University Of California Santa Barbara

NSF Awards · FY 2026 · 2026-10

When large earthquakes strike mountainous regions, they often trigger widespread landslides that threaten communities, damage infrastructure, and disrupt water and sediment systems. These cascading hazards have far-reaching consequences, influencing river systems, water quality, and carbon transport. Understanding earthquake-induced landslides is therefore important not only for reducing disaster risks abroad, but also for improving hazard assessment and infrastructure resilience in the United States, where similar processes occur in regions such as Alaska, California, and the Pacific Northwest. However, major uncertainties remain in how frequently earthquakes generate landslides and how much carbon they mobilize. This project will address these gaps using the Himalayan Mountains as a natural laboratory. This research will quantify the role of earthquake-induced landslides in erosion and carbon mobilization. It will (1) develop landslide inventories and models to estimate long-term seismic landslide fluxes, (2) measure field samples to quantify organic carbon mobilized by landslides, and (3) use geochemical analyses to determine the reactivity and fate of mobilized carbon. Results will advance understanding of interactions among tectonics, surface processes, and the carbon cycle. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2026 · 2026-07

Agrivoltaic systems use the same parcel of land for agricultural (crop or livestock) production and solar photovoltaic electricity generation. This CAREER project will develop modeling tools needed to evaluate the sustainability and scalability of agrivoltaic systems across major agricultural regions of the United States. The project will combine field data, modeling, and design to identify how configurations of agrivoltaic systems affect crop yield and quality, land use efficiency, and electricity generation costs. The project will provide a large-scale assessment of feasibility for multiple key cropping and livestock systems using compatible agrivoltaics system designs. The project will provide opportunities for undergraduates to participate in the research, and it will integrate training in research tools into undergraduate courses. The project will create publicly accessible online tools to communicate agrivoltaic design trade-offs and synergies. The project will also develop crop-specific outreach materials to support farmer decision-making. This project will integrate solar engineering, vegetation, and techno-economic modeling in a unified open-source modeling and design framework to quantify food-energy trade-offs, co-benefits, and land use efficiencies. The central contribution will be an open-source modeling architecture that links spatial and temporal radiation dynamics, energy system design, and crop responses to shading. This framework will enable systematic assessment of agrivoltaic feasibility across diverse climates, agricultural systems, and solar array designs in the United States. The project will develop and validate a national gridded time-series of global and diffuse photosynthetically active radiation to improve modeling of crops grown under partial shade. The project will create a typology of agrivoltaic system designs based on their compatibility with key cropping and grazing systems. The project will also quantify the techno-economic and sustainability performance of agrivoltaic systems at national-scale by advancing and deploying an open-source modeling framework. These activities will focus on trade-offs and synergies in crop productivity, energy generation, and economic viability. A systematic, integrated framework for evaluating agrivoltaic systems as multi-functional land uses across large geographies and diverse agricultural systems will be created. The framework will enable the identification of solar array design, cropping system, and climate conditions combinations that have the greatest food-electricity synergies. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2026 · 2026-06

Animal, plant, and fungal cells are divided by internal membranes that define unique compartments termed organelles. These organelles allow the cell to create different chemical environments. For example, an organelle called the peroxisome sequesters metabolic enzymes. These enzymes may produce toxic byproducts, such as reactive oxygen species, alongside enzymes that degrade those toxic byproducts to protect the cell. Peroxisome function is essential to human health, agricultural success, and offers rich potential for the discovery of novel biochemical pathways in newly sequenced eukaryotes. Furthermore, there is growing interest in synthetic biology in using peroxisomal sequestration to improve production titers of small molecules of commercial interest. However, our ability to predict and control peroxisome function is hampered by our lack of understanding of its membrane proteins, which control the availability of metabolites inside the peroxisome. The goals of this proposal are to understand how cells make, target, and maintain the proteins in the peroxisome membrane. This project sheds light on the evolution of peroxisomes, enabling their use as synthetic organelles, and improving the understanding of their role in disease and biotechnology. In addition, the educational component to this grant improves outcomes in biology education through increasing access to research experiences for transfer students. Membrane proteins control the transport of small molecules across membranes, regulate signaling cascades, and define the unique identity of each organelle. These proteins can reach the peroxisome through two routes: direct insertion from the cytosol, or indirect trafficking from the endoplasmic reticulum (ER). The latter mechanism involves well-defined insertases at the ER but utilizes poorly defined ER-to-peroxisome trafficking machinery. Two proteins, PEX3 and PEX19, are essential to both routes, though their mechanisms of action in each route are poorly understood. The scientific goals of this project are to elucidate how PEX3 and PEX19 interact with each other and peroxisomal membrane proteins to define the minimal requirements for direct insertion. The project discovers the mechanisms used for ER-to-peroxisome trafficking of peroxisome membrane proteins and determines factors that mediate membrane protein stability at the peroxisome membrane. The approaches feature peptide arrays for assessing the binding preferences of Pex3 and Pex19, Laurdan anisotropy for assessing membrane fluidity and fluorescence imaging for localization studies. Improved understanding of peroxisome membrane protein biogenesis has the potential to reveal a novel mechanism of membrane protein insertion, improve predictions of peroxisome proteomes and metabolic function, and elucidate the contributions of peroxisome membrane quality control to disease outcomes. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2026 · 2026-05

Research in the Arctic generates vast amounts of valuable scientific information about land, water, oceans, ecosystems, technology, infrastructure, and human communities. These data come from many disciplines, including the geological sciences, biological sciences, social sciences, and others. Data also span many types such as satellite observations, field experiments in marine, terrestrial, and aquatic systems, laboratory analyses, surveys, and computer models, among many others. The Arctic Data Center serves as the primary data and software repository for Arctic research projects funded by National Science Foundation’s Office of Polar Programs and ensures that these research products are securely preserved, carefully documented, and openly shared to ensure long-term access and utility. Preserving and disseminating scientific data and results is fundamentally important to scientific research advances, enabling researchers and the broader society to understand, validate, and build upon these scientific results. By maintaining data archival services and high standards for data quality and organization, the Arctic Data Center extends the impact of NSF investments by enabling new research, discovery, collaboration, and practical applications of NSF-funded research well beyond the life of individual projects. Over the next five years, the Arctic Data Center will maintain reliable, secure computing systems and services for preserving and sharing Arctic research data that meets FAIR principles: Findable, Accessible, Interoperable, and Reusable. The Center will continue expanding its archive to accommodate large datasets, model outputs, and complex observational collections. Activities will focus on three integrated priorities: advancing cyberinfrastructure, providing high-quality support services, and deepening engagement with the research community. Cyberinfrastructure research will expand interactive data portals, cloud-ready datasets, and AI-ready data to enable more efficient analysis and broader reuse of Arctic research. Support services will assist researchers in organizing, curating, and preparing their data for publication, helping maximize the usability and impact of research outputs. Community engagement efforts will focus on translating data for impact, providing accessible information and resources, teaching effective data science skills through short-courses that build capacity for researchers, and gathering feedback to guide ongoing improvements to the Center’s infrastructure and services. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2026 · 2026-05

PROJECT SUMMARY The project supports ongoing efforts in the Shea group geared at developing new computational methodologies and tools to study the liquid-liquid phase separation (LLPS) of intrinsically disordered proteins (IDPs). Biomolecular condensates formed by LLPS play a range of vital physiological roles in the body, but under aberrant conditions, they can transition into amyloid fibrils, a process linked to disease. The project will meld artificial intelligence protein-language models with a multiscale computational framework to accurately simulate the dilute and dense LLPS phases, characterize the role of water, co-solvents, and high pressure in modulating assembly, and identify new LLPS-prone sequences in the human proteome. The proposal consists of three research projects. Project 1 involves the development of a tightly integrated multiscale computational approach bridging the atomistic to mesoscopic time and length scales. The relative entropy approach will be used to generate chemically accurate protein and water coarse-grained models from atomistic simulations, which will be used as input for efficient field theoretic simulations. The latter will be used to generate phase diagrams for the LLPS of the microtubule-binding Tau protein and Elastin-Like Polypeptides (ELPs), with field theoretic outputs backmapped to generate atomistic, solvated condensate structures that can be directly compared to experiment. Project two involves the development of new high pressure Kirkwood-Buff force fields for the osmolyte trimethylamine N-oxide (TMAO) from experimental Kirkwood-Buff Integrals, and their application to the study of TMAO’s counteraction of high-pressure denaturation of ELP condensates. Project 3 involves developing new artificial intelligence protein language model tools to mine the IDRome – the 28k proteome of intrinsically disordered regions – for new LLPS-prone and co-condensating sequences. The research will lead to state-of- the-art computational tools that will be deposited in Github and made freely available to the broad scientific community, to new physical insights into osmolyte and pressure modulation of LLPS, and to the discovery of new LLPS-prone sequences. The research will inform on conditions that promote functional forms of LLPS as well as lay the foundation for rational therapies for condensate-linked diseases.

NSF Awards · FY 2026 · 2026-05

Polyethylene is a common and useful plastic. It is an inexpensive material that is manufactured, used, and discarded in vast quantities every year. This project aims to find a new way to recycle waste polyethylene by adding enough value for recycling to be economically viable at scale. The aim is to disassemble long polyethylene molecules into much smaller molecules that can be used as chemical building blocks. The challenge is to use a carefully timed sequence of chemical reactions to break some of the strong bonds in polyethylene molecules selectively. The speed at which each reaction proceeds must be known and controlled so that the various steps work seamlessly together. The speed of each reaction will be controlled by designing a catalyst that directs the reactions and the rates at which they proceed. The resulting knowledge will allow polyethylene to be converted into molecules whose intrinsic value to the chemical industry will pay for collection, sorting, and processing of waste plastic. The project will target molecules used to make new plastics, lubricants, motor oils, and surfactants. The project will investigate the empirical and microkinetic rate laws for the two key steps in polyethylene disassembly to higher olefins: olefin metathesis and olefin isomerization. The relative rates of these two reactions determine the average chain length of the product olefins. Both reactions will be catalyzed simultaneously by a bifunctional heterogeneous catalyst. Kinetic profiles will be recorded to extract reaction orders, rate constants, and activation barriers for productive and unproductive reaction steps, using global analysis. Multiparameter models will be built and tested to identify and predict the conditions (e.g., pressure, temperature, catalyst concentration) for selective polyethylene depolymerization to olefins of any desired chain length. The predictions will be tested by performing the reaction and assessing the product distribution, and the results will be used to further refine the model. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2026 · 2026-05

Modern technologies depend on understanding how atoms are arranged inside materials. This atomic arrangement determines how materials conduct electricity, withstand extreme environments, store energy, and perform in applications ranging from microelectronics to national defense systems. X-ray diffraction (XRD) and related scattering techniques are powerful tools for revealing atomic structure, yet analyzing XRD data is a complex task, especially since it is usually large in volume. Although thousands of diffraction patterns are generated every year in laboratories and national facilities, the results remain scattered across publications or stored locally without standardized formats, limiting its reuse and slowing scientific progress. There is no public database of experimental powder diffraction data. This project addresses this need. This project will develop DiffAI, an open, community driven platform that will host public experimental powder diffraction data, associated metadata, and provide artificial intelligence (AI) tools for automated analysis. These will enable more accurate structure determination of materials with complicated atomic arrangements, such as quantum materials that may underlie future quantum information technology. By making high-quality diffraction data findable, accessible, and reusable, DiffAI will accelerate and lower barriers for materials discovery. By democratizing access to experimental data and machine learning models, DiffAI will enable efficient analysis of diffraction data and foster collaboration within the global research community. Through open-access tools, student training, and community workshops, DiffAI aims to establish a global standard for sharing and analyzing diffraction data, ultimately driving progress in materials characterization and discovery. This project advances the foundations of scientific cyberinfrastructure in three key ways: 1) A novel, extensible data architecture for experimental diffraction that will combine metadata schemas, JavaScript Object Notation (JSON) based data records, and a public data repository for experimental powder diffraction patterns that supports scalable, persistent storage of heterogeneous diffraction datasets. Persistent Digital Object Identifiers (DOIs), curated releases, and open APIs will facilitate reproducible workflows and long-term sustainability. 2) Automated agentic large language model (LLM) workflows for large-scale data extraction and digitization that identify relevant literature, detect and classify XRD figures, extract labels, and digitize plots into machine-readable formats. The team also plan to develop software tools for more automated data and metadata capture from laboratory instruments and synchrotron X-ray and neutron diffractometers at national laboratories, thereby creating a generalizable blueprint for automated experimental data recovery, an emerging need across multiple scientific domains. 3) Building on prior NSF work, DiffAI will implement domain-adapted AI models integrated into cyberinfrastructure that bridge synthetic training sets with real experimental data for automated XRD data and metadata validation tasks. These will enable more accurate structure determination for complex martials, such as quantum materials. Collectively, these advances will provide a scalable, community-driven cyberinfrastructure element that enables modern, AI-ready diffraction workflows. This award by the Office of Advanced Cyberinfrastructure is jointly supported by the Materials Research Section within the Directorate for Mathematical and Physical Sciences. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2026 · 2026-04

Generative artificial intelligence (AI) now produces text at scale, which creates urgent needs for trustworthy ways to identify and trace AI-generated content. This project advances watermarking, a family of methods that embed a hidden signal into generated text so it can be identified later, while keeping the text useful and natural. A major goal is to enable more reliable attribution than current approaches, including the ability to encode more than a single yes-or-no identifier so that content can be traced to a specific model, system, or authorized use. The project also strengthens resilience against attempts to erase the watermark or to fabricate text that falsely appears watermarked. Outcomes support responsible use of generative AI in research, education, and society by improving tools for protecting intellectual property in datasets, increasing trust in automated reviews and other AI-assisted writing, and supporting secure communication among AI systems. The project’s educational activities develop course modules and training experiences that prepare students to reason about reliability, security, and trade-offs in generative AI, and they broaden participation through mentored research experiences, community engagement, and outreach activities. This project studies how to embed multi-bit information into text during large language model generation while preserving text quality and providing reliability, robustness, and security guarantees. The research frames large language model watermarking as a distributional information embedding problem in which the generation process is shaped so that the resulting text remains high quality yet carries decodable information. Because generated text is produced sequentially rather than as independent samples, the project develops theory and algorithms that explicitly address this dependence and that measure distortion using distribution-based criteria. The research characterizes fundamental trade-offs among detectability, text quality, information rate, robustness to removal, and resistance to spoofing. These results guide the design of efficient, deployable multi-bit watermarking algorithms with provable performance guarantees, along with authentication mechanisms that help distinguish genuine watermarks from adversarial forgeries and help detect removal attempts. The project also develops practical methods that extend beyond basic AI versus human attribution, including dataset intellectual property protection, in-context watermarking for detecting AI-generated reviews, and secure message passing among large language model-based agents. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2026 · 2026-04

Summary. The plasma concentrations of many metabolites are strictly controlled. Glucose, of course, is a well- known and obviously important example. But because of the critical roles they play in protein synthesis, intermediary metabolism, and neurochemistry, homeostatic control over the concentrations of many amino acids is similarly tight and similarly vital, with their dysregulation similarly leading to disease. The acute (i.e., minutes- scale) mechanisms underlying this homeostasis, however, are far less well characterized than those underlying blood sugar control. In no small part this discrepancy is due to the cumbersome, costly, and poorly-time-resolved nature of current methods for measuring any metabolite other than glucose, all of which rely on sampling (e.g., blood draws, microdialysis) and benchtop analysis. Against this background, the premise of our work is 3-fold: First, that improved understanding of metabolic homeostasis is required to reach a complete, systems- level understanding of physiology. Second, that achieving this will require orders of magnitude improve- ment in the time-resolution of metabolic measurements. Third, that, when coupled with the powerful aptamer discovery strategies we have developed, electrochemical aptamer-based (EAB) sensors are the only technology on the horizon that can enable this paradigm shift. EAB sensors, a platform technology we invented, have already been shown to support seconds-resolved, real-time, multi-hour measurements of more than 2 dozen different molecules in the veins, brains, and solid peripheral tissues of living subjects. Building on this, here we propose to adapt this now well-established (e.g., independently reproduced by others) technology to an important and novel problem: the study of metabolic homeostasis, placing particular emphasis on the amino acids involved in nitrogen and energy metabolism (glutamine, alanine, arginine), neurotransmitter synthesis (the aromatic amino acids), and metabolic syndrome/diabetes (the branched-chain amino acids). To achieve this goal, our established, multi-disciplinary team, whose expertise spans aptamer selection, device design and optimization, and animal studies, employs a rigorous discovery pipeline and multiple layers of independent internal replication to ensure our findings are robust and reproducible. By providing a window into metabolite dynamics at physiologically-most-relevant, seconds-to-minutes time scales, the proposed technology will provide unique opportunities to test and expand models of acute metabolic homeostasis throughout the body.

NSF Awards · FY 2026 · 2026-04

Nontechnical Description: Modern technologies that drive the U.S. economy and national security—such as semiconductors, quantum computing, secure communications, biomedical devices, and clean energy systems—depend on the ability to manufacture structures at extremely small scales, yet today’s most powerful fabrication tools can only make flat, two-dimensional patterns, even though many next-generation devices require true three-dimensional structures at the nanoscale. This project will acquire a state-of-the-art 3D nanoprinting instrument for the University of California, Santa Barbara Nanofabrication Facility that uses advanced laser-based printing to rapidly build three-dimensional structures smaller than a thousandth of a human hair without time-consuming and expensive designs, photomasks, and multi-step processing, enabling researchers to test new ideas much faster than is currently possible. The instrument will support research in areas central to the national interest, including semiconductor manufacturing, quantum information science, secure communications, biomedical engineering, and advanced sensing, with applications such as more efficient quantum communication chips, improved medical implants for bone repair, ultra-sensitive detectors for new materials, and technologies that could improve batteries, clean water systems, and medical diagnostics. The project will also train undergraduate students, graduate students, postdoctoral researchers, and community college trainees in advanced manufacturing skills, helping build a diverse and highly skilled workforce for industries critical to U.S. competitiveness, while strengthening partnerships between universities and industry so that new ideas can move more quickly from the lab to real-world impact. By enabling discoveries not possible with existing tools, expanding access to cutting-edge infrastructure, and preparing the future workforce, this project directly supports NSF’s mission to promote scientific progress, advance national prosperity, and strengthen national security. Technical Description: This project will acquire and deploy a maskless two-photon photolithography (2PP) 3D nanoprinting system in the UCSB Nanofabrication Facility, enabling direct-write fabrication of three-dimensional micro- and nanostructures with sub-100 nm resolution, 100 nm alignment accuracy, and rapid write speeds, overcoming the planar limitations of conventional lithography. The instrument will support research in integrated quantum photonics by printing microlenses, mode converters, and beam-steering elements directly on chips and fibers to improve coupling efficiency for quantum light sources and quantum networks; in trapped-ion quantum computing by fabricating three-dimensional electrode geometries with improved trapping strength, optical access, and scalability; in biomaterials and tissue engineering by nanoscale patterning of biocompatible materials and microfluidic devices to study cell and extracellular vesicle interactions for bone repair; in nanofluidics by fabricating precisely controlled nanochannels for studying ion transport relevant to energy storage, desalination, biosensing, and neuromorphic computing; and in scanning magnetometry by creating three-dimensional nanoSQUID structures for high-resolution imaging of quantum materials. The system will be integrated into the existing Nanotech infrastructure serving hundreds of academic and industrial users annually, with professional staff providing installation, maintenance, training, and development of standard fabrication recipes, and access managed through an online scheduling system. It will be embedded into graduate coursework, NSF-supported training programs in quantum information science, and community-college workforce initiatives, providing hands-on experience with advanced manufacturing while enabling rapid prototyping of complex 3D nanoscale devices that will accelerate discovery, strengthen university–industry collaboration, and support U.S. leadership in advanced manufacturing, quantum technology, and biotechnology. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2026 · 2026-02

Oaks dominate many ecosystems of California and the United States, making them crucial to biodiversity and economically valuable for timber products, such as lumber and furniture wood. Recent research has demonstrated that oaks are maladapted to current environmental conditions, being better adapted to cooler environments present 20,000 years ago. This unexpected maladaptation results in tree mortality, reduced growth, and increased susceptibility to pathogens. The project investigates the understudied concept of oak maladaptation by linking physiological traits, tree performance and fitness, and genomics. This project will investigate two widespread, ecologically important California tree oak species--Quercus lobata (valley oak) and Q. agrifolia (coast live oak)--which often grow together, but have different physiological responses to temperature and drought. Project goals are to understand how physiological traits determine tree growth, survival, and reproduction, whether these traits are genetically based, and how the underlying genetic gradients across the landscape can be used to predict which tree populations are most vulnerable and which are most resilient. Findings will both inform management strategies for oak restoration and conservation in areas where oaks have been removed by harvest or wildfire and also provide a case study for other forest tree species. The research will enhance the STEM workforce by educating students and postdoctoral scientists in cutting edge concepts and tools in integrative biology, ecology, evolution, and forestry. This project is an integrative study of the mechanistic and fitness response of trees to their environments using the understudied but ubiquitous phenomenon of maladaptation. Trees are particularly vulnerable to maladaptation due to their long generation time and long life span that can result in individuals being out of sync with current environments. Through these two contrasting California oaks, the project will first identify the physiological traits associated with response to high temperatures and drought through greenhouse and field experiments. Second, the studies will see whether traits contribute to the survival and growth across a tree’s life history of seedlings, saplings, and young adult trees, and determine how much key traits are genetically based. Third, a landscape genomic study will be conducted to identify geographic regions of maladaptation for each species based on genomic markers, and test whether these areas are the same as predicted to show maladaptation using empirical findings from physiological and fitness studies. This information can be used to identify seed sources for restoration and management of oak projects. This research will address the knowledge gap between selective processes and mechanisms affecting adaptation versus maladaptation. It will also demonstrate the innovative use of landscape genomics tools to detect maladaptation across a species range. By linking phenotypic mechanistic findings and relative fitness of trees with genomic information, this project will demonstrate how functional genomics can inform tree conservation. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2026 · 2026-01

The oldest and most stable parts of the Earth’s continents are called cratons. When continents break-up, or rift, these stable crustal areas split apart and allow magma to rise from Earth’s deep interior. This forms new crust. When this happens, reservoirs of economically valuable volatile elements like helium and hydrogen get stuck underneath the craton, forming a reservoir. Geologically important volatiles like carbon dioxide also get stuck, but how and why this happens is not well understood. When cratons eventually break apart, these gases are released to the atmosphere and have potentially significant impacts. The goal of this work is to determine the processes that control how volatiles are made and stall, how they move through the crust, and how they are released at the surface. This team will focus on the Tanzania Craton. Graduate students will be trained in field and laboratory techniques, data interpretation and application of these techniques as they relate to economically valuable gases. This project is a comprehensive study of the volatile gases that are being emitted from gas and water seeps along the flanks of the Tanzanian craton - a region where the stable continental craton is actively being “cracked” by rifting and simultaneously heated by plume-induced volcanism. The overall aims are to understand: 1) the mechanisms by which gases have been produced and stored in stable cratons over >109-year timescales, and 2) how they are liberated and transported to the surface during cratonic breakup. The study primarily focuses on helium (He) and nitrogen (N2) and their isotopic characteristics, which are the main constituents of cratonic gas accumulation, but other noble gases (Ne, Ar, Kr, Xe) and their isotopes, CO2, CH4 (as well as their isotopes) and H2 in seeps will also be measured. Field-and lab-generated gas chemistry results will be used to form an integrated model of gas formation and transport along the flanks of the Tanzania craton. Volatile fluxes will be calculated to understand the extent of gas release when a cratonic region is disrupted by rifting and volcanism. Constraining how volatiles are accumulated and released during steady-state rifting and magmatic conditions will allow characterization of cratonic volatile inventories and fluxes. This information will provide valuable context to researchers studying the effects of gases abruptly released from the stable craton to the atmosphere as well as the formation of economically valuable gas reservoirs of helium and hydrogen. This award was made possible through the NSF/GEO-UKRI/NERC lead agency opportunity. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2026 · 2026-01

Quantum computing offers the ability to unlock cryptographic advancements that are beyond the reach of existing security technologies. This includes not only strengthening the mathematical foundations of existing cryptographic schemes but also enabling entirely new cryptographic concepts. This project seeks to chart a clearer understanding of the complexity-theoretic foundations that underlie quantum cryptographic systems. The resulting insights will help in precisely characterizing the mathematical assumptions needed for quantum cryptographic primitives. The project also aims to develop new protocols and techniques for secure interaction with quantum information, including ways to verify and protect quantum information. Such protocols could potentially lead to applications of quantum computers and quantum networks in the future. In addition to contributing to foundational science, the project will support educational activities, outreach, and international collaboration. The project undertakes a broad theoretical study of the complexity assumptions and structural underpinnings of quantum cryptography. It aims to clarify the hierarchy of quantum cryptographic primitives and determine which assumptions are necessary or sufficient for constructing various protocols. The research will explore idealized models such as the quantum random oracle model, the common Haar state model, and the Haar random oracle model, in order to probe feasibility and impossibility results. The investigators will also analyze complexity-theoretic reductions and separations between different cryptographic primitives, and employ recently developed unitary complexity theory to better understand their security. A further goal is to design new quantum cryptographic protocols, including interactive and zero-knowledge protocols for preparing quantum states, and to explore notions such as proofs of quantum knowledge. By pursuing these directions, the project seeks to build a coherent, complexity-theoretic framework for quantum cryptography. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2026 · 2026-01

Moving people to 'better' places underpins the rationale for over 42 billion dollars in federal spending on flagship programs in affordable housing and social policy. Yet remarkably little is known about how much and where integration actually occurs. This project deploys a national, large-scale, and mobility-based test of the major theories of how integration happens using cell phone location data. Case studies of metropolitan areas identify additional characteristics of places that correspond with more integration. Findings provide new evidence on the effects of housing policies, such as the social outcomes of vouchers and zoning incentives for mobility, as well as context-specific mechanisms and enabling conditions that may yield insights and inform place-based policies. The publicly available data products from this research enable scientific inquiries on mobilities across multiple spatial and temporal scales and set the stage for a wider range of work on related issues and their equity impacts. This project makes a novel intellectual contribution to questions of 'people versus place' and the study of human mobility data in the geospatial and social sciences. The research tests the tension between theories on the power of proximity and opportunity for individual outcomes, which drive most affordable housing policies in the US, and those of case studies that show persistent patterns of experienced segregation and exclusion inside mixed-income areas. Geospatial, statistical, and qualitative methods examine segregation and access to opportunity to incorporate a wider range of experienced contexts through human mobility data in a granular and spatiotemporally contingent manner. Findings nuance localized heterogeneity and persistent 'microsegregation' dynamics of high relevance to social and housing policy and contribute new knowledge to current prominent debates on the role of socioeconomic and racial integration in promoting opportunity in affordable housing. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-10

Solid catalytic materials, such as zeolite aluminosilicates, are at the heart of petroleum and natural gas conversion. Some solid catalytically active materials are now known to be dynamic, with metal ions that move throughout the porous solid structures. While this mobility has important influences on the catalytic activity of the zeolite, and therefore the productivity of the industrial process, understanding, quantifying, and tracking this mobility is exceptionally challenging. In this emerging paradigm of dynamic catalytic materials, signals must be identified to track the mobile components and new tools developed to probe their behaviors and contributions to reactivity. This Designing Materials to Revolutionize and Engineer our Future (DMREF) project will investigate the dynamic behavior of technologically-important metal cations such as gallium and copper, dispersed in porous oxide materials. Specifically, the project will explore the spatial extent and timescale for ion mobility, the role of adsorption (determined by the size of the ion and its oxidation state, as well as the nature of the oxide support), the effect of temperature, pressure, protons, and the availability of ligands. Spectroscopic fingerprints for ion mobility will be identified and used to probe their dynamic behavior. Guided by theoretical simulations, these spectroscopic fingerprints will aid in interpreting the measurements and describing the reaction mechanisms. The data and models generated by the project will allow researchers to predict temperature regimes that mark the onset cation mobility, as well as how cation mobility influences catalytic performance, activation during start-up, and deactivation. This will provide insight into how to intentionally synthesize materials with a desired type of dynamic behavior, account for the contributions of mobility to the technological performance of materials, prolong their useful life, and regenerate them by inducing mobility to cause redispersion of the ions. Another important outcome is the training of graduate students and postdoctoral scholars in collaborative research at the intersections of synthesis, spectroscopy, theory and simulations. They will work across disciplines with researchers in the US and abroad, in academia, national labs, and industry, to tackle challenging problems in materials design. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-10

This project examines the effects of natural disasters on the mobility of individuals. Owing to impacts on critical infrastructure and local economies, people often change how and where they move following natural disasters. Understanding these changes is important for emergency response and long-term urban resilience planning. In this project, the researchers develop advanced computational solutions based on spatial analytics and locational data, such as information from mobile phones, to study how communities respond to disasters, especially wildfires. By looking at anonymous movement patterns, the project explores how people react to emergencies and how their ability to move varies across a geographical region. Open-source tools are developed that make it easier for researchers to investigate disaster impacts. These tools help to improve decision-making in urban planning and emergency response. In addition, the project provides students with opportunities to develop valuable analytical skills, helping to prepare them for future careers in STEM. There is a need to understand better how disasters affect people’s movements in real time, especially at a local level during different stages of an event. To address this need, the project creates an integrated spatial analytical and modeling framework that combines large-scale anonymous movement data, such as data from mobile phones, with geographic, demographic, and environmental information in order to study travel patterns and logistical vulnerabilities during crisis events. Specifically, the project pursues three goals. First, it uses different types of mobile phone data as behavioral markers to learn and model how people’s movement patterns change during various phases of a disaster, with particular attention on access and placement of resources. Second, the project develops new machine learning and spatial analysis methods to enhance the representation of mobile location data for modeling and summarizing population-level movement patterns. Third, the researchers are implementing and evaluating open-source software that integrates the methods for use by practitioners. The case studies assess how well the new methods can elucidate geographical areas where people may have trouble accessing needed resources during natural disasters. The results help to improve predictions of how people behave in emergencies and support better assessments of community responses based on data-driven insights. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-10

Large-scale networks of interconnected dynamical systems are ubiquitous in both nature and engineered systems. For example, the AC electric transmission grid is arguably one of the most complex machines ever built. Its stability requires the operation of thousands of generators in synchrony to within milliseconds. This synchrony is achieved through networked interconnections and dynamic interactions. The study of these network dynamics is essential in characterizing the resilience of such critical infrastructure to disturbances, uncertainties and exogenous shocks. Networked oscillators operating in various states of partial or complete synchrony are also ubiquitous in biology, from interconnected neurons to the synchronization of fireflies. The commonality between all these disparate networked systems is in the underlying mathematical structure and phenomena. Thus the study of synchrony phenomena in one type of network can inform the understanding of another network that at first might seem to be a very different system. The proposed research aims at studying a recently discovered type of network vulnerability that arises from certain patterns in the way some networks are interconnected. Mathematically, this phenomenon is known as the localization of eigenvectors that describe the network structure. We study the intriguing mathematical similarities between these phenomena in macro-scale networks on the one hand, and those that occur in semiconductor physics known as Anderson Localization on the other. It appears that not all parts of a network are equally vulnerable, and different types of shocks and perturbations lead to different behaviors of the network, some more problematic than others. Our research aims to uncover the fundamental underlying mathematical reasons for both network resilience and network vulnerability and apply those findings to real-world networks such as those in the AC transmission grid among others. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-10

Immersed granular materials such as river and seabed sediments and industrial slurries consist of small particles dispersed in a liquid. When these particles stick together, the bulk behavior of the slurry becomes highly complex. These cohesive effects determine whether a riverbed erodes, a coastal slope collapses, or an industrial process is successful. Despite its importance, predicting the bulk behavior of cohesive immersed grains is difficult. This project addresses the challenge of predicting how microscopic adhesive forces between individual particles control the large-scale flow of immersed cohesive granular materials. The resulting knowledge will help improve models for underwater sediment transport, water treatment facilities, and industrial slurries. The project will combine a novel laboratory approach, utilizing controllable "sandcastle-like" bonds between particles with advanced particle-resolved numerical simulations that connect the particle and bulk scales. The project also includes a strong educational component that will provide research and training opportunities for high-school, undergraduate, and graduate students to develop the next-generation scientific workforce. The goal of this project is to develop a quantitative framework connecting particle-level cohesion to the macroscopic flow and rheology of immersed granular materials. The project will integrate laboratory experiments with particle-resolved numerical simulations. The experiments will directly measure the adhesive force induced by capillary bridges between immersed particles, characterize the dynamics of cohesive particle clusters using high-speed imaging, and quantify bulk flow properties including yield stress in canonical configurations like granular collapses and rotating drums. These results will be used to develop and validate a cohesive force model for the simulations, which will then probe the microstructural origins of the bulk response of the cohesive granular material. The primary scientific contribution will be the formulation of data-driven, continuum-level constitutive laws for cohesive immersed granular flows. This work will develop a validated computational tool and provide a foundational understanding to improve predictive models for cohesive sediment transport and other applications. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-10

Throughout mathematics and computer science there is an over-arching connection between structural and descriptive mathematical statements and efficient algorithms. Here efficient algorithms mean polynomial time algorithms. These are algorithms whose running time scales well with the size of the input data that they are supposed to process. When you double the size of the data, the time taken to process it also get doubled, or at worst multiplied by a fixed constant. Efficient algorithms permeate everything, and so it is of utmost importance to know which problems can and can't be solved efficiently by computers. Yet, for some fundamental problems, such as the Independent Set problem (given a social network, find the largest group of people who do not know each other), researchers have been unable to establish precisely when an efficient algorithm exists and when it does not. Quite recently a lot of progress has been made on this problem, and on other related problems, by considering quasi-polynomial time algorithms instead of polynomial time algorithms. These are algorithms that are almost as efficient as polynomial time algorithms, but not quite. In this project the investigators will build a theory of quasi-polynomial time algorithms for graph problems. This will also lead to new and fundamental descriptive mathematical theorems, especially within the field of graph theory. Despite tremendous effort, a number of fundamental computational problems in algorithmic graph theory still resist classification into polynomial time solvable or NP-hard. On the other hand, there have been a number of recent breakthrough results, many of which were co-authored by the investigators, establishing quasi-polynomial time algorithms for problems for which the existence of polynomial time algorithms remains as a central open problem in the field. The quasi-polynomial time algorithms effectively rule out the possibility of NP-hardness results for the considered problems. These algorithms are built on structural insights specifically tailored to quasi-polynomial time, rather than polynomial time, algorithms. The structure theory underlying quasi-polynomial time graph algorithms is rapidly developing into a research area in and of itself. In this project, the investigators will spearhead the development of this exciting new direction and build the structure theory for quasi-polynomial time graph algorithms. In many important aspects the new structure theory will serve as an analogue of the famous graph minors project for induced minors. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2025 · 2025-09

Project Summary The ability to navigate is a crucial component of daily life, and around the emergence of puberty, men begin to outperform women in navigation tasks that require an accurate “cognitive map,” or an internal representation of an environment. After puberty, there are two critical aspects to ovarian hormones that potentially shape this trajectory: 1) the short-term fluctuations that occur during the menstrual cycle, and 2) the longer-term, sweeping changes in hormones that occur across the menopausal transition. Understanding the role of ovarian hormones on navigation ability is important because the ability to navigate is one of the earliest cognitive abilities to be affected in the development of Alzheimer’s Disease (AD), and women account for 2/3rds of all late-onset cases. The neural circuitry implicated in AD and spatial navigation overlap substantially (e.g., the medial temporal lobe [MTL]), and in animal models and some human studies, estradiol has a positive impact on the ability to navigate. However, little is understood about how sex hormones affect navigation in women, and if aging exacerbates these effects. Even less is known as to how these mechanisms contribute to the disproportionate rate at which women are affected by AD. This proposal will establish the role of ovarian hormones in acquiring spatial knowledge, both at the cycle-phase level and in regards to endocrine aging. In the F99 phase (Aim 1), I will determine associations between ovarian hormones and navigation in young women between the ages of 18-40. First, I will assess the impact of the menstrual cycle on navigation ability and strategy (N=60) in naturally cycling young women across two timepoints–one of low estradiol (the early follicular phase) and one of high estradiol (the ovulatory window/luteal phase). The study will take a within-subjects approach to account for individual variability and isolate effects of estradiol on navigation ability. Next, I will determine the role of ovarian hormone suppression on navigation ability and strategy in young women (Cases) compared to age-matched women, half of which are naturally-cycling and half are on a progestin-only oral contraceptive (Controls, N=40 in each group). Cases will undergo chemically-induced ovarian hormone suppression for 3 months as treatment for endometriosis, and Cases and Controls will be tested on their navigation ability and strategy before and after the time course of Cases’ treatment. In the K00 phase (Aim 2), I will take my skills and insights gained from the F99 phase, including fundamental knowledge of neuroendocrine methods and spatial cognition, and establish the relationship between reproductive aging, brain structure, and biomarkers of AD (via neuroimaging of the MTL and pTau-217). To do this, I will leverage a community cohort (N=180) of midlife women (ages 45-55, stratified into three groups based on menopausal status) to collect hormonal, MRI, and navigation data to build a cohesive understanding of these mechanisms as they pertain to AD risk. Together, this proposal will identify the role of ovarian hormones and endocrine shifts on navigation, as an early diagnostic target for AD and to improve women’s health and outcomes.

NSF Awards · FY 2025 · 2025-09

NONTECHNICAL SUMMARY This award supports theoretical and computational research, and education on active matter. Active matter refers to ``materials’’ formed not of atoms or molecules, but of self-powered entities, such as birds, living cells, or man-made microswimmers, that take energy from the environment to self-organize and produce coordinated motion. An example is a bacterial suspension. Each bacterium is an active particle that swims by consuming nutrients. A dense swarm of bacteria behaves collectively as a living fluid that can flow with no externally applied forces or ``freeze” into a solid-like biofilm – a highly resistant bacterial aggregate like the tartar that forms between our teeth. This type of emergent behavior, where a collection of many interacting entities exhibits large-scale spatial or temporal organization in a state with novel macroscopic properties, is familiar in inanimate or passive matter (e.g., the transition from water to ice as one lowers the temperature), but acquires a new unexplored richness in active systems that are tuned not by an external “knob”, like temperature, but by energy generated internally by each individual. Previous active matter research has focused largely on the behavior of active fluids that exhibit self-sustained, often chaotic, flows. The first part of the work carried out in this project focuses on the largely unexplored behavior of active solids where energy input at the local scale can drive global oscillations and shape changes. Active solids are realized in many biological contexts, such as in the development of organs and organisms, where collections of living cells behave like solid-like materials on experimentally relevant time scales and undergo self-driven dramatic shape changes to achieve the desired form. Using theory and computation, the PI will develop a mathematical model of active solids that incorporates the feedback between mechanical forces and biochemical signaling. The model will be applied to specific examples of animal morphogenesis, such as that of the freshwater polyp Hydra which is extensively studied in the laboratory under controlled conditions. This work will yield critical understanding of how biological systems modulate energy input and dissipation in space and time to achieve target shapes and control transitions between target shapes. It will additionally pave the way to formulating rules for the design of self-shaping materials. In a related project, the PI will work with an experimental collaborator to quantify and control the spatial and temporal organization of complex active fluids built from proteins, with the ambitious long-term goal to mimic nature and construct materials capable of autonomous motion and reconfigurations. In addition to lead to fundamental advances in physics, the proposed research will impact other fields, from biology to engineering. It will serve as a framework for the training of undergraduate and graduate students and postdoctoral researchers at the interface of physics, engineering and biology, hence contributing to the development of a strong STEM workforce. TECHNICAL SUMMARY This project will combine theoretical models and numerical simulations to address a number of open questions in active matter physics. The research is organized around three specific objectives. 1.) Active elasto-nematic as a model for self-shaping synthetic and living matter. Motivated by experiments on Hydra morphogenesis, the PI will formulate a continuum model of active elasticity that incorporates the feedback of mechanics and chemical activation. Using this model the PI will quantify the relative roles of geometry and stress/strain-driven biochemical feedback in controlling the structure and collective dynamics of active solids, where cell density and activation at the cellular scale control global temporal oscillations and spatial patterns. This work has implications for a broad range of biological processes, from morphogenesis to cancer progression. 2.)Active bilayers, micelles and foams. Motivated by recent experiments by collaborator Dogic, the PI will couple phase separation and flocking dynamics to examine the role of polar microtubules- kinesin 4 constructs as ``active surfactant’’, capable of organizing in self-reconfigurable structures, such as active bilayers, micelles and foams. This work will advance our understanding of the mechanisms that sub-cellular organization, with potential applications to the creation of new materials for drug delivery. 3.) Cell migration in crowded environments. New models of cell motility that couple reaction-diffusion to mechanics will be employed to investigate how cells and cell groups explore their environment and adapt to it to migrate in crowded settings. The proposed research will engender fundamental advances in nonequilibrium statistical physics, open new strategies for the design and assembly of active and reconfigurable materials, and develop theoretical models relevant to biological processes, from morphogenesis to cancer invasion. The project has a strong educational component aimed at training students and postdocs with robust quantitative skills and expertise at the interface of physics and biology. The PI will continue to play a significant role in the profession through the organization of conferences and advanced schools and her participation in review and advisory panels. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2025 · 2025-09

PROJECT SUMMARY (See instructions): We live in diverse environments featuring dynamic, interacting stimuli that can be viewed from an arbitrary set of perspectives and approached under a variety of goal scenarios. However, modern cognitive and visual neuroscience studies often stand in stark contrast to this reality of our lived experience: typically, participants view isolated, simplistic stimuli while brain activation is recorded and reconstructed or decoded neural representations are quantified. Reconstructing naturalistic visual images from fMRI data presents a challenging task: existing approaches require dozens of hours of 7T fMRI data per participant and extensive compute capability, each of which render these methods inaccessible to traditional cognitive neuroscience labs. Our long-term goal is to develop data-efficient methods to enable use of natural stimuli in cognitive neuroscience studies which characterize neural information content and transformations with cognitive operations. Our objective in the present proposal, which is the next step towards our long-term goal, is to demonstrate the efficacy of natural image reconstruction as an experimental tool in cognitive neuroscience via implementing biologically-inspired optimizations to existing approaches and benchmarking their performance on newly-acquired cognitive task data. Our work involves 3 specific aims: (1) maximize data and computational efficiency of image reconstruction approaches, (2) improve image reconstruction using neuroscience principles, and (3) investigate the impact of cognitive tasks on reconstructions from neural representations of natural images. Across all Aims, we will implement and optimize a novel Al-based image reconstruction approach applied to fMRI data. In Aim 1, we will focus on establishing the efficacy of our approach for training decoding models for new participants using minimal new fMRI data. In Aim 2, we will augment our method by incorporating principles from visual neuroscience as informative priors, like spatial receptive fields estimated from retinotopic mapping and feature-selectivity estimated from functional localizers. In Aim 3, we will test our approach by acquiring several benchmark cognitive neuroscience datasets measuring how reconstructed representations are impacted by visual cognitive task demands including visual attention and working memory. This work is innovative because it enables natural image reconstruction with tractable datasets in cognitive neuroscience labs. This work is significant because it will lead to a new understanding of the neural codes supporting visual perception, attention, and working memory, which are impacted in a host of neurological and psychiatric disorders.

NSF Awards · FY 2025 · 2025-09

The seafloor sediment provides an important archive of information about Earth’s past. Sediment accumulates nearly continuously for thousands to millions of years. Interpreting the geologic and environmental changes recorded by these sediments relies on knowing the age of each sediment layer. Researchers often use software to create “age models” that estimate sediment age and the uncertainty of that age. This project aims to improve the accuracy of sediment ages. It will compile radiometric ages in over 250 marine sediment cores. This new data will increase the constraints on the new modeling software, BIGMACS, by tenfold. This improvement will result in more accurate sedimentation rates, reduce age-model uncertainty, and broadly improve paleoclimate data compilations. This new software will be freely available to the scientific community. The project will advance the career of a postdoctoral researcher in applied math and geosciences, train graduate students in interdisciplinary paleoclimate studies, and expose an undergraduate student to research. The accuracy of paleoclimate reconstructions used to validate the climate models rely on age models when identifying cause-and-effect relationships, creating snapshots of the climate at a specific point in time, or characterizing the magnitude of natural variability on different timescales. Such information is crucial for testing the effectiveness of climate models and improving their ability to simulate potential future climate states. Several software packages exist that use statistical methods and different assumptions about variability in sediment accumulation rates to produce age models that allow for ages to be estimated at depths between directly dated sediments, for every depth in a sediment core. However, very few studies have measured variability in ocean sedimentation accumulation rates or tested the statistical models used by these software packages and how they affect reconstructions of Earth’s past. This study will employ two different techniques to measure sedimentation accumulation rate variability over the past 50,000 years using data from approximately 250 ocean sediment cores. These measurements will then be used to estimate parameter values that improve the statistical models used by age modeling software. The principal investigators will also develop improved statistical methods for a previously published software package to generate more accurate results. The improved model will also be made available as open-source, such as Python, for greater accessibility. The study also investigates how estimates of past climate change are impacted by different age modeling software packages and updated estimates of sedimentation rate variability. This project benefits the broader scientific community by providing improved age modeling tools for reconstructing past climate change and provides interdisciplinary training for the next generation of scientists, including graduate and undergraduate students in Earth Science and an interdisciplinary early career researcher in Applied Mathematics and Paleoclimate. 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

Non-Technical Summary: Self-assembly refers to the spontaneous emergence of well-defined structures from an initial disordered state. Common examples include the formation of soap films, the folding of proteins and nucleic acids, as well as the formation of crystals. Self-assembly is a powerful bottom-up approach to materials synthesis. While capable of generating intricate structures, equilibrium self-assembly suffers from limitations. It requires microscopic constituents that exhibit riotous dynamics driven by thermal noise. To overcome these limitations, this project will develop a paradigm of active assembly for generating materials from building blocks that exhibit no thermal motion. Non-thermal Velcro-like bundles of filaments are placed in an active fluid. Spontaneous flows generated by active fluid endow passive bundles with enhanced dynamics. These bundles move chaotically, stick to each other, generating permanent connections, and assembling three-dimensional elastic networks, whose structure and mechanical properties cannot be realized with conventional self-assembly methods. The proposed research provides a powerful platform for generating new materials with unique properties. From a societal perspective, the proposed project will provide rigorous interdisciplinary training to graduate students. The project will also provide invaluable research opportunities and extensive mentoring to undergraduate students from UCSB and throughout the California State educational system. Finally, the project pursues extensive outreach activities targeting the general public and K-12 education, enhancing the public awareness of materials research. Technical Abstract: Self-assembly is a versatile paradigm for engineering materials with intricate structures and targeted mechanical properties. Well-understood equilibrium statistical mechanics provides a quantitative relationship between the interactions of the microscopic building blocks and the ensuing macroscopic properties of the target assemblage. Notwithstanding its considerable successes, equilibrium self-assembly suffers from limitations. Self-assembly demands an equilibrium environment wherein all intermediate states exhibit thermal motion. To ensure that the process reaches the target state, one has to balance the strength and specificity of the attractive interactions against the characteristic thermal energy. This project aims to extend the capabilities of equilibrium self-assembly by establishing the foundations of active assembly. Chaotic flows generated by an active fluid endow passive molecular building blocks with enhanced stochastic dynamics and excess energy that are not accessible in equilibrium. This overcomes energetic barriers that trap the equilibrium system and allows for the exploration of a much larger landscape of accessible states. In active assembly, the building blocks move throughout the sample, encounter each other, and bind together to give rise to soft materials with unique structures, shapes, mechanics, and dynamics. When compared to equilibrium self-assembly, active assembly extends the manifold of accessible structures, overcomes kinetic trapping associated with equilibrium, and enables the assembly of mesoscale building blocks that do not exhibit thermal motion in the absence of activity. In the first aim, actin filaments and their crosslinkers are placed into a microtubule-based active fluid. Being advected by the active flows, actin filaments efficiently explore the accessible phase space and assemble into elastic networks whose architectures are not accessible using conventional protocols. State-of-the-art microscopy and quantitative image analysis elucidate the kinetic pathways of network assembly in real-time with near-molecular detail. In the second aim, the rheological properties of the assembled elastic network are correlated to the microscopic network structure. These unique features will enable a study of the non-thermal transition from floppy networks lacking a finite shear modulus to networks with a finite rigidity. 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 overall objective of this project is to produce a new tool for accurate and reliable wind observations from coastal high-frequency (HF) radar at the same resolutions as currents and demonstrate their validity via comparisons with in-situ observations. Considering the kilometer scale variations in wind and currents will lead to advancing our knowledge of the coupled ocean-atmosphere system. High frequency radar-based surface wind observations will fill a critical gap in our capability to observe and understand coastal ocean dynamics and ocean-atmosphere interactions. In HF frequency radar-based efforts, signal attenuation, or path loss, is largely related to the wind field itself, which represents a key difference from satellite-based wind extractions. To resolve some systematic issues of previous HF radar-based methods, PIs propose to finalize a generalized method for wind extraction process using a ‘tomographic’ approach. This new approach will be demonstrated through the production and analysis of two long records of coastal ocean winds and currents in two geographically distinct coastal regions. 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.