North Carolina State University

NSF Awards · FY 2025 · 2025-08

This award supports contributions to two experiments designed to provide high precision measurements of neutron decay: Nab, at the Spallation Neutron Source in Oak Ridge, Tennessee, and UCNτ+ at the Los Alamos Neutron Science Center. These measurements should define the most precise characterization to date of the weak nuclear force, one of the four fundamental forces. This force causes neutron decay, which can be used in sensitive searches for new interactions or particles. The Nab experiment relies on two state-of-the-art silicon detectors to detect neutron decay products (protons and electrons). The NCSU group will continue their work characterizing the electron response and analysis of the wave-form data from these detectors, concentrating on event reconstruction using detailed detector models together with algorithms developed using recent data. The UCNτ+ experiment is designed to store and count ultracold neutrons in a magnetic trap to determine the neutron lifetime. The NCSU group will contribute to an improved understanding of experimental effects associated with ultracold neutron energy spectra and neutron transport, as well as the analysis of lifetime data. The broader impact of this work stems from the impact the experimental data has on particle physics and astrophysics, and the training we provide for undergraduates, graduate students and postdoctoral research associates. For the Nab experiment, a wave-form model was developed during the previous funding period, together with a procedure to determine the impurity density profile for the Nab silicon detectors. A waveform template library is now being developed which can be applied to noisy experimental data to permit efficient event topology reconstruction. A specific target of interest for the Nab collaboration will be to explore machine-learning algorithms for the event reconstruction process. This effort will be coupled with our on-going effort to provide high precision calibration of the electron response function and bremsstrahlung losses for the Nab detectors. For UCNτ+, our group has established simulations of a new “elevator” adiabatic loading system and multiple instruments to determine the impact of variations in the loaded ultracold neutron spectrum to the trapped populations used to measure the lifetime. 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

The ability of plants to regrow an entire organism from just a few cells is a truly remarkable feat of nature, widely used in agriculture and biotechnology for centuries. For example, methods like taking cuttings from a plant to grow new ones, or grafting different plant parts together, rely on this natural capacity. Modern applications, such as developing improved crop varieties through genetic engineering, also depend heavily on understanding and harnessing this unusual plant characteristic. Despite its importance, the precise molecular mechanisms that allow a differentiated plant cell to essentially "reset" and begin forming a new plant are not fully understood. This project aims to unravel these fundamental processes, which are crucial for overcoming current limitations in developing new and improved plant varieties that can better withstand environmental challenges or produce more food and resources. By understanding how plants regenerate, this research will provide foundational knowledge to improve crops and contribute to a more sustainable future for agriculture. Broader Impact activities will include training of students and outreach to the local community via the Plants4Kids program in North Carolina and Science Bound Saturday events at Iowa State University. This project focuses on elucidating the molecular mechanisms by which somatic plant cells reprogram to regenerate organs and whole plants. While plant hormones, particularly auxin, cytokinin, and ethylene, are known to be critical regulators of this process, the specific roles of local auxin biosynthesis and its interplay with other hormonal and regulatory networks remain largely unexplored. The research aims to: 1) identify specific auxin biosynthetic genes involved in the initial stages of cellular reprogramming using a comprehensive collection of whole-gene translational reporters; 2) determine the functional significance of localized auxin biosynthesis in promoting plant regeneration; and 3) delineate the regulatory networks downstream of these key auxin biosynthetic genes involved in callus formation and subsequent plant regeneration. This will be achieved through single-nuclei RNA-seq analysis at precisely chosen time points, guided by a novel triple hormone response sensor that provides cellular resolution of auxin, cytokinin, and ethylene activities, linking transcriptional changes to morphological events. This award is co-funded by the IOS-Developmental Systems Cluster and the IOS-Physiological Mechanisms and Biomechanics 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

Data reduction holds paramount importance in scientific endeavors and various data-intensive domains. This necessity is compounded by the unprecedented surge in data volume propelled by advancements in facilities and scientific research. Data compression stands as a prevalent method for data reduction. But prevailing solutions are hindered by a fundamental constraint: the requirement for decompression prior to processing. This introduces three primary challenges: (i) heightened strain on storage and memory resources, (ii) potentially lengthy decompression times for sizable datasets leading to significant workflow delays, and (iii) sometimes loss of accuracy when applications are compelled to operate on partial data views for space shortage. Aiming to develop a set of novel programming and runtime techniques, this project will create Efficient Processing without Decompression (EPOD), a novel approach to data reduction by lossless data compression while maintaining direct processability (without decompression) in the compressed state. The success of the project is poised to yield significant impacts, potentially reducing data sizes in numerous domains by one or two orders of magnitude without quality loss, while concurrently expediting data processing by multifold. The technique will help accelerate scientific research as well as improve the efficiency and productivity in various data-intensive domains. To develop the idea of EPOD into a new paradigm and solution for data reduction, this project includes five research activities: (i) developing the basic EPOD method and expanding its data coverage to floating-point datasets and so on; (ii) creating multi-level support of EPOD operations (i.e., data accesses or manipulations working directly on the EPOD compressed format) in forms of an EPOD library and a scalable large language model-based EPOD synthesizer; (iii) enabling continuous compression for streaming to make EPOD seamlessly integratable in streaming workflows of scientific facilities; (iv) developing advanced optimization of EPOD to maximize the efficiency and scalability; (v) integrating with existing data analytics ecosystem and complementary techniques and applications, and demonstrating the impact on a broad range of scientific domains and 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 · 2025-07

Geometric topology is a field which studies the intrinsic structure of shapes. The primary investigator (PI) will focus on properties of knots, three- and four-dimensional shapes, which appear in aspects of biology, physics, and computation. The PI will develop tools which help to measure the complexity of these objects, as well as constructing exciting new topological structures in four dimensions. Broader impacts will be through mentoring at the undergraduate and graduate levels, organizing seminars, and disseminating mathematical resources. The PI will produce new exotic smooth four-manifolds using hands-on constructions and invariants from Heegaard Floer homology. He will also study surfaces in four-manifolds, including finding exotic embeddings of surfaces in four-manifolds and constructing new non-orientable surfaces in the four-sphere. The PI will apply instanton gauge theory to a number of topological problems. For example, he will make progress on the Cosmetic Surgery Conjecture using the Chern-Simons filtration; show that nullhomotopic knots are determined by their complements, answering a question of Boileau from the famous Kirby Problem list; and constrain the Heegaard genus of three-manifolds through homology cobordism invariants. The PI will establish foundational results about Heegaard Floer homology, including giving a topological characterization of Heegaard Floer homology solid tori and establishing structural results about the module structure on Heegaard Floer homology. The PI will also continue mentorship of PhD students; organizing seminars and working groups with the goal of enhancing the mathematical community in the Research Triangle region; and writing Mathematical Reviews. 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

Machine learning (ML) techniques play a pivotal role in modern artificial intelligence (AI) systems, but they remain notably vulnerable to disruptions caused by security attacks. These vulnerabilities can severely compromise AI system performance or be exploited maliciously, posing significant economic, ethical, and societal risks. For example, placing a small sticker on a stop sign could cause a self-driving car's perception system to misinterpret it as a speed limit sign, leading to potentially catastrophic consequences. As the reliance on AI grows, ensuring the secure, robust, and resilient operation of ML systems becomes increasingly essential. However, most robust ML research has focused on static, closed-world scenarios that fail to address the complexities of dynamic, real-world environments. This award aims to develop transformative methods to enhance the resilience and reliability of ML systems in these challenging settings. The outcome of this project promises broad societal benefits, including safer and more dependable AI applications in diverse fields such as biology, healthcare, cybersecurity, and manufacturing. Additionally, the project will transform AI education by integrating ML robustness as a foundational theme, preparing future workforce to tackle emerging challenges in trustworthy AI, and fostering public awareness of AI risks and mitigation strategies through extensive outreach. This award seeks to advance AI research by addressing three key challenges in open-world environments. First, it will develop novel techniques to enhance robustness generalization across data distributions, which mitigates robustness degradation under distribution shifts. Second, it will introduce new learning algorithms to ensure robustness against multiple attacks simultaneously. Third, this project will devise certification approaches to evaluate robustness in dynamic environments. Comprehensive evaluations will be conducted using public datasets and real-world applications, supported by collaborations with academic, governmental, and industrial partnerships. The success of this CAREER project will pioneer new frontiers of robust ML and establish solid foundations for building next-generation robust and trustworthy AI in the real world. 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

Rural mountain communities like Appalachia face increasing risks from extreme weather events like inland flooding, as tragically demonstrated by the catastrophic impacts of Tropical Storm Helene in 2024. These communities experience disproportionate health consequences from such disasters, including respiratory illnesses, waterborne diseases, and mental health disorders, yet receive limited research attention and resources compared to coastal and urban areas. This Research Coordination Network (RCN) serves the national interest by advancing scientific knowledge and public welfare through an interdisciplinary collaboration of geoscientists, public health professionals, social scientists, and community leaders to identify evidence-based strategies for enhancing flood resilience, targeted adaptation solutions, and reducing health disparities in rural communities. Through knowledge sharing and coordinated research, the network will have broader impacts that include actionable recommendations to protect community health, strengthen rural infrastructure, and build economic resilience. The findings will be designed for scalability, enabling broader application to other rural communities nationwide. This project promotes scientific progress by equipping communities with the tools to mitigate and adapt to extreme weather events. The research coordination network will operate through five interdisciplinary working groups addressing critical themes: (1) Geophysical and weather related disaster modeling for flood prediction, developing high-resolution flood forecasting models tailored to rural and mountainous regions; (2) Flood exposure and health outcomes, investigating short- and long-term health effects of inland flooding; (3) Social and economic dimensions of flood vulnerability, exploring how structural and sociodemographic factors shape resilience; (4) infrastructure resilience and adaptation strategies, assessing flooding impacts on essential systems in communities with limited resources; and (5) Community-led adaptation and policy implementation, collaborating with stakeholders to integrate indigenous knowledge systems into resilience planning. The research network will implement a structured five-year coordination plan featuring quarterly virtual meetings, annual World Café-style collaborative sessions, pilot funding opportunities for innovative research, and developing a Rural Resilience Network Exchange – a digital platform for data sharing, community engagement, and knowledge dissemination. This network will facilitate interdisciplinary collaborations across institutions through virtual workshops and student-led research initiatives embedded within working group activities. The project will tackle methodological challenges in flood hazard assessment by integrating cutting-edge atmospheric and environmental data with health and socioeconomic indicators, creating a more comprehensive framework for understanding and mitigating flood impacts in rural communities. Through these coordinated efforts, this research network will produce peer-reviewed publications, policy briefs, and community-engaged research products that advance scientific knowledge and inform practical resilience strategies for Appalachia and rural regions globally. 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

Legacy spectrum sharing systems, like Spectrum Access System (SAS) used in the Citizen Broadband Radio Service (CBRS) band, work effectively for static or slowly changing, i.e., slow-moving, federal systems. On the other hand, for other mid-band frequencies such as the lower 3 GHz band, these SAS-like systems face issues such as high operational latency, reliance on expensive sensors, and lack awareness of factors such as weather and traffic types. This project introduces a novel Open Dynamic Spectrum Management (O-DSM) system, built on data-driven, standard-compliant Open Radio Access Network (O-RAN) architecture, to enable a faster, more efficient, and context-aware spectrum sharing between 5G/6G networks and Federal/non-Federal users. The proposed research aims to develop an O-DSM system to improve spectrum sharing between commercial networks and Federal/non-Federal users across mid-band frequencies like the lower 3 GHz (3.1 – 3.45 GHz) and FR3 bands (7 – 24 GHz). Key goals include (1) developing artificial intelligence (AI)-driven methods for rapid, precise detection of incumbent radars even in lower signal-to-interference-plus-noise regime, (2) developing intelligent, context-aware, closed-loop dynamic spectrum control algorithms using digital twin and O-RAN capabilities, (3) creating an AI-assisted framework for testing real-time performance, conformance and security, and (4) prototyping the O-DSM system for evaluation to ensure effective 5G/6G coexistence in mid-band frequencies. This research has the potential to catalyze whole-of-nation broadband network deployments, seamlessly with Federal/Non-Federal systems across mid-band frequencies. It is expected to promote economic growth and solidify U.S. leadership in wireless technology. The project will offer valuable insights to regulatory bodies like the FCC (Federal Communications Commission), addressing key spectrum coexistence issues in mid-band frequencies. The program also includes a strong education component, cultivating future technology leaders through lectures, projects, hands-on training, and mentorship. High school students will also engage in engineering experiences via summer camps. 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

Plants grow and adapt through complex developmental processes, from root formation underground to leaf and flower development above. Understanding how plants build and shape themselves not only advances science but also helps address real-world challenges in agriculture resilience and efficiency and food security. The 2025 FASEB Conference on Mechanisms in Plant Development will bring together leading scientists and early career researchers to share discoveries about how plants grow and respond to their environments. Taking place in Southbridge, Massachusetts, this meeting is the only recurring international forum fully focused on plant development. This conference ensures shared open discussion of the latest scientific tools, like single-cell technologies and live-cell imaging, so that more researchers can use these new techniques to explore plant systems in greater detail than ever before. The meeting also offers dedicated workshops and networking opportunities for students and early-career researchers to build skills and mentorship relationships, fostering a new generation of plant researchers. These efforts contribute to a highly trained workforce capable of developing innovative solutions for agriculture, while also strengthening the U.S. leadership in plant science and biotechnology. The 2025 FASEB Conference on Mechanisms in Plant Development will convene a multidisciplinary community of scientists exploring the genetic, molecular, and cellular mechanisms underlying plant development. With rapid advancements in single-cell transcriptomics, super-resolution microscopy, and computational modeling, the field is poised to make transformative discoveries in developmental biology. This meeting will feature sessions on key topics such as morphogenesis, growth control, hormonal signaling, patterning, and root architecture, with an emphasis on how plants establish structure and respond to environmental signals. Scientific sessions will include 32 invited talks and 22 selected presentations from emerging researchers, fostering a comprehensive exchange of unpublished research. Two keynote lectures will honor the groundbreaking contributions of Philip Benfey and Joanne Chory, whose work has significantly shaped the fields of root development and plant hormone signaling. A core focus of the meeting is cross-disciplinary dialogue, bringing together experts in molecular genetics, cell biology, biophysics, and systems biology to explore new conceptual frameworks and methodologies. Professional development workshops will support early-career researchers through targeted training in publishing, grant writing, interdisciplinary research, and mentorship. By combining cutting-edge research with training and networking, this conference will advance the frontiers of plant developmental biology and support the next generation of scientific leaders. 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 CAREER project, funded by the Chemical Mechanism, Function, and Properties Program of the Chemistry Division, focuses on uncovering conventionally overlooked chemical mechanisms underlying the variability in fluorescence spectra of single molecules of the same dye species across space and time. Led by Professor Yang Zhang of the Department of Textile Engineering, Chemistry, and Science at North Carolina State University, the project aims to advance the understanding of these mechanisms to develop innovative chemical strategies for illuminating and visualizing biological and artificial nanoscopic processes. Situated at the intersection of color/dye chemistry (organic, analytical, and physical), single-molecule imaging, and deep learning, the project is uniquely positioned to foster interdisciplinary education across all levels. The educational initiatives will integrate color chemistry and machine learning into curriculums spanning K-12 through graduate studies. Additionally, outreach activities will include organizing statewide competitions for K-12 students through the North Carolina Science Olympiad, further promoting scientific engagement. The long-term objective of this project is to understand and control single-molecule fluorescence spectral heterogeneity (smFLUSH) across diverse families of organic fluorophores, including Rhodamine, Boron Dipyrromethene (BODIPY), Cyanine, and Squararine dyes. Mechanistic insights into smFLUSH are critical for guiding the design of next-generation fluorescent probes that meet the demands of advanced multiplexed and functional imaging capabilities in cutting-edge single-molecule super-resolution fluorescence imaging technologies. To achieve these goals, the project focuses on (a) developing a high-throughput single-molecule spectroscopy platform coupled with deep-learning-enabled imaging analytics for systematic characterization of smFLUSH, and (b) synthesizing model compounds to validate structure-property relationships observed in existing fluorophore families. Key research questions include: (1) How can smFLUSH be statistically characterized with precision, accounting for noise uncertainties? (2) How do intrinsic fluorophore structures and environmental interactions influence smFLUSH across different dye families? (3) What chemical modifications can be employed to manipulate and control smFLUSH? By addressing these questions, the project will pave the way for transformative advancements in single-molecule super-resolution imaging, enabling the visualization of biological and artificial processes at an unprecedented level. 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

The evolution of next-generation (NextG) mobile and wireless networks is driven by a move toward higher carrier frequencies, such as millimeter-wave (mmWave) bands. Higher frequencies provide higher capacity but also have a much shorter distance range for coverage compared to lower-frequency signals. This means that a single access point (AP) or a base station cannot cover a large area leading to smaller areas (cells) with each AP handling a smaller number of users. This dense deployment of small-cell APs necessitates a heightened level of intelligence and timely situational awareness to enhance network resilience and self-reconfigurability in the face of various challenges like network or AP failures. To tackle these challenges, this project pursues a novel resilience-native network paradigm called CatFly, which embraces a data-driven learning approach that utilizes a digital replica of the physical network with sufficient details to swiftly achieve preemptive operations against disruptions. Armed with this hybrid digital-physical (HDP) intelligence, networks are always ready and responsive, employing outcomes of their what-if analysis to reconfigure the physical network and ensure resilience. The project aims to utilize a combination of techniques such as network optimization, graph theory, machine learning, experimental measurements, and models that mix physical and virtual contexts. The project will advance networking technologies in three inter-related thrusts, followed by a comprehensive system-level analysis and validation, including: 1) Generalized prediction frameworks that learn critical performance indicators for spatio-temporal awareness and cross-domain knowledge, linking them to handle network disruptions; 2) Multi-scale approach to creating and evolving a network digital replica with sufficient details that is faithful to the physical network to assist with resilience-centric awareness; 3) Hybrid digital-physical optimization with a stabilization-driven mechanism to uncover multi-dimensional physical reconfigurations. The project will develop a comprehensive educational plan that includes a pre-college STEM virtual laboratory, new network intelligence-centered course materials, and hands-on activities using the designed software tools and testbeds. A project website that provides access to code, data, and educational related resources will be actively maintained and updated for the duration of the project. 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

Mathematical models arising in areas such as data analysis, artificial intelligence, geophysics, signal processing, and medical imaging are increasingly complex due to their large size and the presence of random perturbations. This project will investigate foundational principles governing the mathematical representation and the numerical solution of such large-scale random models. New strategies and methodologies based on geometric principles to effectively incorporate randomness in the underlying mathematical representations and in the design of efficient randomized solution algorithms will be developed. Graduate students will be trained as part of the research plan. This project focuses on models and convergence principles for dealing with stochasticity in a wide range of algorithms for solving various types of equilibrium problems arising in convex feasibility, best approximation, convex optimization, fixed point, variational inequality, and monotone inclusion problems. A flexible geometric framework will be developed that captures a broad array of existing algorithms while furnishing an effective pattern for designing new ones. The tools to be developed in the project aim at providing common principles to analyze the asymptotic behavior of stochastic algorithms at several levels: stochastic operator approximations, random coordinate updates, and random operator activations. The two generic classes of problems considered are convex feasibility problems and multivariate systems of structured monotone inclusions. Extensions beyond the monotone/convex setting are also planned. The theoretical and algorithmic findings will be applied to problems in the areas of signal processing, artificial intelligence, machine learning, inverse problems, statistical biology, and medical imaging. 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

Accurately predicting the ecological effects of a warming planet is essential for lessening ongoing economic and societal harm. However, the potential impact of global temperature rise predicted for Earth by the end of the 21st century cannot be studied on the scale of human history alone. Fortunately, essential real-world data on the outcome of rapid and extreme warming is preserved in our planet’s deep-time rock record. Approximately 95 million years ago, Earth transitioned through an interval of global change known as the Cretaceous Thermal Maximum (KTM) with profound repercussions. Global temperature increase during the KTM matches predictions for Earth's near term, making the event a critical case study for our planet’s imminent future. Research demonstrates that during the KTM, 80% of marine life went extinct due to increased ocean temperatures and oxygen starvation. However, scientists do not yet understand the impact of warming on land. Our team of Earth and Life scientists will address fundamental questions about the KTM, producing results directly relevant to society's health and economic well-being. The project will generate freely accessible databases of temperature and precipitation records, species diets, migration and range patterns, plant community compositions, and landscape changes. A sustainable network of labs will use these databases to calculate the duration, rate, and magnitude of extinction and recovery and identify factors affecting ecosystem resilience, such as shifting habitats and destabilizing food webs. A cross-disciplinary postgraduate research exchange program will arm the next generation of scientists with the broad skill sets necessary to tackle some of humanity's forthcoming grand challenges. Finally, the project will increase STEM opportunities for youth via co-created teacher resources and a public science project that empowers secondary school students to contribute directly to scientific research. Approximately 95 million years ago, ecosystems transitioned through an understudied hyperthermal event, the Cretaceous Thermal Maximum (KTM), driven by increasing atmospheric CO2. Global temperature rise during the KTM was triple projections for Earth by the end of the 21st century—making the event a critical case study for predicting tipping points of functional ecosystem decline (economic risk) in as-of-yet unrealized planetary states. Previous studies have documented KTM's marine impacts, including global ocean deoxygenation and cascading extinctions; however, scientists currently lack essential data on terrestrial outcomes. This project will formulate comprehensive, open-access databases that enable cross-disciplinary study of the KTM aftermath. Research will focus on Mongolia's Gobi Basin and North America’s Western Interior Basin, which together preserve the world's richest records of Cretaceous terrestrial life. Data generated will include floral and faunal biodiversity and spatiotemporal records, as well as biofunctional traits such as niche guild, migration and range potential, habitat requirements derived from geochemical analyses, temperature and precipitation proxies, constrained by radioisotopic ages determined using C-isotope chemostratigraphy, eggshell and pedogenic carbonate, and zircon. By integrating across Earth-life systems, the project will tackle a series of hierarchical objectives, including establishing a refined chronology of ecosystem change, calculating the rate and duration of destabilization and recovery, assessing trends and drivers of habitat evolution, and exploring the impact of extreme warming on ecosystem resilience, functional biodiversity, and species threat. Beyond propelling comparative research on ancient hyperthermals, the collaboration will enable a cross-disciplinary postgraduate research exchange program to arm the next generation of scientists with the multifaceted skill sets necessary to tackle grand challenges. Finally, broader engagement objectives will increase scientific literacy and inspire youth to pursue STEM careers via a public science program that enables secondary school students to discover new biodiversity records, contributing directly to data collection and through co-created teacher resources. This project is funded by the BIO/DEB Biodiversity of a Changing Planet (BoCP) Program, the Division of Earth Sciences (EAR) and the GEO/EAR Life and Environments through time (LET) 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 collaborative project will create computer simulations that will help scientists study how organic molecules form crystals when mixed with a liquid and placed inside materials with tiny holes. This is important because the way crystals form can affect how well medicines work and how certain chemicals are used in defense technology. For example, in drug-making, specific crystal shapes of a medicine's active ingredient can make it more effective. The same idea applies to creating special chemicals for other uses, like materials for the military. By learning how tiny spaces (measured in nanometers) affect crystal formation, scientists can control the process better. This could lead to new medicines and stronger materials that help keep people safe. The computer tools and programs created in this project will be shared with other scientists and companies through websites like GitHub and nanoHUB. The project will also train students, from high school to PhD level, in using computer simulations. They will work with industry experts and create fun science comics to get more early career researchers excited about STEM fields. This project will help understand how confinement in nanopores affects crystal nucleation of solutes in solution. Most studies of nucleation in confinement have been experimental, with the results explained using classical theories that break down for nanometer-pore sizes. Molecular simulations in this field require the use of rare event methodologies in the Grand Canonical Ensemble (GCE), as the modeled confined solution is in equilibrium with a bulk phase with experimentally accessible properties. However, most rare events methods are based on molecular dynamics, which is difficult and computationally expensive to extend to the GCE. Simulations require challenging insertions and deletions of molecules in a dense solution confined inside nanopores. These challenges will be addressed by developing a new simulation method that combines the String Method in Collective Variables (SMCV) and Voronoi Milestoning with the continuous fractional component Monte Carlo method, all in the GCE, to model solute precipitate nucleation in confinement. These computational tools will then be used to determine how surface wall solvophobicity, pore size and pore geometry, and the local structure of the solvent near the pore walls, affect the solute nucleation rate and formation of crystal polymorphs. 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

Scientists are increasingly incorporating machine learning (ML) and artificial intelligence (AI) techniques into their applications to accelerate and enhance scientific research and discovery across a wide range of disciplines. For example, machine learning has been successfully integrated into tools for weather forecasting, earth sciences, astronomy, high-resolution imaging, genomics, and molecular biology. However, the ever-growing size of scientific datasets results in prohibitive hardware resource costs, significantly complicating the deployment of these applications on high-performance computing platforms at scale. Lossy compression — a data reduction technique that significantly reduces dataset size by removing redundant or less important information — has proven effective for many scientific datasets, including those from cosmology and structural biology. Despite its promise, integrating lossy compression into AI-driven scientific applications remains a non-trivial challenge, requiring broad expertise in data compression and machine learning, as well as a deep understanding of application requirements, system considerations, and their interactions. These complexities hinder the adoption of this powerful data reduction technique in scientific applications. The overarching goal of this project is to address this gap by providing a cyberinfrastructure that seamlessly and adaptively integrates lossy compression into deep learning pipelines within scientific applications. This integration will reduce memory usage and communication overhead, enabling AI-for-Science applications to scale to massive datasets. The design includes several key innovations. First, it features a user-friendly interface that allows users to define accuracy requirements — which may evolve during application execution — and to instantiate different compressors, supporting both customization and extensibility. Second, it provides a software layer that integrates with popular deep learning frameworks, such as PyTorch, enabling compression to be applied to existing neural network models with minimal code modifications. Third, it incorporates an adaptive execution engine that dynamically selects the appropriate compressors and error bounds based on the desired accuracy, data characteristics (e.g., smoothness, value range, sparsity), model structure, and system configuration. The cyberinfrastructure will support both existing and emerging machine learning accelerators and will be released as open-source software, accompanied by documentation and training materials to promote adoption within the scientific and computing communities. Ultimately, this project has the potential to benefit the broader community by enabling scalable, AI-driven scientific discovery. 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 · 2025-06

Deep Learning (DL) has improved scientific applications across various scientific domains, including high-energy physics, meteorology, agriculture, and material science. This project introduces DLToolkit, a performance profiling infrastructure tailored for domain scientists to analyze and optimize science-driven DL applications. This project also contributes to education and supports broader usage; the outcomes of this project will be integrated into the Computer Science (CS) curriculum, and both George Mason University and the University of California - Merced are minority-serving institutions, offering opportunities for delivering knowledge about cutting-edge techniques to underrepresented students. Together with industry and national laboratory partners, the project will also provide research training, symposia, and internship opportunities for students, aiming to foster a cohort of performance engineers. The overarching objective of this project is to improve scientific DL applications. The intellectual merits include three novel profiling capabilities: (a) synergistic tool-framework profiling to streamline extensive domain-specific knowledge from existing DL frameworks to DLToolkit, significantly lowering the barrier for domain scientists to use DLToolkit; (b) just-in-time (JIT)-aware profiling to ensure precise yet lightweight attribution of performance events to complex JIT-compiled DL operators; and (c) tensor-centric profiling to provide a holistic view of tensor operations’ impact on model performance. By uniting these capabilities within DLToolkit, this project will create a cohesive infrastructure for domain-specific performance profiling to empower scientists with critical insights to optimize their DL applications, accelerating scientific research and innovation. 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 · 2025-06

This award supports the participation of early career U.S.-based researchers in the “International Workshop on Applied Probability: Probability, Statistics and their Applications (IWAP 2025),” which will take place June 9-13, 2025 in SAS Hall of North Carolina State University. In light of the many applications of probability and statistics in public policy, decision theory, social and health sciences and various scientific fields, the conference will offer a platform for advancing new statistical and mathematical methodologies and computational procedures. In addition to research presentations, there will be ample networking opportunities throughout the event, helping to facilitate the exchange of ideas. Thus, while the funding will primarily support students and participants with recent Ph.D.’s, and will help to educate and bring them to the forefront of this active and important research area, the conference will be beneficial for all of the approximately 50 to 60 workshop participants that are expected. The conference will consist of eight plenary talks given by top researchers. There will also be both invited and contributed sessions. Topics that will be presented include scan statistics, graphs/networks, statistical machine learning, variable selection and quantum computing, with applications to such areas as genomics, chemistry, individualized treatment regimes in medical research, network traffic, economic forecasting, medical imaging, cluster, anomaly and change point detection, and forecasting tourist arrivals. See https://iwap2025.weebly.com/ for more information about IWAP 2025. 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 · 2025-06

Sensors are an indispensable part of our lives, assisting society’s transportation, health, safety, and communication needs. Conventional sensing approaches acquire data in a fixed fashion, independent of the task for which the data is being utilized. In addition, each of data acquisition, reconstruction and inference blocks in the data processing pipeline is independent of one another and optimized separately. This approach has led to exponential rates of data generation that creates an unbearable demand for power, storage, processing, and communication requirements in today’s sensing systems. The goal of this project is to advance the science of learning-based sensing and processing technologies by developing an adaptive, task-oriented and physics-aware data-to-decision pipeline, which jointly optimizes data acquisition, reconstruction, and inference stages in a data-driven learning framework. The proposed research will establish the foundations of future smart, adaptive, and resource-efficient sensing systems for a variety of applications, including biomedical imaging, remote sensing, radar, and wireless communications. This project has three interconnected objectives (i) Developing learning-based physics-aware multi-dimensional signal reconstruction techniques through foundational relations to regularized inverse problems and explainable architectures inspired from existing signal processing models, (ii) Developing mathematical and learning-based adaptive and task-oriented measurement design approaches with jointly optimized sensing, reconstruction and processing blocks, and demonstrate its impacts on real-world problems, (iii) Developing a learning-based data-to-decision framework, which infers actionable information (classification, parameter estimation) directly from low number of learned measurements. The central theme of planned synergistic educational and outreach activities is to increase the scientific literacy of both the K-12 and university students and the public regarding sensing systems, signal processing, and machine learning. Because sensing technologies are on the frontier of how information is perceived and extracted, and are essential to a wide range of applications, this project will have a high impact on sensing technologies being developed to improve the quality of our daily lives, ranging from applications of cameras to biomedical imaging, or from smart home technologies to autonomous vehicles. 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 · 2025-06

This grant will support research on new methods for embodying intelligence in millimeter- to centimeter-scale soft robots. These robots have a layer of flexible plastic as a frame, supporting a second layer of flexible piezoelectric polymer. Piezoelectric materials contract or expand in response to applied voltage, allowing them to be used as actuators. Alternatively, they generate a voltage when twisted or stretched, allowing them to be used as sensors instead. Configuring different regions of the piezoelectric layer as sensors or actuators, and interconnecting those regions through customized circuits, causes deformation waves to travel through the robot structure. These propagating deformations can propel the robot forward or backward, or turn the robot clockwise or counterclockwise. Different types of deformation waves might also, for example, have different heights, potentially allowing the robot to pass over an obstacle or to squeeze under a small gap. Contacting objects in the environment can change the wave propagation pattern, in turn changing the robot's motion. This project will proceed in three parts. First is a study of the different ways that deformation waves can travel in the robot structure, and how these different types of waves can be shaped and switched by different patch geometries, interconnection patterns, and contact with surrounding objects. Next is understanding how the different types of waves can be used to accomplish basic types of movements. Finally is combining these effects to achieve goals like navigating an obstacle field or escaping a maze. Ultimately these capabilities could allow many such small, flexible, low-power robots to search a disaster site for people in need of help, to monitor dry brushland for fires, or to patrol sensitive areas for intruders. The outreach activities through established summer programs and other educational initiatives will positively impact science, technology, engineering, and mathematics education of K-12 students. This research intends to bridge the fundamental knowledge gap between active mechanical metamaterials and soft robotics. It aims to embody physical intelligence in active mechanical metamaterials for creating a new class of intelligent maneuverable soft robots with reduced control burden. The new soft robots will leverage embodied materials intelligence from constituent soft electroactive polymers capable of both self-sensing and actuation, as well as mechanical intelligence from periodic architectures that localize multimodal dynamic wave modes at the boundary for distinct adaptive behaviors. This research looks to generate new knowledge on wave localization mechanisms, dynamic energy localization, and path reconfiguration. These look to enable the creation of intelligent maneuverable soft robots capable of energy efficient and robust movement through challenging terrains, as well as self-adaptive locomotion and obstacle-responsive behavior in complex unstructured environments. 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 · 2025-05

This I-Corps project is based on the translation from lab to market of a manufacturing solution to affordably produce acrylic fibers in the U.S. This solution addresses the current reliance on imported acrylic fibers, especially given their importance in manufacturing military, outdoor, and apparel applications. The commercialization of this solution has the potential to benefit the U.S. economy and society by introducing competitively priced acrylic fibers that are domestically made. The acrylic fiber market is worth $1 billion annually. This I-Corps project utilizes experiential learning coupled with a first-hand investigation of the industry ecosystem to assess the translation potential of a more affordable method of manufacturing petroleum-based polyacrylonitrile (PAN) into specialty acrylic fibers and precursors to carbon fiber. The current process of spinning PAN-based fibers relies on volatile organic compounds (VOCs) that are flammable and toxic. This new solution consists of manufacturing PAN-based fibers by the process of melt spinning at high speed, offering a low energy approach to fiber manufacturing. The benefits of this approach include a reliable, domestic supply of acrylic fibers for U.S. textile fiber mills at a potentially lower cost. 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 · 2025-05

Nearly one-third of the global population experiences unreliable electricity access, and a U.S. Department of Energy report estimated that the total cost of power outages to American businesses is around $150 billion every year. As power grid simulations grow in complexity and scale, there is an urgent need for more efficient computational models to meet real-time decision-making demands. Traditional simulation approaches struggle to parallelize efficiently, especially for large systems like the Eastern Interconnection with over 70,000 buses. The emergence of Graphics Processing Units (GPUs) and artificial intelligence (AI) models offers promising alternatives for accelerating complex simulations. The main idea is to train neural network surrogates of numerical models, and once pre-trained, the networks can generate simulations with much faster speed and efficient scaling. This project develops a novel AI-surrogate enhanced cyberinfrastructure for accelerating power grid simulations. The resulting framework will lower barriers for power grid engineers to adopt AI surrogates, enabling interdisciplinary research and education between power systems and computer science domains. The project will deliver three key innovations: (1) program-behavior analysis to identify optimal code regions for AI surrogate replacement; (2) semi-automatic AI surrogate construction that incorporates domain-specific physical knowledge; and (3) heterogeneous computing with multi-fidelity modeling that dynamically balances AI surrogates and traditional models across computing resources. The methodological approach combines static and dynamic code analysis, neural network training with physical constraints, and adaptive scheduling algorithms for CPU/GPU resources. The project aims to transform AI surrogates from auxiliary tools into essential elements for power grid planning. 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 · 2025-05

Rare Earth Elements (REEs) are hard to obtain in highly purified forms. They are used in everything from cell phones to cars. REEs can be obtained through conventional mining operations or salvaged from waste streams of various industries. The challenge is to first recover REEs in bulk and then purify specific REEs from resulting mixtures. This is made difficult because of their chemical similarities. Current purification processes present environmental and safety issues. This project will leverage microorganisms that grow in nearly boiling water. They are found in acidic hot springs in Yellowstone National Park and ocean floor thermal vents. The organisms will be used to recover REEs and purify them through novel bioseparation processes that are safe, efficient and environmentally benign. The project will provide local high school students with opportunities to visit NC State University’s biotechnology facilities and undergraduate students with REE case studies relevant to their engineering curriculum. The biological properties of extremely thermoacidophilic microorganisms that naturally inhabit REE-rich geothermal environments will be exploited to develop strategies for sustainable recovery and purification of REEs. Specifically, the organisms will liberate REEs from coal and from by-products of a variety of industries. Furthermore, protein scaffolds and REE-binding proteins derived from these microorganisms will be engineered to develop scalable separation processes to purify REEs. Select extreme thermoacidophiles and metagenomic samples recently obtained from hot springs in Yellowstone National Park will be examined for evidence of REE utilization. If detected, metabolic roles and associated proteins will be determined. Extreme thermoacidophiles that oxidize reduced iron and sulfur compounds as energy sources will be used to liberate REEs from coal and coal fly ash through bioleaching. Subsequently, yeast surface display-based methods will be used to directly evolve and tune thermophilic binding proteins for affinity to specific REEs. These will be deployed in multi-stage, affinity chromatography purification schemes to recover individual REEs. Advanced analytical methods for physical, chemical, and biological characterization will support the work. Scanning Electron Microscopy coupled with Energy-Dispersive X-ray Spectroscopy (SEM-EDS) will be used to assay for REEs bound to magnetic nanoparticles. This will serve as a first step in yeast surface display methods for screening proteins to be used for purification of these elements from solutions and from each other. 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 · 2025-05

This EArly-concept Grant for Exploratory Research (EAGER) project funds research that aims to advance the fundamental understanding of a hybridized 3D-printing approach that has significant application potential, such as in composite electrodes. Current 3D printing methods such as stereolithography and digital light processing (DLP) can manufacture freeform polymer matrix composites with randomly oriented heterogeneous fillers or fibers, but they have not been studied for creating connected and continuous fiber composite structures. Continuous fiber composites in industry are utilized for enhancing strength and toughness, as well as for imparting certain thermal, electrical, or ionic transport functionalities into a polymer matrix composite. Continuous filler composites are typically made via molding techniques with significant manual effort. There is an urgent need for further scientific exploration in utilizing continuous fillers to maximize electrical and thermal performance and efficiency in freeform fabricated polymer matrix composites. The approach investigated in this project combines multiple manufacturing principles: acoustic manipulation (via acoustophoresis) to agglomerate and align discrete particles in a polymer vat, photopolymerization (via DLP) to define the geometry of the component in each layer, and photonic sintering (via intense pulsed light sintering, IPLS) to create connected and fused fibers from the particles aligned through acoustic manipulation. Successful completion of the project looks to enable a new freeform manufacturing approach for heterogeneous materials, and the approach could be extended to include other fillers such as piezoelectric materials or ionically conductive materials to realize advanced functionalities. Apart from acoustophoresis for particle alignment, DLP printing and IPLS have not been explored as an integrated process due to their significant processing differences. Their joint effect on the process performance is not well understood. While IPLS utilizes high energy dosages (J/cm2 - kJ/cm2) and broad wavelength spectral distribution during illumination, DLP printing, meanwhile, utilizes relatively low energy (mJ/cm2) and a very narrow wavelength spectral distribution to induce polymerization of the intended geometry. This EAGER research project explores their joint effect to synergistically process two heterogeneous material types: polymer resin (low temperature processing, low light energy) and metal (locally high temperature processing, high light energy). To combine DLP printing and IPLS, photopolymerization will be induced via red light illumination, with IPLS being performed via UV/Blue light illumination. Additionally fundamental relationships look to be established between acoustic stimulus (frequency, amplitude, duration), embedded particle properties (size, concentration within resin, and shape), and would-be sintered fiber dimensions (width, height, spacing). 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 · 2025-04

NON-TECHNICAL SUMMARY Enhanced electrical and electromechanical responses in ferroelectric materials often arise at chemically induced phase boundaries where multiple structures coexist—for example, the well-known morphotropic phase boundary in lead zirconate titanate. At these phase boundaries, external stimuli, such as electric fields, can trigger interconversion between different structures, leading to colossal physical responses that can be utilized for various functional applications. However, these chemically induced phase boundaries usually involve complex chemical compositions that introduce disorder and heterogeneity. Additionally, these materials often contain toxic lead, raising significant environmental and health concerns. To address these challenges, the research team aims to develop strain-engineering pathways to create phase boundaries in lead-free ferroelectric oxide heterostructures and membranes beyond the traditional chemical method. These new strain modalities open opportunities to explore, manipulate, and harness novel functionalities in oxide materials for next-generation applications. Aligned with the research, this program supports the launch of a teacher-training workshop for the special education teachers in K-12 public schools and offers research internships for high school students. These efforts aim to break barriers to STEM careers for students to promote a stronger workforce. Additionally, this program develops hands-on and online course modules to foster greater interest and access in functional thin-film materials research for undergraduates and graduate students. TECHNICAL SUMMARY This program aims to advance strain engineering beyond traditional heteroepitaxy to create strain-induced phase boundaries in lead-free ferroelectric thin-film heterostructures and membranes. By leveraging anisotropic epitaxy and dynamic strain tuning, the program seeks to generate coexisting and bridging phases with enhanced dielectric, piezoelectric, and ferroelectric properties in lead-free sodium niobate heterostructures and membranes. This program employs atomic-scale epitaxy and chemical lift-off techniques to synthesize epitaxial heterostructures and membranes while applying mechanical tuning to control the competition between nearly degenerate polymorphs near phase boundaries. A broad set of structural and property characterization tools are utilized to establish strain-structure-property relationships, enabling the rational design and precise control of strain to unlock new phases with superior electrical and electromechanical performance. In parallel, the program integrates educational initiatives to foster a stronger STEM workforce, with a particular focus on engaging students. These initiatives include teacher-training workshops for K-12 special education teachers, research internships for high school students and undergraduate students, as well as hands-on and online course modules focused on functional thin-film materials. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-04

NON-TECHNICAL SUMMARY: Nature is a constant source of inspiration for both art and science, particularly in the fields of materials design and innovation. Over the past 30 years, scientists have discovered renewable nanomaterials—tiny particles made from common, renewable resources like wood and crops. These materials combine the benefits of advanced technology with sustainability and safety, offering potential solutions to global challenges. Cellulose nanocrystals (CNCs), which are tiny, rod-shaped particles, are an example of renewable nanomaterials. CNCs can naturally form strong, color-producing structures, which have potential uses in products like coatings, textiles, and electronic devices. However, the exact factors that control how CNCs assemble and create colors are not yet well understood. This research focuses on studying how different surfaces and drying conditions affect the assembly of CNCs. By uncovering these details, this project seeks to create sustainable colors and materials, leading to innovations like smart windows, anti-corrosion coatings, and even semiconductors. This project also includes a significant educational component that focuses on engaging college students, middle-school teachers and the public in science, technology and sustainability. This project specifically fosters interdisciplinary educational opportunities, involving hands-on learning, research experience, peer-teaching and mentoring, and partnerships between academia, local schools and communities. TECHNICAL SUMMARY: The goal of this CAREER program is to develop a scientific foundation and an interdisciplinary, inclusive education platform to harness the self-assembly of renewable nanoparticles on solid surfaces for the controlled design of advanced sustainable materials and bio-inspired colors. Of particular interest are cellulose nanocrystals (CNCs), plant-based nanoparticles that spontaneously self-assemble in aqueous suspensions to form chiral nematic structures at critical concentrations. These helicoidal structures achieve an arrested state which may be characterized as strong, color-generating nanostructures as a result of drying the colloidal state into a coating or a film (a process referred to as EISA or evaporation-induced self-assembly). The substrates onto which the dispersions dry are hypothesized to critically influence the self-assembly of CNCs, which in turn controls the generated visible colors. This program, therefore, aims to (1) elucidate the role of mounting substrate effects on the assembly and anchoring of these CNCs and (2) investigate the influence of drying dynamics on EISA, known to primarily govern color generation. A series of mounting substrates of varying surface properties and topography is used to investigate the deposition of CNC mono- and multilayers and assess the influence of substrate properties and external drying conditions on the drying dynamics of CNC cholesteric structures and the formation of colors. The outcomes of aims (1) and (2) are to be integrated into a novel interdisciplinary, multi-generational educational platform that will train the next generations of leaders in bio-inspired and sustainable colors for materials design and innovation. This platform will also aim to (3) develop a globally competitive STEM workforce to increase the engagement of younger generations from all backgrounds and the broader public in science, technology and sustainability. 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 · 2025-04

This project will hire a new technician for North Carolina State University’s (NC State) Paleo Isotope Laboratory (PIL). This new position will help the PIL to study ancient Earth’s geological and environmental processes and develop new analyses. The technician will train students and researchers to perform research that uses clumped carbonate isotope geochemistry. This will create a hub for this type of analysis at NC State, strengthening scientific resources across the region. This position will also make the PIL’s resources more available for applied research projects from external users. Finally, this work will support public outreach on Earth history and connect local youth to STEM opportunities. The new technician will contribute to directed research at the PIL, which focuses on resolving the conditions and drivers of changing climatic conditions and ecosystem impacts in the past. Specifically, this involves developing better analytical and interpretive approaches for terrestrial geochemistry proxies including carbonate clumped (Δ47) isotopes. Clumped isotope geochemistry is a rapidly growing area of paleoclimatology, because it offers a means to directly measure mineral temperatures and characterize changes in source fluids. This provides information to determine reconstructions of environmental temperatures, sediment burial and maturation histories, changes in water or hydrothermal fluid sources, feedback between tectonic uplift and climate, and even evaluating thermal strategies of organisms. The technician will assist with management and repair of specialized IRMS instrumentation, data collection and experimental design for carbonate standard and clumped isotope studies, training students and visiting researchers in isotope systematics and applications, and contributing to public outreach events, thereby contributing to these efforts for both ongoing and future NSF-funded projects. Moreover, the added personnel support from a dedicated technician will enable the PIL to help serve the scientific goals of dozens of research groups in the Southeastern U.S. and beyond which are devoted to better understanding paleoclimatic, biogeochemical, and depositional processes, and grow the interdisciplinary research portfolio and service-center capacity of the PIL. 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.