Oregon State University

NSF Awards · FY 2026 · 2026-09

Clean and reliable water treatment is critical to protect public health. However, most treatment decisions still rely on simple models that do not reflect real‑world conditions. Artificial intelligence (AI) has the potential to help engineers make more informed decisions. This CAREER project will develop AI models that help engineers understand how and why treatment processes work at full-scale. The project will reduce risk, improve reliability, and expand access to advanced tools for communities with limited technical resources. The outcomes of this research will be shared with water utilities and used across many treatment systems. The project will also address a national need for a workforce that can use AI responsibly by integrating data science into environmental engineering education. This project will support safer water systems, prepare future engineers, and show how AI can be used as a tool for scientific discovery. This CAREER project will develop an application‑driven AI framework for modeling engineered environmental systems, using water and wastewater disinfection as a representative, high‑risk unit process. The research will integrate multi‑facility operational and water quality data with hybrid modeling approaches that combine physics‑based process models and machine learning (ML). These approaches will include mechanistic ordinary differential equation models coupled with ML components, physics‑informed neural networks, and embedded neural differential equation formulations that constrain learning using known physical, chemical, and biological relationships. Model development and evaluation will explicitly address challenges common to environmental datasets, including data sparsity, autocorrelation, measurement uncertainty, and site‑specific variability, through time‑aware validation, uncertainty quantification, and risk‑based performance metrics. Mechanistic insights inferred from the models will be tested using a pilot‑scale disinfection system to distinguish true process behavior from artifacts introduced by data collection or modeling practices. The project will also develop protocols for model reuse and adaptation using transfer learning and privacy‑preserving federated learning, enabling models trained on multi‑facility data to be applied in data‑limited systems without sharing raw data. Together, these methods will advance the scientific use of AI in environmental engineering by enabling mechanistically grounded discovery, improving generalizability across real systems, and establishing a foundation for trustworthy, reusable models for infrastructure 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 2026 · 2026-09

The real world is too complex to model accurately. Autonomous agents and robots that perform complex tasks in the real world, ranging from handling inventory in warehouses to driving, will inevitably encounter scenarios that are not fully described in their symbolic models used for decision-making. To handle such unexpected scenarios, agents often rely on human assistance to complete the task, restore safety, or refine the model. While these interventions can restore safety in the short term, they are reactive, require extensive human effort, and fail to generalize. Consequently, translating empirical success from structured environments to real-world settings remains challenging and hinders long-term autonomy. Just as human decision-makers adapt by reflecting on their limitations and seeking information or assistance, as needed, reliable long-term autonomy requires autonomous systems to proactively recognize their model limitations, seek additional information to refine their models, produce context-appropriate behaviors, and recover from unexpected runtime errors, with minimal reliance on humans. This project will develop an introspective reasoning paradigm for autonomous agents, grounded in three core properties: (1) model cognizance—autonomous and proactive identification and repair of model limitations, through limited user interactions; (2) contextual planning—prioritizing context-appropriate objectives and balancing tradeoffs in multi-objective planning, by determining ``what matters when''; and (3) resilient execution—detect and recover from unforeseen execution-time errors with minimal disruption to task performance, via self-monitoring and online replanning. By closing the loop between self-monitoring and behavior adaptation, this project lays the foundation for autonomous systems that operate reliably in unstructured environments, while gradually reducing the human effort involved. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2026 · 2026-06

PROJECT SUMMARY Chronic sleep disruption has recently been implicated in osteoarthritis (OA) pathology. Sleep is chronically disrupted for the millions of American workers with schedules outside of normal 9am- 5pm hours (shift workers) which are up to 49% more likely to develop OA relative to daytime counterparts. These data indicate sleep plays an important role in OA onset and likely progression; however little is known about how sleep relates to OA and there are few animal studies examine how disrupted sleep relates to OA onset and progression. This proposal intends to address that knowledge gap by leveraging a unique expertise in animal phenotyping of both sleep and post-traumatic OA (PTOA) with novel noninvasive sensor approaches to investigate the first connections between shift work schedules that disrupt sleep and PTOA pain, disability, and tissue damage in both sexes. The overarching objective of our research program is to quantify causal links between sleep and PTOA pain, tissue damage, and disability in preclinical models using the following aims. Aim1 will investigate whether shift work sleep schedules exacerbate PTOA pain, disability, and tissue damage in a surgical mouse model of knee PTOA. we hypothesize that mice experiencing shift work schedules disrupting sleep will have worse PTOA outcomes in pain, disability, and tissue damage compared to mice with normal sleep schedules. Results of this aim will provide the first rich datasets evaluating sleep impacts on PTOA, opening new avenues of inquiry for subsequent investigations of mechanistic sleep impacts in PTOA, other OA, and the clinic. Aim 2 will Determine whether sleep-disrupting shift work schedules attenuate PTOA pain relief from the promising analgesic and potential disease modifying drug, Celecoxib. We hypothesize that Celecoxib analgesia will be reduced in animals with disrupted sleep compared to those with normal sleep. Results of this aim will generate knowledge of cyclooxygenase-2 roles in the sleep-PTOA axis, providing the first evidence that sleep disruption may alter efficacy of Celecoxib with implications for other forms of OA and drugs with similar mechanistic pathways. Overall, This work will determine whether shiftwork schedules exacerbate preclinical PTOA outcomes and response Celecoxib. This information will help clinicians identify higher-risk patients and recommend more effective PTOA pain management regimes, furthering the goals of NIAMS by increasing our understanding of understudied risk factors that contribute to OA onset, pathogenesis, and variability in health outcomes.

NIH Research Projects · FY 2026 · 2026-06

PROJECT SUMMARY This project aims to develop cryopreservation methods to extend organ viability from hours to years. This would improve transplantation by allowing more effective organ allocation, lower discard rates, improved immunological matching, reduced chronic rejection, and more flexible surgery scheduling. Current efforts to cryopreserve organs use cryoprotective agents (CPAs) to prevent ice formation, but these efforts are limited by the toxicity of existing CPAs. Previous research has mainly focused on <10 molecules out of the ~40 that have been tested as CPAs. However, the number of potential molecules is in the millions. This project leverages novel high throughput screening methods and machine learning to explore this vast chemical space to identify new CPA mixtures with reduced toxicity. The central hypothesis is that high throughput screening of CPAs for cell membrane permeability, toxicity, and promotion of glass formation can be combined with machine learning and decision-making algorithms to enable discovery of novel low toxicity compositions for organ cryopreservation. The project has four specific aims: Aim 1: Screen for chemicals with high membrane permeability and low toxicity. High throughput experiments will be combined with virtual screening using machine learning models to discover new molecules with promising properties for cryopreservation. Aim 2: Identify CPA interactions that reduce toxicity. To uncover synergistic CPA interactions, high throughput experiments will be performed to compare the toxicity of binary CPA mixtures to single-CPA solutions. The resulting data will be used to train a model for predicting the toxicity of CPA mixtures. Aim 3: Quantify the glass forming abilities of CPA mixtures. A high throughput approach will be used to determine the concentration required to form a glass upon cooling for each CPA mixture. Aim 4: Optimize CPA mixtures for organ cryopreservation. An iterative approach combining mathematical optimization, high throughput testing, and model retraining will be used to identify CPA compositions that can prevent ice with minimal toxicity. Overall, this work will establish high throughput screening and machine learning as a platform for discovery of novel CPA mixtures with reduced toxicity, laying the groundwork for future efforts to cryopreserve human organs.

NSF Awards · FY 2026 · 2026-06

Decades of fuel accumulation and increasing aridity are causing more frequent wildfire in many western forests. Frequent fire often reduces soil fertility in forest types with low precipitation. However, it is unclear whether this is also the case for more productive, wetter forest types, like those found in the western Cascade Mountains of the Pacific Northwest, where fire has been less frequent historically. This goal of this research is to evaluate interactions among regenerating vegetation, soil fertility and microbial communities after single high severity fires and short-interval reburns. Ultimately the scientific knowledge gained from this project will help predict the time scale and the trajectories of soil recovery after wildfires in coniferous forests with high precipitation. The research team engages with land managers to understand how to improve post-fire forest regeneration and restoration in the region. This project also collaborates with Outdoor School educators resulting in hands-on and field-based activities to increase the wildfire and scientific literacy of students. The overarching goal of this research is to characterize the patterns and timescale of soil recovery after fire. The key is understanding the complicated mechanisms underlying the interactions among plants, soils, and microbes after high-severity fires. The project uses a combination of field sampling, field manipulations and laboratory experiments to test the central hypotheses about these mechanisms. Initial surveys of vegetation and soil properties establish baseline patterns, and sites that have burned at different times provide a picture of soil recovery. Complementary mechanistic work characterizes contributions of photodegradation to litter decomposition, and disentangles the effects of microbial communities and soil properties. Once the framework is developed to assess wildfire impacts, the future trajectory of these forests can be better understood. Further, there are direct links to help prioritize forest rehabilitation and restoration efforts in coniferous forests with high precipitation. 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

Expanding access to foundational computer science education has become increasingly important as rapid advances in artificial intelligence reshape daily life, work, and civic participation. Many schools, particularly those in rural communities, lack the staffing, resources, and sustained support needed to offer high‑quality introductory computer science courses that prepare students to navigate and contribute to an increasingly digital society. This project expands the capacity of an established statewide partnership, Computer Science for Oregon, to support teachers and school leaders as they integrate newly enhanced artificial intelligence content into a widely adopted foundational computer science course. The project strengthens an existing network of educators across the state, expands access to effective instructional practices, and supports districts in developing sustainable approaches to computer science and artificial intelligence education. By assisting teachers and school leaders during real‑time classroom implementation and building communities of practice, the project promotes the progress of science, advances educational opportunity, and helps ensure that all students can engage meaningfully with emerging technologies that shape the nation’s future. Accordingly, this project contributes to the goals stated in the Dear Colleague Letter NSF 25-035 regarding advancing AI education for the American youth. The project focuses on supporting the implementation of a revised Exploring Computer Science curriculum that now includes artificial intelligence and machine learning, the role of data in artificial intelligence systems, human and computational models of intelligence, natural language processing, algorithmic bias, and the societal impacts of artificial intelligence. Research activities and implementation support center on four components: individualized coaching for teachers during the academic year; statewide virtual professional learning communities that foster collaboration and knowledge sharing; structured guidance for school and district leaders as they integrate artificial intelligence and computer science into staffing, scheduling, and program plans; and the dissemination of teacher‑led action research on classroom practices related to artificial intelligence instruction. Together, these activities expand the reach of an eight‑year statewide effort, strengthen the professional capacity of educators, and generate practitioner‑informed insights into effective artificial intelligence instruction. The project’s integrated model is expected to improve the quality and consistency of artificial intelligence and computer science education across diverse regions, while building long‑term infrastructure to sustain universal engagement in foundational computing opportunities. 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

Non-technical description Every living cell is surrounded by a very thin membrane barrier that keeps most large and water-soluble molecules out of the cell. The membrane protects the cell, but it also makes it hard to deliver useful cargo molecules, such as drugs, into cells. Short molecules called cell penetrating peptides can sometimes carry cargo across this barrier, but most known examples work inefficiently and tend to trap their cargo inside internal compartments where the cargo cannot do its job. Recently, researchers discovered a new class of peptides that behave differently. These peptides can move directly across the cell membrane and deliver cargo molecules with much higher efficiency and without entrapment. The goal of this project is to understand how these unusual direct delivery peptides cross cell membranes and why they work better than earlier examples. The team studies how the peptides interact with the lipids that make up cell membranes and how the chemical complexity of real cell membranes affects the interactions. The team develops laboratory membrane systems that closely mimic natural cell membranes to study the interactions in detail. By uncovering the rules that allow peptides to cross membranes directly, this work helps scientists design new molecules to deliver useful cargos into cells. The project also supports the training of undergraduate and graduate students in interdisciplinary research and shares results through publications and outreach activities that introduce students to the science of cell membranes and biomaterials. Technical description The plasma membrane of a cell prevents most hydrophilic macromolecules from entering the cytosol. Classical cell penetrating peptides such as tat and penetratin can deliver these cargos, but they rely on endocytosis. This pathway is inefficient and often traps the cargo inside intracellular vesicles where they are degraded. Recently discovered direct delivery peptides perform much better and use a different mechanism. These peptides can deliver many kinds of cargo to the cytosol at low concentrations and with high efficiency. Current evidence suggests that they cross the plasma membrane by direct translocation, but the molecular basis of this process is still unclear. The goal of this project is to identify the peptide structural features and peptide–lipid interactions that allow efficient direct plasma membrane translocation. The central hypothesis of this work is that these peptides adopt flexible conformations that promote strong interactions between arginine side chains and lipid headgroups at the membrane surface, along with cooperative interactions involving aromatic residues. To test this idea, the team carries out systematic structure–activity and mechanistic studies that compare classical cell penetrating peptides with direct delivery peptides under the same experimental conditions. Plasma membrane lipids are isolated from plasma membrane–derived vesicles and used to build customizable lipid mixtures that reproduce the chemical complexity of biological membranes. The team measures peptide binding, translocation efficiency, structural dynamics, and peptide–lipid interactions in these systems and relate those properties to delivery activity. These studies define the molecular principles that allow efficient membrane translocation and guide the design of new peptide-based delivery materials that can transport a wide range of cargos into living cells. 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 · 2026-03

With support from the Environmental Chemical Sciences (ECS) program in the Chemistry Section, Professor Alison Bain at Oregon State University is investigating the impact of atmospheric micro- and nanoplastics on the physical properties of mixed-phase aerosol droplets. Field studies now routinely find micro- and nanoplastics in atmospheric aerosol samples. However, the impacts of these plastics on aerosol-phase properties and processes are not well understood. Professor Bain and her students will conduct macroscopic and aerosol droplet-scale experiments utilizing plastics of different chemical structures and morphologies to systematically study the impacts of the timescale of environmental aging on the surface morphology and chemistry of micro- and nanoplastics; the uptake of water and other chemical species; and the mixing of micro- and nanoplastics with existing aerosols. Their studies could establish a fundamental understanding of the impact of micro- and nanoplastics on aerosol physical properties, which could significantly contribute to the scientific understanding of the interactions between atmospheric plastics and other aerosols. The educational efforts of this project include training undergraduate students to efficiently handle large datasets common in analytical and environmental chemistry, and developing science lessons investigating microplastics for students in rural Oregon. This project will investigate how plastic type and the changing surface chemistry and morphology during environmental aging impact aerosol properties and processes. Using a wide variety of plastics commonly found in the environment, such as polyethylene, polystyrene, and nylon, Professor Bain and her team will age plastics in the lab and investigate the sorption of water and surface-active organic molecules to and from plastic surfaces. Additionally, they will develop new instrumentation to investigate the physical processes of mixed plastic-aqueous aerosol at the single particle level. Specifically, this project will investigate where these nano- and microplastics are located in mixed-phase systems, and how they might alter aerosol hygroscopicity and the partitioning of surface-active molecules. The education efforts will integrate technical computing into undergraduate analytical chemistry, where students will learn to work with large datasets and leverage AI to efficiently develop code for data analysis. Additionally, in collaboration with the SMILE (Science & Math Investigative Learning Experiences) program, a hands-on science lesson characterizing microplastics will be developed for middle and high school students. 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 · 2026-03

Large igneous provinces are features in which massive volumes of magma are emplaced over a short period of time. Ontong Java Nui is a massive large igneous province in the Pacific basin that formed ~119 million years ago and may have been the driver of a global anoxia event in the ocean basins. This project will conduct high-resolution age experiments on lavas from Ontong Java Nui. The results will confirm whether the eruptions happened at the same time as the global anoxia event and thus may have been its cause. The results will clarify how large volcanic events can alter ocean chemistry and environmental conditions that influence marine ecosystems and the distribution of oxygen in the oceans. The project will also provide training for early-career scientists, strengthen analytical capabilities, and engage broader audiences through research and outreach activities. Ocean anoxia event 1a is marked by the deposition of organic-rich marine sediments globally over a short (1.1 million years) duration. The primary hypothesized causal mechanism for the event is the emplacement of massive Ontong Java Nui LIP, which is composed of the Ontong Java, Manihiki, and Hikurangi Plateaus. Recent high-resolution ages of the Ontong Java Plateau indicate an emplacement prior to the anoxia event. This project will conduct 40Ar/39Ar high-resolution age determination experiments on samples from the Manihiki and Hikurangi Plateaus. The results will address three primary questions: 1) What was the timing, duration, and potential pulses of the Manihiki and Hikurangi Plateau emplacement? 2) Is the potential shallow or subaerial outgassing of the Manihiki Plateau contemporaneous with ocean anoxia event 1a? 3) What are the rates of crustal growth associated with the two episodes of plume-triple junction interaction that coincided with the breakup of Ontong Java-Nui? The results will have implications for how marine LIP emplacement affect ocean conditions. 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 · 2026-03

This project will develop, optimize and evaluate the analytical performance of a new tool, termed Thermal Ink Jet Spray (TIJ Spray). TIJ Spray will enable the direct injection of low input samples, including individual cells, into mass spectrometers for multi-omics analysis to enable discovery of cell phenotypic heterogeneity of drug response. TIJ Spray in conjunction with ion mobility spectrometry and integrated with high-resolution tandem mass spectrometers (IMMS) promises of providing the necessary throughput, sensitivity and specificity for detecting and characterizing cell type heterogeneity, subpopulation heterogeneity and drug response based on molecular ion profiles. Comprehensive characterization of the proteome and metabolome of individual cells allows inferring the functional state of individual cells and reveals functional phenotype heterogeneity. Knowledge of existence of functionally heterogeneous phenotypes is of relevance for the biology of tumors and tumor microenvironments because of the presence of highly heterogeneous cell populations and possible presence of cells of the same type but displaying different functional and metabolic phenotypes. Distinct phenotypes are masked in bulk cell populations, which hinders the discovery and validation of phenotype-specific biomarkers as well as confounds drug discovery. In this high risk/high reward project, we will develop this new technology and demonstrate its analytical performance for multi-omic analysis of single cells by accomplishing the following specific aims: Aim 1: Develop and optimize TIJ spray and its technical integration with IMMS. We will develop TIJ spray and demonstrate its the analytical capabilities in conjunction with IMMS for detection of different classes of analytes including those found in individual cells. Aim 2: Demonstrate TIJ Spray IMMS for multi-omics of single cells for cellular phenotyping and detection of phenotype-specific drug response. We will demonstrate and evaluate performance of TIJ Spray IMMS for obtaining multi-omics data in a pathobiologically relevant context and evaluate its analytical performance for detecting multi-omics changes, and differential drug uptake and response. When successful, we aim for achieving throughput of several hundreds of cells per day resulting in high quality multi-omic profiles for single-cell functional phenotyping. When we can show “fitness for purpose”, TIJ Spray IMMS has the potential to democratize single-cell mass spectrometry making it available for a wider range of laboratories and the biomedical community.

NIH Research Projects · FY 2026 · 2026-02

Natural products (aka specialized metabolites) are small molecules produced by bacteria, fungi, plants, and animals that have found many uses in agricultural, veterinary, and human health. More than half of all currently FDA approved drugs are natural products or derived from natural products. In addition, to their far- ranging biological activities (anti-cancer to anti-bacterial to anti-viral) the pathways that organisms utilize to construct natural products from simple, readily available building blocks, particularly those used by microbes, have triggered much interest. Both as a way to understand the mechanisms and enzymes to allow manipulation of the proteins and pathways to produce new-to-nature natural products with altered biological activity and from an intellectual standpoint. In this proposal, we dissect the mechanism of ureido bond formation in the biosynthesis of three families of bioactive bacterial natural products, the anabaenopeptins, the syringolins, and the muramycins, which derive from diverse bacterial genera. These biologically active molecules contain the proteolytically stable ureido bond, which is a key contributor to their biological activity. While the mechanism of ureido formation in biotin biosynthesis has been studied, we hypothesize that the biosynthesis of the anabaenopeptins, syringolins, and muramycins, which are all assembled through the action of a non-ribosomal peptide synthetase (NRPS), are distinct due to the fact that the intermediates are tethered to the NRPS in contrast to the untethered biotin intermediate, 7,8-diaminononanoate. Our preliminary data suggests that the mechanism utilized in the biosynthesis of the anabaenopeptins is distinct from the mechanism previously proposed for the biosynthesis of the syringolins. This proposal describes a multidisciplinary approach consisting of protein overexpression, isotope labeling, and chemical trapping in Aim 1, which is complemented by cryo-EM, and site directed mutagenesis in Aim 2. These specific aims will allow the elucidation of the mechanism utilized during ureido bond formation while identifying the protein residues and motifs involved in substrate binding and catalysis in proteins found in different bacterial genera and with different protein architectures. The identification of the motifs will allow the proper annotation of ureido bond forming condensation domains found in deposited sequenced genomes, which is an issue as they are currently annotated as having amide bond forming activity by popular annotation software like AntiSMASH. This prevents the accurate prediction of compound structure from genomic data and represents a current hurdle in the field of natural products. The knowledge gained in this proposal will set the stage for the future engineering of new natural products with altered biological activity by introducing ureido bonds to increase proteolytic stability and alter physiochemical properties.

NSF Awards · FY 2026 · 2026-01

Grasses are beneficial to human society by creating habitat for bees, and other pollinators, that ensure crops produce fruit and seeds, improve the quality of water, trap carbon, and provide food for animals upon which people rely for nutrition. However, grasses are a large group of over 11,000 species, which cover ~50% of the earth’s surface, and there are differences in their ability to perform beneficial ecological functions. For example, they can differ in the time of year they grow (also called phenology), how fast they grow, and their ability to tolerate and survive droughts. Importantly, there are often trade-offs between these characteristics, such that species that only grow in the spring may not be very tolerant of drought, and species that only grow during the summer generally do not grow very fast. Therefore, environmental changes during different seasons may prevent some species from thriving in their current locations, altering the ecosystem services they provide. To effectively manage resilient grasslands for the future, we need better information on the phenology, growth rates, and drought tolerance of a broader range of grass species. Most projects that measure these characteristics have focused on trees, leaving major gaps in our understanding of these traits in grasses. Using novel techniques to observe processes occurring inside the leaf and new mapping methods, our project will provide critical information about plant traits and tradeoffs in different environments to help predict how grass distributions will respond to changing weather patterns and environmental conditions. Changes to plant communities are continually occurring as plants disappear, appear, and re-arrange in ecosystems across the globe as rising temperatures and changing precipitation patterns reduce the available water for plant growth. Plant responses to these dynamic conditions dictate whether a species can persist in a region or must shift distributionally. Modern approaches to modeling species distributions rarely include the mechanistic underpinnings of organismal responses but, instead, rely on bivariate relationships between individual traits and annual summaries of abiotic conditions. This approach ignores the fact that networks of traits, rather than any single trait, generate different drought-coping strategies and that drastic differences in grass phenology decouples plant growth conditions from annual summaries of abiotic conditions. To improve predictions of future species distributions and inform restoration projects of ideal seed-mixes, the overall objective of our study is to improve the accuracy of species distribution models through a better understanding of grass species resilience by including trait networks and growth phenology. Using a set of species that spans the entire grass family, the investigators will identify mechanistic trait networks leading to different drought-coping strategies, including mechanisms leading to embolism formation, a key drought-coping trait rarely studied in grasses. Integrating these key traits will provide information on species responses and distribution shifts and the experimental design will also provide information on how these traits may evolve independently or in unison within the grass family. 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 primary objective of the research supported by this award is to inform real-time adaptive automation interventions in safety-critical system operations by leveraging cognitive workload predictions and integrating operator emotional state information. The research seeks to address two critical limitations in current cognitive workload modeling: (1) unreliable ground-truth labeling; and (2) classification model dependence on extensive offline training. By capturing multi-source physiological signals and context-dependent emotional state information, the project looks to formulate a new cognitive workload model to significantly enhance the accuracy of human state assessment. This work seeks to produce an integrated cognitive-emotional state assessment framework that links operator cognitive and emotional states to specific system automation responses. The research plan involves high-fidelity driving simulator experiments to generate a multimodal dataset that captures driver performance, central and peripheral physiological signals, cognitive workload responses, emotional states, and driver feedback during challenging scenarios. Statistical analyses intend to identify how cognitive and emotional states interact, providing a quantitative and probabilistic basis for decision rules to trigger timely, context-aware driver assistance. In parallel, nonlinear deep learning models will be trained to the simulator dataset to predict optimal timing and forms of automation interventions. The primary technical outcome intends to be a novel systems design framework for advanced driver assistance systems, achieving more accurate real-time state assessment and contextually appropriate automation interventions. The research looks to generate fundamental insights on the dynamic interplay of cognitive and emotional states in safety-critical tasks, representing a paradigm shift in how human-centered automation systems can enhance operator safety and trust. The project will also have an educational impact by training graduate students in human factors engineering and intelligent systems design. Finally, research findings will be disseminated broadly through peer-reviewed publications, conference presentations, and other outreach efforts to benefit both the research community and industry practitioners. This award has been funded by the Engineering Design and Systems Engineering, EDSE and Mind, Machine and Motor Nexus, M3X programs in the Division of Civil, Mechanical, and Manufacturing 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 2026 · 2026-01

Rivers transport large amounts of sediment and organic carbon (OC) from land to the ocean, with much of this material deposited on the continental shelf. These shallow environments are therefore important for OC burial and storage. However, knowing the amount and long-term stability of this OC is challenging because of changing conditions in coastal systems. This project will revisit a shelf region off the Umpqua River in central Oregon that was sampled in 2009 to see whether sedimentation and OC deposition have changed over time. The researchers will conduct detailed geophysical mapping and collect new sediment cores for physical and geochemical analyses. By combining these new data with the previous measurements, the researchers can quantify spatial and temporal changes in deposition. They will try to link any observed changes to factors like river sediment load, wave-energy, seasonal low-oxygen zones, and groundfish trawling. A group of undergraduate students will participate in the research as part of a two-year Undergraduate Research Experience. Students will sail on the research cruise, gaining hands-on experience in field oceanography, and will then work in teams to analyze samples, process and visualize data, and communicate results. The Umpqua River depocenter offers a unique opportunity to gain understanding of OC burial flux and stability in shelf environments because it is small enough to be feasibly mapped and sampled in a single expedition, has experienced marked, documented environmental changes, and is representative of marine sediment dispersal/depositional systems globally. This project will combine geophysical mapping with comprehensive re-sampling of locations previously cored in 2009. Sediment multi-cores will be imaged via X-radiography or CT scanning, analyzed for physical properties including bulk density and grain size, and dated using Pb-210 and Cs-137 isotopes. Sediment will be analyzed for OC and nitrogen content and OC composition. If sediment and OC deposition and storage have changed over the intervening years, researchers can use the combined datasets to identify the primary drivers of change and predict the response of OC sequestration to future environmental variability. Conversely, if OC storage has not changed, then it will be possible to compile carbon storage data from multiple decades of studies in U.S. margin environments to generate estimates of total carbon storage. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-10

Many science teachers struggle to make science ideas meaningful and relevant to students. One approach is to connect science to context--that is, students' real-world environments and experiences. Teachers are more likely to connect their instruction to authentic contexts when they have experienced them firsthand, but this is not always possible. This project explores how immersive field science experiences and carefully designed digital resources can help secondary teachers make science more engaging and relatable for students. The research team will study how teachers incorporate what they learn from real-world science experiences into their classroom teaching, and whether online materials can replicate some of the same benefits. By improving both immersive and digital professional learning experiences, the project aims to increase access to and decrease the cost of high-quality instructional supports, especially for teachers who cannot attend traditional field-based learning. Products will include new curriculum materials, professional learning models, and a classroom observation tool to study how teachers make science content more relevant and meaningful. The project will directly support over 60 teachers and 4,000 students in Florida and Oregon and will provide open-access tools and resources for broader use. Moreover, it will lead to a deeper understanding of how teacher experiences with authentic science contexts, whether in real life or with online supports, can translate into more rigorous science instruction. Ultimately, this work will be relevant for science teachers across the U.S. This design and development research project investigates how secondary science teachers contextualize instruction following either immersive field experiences or curated digital supports. The project uses a quasi-experimental, mixed methods design to compare the impacts of "primary contextualization" (learning in real-world contexts) and "secondary contextualization" (learning with context through multimedia) on teachers' instructional practices. Teachers engage with Data Nuggets modules, curriculum units built around real data and stories from scientists, and participate in professional learning focused on connecting these materials to specific field sites. Researchers collect data through classroom observations, interviews, and surveys to identify and examine the instructional moves teachers make to connect content to context. The team is refining a contextualization observation protocol, developing new context-supplemented Data Nuggets lessons, and creating models for optimizing both field-based and online professional learning. This work is generating new insights into how science teachers learn to integrate authentic contexts into instruction, identifies the specific moves teachers use, and advances the tools available for supporting and measuring contextualizing teaching practices. The Discovery Research preK-12 program (DRK-12) is an applied research program that seeks to significantly enhance the learning and teaching of science, technology, engineering, and mathematics (STEM) by preK-12 students and teachers. Projects in the DRK-12 program build on fundamental research in STEM education and prior research and development efforts that provide theoretical and empirical justification for funded projects. 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 aims to serve the national interest by educating a STEM workforce equipped with artificial intelligence (AI) competencies and enable us to compete globally. In response to the growing interest in AI policy, this project will help undergraduates entering the workforce be better prepared to integrate technical knowledge with AI knowledge. This project will lead to the development and implementation of AI educational modules that use policy narratives with computational exercises to educate first-year undergraduate engineering and public policy students. AI capabilities, limitations, and policy competencies will be developed as part of these modules. Specific competencies to be targeted include AI technical limitations, ethical considerations, social implications, professional responsibility, and regulatory frameworks. Assessment of student outcomes will provide insights into learning gains and advancement of competencies. The knowledge gained from this project will contribute to our understanding of AI education and will be disseminated to the STEM education community for broader usage. The integration of technical knowledge with policy in the context of AI applications will help educators cultivate better prepared professionals. The goal of this research is to advance our understanding of how policy and AI pertinent to computational competencies can positively impact student learning towards career readiness. More specifically, the project will use a narrative policy framework to elicit student and expert views and understandings of AI policy. Action research will then be used to develop educational modules for first-year public policy and engineering undergraduates. This study will evaluate the impacts of the modules on students using: 1) quantitative and qualitative analysis on student narratives, pre-post survey information, and observational field notes to determine commons shifts in thinking; and 2) comparative analysis between engineering and public policy students to determine discipline-specific differences. Learning competencies to be targeted include technical, ethical, societal, and regulatory. This project offers the potential to inform other integrative learning practices in support of cultivating better educated STEM professionals. AI competencies are essential for the next generation of STEM professionals and this project will contribute to this knowledge base. The NSF IUSE: EDU Program supports research and development projects to improve the effectiveness of STEM education for all students. Through the Engaged Student Learning track, the program supports the creation, exploration, and implementation of promising practices and tools. 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

Passwords remain the most ubiquitous method for client-server authentication on the Internet, helping billions of people log in to their online accounts every day. The traditional approach to client-server password authentication is vulnerable to numerous attacks on the underlying public-key infrastructure, such as phishing attacks. In recent years, new cryptographic protocols that do not rely on the public-key infrastructure, such as asymmetric password-authenticated key exchange (aPAKE) and its strong variant (saPAKE), have been proposed and analyzed; these methods achieve better security, and are seeing increasing applications in real life. There remains a significant gap, however, between the theoretical security analyses of (s)aPAKE and its applications in the real world: in particular, the exact level of security for (s)aPAKE protocols currently deployed in practice is not sufficiently understood, and there is room for improvement on the efficiency of such protocols. This project aims to push the boundaries of both the construction and analysis of (s)aPAKE in the client-server setting, and bridge the gap between theory and practice. Specifically, the investigator presents new security analyses of existing real-world (s)aPAKE protocols, whose current analyses involve contrived security definitions and models that are not well understood. The new analyses consider to what extent these complications are justified, and whether these protocols achieve more standard security notions in stronger cryptographic models. In addition, the investigator studies new tools that can be used in the construction of (s)aPAKE protocols, to achieve better efficiency measured by the protocols' computational costs, communication costs, and round complexity. Finally, the investigator studies related primitives for client-server password authentication that are widely used in practice yet understudied in cryptography, such as two-factor authentication, and proposes new security models and constructions for these protocols. The project's main impacts are new technology that has broad applications in the protection of secret data in various settings, including the next generation of transport layer security. 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

Metallic glasses are a unique class of metals with a disordered atomic structure, unlike most conventional metals that exhibit an orderly crystalline arrangement. This structural difference gives metallic glasses exceptional strength and hardness but also makes them brittle and prone to sudden failure under tension—limiting their widespread application since their discovery in the 1960s. Researchers have been seeking ways to improve their ductility by introducing soft zones, known as shear bands, which can absorb stress and permit limited deformation. However, existing techniques lack precise control over the formation and behavior of these zones. This project supports fundamental research to develop a new, cost-effective processing method that creates carefully designed patterns of shear bands. This method enables metallic glasses to deform without catastrophic failure by tailoring their internal structure using laboratory-scale equipment not traditionally employed in metal manufacturing. The approach requires minimal capital investment and opens new pathways for processing these advanced materials. By enhancing ductility and enabling structural control, the project broadens the application potential of metallic glasses for advanced engineering technologies. It also contributes to strengthening U.S. manufacturing capabilities and advancing national goals in science, innovation, and economic competitiveness. University students involved in this research will gain valuable training for future careers in advanced materials and manufacturing. Despite their exceptional strength and elastic strain limits, metallic glasses (MGs) have seen limited commercialization due to their near-zero tensile ductility. Shear bands facilitate localized stress relaxation in metallic glasses by forming soft regions within a hard matrix, but controlling their formation and evolution remains a major processing challenge. Broad application of MGs requires processing strategies capable of systematically engineering shear bands to significantly enhance tensile ductility. This project introduces a novel approach for shear band engineering through strategic patterning, seeding, and spreading in metallic glasses, using an innovative combination of Rockwell indentation and high-pressure torsion – techniques not previously recognized for processing metallic glasses. To demonstrate the applicability and effectiveness of this approach, the study will focus on Cu-based and Zr-based MGs, both of which possess high glass-forming ability and are relevant to a wide range of engineering applications. By combining shear band engineering, multiscale microstructural characterization, microscope-based in-situ mechanical testing, and high-performance molecular dynamics simulations, this project will advance the fundamental knowledge of (i) the relationships between processing conditions and shear band microstructure, (ii) the mechanisms underlying shear band-induced tensile ductility, and (iii) microstructure design strategy to overcome the long-standing strength-ductility tradeoff in metallic glasses. 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 project aims to unify the combustion research community's fragmented datasets by creating open-source, standardized databases for gas-phase chemical kinetics experiments and models. The project will develop a machine-readable, web-based, and API-accessible repository that initially compiles public data and later incorporates researcher-contributed datasets, all adhering to FAIR principles to promote accessibility and reuse. The project develops robust cyberinfrastructure to enable, facilitate, and encourage Public Access to and the widespread reuse of experimental and modeling data, stewarding Open Science in the field of gas-phase chemical kinetics, primarily combustion. This capability building project will: 1) devise open data formats for gas-phase chemical kinetics experiments and grow an open database of such measurements; 2) create a database for kinetic models and parameters with standardized formats; 3) build an innovative data repository model based on distributed version control to eliminate barriers to use, reuse, and contribution; 4) collect publicly available experimental and modeling data to bootstrap the databases; and 5) propose workflows and “apps” that use the database infrastructure, which will be useful in their own right, and serve as examples for others to follow in creating their own applications and workflows. This award by the Office of Advanced Cyberinfrastructure within the Directorate for Computer and Information Science and Engineering is jointly supported by the Directorate for Engineering. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2025 · 2025-09

Abstract: Pica is the craving for and deliberate consumption of nonfood items. Geophagy is the ingestion of earth, one of the most common forms of pica. Geophagy is more prevalent among pregnant mothers in sub- Saharan Africa, with reported rates ranging up to 87%. Pica, including the practice of geophagy, has also been documented in the USA; however, the rate of geophagy is lower compared to sub-Saharan Africa. Maternal ingestion of soil/clay potentially exposes the developing fetus to dangerous levels of heavy metals, including lead, arsenic, and manganese, which are known to adversely impact children’s neurodevelopment. Moreover, maternal ingestion of soil/clay may diminish the capacity of the gut microbiota to perform critical functions, which (directly or indirectly) impact fetal brain development. This planning grant will strengthen the collaboration between Oregon State University and two institutions in Ndola, Zambia, including the Tropical Diseases Research Centre (TDRC) and Copperbelt University, School of Medicine (CBU-SOM). Aim 1. To the best of our knowledge, the expertise to assess children’s neurodevelopment is lacking in Ndola, Zambia. Researchers at the TDRC and CBU-SOM will be trained to assess children’s neurodevelopment using the Bayley Scales of Infant Development, 4th Edition (BSID-4). Aim 2. Among 200 HIV-negative mothers/offspring enrolled at parturition, we will assess the practice of geophagy, maternal exposure to heavy metals, and impacts of maternal geophagy on children’s neurodevelopment at 12-months of age using BSID-4. Aim 3. Among enrolled mothers, we will determine whether the community structure of the gut microbiota differs between geophageous and non-geophageous mothers using 16S rRNA gene profiling. Aim 4. Working with an Advisory Board comprised of Zambian public health practitioners, we will investigate sustainable alternatives to the practice of geophagy. Potential interventions will be tested in a larger cohort of pregnant mothers in the full study. This study will increase the capacity of both institutions in Ndola, Zambia, to conduct environmental health research and assess children’s neurodevelopment. The long-term goal of this study is to reduce the practice of geophagy among pregnant mothers, by conducting evidence-based research to inform public health officials concerning the risks of geophagy. Because mothers pass down this practice to their children, reducing maternal ingestion of soil/clay will also likely reduce this practice among their children. The results from this study are applicable to the USA population, where geophagy has also been documented

NSF Awards · FY 2025 · 2025-09

This I-Corps project focuses on investigating the commercial potential of two novel technologies for powder fabrication processing and additive manufacturing that enable the scalable production of metal-graphene composites with tailored thermal and mechanical properties. The innovations target unmet needs across industries that demand lightweight, high-performance metallic structures capable of operating in thermally and mechanically demanding environments. Current manufacturing techniques are limited in their ability to achieve high thermal conductivity and strength simultaneously while maintaining scalability and cost-efficiency. Both techniques provide pathways to overcome those limitations by offering site-specific control of thermal and mechanical properties at the micrometer level. The broader impact of the technologies lies in their potential to improve energy efficiency, reduce material waste, and enhance manufacturing agility in sectors such as aerospace, energy, and advanced transportation, thus supporting national goals in high-tech manufacturing and advanced materials innovation. This I-Corps project utilizes experiential learning coupled with a first-hand investigation of the industry ecosystem to assess the translation potential of the technology. This solution is based on the development of methods that enable the in-situ formation of quasi-continuous graphene structures covering metal powders. The technologies can be integrated into additive manufacturing processes to directly print metal graphene composites. The processes can also be used to prepare graphene-covered metallic powders for additive manufacturing techniques, such as laser powder bed fusion (LPBF), and laser direct energy deposition (LDED). Moreover, the graphene covered metallic powders can be used to fabricate wires for laser wire deposition and wire arc additive manufacturing (WAAM). These methods enable precise control of microstructures in additively manufactured components, tuning the thermal and mechanical performance, and enabling cost-effective, and scalable production of components with enhanced functionality. By simplifying the manufacturing steps and offering microstructural control at the micrometer level, this approach could represent an advance in the additive manufacturing of next-generation metal-graphene composites for mission critical 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 · 2025-09

Informal STEM Learning (ISL) organizations play a vital role in fostering public engagement with science. They have high rates of visitation and are considered among the public as trusted places for education experiences and leisure time activities. ISL organizations are uniquely positioned to support all learners, including intellectually, developmentally disabled (IDD) and neurodivergent individuals, in that they offer choice, multiple ways of engaging, the opportunity for self-directed and self-paced learning, and learners can engage without the pressures of testing or formal assessment. However, despite their potential to support STEM pathways for all learners, ISL settings have not yet reached their full potential in supporting IDD and neurodivergent individuals. This project is twofold building on prior work with neurodivergent individuals. First it examines the degree to which practices and resources developed previously for zoos and aquariums can be implemented in new contexts (e.g., science museums, children's museums, botanic gardens) that are open to all including neurodivergent individuals. Second, it investigates how those strategies and materials, in settings that are open to all, can support IDD individuals. It also explores ISL employment opportunities that are open to all including neurodivergent and IDD individuals. This award is a collaborative partnership between the STEM Research Center at Oregon State University, Zoos and Aquariums for a Neurodiverse Ecosystem (ZANE), the Association of Zoos and Aquariums (AZA), the Association of Science and Technology Centers (ASTC), the Association of Children's Museums (ACM) and the American Public Garden Association (APGA), The project will apply the Facilitating Adoption of Best Practices implementation model to guide the studies and project activities to understand the implementation context, identify barriers and facilitators, and document the implementation process and resulting outcomes. Each aspect of the implementation model will be explored through sub-studies including 1) a state of the field landscape study, 2) a community study, 3) transference panels, and 4) comparative case studies. Each sub-study will apply multiple quantitative and qualitative data collection methods including surveys, interviews, focus group discussions, written reflections and review of artifacts. Quantitative data will be analyzed using descriptive and inferential statistics to describe, compare, and determine causal relationships between independent and dependent variables. Qualitative data from state of the field study, community study and transference panels will be coded and analyzed to identify emergent themes. Data collected as part of the case studies will be analyzed using thematic and cross-case analysis for comparisons across study sites. Resources developed as part of the project will be created through design charettes with ISL practitioners and led by professional organizations representing different ISL sectors. The project will result in new understandings of education, hiring and employment practices in ISL organizations. Project outputs include: 1) expanded toolkit of practices; 2) a repository of supports and resources for greater uptake and use of the toolkit among ISL practitioners and organizations; 3) a community of professionals across the ISL field through professional associations (i.e., AZA, ASTC, ACM, APGA) to engage in capacity building; and 4) research findings. This AISL Integrating Research & Practice award is funded by the Advancing Informal STEM Learning (AISL) program, which seeks to advance new approaches to, and evidence-based understanding of, the design and development of STEM learning in informal environments. This includes providing everyone with multiple pathways for accessing and engaging in STEM learning experiences. 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 · 2025-09

PROJECT SUMMARY Obesity and type-II diabetes are a pandemic, causing grave social and economic burdens in the United States. Epidemiological and animal studies showed that the flame retardants polybrominated diphenyl ethers (PBDEs) and a most widely used current PBDE-alternative – tetrabromobisphenol A (TBBPA) are linked to obesity and diabetes. However, mechanisms governing early life exposure to flame retardants and obesity/diabetes remains unknown; no studies have compared the effect of PBDEs vs. TBBPA, and little is known regarding how early life flame retardant plus additional risk factors (e.g. dietary factors) later in life modulate obesity. The liver- and intestine-enriched pregnane X receptor (PXR) and constitutive androstane receptor (CAR) are novel regulators for obesity. Intestinal inflammation, as triggered by dysbiosis of the gut microbiota, is an early mediator of obesity and type-II diabetes. In the previous grant cycle, we have demonstrated that: 1) early life exposure to the PBDEs and TBBPA produces a pro-inflammatory/pro-obese microbial signature later in life; 2) PXR and CAR are necessary in maintaining the basal healthy gut flora to prevent the blooming of pro-inflammatory microbes; 3) the presence of gut microbiome promotes immunotolerance in the liver, whereas early life exposure to PBDEs produced gut dysbiosis associated with enhanced immune cell hepatic mobilization, reduced PXR/CAR signaling, and predicted liver injuries in adulthood; Early life exposure to an industrial PBDE mixture persistently disrupted the microbial tryptophan metabolism. We also showed that the microbial tryptophan metabolite indole-3- propionic acid (IPA) mimicry FKK6 compound protected against intestinal inflammation associated with increased anti-inflammatory short-chain fatty acids (SCFAs) and the SCFA-producing microbes. Building on our findings, the objective of the renewal is to determine how the host nuclear receptors, gut microbiome, and early life flame retardant exposure interact to modulate the adult onset of obesity and type-II diabetes. Our central hypothesis is: early life flame retardant exposures upset the physiological functions of PXR/CAR, leading to pro- inflammation and pro-obesity microbial metabolites and elevated AhR signaling, which augmented obesity following a 2nd hit (e.g. a high fat diet [HFD]) later in life; whereas the microbial mimicry is a novel remediation strategy. We will test this hypothesis in 3 Aims: Aim 1 will determine to what extent PXR and CAR modulate the inflammation- and obesity-related host and microbial signatures following early life exposure to PBDEs and TBBPA. Aim 2 will determine how early life flame retardant exposure and PXR/CAR interact to modulate the high fat diet-induced obesity later in life. Aim 3 will determine how gut microbiome mechanistically contributes to the divergent roles of PXR and CAR in obesity following the “two-hit” exposure paradigm. The overall impact of this research is that we will determine how TBBPA vs. PBDE exposure in early life, especially in the combination of a high-risk obesogenic adult diet, results in adult obesity. This perspective will be shown through the lens of the important host nuclear receptors, which are well-established mediators of endocrine response.

NSF Awards · FY 2025 · 2025-09

Processes at the ice-ocean interface of marine-terminating glaciers play a critical role in determining the rate of ice sheet mass loss and the depth at which meltwater enters the ocean. Submarine melting along glacier ice faces, traditionally thought to be governed by the strength of subglacial discharge, also influences iceberg calving rates. However, emerging evidence reveals the presence of energetic dynamics elsewhere along the ice face, driving turbulent flows that remain poorly understood and underrepresented in existing models. These dynamics challenge current parameterizations of melt and freshwater flux, underscoring the need to directly validate and improve these frameworks. Specifically, there is the need to accurately represent their role in amplifying feedback loops and nudging the climate system toward potential tipping points relating to accelerated ice loss and disrupted ocean circulation. This project will integrate direct measurements of submarine melt rates and near-ice boundary-layer dynamics at Greenland’s marine-terminating glaciers with numerical simulations to improve the next-generation climate models. Beyond the importance to society and the scientific community, the work will provide mentorship and support for early career researchers, post docs, graduate and undergraduate students, and outreach with a local community as part of a conversation about their changing icy landscape. This proposal will support development of a robust, observationally grounded model for submarine melt prediction at Greenland glacier termini. Current melt parameterizations have largely been formulated for limiting cases where shear or convection dominates and assume simplified geometries and idealized ice and ocean forcing. The investigators recently developed instruments that directly measure the evolving ice boundary and demonstrated that melt is controlled by the interplay between fjord currents, turbulent eddies and near-boundary buoyancy that interact with a complex three-dimensional glacier-ice interface. Moreover, flow along the boundary were found to be significantly more energetic, with melt rates higher than predicted by current theory. This work hypothesizes that a skillful (unbiased) scale-aware melt parameterization will require an improved accounting for all sources of kinetic energy and how they drive the turbulent and diffusive ice-ocean boundary layer. Thus, the investigators propose a focused yet comprehensive set of small-scale measurements of submarine melt and the ice-ocean boundary layer across distinct turbulent regimes. These and larger-scale measurements will be integrated with a suite of numerical simulations to characterize submarine melt rates as functions of temperature, subglacial discharge, fjord dynamics, and other key factors, ultimately providing a framework generalizable to diverse glacier systems. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-09

Hydrologic changes, such as shifting precipitation patterns and changing storage in glaciers, snowpacks, groundwater, and soils makes it increasingly complex to forecast water availability. These changes also make it challenging to accurately quantify water flow and storage. Water tracers are commonly used to determine sources of water based on field measurements. Current water tracers, such as ions and isotopes, are often limited in their ability to distinguish between water sources or pathways, especially when the underlying geologic materials are similar. This project will explore the potential for DNA-derived tracers to overcome these limitations in watersheds where snow and ice are major water sources. The main objective of this research is to establish DNA-derived tracers as a reliable tool for hydrologists to better understand how water flow and storage in catchments is influenced by snow and ice, providing new insights where traditional tracers fall short. By providing more precise tools to assess water flow and storage, this research advances knowledge in both hydrology and environmental DNA tracing, with cross-disciplinary applications in environmental monitoring and biodiversity analysis. The project will benefit society by improving water management strategies in climate-sensitive regions, supporting biodiversity conservation efforts, and contributing to climate change adaptation policies. This bi-lateral international project examines water sources and flow paths in snow and ice-dominated catchments of the Oregon Cascades (USA) and the Swiss Alps. Combining traditional tracers with naturally occurring environmental DNA (eDNA) data will allow a more detailed analysis of water dynamics during hydrologic transitions, such as seasonal shifts. This project will conduct eDNA sampling across four study streams as well across multiple water sources, including snowpacks, glaciers, groundwater, soil leachates, and tributaries. At each site, non-target metagenomics analysis will be conducted on samples of watershed discharge and water sources throughout the catchment to identify the individual genes present in water samples. Two traditional hydrologic tools, end member mixing analysis and concentration-discharge analysis will be applied using eDNA information from different sources as the end members and discharge-dependent target tracer. The combination of traditional and eDNA-based tracers offers a robust mechanism to assess the success of these methods in improving water flow and storage predictions. Success will be measured through data integration, model improvements, and the production of peer-reviewed publications and conference presentations. This collaborative U.S.-Swiss project is supported by the U.S. National Science Foundation (NSF) and the Swiss National Science Foundation (SNSF), where NSF funds the U.S. investigator and SNSF funds the partners in Switzerland. 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.