

Approximately 85% of the matter in the universe is thought to be dark matter. The presence of this mysterious component is well documented in large galaxies, but the amounts of dark matter in galaxies much smaller than the Milky Way are poorly understood. With a new dataset of 100,000 dwarf galaxies, this program will measure their composition and proportion of dark matter, stars, and gas. The principal investigators will also use advanced computer models and simulations to explain the most important physical properties of these dwarf galaxies. This program will develop summer research internships, seminars, and mentoring schemes to increase the retention rates of undergraduate students majoring in physics and astronomy in the States of California, New Jersey, and Ohio. This program will also support research and training opportunities for undergraduate and graduate students in astrophysics. This program will characterize the baryon and dark matter content of massive dwarf galaxies with a united and novel theory and observational approach. This program is made possible by the Merian survey, which is identifying 100,000 well-characterized massive dwarf galaxies. This program will match the internal kinematics of galaxies with their halo mass, using HI line shapes and weak-lensing masses. The principal investigators will use simulations, which successfully reproduce the HI properties of dwarf galaxies, to determine if Cold Dark Matter (CDM) or self-interacting dark matter (SIDM) is a better match to the kinematics and halo properties of massive dwarfs. Using the DESI Y1 data set, the PIs will study how environmental effects impact dwarf properties and study to what degree this impacts weak lensing measurements. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-09

The availability of widely accessible software and procedures for predicting the intricate response of coastal structures to climate-induced extreme hydrodynamic events is paramount for supporting the development of climate-resilient coastal communities. However, the current scientific toolbox for evaluating the response of coastal structural systems to extreme hydrodynamic loads consists of empirical models lacking a solid theoretical foundation, outdated design codes, and oversimplified numerical frameworks that misbehave in scenarios beyond their limited scope. This project aims to address this deficiency by pioneering the development of novel high-fidelity, physics-based numerical methodologies, and open-source, high-performance computational software infrastructure for fluid-structure interaction simulation between extreme hydrodynamic events and coastal structures. This research enables the advancement of knowledge in the field of coastal climate resilience. It aligns with NSF's commitment to promoting the progress of science and facilitating breakthroughs in climate change and resilience. The wide dissemination of the developed computational tools holds the potential to deliver societal and economic benefits by enabling the design of more climate-resilient coastal infrastructure. Moreover, it has the potential to impact multiple scientific fields that involve fluid-structure interaction. Integrated into this research are several education and outreach activities that involve training high-school teachers in climate resilience issues, engaging diverse student cohorts in project participation, and enhancing the curriculum. These activities promote climate change awareness, cultivate interdisciplinary and computational thinking, and foster diversity and inclusion. The technical objective of this project is the development of high-fidelity, physics-based computational tools for coastal fluid-structure interaction under extreme hydrodynamic events. These tools advance mathematical methods, algorithms, and computational software on coastal climate resilience. Specifically, the project introduces the following cyberinfrastructure innovations. First, it employs Smoothed Particle Hydrodynamics to simulate violent free-surface flows and extreme structural deformations, including fragmentation. This approach departs from previous numerical methods on coastal fluid-structure interaction that often relied on mesh-based techniques or rigid body assumptions to represent solid structures. Furthermore, it utilizes a novel pressure projection method that facilitates an efficient and accurate two-way coupling of the fluid and structural domains, leading to high predictive accuracy. The research also delves into advanced numerical approaches for modeling structural damage and fracture. These include phase-field, peridynamics, and microplane models, that result in advanced numerical capabilities for simulating structural failure. In addition to these computational developments, the project uses water flume facilities to provide experimental validation for the developed computational tools. Lastly, the culmination of these computational and mathematical innovations, along with a sophisticated pre-processing module tailored to civil structures, are combined to develop a GPU-accelerated software platform made available to the research community through open-source cloud-based repositories. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-09

This project advances science at the intersection of robotics, artificial intelligence, and formal verification to enable reliable and transparent robot behavior in real-world settings. As robots increasingly assist with complex tasks—from warehouse logistics to supporting independent living—ensuring their safe and trustworthy operation is essential. However, state-of-the-art robot learning methods, such as deep reinforcement learning, rely heavily on opaque neural network controllers that are difficult to interpret, verify, and generalize, limiting their use in safety-critical domains. This research addresses these challenges by developing a new class of interpretable control programs, written in domain-specific languages with automatically-inferred domain knowledge. These programs enable robots to reason over long-term goals, adapt to novel environments, and be certified as safe before deployment. Through this approach, the project aims to improve both the efficiency and reliability of real-world robotic systems. The main contributions of this project are new algorithms for the synthesis and verification of robot-control programs, grounded in formal methods, to support transparent and trustworthy robot learning. The proposed approach synthesizes high-level symbolic programs from low-level reward functions or task specifications in continuous, high-dimensional environments. At its core is abstraction refinement, which automatically generates and iteratively improves symbolic representations of environment states and robot capabilities. These abstractions guide the synthesis of recursive, compositional control programs that generalize to long-horizon and multi-object tasks. Verification is achieved through compositional reasoning, enabling the correctness of a control program to be inferred from modular analyses of its components. This significantly reduces the computational burden of verification in complex environments. The research will be validated on real-world robotic platforms, such as packing and assembly tasks in warehouses, manufacturing, and supply-chain settings, to evaluate the efficiency, scalability, and generality of the proposed synthesis and verification techniques. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-09

This project is using artificial intelligence (AI) to better understand how metals are used by cyanobacteria, the most abundant group of organisms to have ever existed in Earth history. Metals are essential for life and are held tightly by proteins inside of cyanobacterial cells. AlphaFold is a powerful AI tool that can predict the shapes of these proteins and how they hold onto metals. So far, these predictions have not yet been tested with laboratory experiments. In this study, college students are growing cyanobacteria in the laboratory and using advanced X-ray techniques to see how metals are bound inside the cells. By comparing the AI predictions with real data, the team hopes to better understand how metals move through environments when these cells die and break apart. This knowledge will provide a better picture of how dissolved metals are recycled in aquatic ecosystems. The project also includes outreach to schools and communities, including science activities for children, story-writing contests, and support for college students to get involved in science. The laboratory studies focus on resolving the chemical speciation of Zn and Fe in cyanobacteria. Experiments are performed by the researchers to assess whether AI-predicted metal-ligand binding environments reflect the actual speciation of Zn and Fe in living cells. Marine and freshwater cyanobacteria are being cultured under metal-controlled conditions, and proteins expressed under different growth phases are being identified using LC-MS/MS proteomics. The three-dimensional structures of the proteins will then be modeled using AlphaFold, and the protein structures will be annotated to identify putative metal-binding sites, coordination numbers, and ligand identities. These predictions will be experimentally tested using High Energy Resolution Fluorescence Detection (HERFD) X-ray absorption spectroscopy at the Zn and Fe K-edges, conducted at the Advanced Photon Source. Spectral data will be analyzed using linear combination fitting and principal component analysis to quantify the distribution of metals among cysteine, histidine, and carboxyl ligands. It is anticipated that AI predictions will correlate with experimental data, particularly in conserved protein families. These findings will provide mechanistic insights into metal-ligand complexation in cyanobacteria and establish a framework for AI-enabled investigations of metal cycling and biogeochemistry in natural aquatic systems. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-09

Radar technology for monitoring vital signs, including heart rate and respiration rate, shows significant potential in transforming health monitoring via remote, continuous observation of multiple individuals. While phased arrays have been widely used in radar-based vital sign monitoring due to their low cost and effectiveness in single-person applications, they lack the flexibility to generate beams with well-controlled sidelobes required for monitoring individuals in close proximity. This project introduces VSMART (Vital Sign Monitoring via Remote Tracking), a system that integrates hardware, signal processing, and machine learning to enable continuous, remote monitoring of vital signs across multiple individuals. VSMART is designed to reduce interference and deliver high-accuracy performance at low cost. The research also explores the system’s adaptability for remote blood pressure monitoring. VSMART is particularly suited for deployment in crowded critical care environments, assisted living facilities, and nurseries, with applications including apnea detection and identification of sudden infant death syndrome in newborns. Additionally, the project contributes to curriculum development and offers opportunities for both graduate and undergraduate students to engage in research. The research pursues the following objectives: (i) Develop a novel, cost-effective radar transmitter architecture capable of producing well-controlled beams targeted at specific regions of the human body. This innovation centers on enhancing phased arrays with double phase shifters, providing additional degrees of freedom for improved beamforming capability. Unlike traditional arrays, the proposed configuration can control both magnitude and phase of the transmitted waveform at each antenna element, allowing for greater flexibility in beam pattern design. These improvements are achieved with minimal added cost, primarily due to the use of inexpensive phase shifters. For multi-target scenarios, the main beam is successively directed at each target while nullifying interference from others, enabling effective monitoring of multiple individuals. Computationally efficient algorithms for calculating beamforming weights that consider non-ideal characteristics of the phase shifters are developed and a prototype of the enhanced phased array system is constructed. (ii) Develop advanced methods for heart rate and respiration rate estimation using learning-based techniques that reconstruct waveform morphology similar to that of contact-based sensors. This includes the design of a waveform reconstruction network based on Long Short-Term Memory to capture temporal dependencies and a self-attention mechanism to emphasize relevant segments of the radar signal. A signal augmentation framework that simulates realistic radar echoes reflecting human dynamics is implemented. A Conditional Generative Adversarial Network serves as the foundation for generating training data to enhance model robustness. Radar waveforms that preserve the integrity of vital sign information within the radar echoes are also investigated. (iii) Leverage the flexibility of the proposed transmitter system to generate multiple beams focused on different regions of the body, and utilize the reconstructed vital sign waveforms to extract blood pulse information from each region. By analyzing time delays between these signals, estimates of blood pressure are obtained using both traditional physiological models and machine learning techniques. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-09

This project explores a unique type of fungus that parasitizes aspen trees. Known as the aspen bracket, this fungus has a remarkable trait: insects tend to avoid it. This behavior suggests that the fungus — either on its own or in partnership with the aspen tree — may produce substances that are toxic or unappealing to insects. By studying how the aspen bracket functions, researchers hope to discover new insect-repelling or insect-killing compounds that are both effective and environmentally friendly. These natural substances may work in ways not previously seen in fungi, offering new approaches to pest control. The findings could have valuable applications in agriculture, forestry, and public health. In addition to its scientific goals, the project has a strong educational mission: it will involve undergraduate and high school students in hands-on research. By engaging students and sharing results with both the public and the scientific community, the project aims to inspire broader participation in science, technology, engineering, and math (STEM) fields. The metabolic profile of the aspen bracket (Phellinus tremulae) will be characterized with respect to the content and origin of insecticidal or repellent compounds. First, the project will identify and characterize metabolites in the fungus. Both untargeted and targeted metabolomics will be conducted using liquid chromatography–mass spectrometry (LC-MS), and compounds of interest will be purified and tested on insects to confirm their repellent or toxic activity. Second, the origin of these compounds in P. tremulae will be investigated. Comparative analyses will assess the presence of these compounds in wild-collected fungal specimens, host aspen tissues, and cultured fungal samples to determine whether the compounds are biosynthesized by the fungus or accumulated from the host. Lastly, the project will explore the mode of action of these compounds using computational modeling. A suite of in silico approaches will be applied, including molecular docking, molecular dynamics simulations, and free binding energy calculations, to evaluate interactions between the compounds and known insecticidal protein targets. Additionally, chemometric models such as quantitative structure-activity relationship (QSAR), read-across, and q-RASAR, combined with machine learning (ML) and artificial intelligence (AI) algorithms, will be used to predict insecticidal efficacy, potency, and chemical stability. Environmental and human safety assessments will also be conducted computationally to evaluate toxicity and regulatory viability. Together, these approaches may uncover new natural insecticides, reveal previously unknown biosynthetic pathways, and provide a model for integrating metabolomics, pest management, and predictive computational toxicology. The results of this project can be translated into new biotechology, i.e., new pesticides. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-09

This award supports the convening of a series of workshops and collaborations with the Evolutionary Anthropology Society. The PIs organize a series of multidisciplinary workshops that bring together both senior and early career scientists from across the range of Evolutionary Behavioral Science (EBS) disciplines. The workshops will address how EBS approaches can be integrated and translated to other disciplines to expand the science of human behavioral and cultural variation. Broader impacts include the development and mentoring of a scientific workforce. The multidisciplinary perspectives that can be leveraged from EBS encourage new perspectives toward the development of scientific discovery. The project responds to federal priorities for scientific workforce development in areas of integrated science and the scientific understanding of the impacts of biological factors on human behavior and adaptation. The PIs also develop a robust program in collaboration with the Evolutionary Anthropology Society to help build a pipeline to address critical workforce gaps through systematic skills development in integrated social science. Outcomes include the development and adoption of biotechnological innovations and workforce development in areas critical to the development of the bioeconomy. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-08

This award supports the study of a basic property of protons: how the charge of the proton is distributed within it. The motivation is that much of ordinary matter is made up of protons, but even simple properties like the charge distribution are hard to predict precisely, as they arise from complicated interactions of the quarks that make up the protons, rather than from the quarks themselves. The PIs and graduate students use electrons and muons (a heavier relative of electrons) to probe the proton, to measure its charge distribution and also to measure quantum mechanical corrections to determining the charge distribution. In addition to the direct scientific goals of the project, the experiments provide undergraduate and graduate students and other early career scientists with experience and training in working in international collaborations of modern scientific experiments, with state-of-the-art technology. This grant will support the continuation of the experimental nuclear physics research program of the intermediate energy experimental nuclear physics group at Rutgers University. The focus of the experimental activities is MUSE, a measurement of the scattering cross sections of both positively and negatively charged electrons and muons from a liquid hydrogen target, performed at the PiM1 beam line of the Paul Scherrer Institute in Switzerland. During this grant period the group will complete taking production data and work on analysis. The scattering cross sections are used to determine the proton form factors, from which the proton charge radius can be extracted. Comparing scattering of positively and negatively charged particles determines two-photon exchange corrections, which impact the radius determination. Comparing scattering of muons to electrons checks lepton universality, including the consistency of the proton radius when measured by the two species. In addition to the new measurements of MUSE, the group plans to take part in running-related Jefferson Lab experiments and will continue to be involved at a lower level in the analysis and publication of results of experiments run previously. The broader impact of the group has been primarily in the training of undergraduates, graduates, and postdocs. Students and post-docs who have worked with the group have gone on to careers in a variety of areas, including medical physics, national security, and financial systems, in addition to continued work in fundamental physics research. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-08

Wireless network management can benefit from Artificial Intelligence (AI)-based approaches (“AI for Wireless”) and AI applications could be optimized for operating over wireless networks (“Wireless for AI”). This project will demonstrate the design of an experimental testbed for research and evaluation that can benefit both “AI for Wireless” and “Wireless for AI.” The goal is to support an ecosystem built around an integrated wireless and AI infrastructure that can be used by the research community to develop and evaluate novel solutions for problems in advanced manufacturing, smart cities, transportation, and beyond. A key objective is to lower the barrier to entry for AI researchers to use wireless networks and wireless researchers to use AI models by developing onboarding and tutorial materials for new researchers. The resulting proof-of-concept will validate the design principles for a larger-scale testbed. The project will develop a framework for adding AI capabilities to a city-scale programmable wireless testbed. These enhancements will provide researchers with easier access to the wireless infrastructure and AI tools designed for working with wireless data and operating over wireless networks. Specific activities include (a) workshops with academic and industry researchers to share the state-of-the-art and desired future directions that could be supported by the proposed testbed, (b) identifying what existing data sets can be generated from the testbed and demonstrations of how to use AI algorithms on them, along with tutorial and onboarding material for community researchers, and (c) an outreach plan to engage a broader set of stakeholders for developing and using the final testbed. The result will be a prioritized list of functionalities for an AI-enabled testbed, specific tasks to build those functionalities, and target application domains and use cases. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-08

An award is made to Rutgers, The State University of New Jersey, to enable automated deposition of scientific data for three-dimensional structures of large biological molecules (e.g., protein, DNA, RNA) to two important open-access data resources. This information explains how cells in our bodies work at the atomic level and what happens when mutations change cellular function in cancer and other common diseases. It will be made freely available to many millions of basic and applied researchers, working across the sciences in both academia and biopharmaceutical and biotechnology companies, contributing to human health. It will also be made freely available to educators and their students, supporting K-12 and college teaching of the biological sciences, graduate and professional training, and workforce development. The information will also be used to elevate the scientific literacy of the public through outreach activities related to fundamental biology, biomedicine, energy sciences, and biotechnology. The intellectual merits of the project are twofold. First, computer software will be developed to streamline time-consuming tasks that are now carried out manually to contribute scientific information to the Protein Data Bank and the Electron Microscopy Data Bank. These two open-access data resources have a decades-long history of making scientific data generated with financial support from United States federal government agencies freely available to users via the internet. Second, information management by the Protein Data Bank and the Electron Microscopy Data Bank will be improved with new data science and software tools to package information from related structural investigations together, thereby providing a more complete three-dimensional understanding of how the natural world works and how changes in gene sequence lead to changes in function and cause diseases in humans, animals, and plants or promote higher yields of economically important food crops. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-08

This project will develop a next-generation statistical framework to improve the reliability and reproducibility of data science (DS) and artificial intelligence (AI). As DS and AI play an increasingly central role in science, healthcare, technology, and national security, it is essential that the methods used to analyze data are trustworthy and transparent. However, current data analysis tools often rely on the traditional assumption that data come from a specific form of probabilistic model—a practice that often fails to capture the complexity of modern data, leading to misleading conclusions and contributing to a growing crisis of scientific replication. This project studies a new framework called Predictability-Computability-Stability Inference (PCSI) for veridical data science (VDS) to help ensure that conclusions drawn from data are not only accurate but also stable, interpretable, and computationally practical. The research will also help train the next generation of data scientists, promote interdisciplinary collaboration, and support the responsible development of AI. By improving how uncertainty is measured and communicated, the project serves the national interest by strengthening scientific research integrity and public trust in data-driven decisions. The PCSI approach evaluates multiple predictive algorithms and filters out those with insufficient performance, avoiding dependence on any single model and focusing uncertainty assessment on those that are adequately predictive. It uses multiple bootstrap samplings to address uncertainty in an integrated manner with the new form of uncertainty in PCSI: stability over pred-checked algorithms. It also employs a novel multiplicative calibration technique to ensure valid prediction coverage, improving robustness to subgroup structures. The project specifically aims to advance the PCS framework for veridical data science (VDS) by developing PCSI methods for key areas of machine learning, including classification, deep learning, and ensemble learning. The research consists of three thrusts: (1) developing PCSI for classification to improve uncertainty quantification, robustness, and accuracy in both binary and multi-class settings; (2) designing PCSI methods for deep learning and large language models using computationally feasible perturbations and calibrations to enhance stability, interpretability, and performance in modern AI; and (3) establishing theoretical foundations for PCSI and PCS-guided ensemble learning, showing that even under model mis-specification, PCSI can remain valid and outperform existing methods such as conformal inference under reasonable conditions. These developments will result in statistically sound, computationally efficient tools, along with software, publications, and educational materials to broaden participation and ensure broad dissemination. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-08

In many species, competition for mates occurs at multiple levels, from seasonal behavioral displays to less apparent changes in physiology. Such competition shapes the reproductive strategies of both males and females in a species, but the genetic mechanisms underlying these strategies are not well understood. This doctoral dissertation project assesses the impact that female reproductive status has on male physiology in a non-human primate species, with particular focus on genomic and transcriptomic factors related to gene expression and resulting changes in cell production. The study advances knowledge about specific reproductive mechanisms with translational potential for fertility studies, supports undergraduate and graduate science training and mentorship, and generates new genomic data resources. The project's methodological approach to the analysis of tRNA advances priorities in biotechnology. Reproduction competition at the cellular level is a significant, but understudied, aspect of sexual selection in primates. The study combines transcriptomic and evolutionary genomic analyses to understand the molecular mechanisms regulating reproductive seasonality in primates. Novel transcriptomic data are used to elucidate how seasonal reproduction influences the molecular basis of gamete production and competition in a seasonally reproducing primate. The study also investigates the mechanisms by which male reproduction in primates is shielded from threats including inflammatory immune response and oxidative stress. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-08

Hydrogen is a highly promising clean energy fuel. This project aims to accelerate the discovery of advanced materials for advanced hydrogen-based energy technologies. The research focuses on Metal–Organic Frameworks (MOFs), a class of porous materials with highly tunable structures and chemistry. These materials show exceptional promise in applications such as water splitting and proton conductivity, which are critical for efficient producing, storing, and using hydrogen. The project brings together a multidisciplinary, international team from the University of Manchester (UK) and Rutgers University (USA), which combines theoretical modeling, molecular simulations, machine learning, and experimental techniques to better understand how the structure of these materials influences their function at the molecular level. These insights will guide the design and synthesis of new MOFs with enhanced performance, ultimately contributing to advanced energy solutions. The success of the proposed interdisciplinary research program will have significant intellectual merit and broad societal and environmental impact. The project is expected to have long-term benefits on both fundamental science and potential commercial innovation. This project seeks to establish practical design rules for synthesizing Metal–Organic Frameworks (MOFs) with enhanced performance in hydrogen-related processes, specifically proton conductivity and photocatalytic water splitting. Building on recent discoveries at the University of Manchester of MFM-300(Cr)·SO4(H3O)2 and MFM-808-SO4 structures that exceed the proton conductivity of benchmark materials and demonstrate efficient hydrogen evolution under visible light, the research will combine ab initio modeling, molecular dynamics and Monte Carlo molecular simulations with experimental synthesis and characterization. The specific objectives of this research proposal are i) to elucidate molecular phenomena associated with water adsorption, proton transport, and water splitting in these materials on a fundamental molecular level, as well as the effects of structural flexibility and sulfonation on conductivity and catalytic activity ii) to use the identified patterns and factors responsible for the enhanced proton conductivity and water splitting activity to design and discover new MOF architectures with advanced properties iii) to synthesize and characterize new promising MOFs, expanding the range of materials and operation conditions for proton conductivity and hydrogen evolution. Fundamental questions, such as how framework topology, hydration states, and functional group distribution influence transport behavior, will be addressed through a series of research tasks focused on modeling water cluster networks, tuning chemical functionality, and simulating deformation-driven transport effects. These insights will be translated into computational screening and machine learning-guided discovery of new MOF candidates with superior properties, followed by targeted synthesis and performance testing. The outcomes of this research will push the boundaries of materials design for hydrogen technologies and provide a deeper understanding of the structure–function relationships that govern MOF behavior under a broad range of engineering conditions. This collaborative U.S.- U.K. project is supported by the U.S. National Science Foundation (NSF) and the Engineering and Physical Sciences Research Council (EPSRC) of United Kingdom Research and Innovation (UKRI), where NSF funds the U.S. investigator and EPSRC funds the partners in the U.K. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-08

Professor Jeehiun Katherine Lee of the Department of Chemistry at Rutgers University will utilize funding provided by the Chemical Mechanism, Function, and Properties Program of the Chemistry Division to explore organic reactivity and mechanism. One focus will be on reactions catalyzed by N-heterocyclic carbenes (NHCs). NHCs are novel organic substances that increase the speed of various important chemical reactions. Their mechanisms, however, are still poorly understood. The work will utilize both computational and experimental methods, primarily in the gas phase, to reveal intrinsic reactivity. The project has the potential to improve understanding of these catalytic compounds, which will in turn improve their efficiency and utility. This would potentially positively impact synthetic methods that utilize NHCs, which will in turn impact many areas of chemistry, including pharmaceuticals and other useful chemicals. Another focus will be on the properties of reactants, the "players" in a reaction. The properties of "nucleophilicity" and "electrophilicity" will be examined in the absence of solvent, to better understand inherent reactivity of organic species. These fundamental mechanistic studies will also provide education and training for scientists at all levels. N-heterocyclic carbenes (NHCs) are important players in organic chemistry, both as ligands and as catalysts. As organocatalysts, NHCs are best known for their ability to effect Umpolung: the reversal of polarity/reactivity of the carbonyl carbon of a given substrate. These reactions are of great synthetic utility, but mechanistic questions abound. Because examination of these reactions can be complicated by solvent, this chemistry will be examined in the gas phase, using both experimental (mass spectrometric) and computational (quantum mechanical) methods. The mechanisms by which the polarity reversal is incurred will be examined, with the overall goal of developing improved catalysts. Also, gas phase nucleophilicity and electrophilicity of silane and carbon hydrides will be probed, to move toward the development of a gas phase nucleophilicity-electrophilicity scale. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-08

This grant will support research aimed at advancing our understanding of quantum materials, a class of materials that exhibit unique behaviors due to their underlying quantum properties. These materials can conduct electricity without loss (superconductivity) or have exceptional sensing abilities, making them crucial for next-generation technologies like quantum computing, information storage, and high-efficiency power systems. For example, high-temperature superconductors could enable revolutionary applications such as controllable nuclear fusion, levitating vehicles, and more efficient power grids. Therefore, this research aims to develop new models and methods for designing quantum materials by linking macroscopic continuum theory with quantum field, electric field, and magnetic field interactions. By integrating these approaches, the research will provide fresh perspectives on improving properties like superconductivity and creating new materials for energy harvesting, sensing, and actuators. The work will also play a significant role in maintaining the U.S. leadership in technological innovation. Additionally, this project will encourage broader participation in research by involving students in quantum materials studies and promoting interdisciplinary education in mechanics of materials and quantum engineering. A theoretical and computational modeling approach will be established to study and design quantum materials. Specifically, the approach will link continuum mechanics concepts with a suitable quantum mechanical-based order parameter to enable a fresh perspective on quantum engineering. The work will explore the modeling of several distinct aspects of quantum materials: (i) Impact of strain and/or strain gradient, anisotropy, and flexoelectricity in quantum superconductors to provide a new direction in terms of improving the critical temperature and current, (ii) A three-way coupling between quantum field, electric field, and magnetic field in nanostructures, mediated by strain and/or strain-gradient leading to a novel class of quantum materials for sensing and energy harvesting, and (iii) The production of mechanical deformation through alteration in the quantum field (e.g., by laser or change in quantum confinement) leading to a novel class of quantum actuators. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-08

Understanding the fundamental forces of nature remains one of the most profound goals of modern science. The discovery of the Higgs boson has highlighted a long-standing mystery: why is gravity so much weaker than the other fundamental forces? Many models that attempt to explain the structure of the universe suggest the existence of new, undiscovered particles and interactions. With this award, researchers at Rutgers University are embarking on an innovative project at the Large Hadron Collider (LHC) to search for signs of new physics that may bridge this gap. By using advanced machine learning techniques and novel data analysis strategies, the team aims to uncover faint signals of new physics processes. Looking to the future, the group has a critical role in both building advanced detector components and developing the technologies needed to analyze the vast increase in collision events at the upgraded High Luminosity LHC. These efforts will enable scientists to probe fundamental questions about the universe more precisely than ever before, hopefully shedding light on the origin of mass and the nature of dark matter. The group also prioritizes the mentoring of young people: postdocs, graduate and undergraduate students, as well as high school students through the QuarkNet program which engages them in hands-on particle physics research. This project aligns with the NSF mission by promoting scientific progress, supporting STEM education, and potentially reshaping our understanding of the universe. This award will allow the Rutgers group, a long-standing member of the CMS experiment at the LHC, to conduct targeted searches for beyond-the-Standard-Model (BSM) physics with a focus on low-mass signatures accessible through nontraditional datasets such as Scouting and Parking streams. These enable exploration of regions previously considered inaccessible at hadron colliders. The group applies advanced machine learning techniques—including adversarial networks for background suppression, autoencoders for anomaly detection, and deep learning-based image classification—to enhance real-time and offline event selection sensitivity under high-background conditions. The project also involves R&D on hardware-level triggering, fast reconstruction algorithms, and interpretable AI models with robust control of systematics. In support of the High-Luminosity LHC upgrade, the group leads module production, assembly, and testing for the CMS Outer Tracker, and contributes to the design of the Track Finder and Global Track Trigger systems for identifying high transverse momentum tracks. These integrated efforts are designed to extend the LHC’s sensitivity to electroweak- and sub-electroweak-scale phenomena, enabling precision tests of theoretical models and potential discovery of new particles or interactions. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-08

This doctoral dissertation incorporates ethnoarchaeology, experimental archaeology, and microarchaeology to examine the technological development of early human groups. This study focuses on fire use and habitat arrangement, features that have proven critical importance for understanding ancient human behavior due to their ability to inform important aspects in evolutionary history, such as increasing cognitive complexity, changing social structures, and adaptations necessary for survival. This project offers training to undergraduate and graduate students in analytical methods such as micromorphology, Fourier-transform infrared spectroscopy (FTIR), phytolith analysis, and microcharcoal quantification. This project will disentangle two critical challenges facing Paleolithic archaeology, identifying the function of hearth features and detecting the presence of habitation materials in the archaeological record. Leveraging ethnoarchaeological data, hearth features with known functions will be sampled for microarchaeological analysis, providing critical analogs that can be used to better understand the function of hearth features found in Paleolithic contexts. Experimental archaeology will involve the analysis of a 14-year-old hearth and habitat feature, which will provide important information on how these features are preserved in the archaeological record. These insights will then be applied to the Middle Paleolithic site which features a series of complex hearths and potential habitat material, allowing for a precise understanding of how ancient groups who occupied this site organized and interacted with their domestic living areas. This multi-disciplinary study will allow for a broader understanding of what conditions are necessary for human technological development. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-08

With the support of the Macromolecular, Supramolecular and Nanochemistry Program in the Division of Chemistry, Professor Mark C. Lipke of Rutgers University will study how acid-base chemistry and electron transfer processes are altered inside nanoscale molecular containers, thereby providing knowledge that may aid the design of catalysts, sensors, and other chemically functional materials. Graduate students, undergraduate students, and high school interns engaged in this research will receive research training in fundamental organic, inorganic, and physical chemistry, as well as advanced topics of nanoscience, electrochemistry, and energy science. The Lipke group will also engage in educational outreach with local high schools to promote careers in science by discussing modern research topics and career paths to becoming scientists. It is well known that nanoconfinement can alter the rates and thermodynamics of proton and electron transfer processes. However, past studies of this behavior have not fully addressed the complex interplay of factors—especially the hydration of the confined environment—that tune proton and electron transfer. In ordinary solutions, acid-base chemistry and electron transfer chemistry are strongly influenced by the interactions of charged chemical entities with solvent molecules, such as the transfer of protons (H+) from an acidic molecule to water molecules to generate solvated protons known as hydronium ions. Such chemistry can be altered significantly in molecular containers that only have enough space to hold a small number of solvent molecules since these tightly confined spaces prevent solvent molecules from moving freely. The Lipke group will study these effects in well-defined molecular containers to shed light on how such structures can be used to deliberately control proton and electron transfer events that underpin important chemical functions, such as electrochemical synthetic methods. The Lipke group has developed robust redox-active porphyrin nanocages that enable clear measurements of how hydration and other variables influence acidity and redox chemistry. The nanocages can host a variety of acid-functionalized guests and display redox activity that can be characterized clearly by electrochemical methods, thereby providing systems for measuring H+ and H+/e- transfer processes in a confined environment. Furthermore, the uptake of water by the host-guest complexes can be characterized by 1H NMR spectroscopy in organic solvents, providing a way to reliably test the effects of hydration on H+ and H+/e- transfer. These nanocages will be used to examine how different structural and chemical parameters, such as size, hydrogen-bonding ability, hydrophobicity, and counterions, affect H+ and e- transfer under conditions in which the pore is hydrated. Complementing experimental studies, Professor Lipke will collaborate with a computational chemistry expert, Professor Richard C. Remsing, who will employ molecular dynamics and density functional theory calculations to elucidate atomic level explanations of experimental observations. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-08

One of the foundational challenges in computer science is designing data structures that are both space- and time-efficient. Efficient data structures directly impact the performance, energy use, and cost of computing systems that underlie national-scale infrastructure—from supercomputers to cloud services to embedded systems in defense and transportation. In recent theoretical work, the PIs introduced a new technique, the tiny pointer, with the potential to substantially reduce the memory footprint of pointer-based data structures, enabling performance and efficiency gains across a wide range of systems. This project aims to translate tiny pointers from a theoretical insight into a deployable, foundational systems tool. By making tiny pointers broadly applicable, this research supports the national interest in advancing high-performance, resource-efficient computing critical to economic competitiveness, technological leadership, and secure infrastructure. At a high level, tiny pointers are compressed representations of memory addresses that retain compatibility with modern hardware and software systems. This project aims to develop foundational techniques for implementing tiny pointers, and the project has three primary goals: (1) to establish principles for building practical, scalable, and safe tiny-pointer abstractions; (2) to design and implement a high-performance library and OS module that expose these abstractions to both kernel and user-level applications; and (3) to demonstrate space and performance gains across a broad range of systems. Key applications include classical data structures (e.g., hash tables and trees), OS primitives such as page tables and page cache indexes, and flash translation layers (FTLs) in SSDs. For instance, by compressing pointers in x86-64 page tables, the project aims to increase fanout and reduce walk latency, potentially enabling a shift from 4-level to 3-level page tables. Similar benefits are expected for DRAM-resident mapping tables in SSDs. The project will also explore the impact of tiny pointers on systems such as IOMMUs, key-value stores, file systems, and large language models. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-07

Extreme weather-related challenges pose a huge threat to agriculture by simultaneously imposing multiple stresses on crops. Microbes, such as beneficial bacteria in the environment, have been shown to enhance plant resilience to some abiotic stresses. However, we do not know what role that bacteria communities can play in multi-stress conditions. In this project, we will use duckweed, an underutilized aquatic plant, as a model to study resilience to multiple stress combinations, such as high temperature and low nutrient levels, in the environment. Duckweed's small size and rapid growth make it an ideal model for such a study. These unique characteristics allow us to test a larger matrix of stress combinations with duckweed than would be possible in any other flowering plant. Furthermore, duckweed itself shows promise as a sustainable food and fuel source due to its ability to grow without arable land, its high productivity, and exceptional nutritional value. The knowledge gained from duckweed about the role that bacteria communities can impart for stress resilience will also likely be directly transferable to crop species, as we have already shown that duckweed-associated bacteria can affect the growth of other land plants. In this project we aim to quantify the effects that a synthetic community of duckweed associated bacteria may play across the largest matrix (6,480 G x E combinations) of environmental stresses examined in a plant-microbe study to date. This ambitious project brings together expertise in different methodologies, including metabolomics (Germany), ecological genomics and transcriptomics (US), as well as morphological and ionomic phenotyping (UK), leveraging state-of-the-art technologies from each of the three partnering countries. This highly interdisciplinary study will determine which components of the microbial community are required for a subset of plant-environment responses. As such, the success of the project will deliver a pathway to increase resilience of crop production by demonstrating how synthetic bacterial communities can be used to enhance large-scale cultivation of duckweed and how they may influence the stress resilience of other crop plants. Duckweed has recently been recognized as a novel food source in the US and EU. Thus, direct beneficiaries of this work include companies looking to rapidly scale up duckweed production, as well as researchers who are interested to translate findings from model plants in the laboratory to improve sustainability of commercial crops. This award was co-funded by the Plant Genome Research Program and Plant Biotic Interactions Program. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-07

This research project seeks to uncover the fundamental principles governing how cells sense and respond to mechanical forces at their surface, a process critical to numerous biological functions and human diseases. By focusing on mechanosensitive membrane proteins, this work will provide new insights towards understanding cell migration, tissue homeostasis, and touch sensation. The project will also enrich interdisciplinary education through integrated teaching, mentoring, and outreach activities, benefiting students at multiple levels. The research will employ advanced microscopy and biophysical tools to systematically investigate the interplay between cell membrane properties (e.g., tension, curvature, composition, and dynamics) and the subcellular behavior (e.g., distribution and activity) of mechanosensitive membrane proteins. Using Piezo channels as a primary example, the study aims to establish a thermodynamic framework for understanding mechanosensitive membrane proteins behavior at the subcellular level and explore their dynamic behavior within the cell membrane. This multifaceted approach will generate a comprehensive understanding of mechanosensing at the subcellular level, which could lead to potential applications in therapeutic development for various mechanobiological diseases. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-07

NONTECHNICAL SUMMARY Two-dimensional materials are composed of atomically thin sheets of atoms that can be manipulated with an unprecedented level of control. For example, in addition to being able to isolate and stack these atomically thin materials, it is now possible to twist a pair of them to within 0.1 degrees of accuracy. As a result, new arrangements of atoms form when placed on top of each other in this fashion that would not form in nature. At the same time, varying the density of electrons in these devices changes the system from a semiconductor to a superconductor, metal, or insulator providing an amazing amount of control and emergence not previously seen in a single device. These advances raise several new fundamental questions that need to be theoretically and computationally addressed to explain the experimental observations. The amazing tunability of these systems makes them promising candidates for the construction and application of novel technological devices that can be used in future electronics and atomic-scale measurement devices. This research project takes into account realistic effects of the twisted materials to understand how they impact the overall response of the electronic system to imperfections and randomness in the materials, to develop new probes to be used in experiment to ascertain physical effects that are currently out of reach, as well as to find new phases of matter that have yet to be discovered that result from bringing these novel systems in contact. The educational efforts of this proposal focus on career development inside and outside of academia that fosters multifaceted learning and career opportunities for PhD students through direct interactions and exposure with industry professionals and alumni. The PI’s research utilizes a multi-level mentorship program that integrates teaching with research in working with and advising graduate and undergraduate students. Lastly, the computational software developed in the PI’s lab will be made open-source to enhance scientific progress and infrastructure. TECHNICAL SUMMARY This project supports research and education in theoretical condensed matter physics focusing on the role of disorder, quasiperiodicity, and magnetism in twisted two dimensional materials and at interfaces of topological surface states. The rise of two-dimensional materials has ushered in a new era in condensed matter physics, which at the same time has raised several novel questions regarding the microscopic atomic details of the atoms as the layers are twisted and stacked on top of each other. This research aids in the general understanding of these materials by: i) Theoretically investigating the effects of twist-angle disorder in twisted bilayers of graphene and transition-metal dichalcogenides, as well as using vacancies and their scanning-tunneling microscopy response as a probe of twisted bilayer graphene; ii) Determining what kinds of symmetry-broken and strongly correlated states can emerge on the quasiperiodic structures that form from the relaxational process in twisted bilayer graphene close to aligned with hexagonal boron nitride; iii) Understanding how to manipulate the topological Weyl Fermi-arc surface states by coupling them with classical spin ice, quantum spin ice, or a super lattice potential. The PI will employ several computational approaches that combine the kernel polynomial method with the model building capabilities of Wannierization, and many-body approaches that include Hartree-Fock and the dynamical mean-field theory, to describe a wide range of two-dimensional materials that lack translational symmetry. The ultimate goals of this research are first to understand how realistic effects that are innate in these systems affect their behavior, second to use these properties to our advantage to measure physical aspects of the device that have been hitherto out of reach, and lastly to discover novel phases of quantum matter that result when we these systems become strongly interacting. The educational efforts of this proposal focus on career development inside and outside of academia that fosters multifaceted learning and career opportunities for PhD students through direct interactions and exposure with industry professionals and alumni. The PI’s research utilizes a multi-level mentorship program that integrates teaching with research in working with and advising graduate and undergraduate students. Lastly, the computational software developed in the PI’s lab will be made open-source to enhance scientific progress and infrastructure. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-07

Cancer is the second leading cause of mortality, accounting for more than two million diagnosed cases annually in the US. Blood flow plays a crucial role in cancer progression and treatment. To survive and proliferate, cancer cells require oxygen which is supplied by the red blood cells as they flow through the capillary blood vessels, the narrowest vessels in the body. In a healthy tissue, the capillary vessels are well-organized in a hierarchical network. In contrast, in a cancerous tissue the capillary network is not well-organized, and the vessels are structurally abnormal, resulting in significant changes in blood flow pattern compared to healthy tissues. While the altered blood flow may trigger further proliferation of cancer cells, it is also a major bottleneck for effective treatment of the disease. The goal of this award is to develop highly accurate computer simulations using images of cancerous vessel networks to predict the altered blood flow pattern and to elucidate new mechanisms of blood flow anomalies as mediated by the coupling of red cell dynamics and vascular structural abnormalities. The proposed study could lead to identification of specific mechanisms that could be targeted to improve some treatment modalities. In addition to gaining new knowledge, the project will provide broader impacts through the translational aspect and societal benefit of the proposed research, and through integrated K-12 outreach activities, and mentored undergraduate and graduate student research. The proposed research will provide new knowledge about the physics of red blood cell (RBC) suspension altered by the vascular abnormalities of cancer. Angiogenic periphery and tumor core will be considered. Specific activities to be considered are: (i) building in silico models from high-resolution, 3D in vivo images of vascular networks, followed by high-fidelity, first-of-its-kind fluid-structure interaction simulations of deformable RBC suspension with coupled finite-element, finite-volume and immersed-boundary methods, and creating a data bank of tumor microvascular hemodynamics; (ii) elucidating diverse RBC and microcirculatory flow dynamics that have not been previously addressed for cancerous vasculature, such as RBC flux partitioning, 3D nature of hemodynamic forces and cell-free layer, alteration in blood viscosity as illustrated by the Fahraeus-Lindqvist effect; (iii) isolating the vascular and cellular metrics that could potentially be targeted to mitigate the blood flow anomalies. The influence of hypoxia- and acidosis-induced changes in RBC dynamics and their impact on flow anomalies will also be addressed. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-07

This project includes a multi-faceted analysis of marine carbonyl sulfide emissions to be coordinated by a collaborative team from institutions in the US, Germany, and Israel. Carbonyl sulfide is a trace gas capable of providing insight into the global carbon cycle. Mass balance estimates from isotopes and atmospheric inversions both suggest the missing sources of carbonyl sulfide are tied to marine fluxes. This effort will significantly increase understanding of the marine source of carbonyl sulfide (OCS) by conducting a series of coordinated experiments that combine: (1) direct marine flux measurements of OCS; (2) dissolved measurements of OCS and its isotopologues and precursor gasses; and (3) data assimilation and modeling. The project includes an extended field campaign to continuously measure the direct fluxes of OCS. The team will collect data using an air-sea interaction tower on the US Atlantic seaboard (near Martha’s Vineyard, MA) and in Bolkins Eck (Bering Sea), as well as using shipboard measurements to quantify fluxes and resolve sources (via sulfur isotopes) of OCS from these coastal sites. The project includes training for early-career scientists and graduate students from a team of experienced scientists. This work is supported by the Atmospheric Chemistry and the Chemical Oceanography Programs. 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.