Colorado State University

NSF Awards · FY 2026 · 2026-08

Accurate models of how different natural hazards will affect local infrastructure and community response are needed to help plan for these events and make communities more resilient. IN-CORE is an open-source software capable of modeling whole communities and cities subjected to natural hazards, from initial impact through recovery. This project will create an ecosystem of researchers, coders, insurance companies, community resilience planning professionals, government agencies, and other community stakeholders to support the long-term development, growth, and maintenance of IN-CORE. Key activities will include the establishment of a governance plan, recruitment of new users and software contributors, and other community building exercises and coordination mechanisms. The IN-CORE software facilitates the development of scalable, interoperable applications that support optimized allocation of limited resources for hazard mitigation, planning, and post-disaster recovery. IN-CORE unites subject matter experts from various domains, enabling cross-disciplinary methods and approaches to community resilience planning. The open-source ecosystem will broaden the impact and sustainability of IN-CORE through further development of interoperable tools, reproducible workflows, and transparent data-sharing protocols. Datasets provided by the ecosystem can be used to validate and verify new models and implementations to advance resilience engineering, planning, disaster risk management, and other fields. Further, the ecosystem is envisioned as becoming a sustained community of practice for community resilience research. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2026 · 2026-05

PROJECT SUMMARY / ABSTRACT American Indian and Alaska Native (AI/AN) adults experience disproportionately high rates of type 2 diabetes (T2D) and face significant barriers to effective diabetes management. Group-based nutrition education has proven effective for improving nutrition self-efficacy, dietary behaviors, and glycemic management, but tailored programs for AI/AN communities are limited. In response, our team developed and pilot tested “What Can I Eat?” Healthy Choices for AI/ANs with T2D (WCIE) with support from the PI’s K01 (K01DK128023). WCIE is a tailored, 5-session, group-based diabetes nutrition curriculum supported by social cognitive theory. It includes emphasis on peer-to-peer discussions, traditional AI/AN foods, and the American Diabetes Association (ADA) diabetes plate method for meal planning. WCIE has demonstrated feasibility, acceptability, and early evidence of effectiveness. It has been endorsed by the ADA and disseminated via “Train the Trainer” workshops across 50+ AI/AN-serving organizations across the United States. However, preliminary feedback from trained WCIE educators reveals a critical implementation gap: challenges to delivering WCIE with fidelity. Examples of these challenges include: staffing, scheduling, and resource limitations. These modifiable barriers threaten the scalability and sustained impact of WCIE. To address this challenge, this R03 project enhances the PI’s K01 randomized controlled trial evaluation of WCIE by applying implementation science methods to systematically identify contextual determinants of WCIE delivery and co-develop feasible implementation strategies. Guided by the Consolidated Framework for Implementation Research (CFIR 2.0), Aim 1 will involve semi-structured interviews with 40–60 key informants from 20 AI/AN-serving health organizations to explore barriers and facilitators of WCIE implementation. Aim 2 will conduct implementation mapping using the CFIR-Expert Recommendations for Implementing Change (ERIC) tool, including focus groups and consensus-building with participants from Aim 1 to generate and prioritize a context-sensitive menu of implementation strategies. Deliverables include a refined implementation strategy menu and a step-by-step guide for applying the CFIR- ERIC mapping tool in partnership with AI/AN organizations. This work will directly inform a future hybrid effectiveness-implementation R01 trial. By improving fidelity to group nutrition education in diverse AI/AN contexts, this project addresses a critical gap in diabetes care, enhances community-driven implementation efforts, and strengthens the Early Stage Investigator’s career as a diabetes translation researcher. The study is innovative in its use of CFIR 2.0 with AI/AN-serving organizations and the inclusion of a rigorous, community- engaged implementation mapping process. Its impact extends beyond the WCIE curriculum, offering a replicable model for scaling culturally tailored interventions in resource-constrained settings. Findings will directly benefit health organizations serving AI/AN populations by improving T2D outcomes through more sustainable, context- aware program delivery.

NIH Research Projects · FY 2026 · 2026-05

PROJECT SUMMARY/ABSTRACT The incidence of youth-onset type 2 diabetes (T2D) is on the rise, with a 95% escalation rate since 2001, and a staggering 77% increase during the COVID-19 pandemic. Regular physical activity is a cornerstone of T2D treatment. Yet, physical activity engagement is alarmingly low among youth with T2D, and effective interventions are lacking. Dr. Gutierrez-Colina’s long-term career objective is to improve health outcomes for youth with T2D by developing state-of-the-art mobile health (mHealth) interventions that provide personalized support for T2D self-management. The goal of this K23 proposal is to develop a novel just-in-time adaptive intervention (JITAI) that integrates dissemination and implementation science with real-time assessments of physical activity barriers and facilitators to deliver tailored strategies for promoting physical activity in adolescents with T2D. In Aim 1, a sequential exploratory design will be used to identify time-varying factors (e.g., motivation, fatigue, self- regulation) that influence physical activity engagement in adolescents with T2D. Qualitative interviews with N=18 T2D stakeholders will be conducted to gather in-depth feedback about physical activity barriers and facilitators. Qualitative findings will be integrated into the development of a 2-week ecological momentary assessment protocol designed to examine daily temporal associations between real-world barriers/facilitators and physical activity in the daily lives of N=25 adolescents with T2D. In Aim 2, the intervention components of a physical activity JITAI (e.g., personalized text messages, tailoring variables) will be co-developed with an advisory board of adolescents and caregivers. Intervention development will draw from dissemination and implementation science frameworks, as well as the “Capability, Opportunity, and Motivation (COM-B) Model,” a well-established theory of health behavior change. The JITAI will target activity barriers/facilitators related to Capability, Opportunity, and Motivation and deliver tailored support at the right time, in the right dose, and only when needed. In Aim 3, a sequential factorial experimental design will be used to pilot a 28-day micro-randomized trial of the physical activity JITAI with N=30 adolescents. Study feasibility and acceptability will be evaluated through usability surveys and post-intervention end-user interviews. Findings will generate critical data to inform an R01 application focused on a full-scale micro-randomized trial to optimize the physical activity JITAI. The proposed K23 research and career development plan will be supported by an outstanding mentorship team and a rich research environment at Colorado State University and the University of Colorado/Children's Hospital Colorado. Completion of the K23 training goals in (1) qualitative/mixed methods, (2) JITAI intervention development with an emphasis on dissemination and implementation science, and (3) the design and evaluation of micro- randomized clinical trials will equip Dr. Gutierrez-Colina with essential expertise in digital interventions and the rigorous methods involved in their evaluation. This training will directly support her successful transition to an independent research career focused on advancing personalized digital interventions for youth with T2D.

NIH Research Projects · FY 2026 · 2026-05

PROJECT SUMMARY This proposal aims to train a DVM-PhD student for a career as a clinician-scientist uniquely prepared to advance the fields of immunology and vaccine development. This project will develop the applicant’s skills in computational biology, in vivo modeling, T cell immunity, vaccine design, grant writing, mentorship, and clinical skills. Rotavirus remains a major cause of morbidity and mortality despite the availability of four attenuated live vaccines. These vaccines exhibit high efficacy in some populations but is much lower in others. Additionally, the safety of attenuated vaccines remains a concern. One cause of oral vaccine failure is environmental enteric dysfunction (EED), a subclinical condition defined by intestinal inflammation, increased mucosal permeability, and aberrant T cell signaling. To address the gap in vaccine efficacy and safety, next generation subunit vaccines and mucosal immune stimulating adjuvants are needed. We have developed a novel probiotic-based vaccine platform using the bacterium Lactobacillus acidophilus. Recombinant Lactobacillus acidophilus (rLA) represents a desirable vaccine delivery platform because it can be engineered to express viral antigens and adjuvants, engages with the mucosal immune system, survives the harsh intestinal tract, can be lyophilized, and delivered orally. T cells play key roles in antiviral immunity through direct cytotoxic killing of infected cells, orchestrating antibody-mediated immunity, and modulating the local immune system through the production of potent cytokines. However, little is known about T cell induction by rLA vaccines despite T cells being the most abundant immune cell in the human small intestine. These studies will determine how T cell immunity is induced by rLA-delivered model peptides and the effects of three novel adjuvant strategies on immune induction. T cells also play a role in the development of vaccine failure in EED, with a regulatory T cell population likely being the primary cause of oral vaccine failure. I hypothesize that rLA vaccines represent a potential tool to overcome EED-related vaccine failure because it induces antigen-specific Th1 immunity to LA- delivered antigens. These studies will utilize a murine model of EED wherein mice will be vaccinated with a novel rLA-based rotavirus vaccine expressing a protective rotavirus epitope and two immune-stimulating adjuvants to test the novel rLA vaccine in a relevant clinical state of vaccine failure. Immunity to the candidate rLA rotavirus vaccine will be compared to the gold standard attenuated rotavirus vaccine which exhibits limited efficacy in many neonates and infants. Antibody-mediated immunity, antigen-specific T cell immunity, and immune cell immunophenotyping will be performed to determine immunologic consequences of the candidate rLA rotavirus vaccine. Overall, these studies aim to improve the efficacy of an rLA-based vaccine platform through mechanistic studies of T cell immunity and in vivo studies using a relevant model of EED.

NSF Awards · FY 2026 · 2026-04

Liquid helium (LHe) is a strategically important and finite resource that enables research at the frontiers of quantum science and engineering, biomedical fields, and fundamental physics. At Colorado State University (CSU), LHe supports rapidly growing programs in quantum materials, quantum computing hardware, nanophotonics, precision measurements, particle physics, and nuclear magnetic resonance (NMR) spectroscopy. Responsible recovery and recycling of LHe are essential for both sustainability and long-term research productivity. This award will expand CSU’s helium recovery infrastructure by adding a second facility to an existing facility, which supports NMR instrumentation widely used in chemical, biological, and materials research. The new facility will more than double the current liquid helium production at CSU, increase on-site reserves, enhance resilience to supply fluctuations, and strengthen conservation through improved recovery and reduced losses. The new facility will focus on supporting the needs of quantum science groups and serve as an emergency backup for the NMR liquefier. By increasing institutional helium recovery and production, the project reinforces national scientific leadership and innovation capacity. The award also advances workforce development. Hundreds of students annually engage with helium-dependent instrumentation through coursework and research training. Expanded infrastructure increases hands-on access to state-of-the-art cryogenic science, preparing a skilled workforce for careers in quantum technology, biomedical research, and other high-technology sectors. By strengthening critical research infrastructure and promoting responsible stewardship of a strategic resource, this project advances NSF’s mission to promote the progress of science and benefit the nation’s health, prosperity, and security. This project will enable installation of a helium liquefaction system on the north CSU campus with a storage capacity of 150 liters and a liquefaction rate of up to 28 liters per day. The new facility will more than double CSU’s total LHe production capacity and increase available reserves, while providing redundancy to the existing south campus NMR liquefaction system. The installation builds on more than a decade of institutional experience in helium recovery, compression, and reliquefaction. Research enabled by the system includes investigations of tunable magnetism and spin transport in quantum materials; heterostructures of two-dimensional materials; radio-frequency devices for quantum computing and accelerator applications; precision tests of fundamental physics using trapped ions; development of liquid-helium-based particle detectors; and quantum photonic measurements. These experiments require ultra-low vibration environments, highly stable temperatures, and/or sustained cooling power not achievable with dry closed-cycle cryogenic systems alone.In addition to supporting quantum and fundamental physics research, the facility will serve as overflow and emergency backup for NMR instrumentation, including newly installed 400 MHz and 600 MHz systems used by researchers across four colleges and ten departments. The north campus installation will eliminate routine dewar transport, reducing helium loss and operational risk. 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

This project involves the purchase and setup of a field-deployable, continuous-flow instrument for real-time sampling and characterization of bioaerosols. Applications to potentially be advanced through the acquisition of this instrument are (1) bioaerosol, including pollen, lifecycle studies in grassland and forested ecosystems; (2) pathogen transport studies, including indoor settings, agricultural lands, and dusty regions; (3) biological ice-nucleating particle typing and identification; and (4) identification of airborne microplastics and other novel particles in lab and field studies. The project provides training opportunities for students and early career researchers, and a team of collaborators has been identified who will utilize the instrument. Lack of adequate technology has been a limitation in characterizing atmospheric bioaerosols. It is anticipated that the acquisition of the SwisensPoleno Jupiter instrument will provide new insights in this underexplored area. The instrument combines state-of-the-art measurement methods with artificial intelligence databases for automatic particle identification. The sensor train includes optical sizing, fluorescence, polarization, and holography. The instrument operates at a high flow rate and is non-destructive, enabling its coupling to other continuous-flow devices for further single-particle characterization, or collection onto substrates for microscopy or bulk analyses. 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

Project Summary Homeostatic Synaptic Plasticity (HSP) is the ability of neurons to exert compensatory changes in synaptic strength in response to altered neural activity, thereby acting as a critical protective mechanism during physiological processes such as learning/memory and development, as well as pathological conditions. Although nearly all HSP studies have focused on glutamatergic synapses, there is substantial evidence that cholinergic regions of the brain also experience significant changes in neural activity in multiple disorders and diseases, including Alzheimer’s disease (AD). Nevertheless, cholinergic HSP, especially at central synapses, and the mechanisms that regulate it have remained remarkably understudied. In the proposed studies, we will use the primarily cholinergic CNS of Drosophila as a genetically tractable model in which to unravel the molecular mechanisms underlying and regulating cholinergic HSP. This is currently the only model that has been developed to study central cholinergic HSP. In the proposed studies, we will validate cholinergic HSP in neurons in their intact circuitry for the first time. We will also use this model to address outstanding questions about the mechanisms that underlie and regulate HSP. We will dissect the role of the ER protein NACHO in the inactivity- induced up-regulation of the Drosophila α7 nAChR that underlies HSP. We will also examine in vivo the role of the transcription factors, NFAT and CREB, in regulating HSP, and identify downstream ion channel gene targets that contribute to HSP. Since ion channel/receptor genes and cellular regulatory mechanisms have proven to be highly conserved across species, we expect our findings to inform our understanding of cholinergic HSP in mammalian systems, and have broader, potentially, therapeutic, value.

NIH Research Projects · FY 2026 · 2026-03

To develop the HIV-eCaDI device we are developing for our NIAID project R61 AI181052 “Development of a robust HIV-1 diagnostic system (HIV-eCaDI) for at-home testing” we prepare, quantify and validate HIV virions for testing as well as test that our HIV sample inactivation is effective to maintain biosafety during testing. We utilize a combination of chemiluminescent cellular cytopathology assessment for HIV-induced cell killing and absorbance-based ELISAs for p24 antigen quantification and are developing a fluorescence polarization assay for intact virion quantification. The 16-year-old Perkin-Elmer Victor X5 multimode platereader that we use heavily in our BSL-2 laboratory to perform each of these assays recently had a critical control board failure and replacement parts are no longer commercially available, which makes performance of these necessary HIV assessment assays impossible. The objective of this supplement is to correct this limitation by replacing our Victor X5 platereader with an equivalent modern Victor Nivo platereader so we can continue to quantify and validate high-quality HIV samples necessary for quantitative evaluation of the HIV-eCaDI devices.

NSF Awards · FY 2026 · 2026-02

The 2025 Dragon Bravo Fire burned more than 580 square kilometers of watersheds in the Grand Canyon. As a result, large volumes of coarse sediment may be delivered to the Colorado River over the coming years. The burned areas are now highly susceptible to post-fire flash floods and debris flows. Upon reaching the river, the materials moved by these events can form debris fans that constrict the river channel, generate rapids, and reorganize sediment storage. Such changes in the nature of the river threaten infrastructure, river navigability, and visitor and boater safety in the Grand Canyon corridor. The increased likelihood of debris-flows creates a unique opportunity to investigate high-energy flow dynamics in a natural, rapidly evolving setting. The knowledge gained from this project will help improve prediction and inform decision-making in fire-affected landscapes. It will support assessment of navigation hazards, sediment impacts, and infrastructure vulnerability. The project activities will include outreach and data-sharing with local stakeholders and hands-on scientific training for students. Despite their strong influence on river morphodynamics, the hydraulics of fan-rapid complexes are seldom quantified at the spatial and temporal resolution needed to understand how sudden sediment inputs reorganize flow. The Dragon Bravo Fire provides an opportunity to establish detailed hydraulic and geomorphic baselines and examine how channel constriction, high-energy flow, and sediment delivery shape rapidly varied flow profiles in confined canyon systems. This project will advance a theory-driven understanding of rapid morphology and undular hydraulic jumps by linking debris-fan geometry, specific energy distribution, and free-surface wave fields along the Colorado River. By evaluating how constriction, hydraulic roughness, slope, and coherent flow structure govern the emergence and wavelength of stationary waves, the project will test whether surface-wave patterns reliably diagnose underlying hydraulic states in steep rivers. These insights will clarify how extreme sediment inputs reshape rapids, sediment routing, and energy gradients, strengthening both fundamental hydraulic theory and applied river management capabilities. 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

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.

NIH Research Projects · FY 2025 · 2026-01

PROJECT SUMMARY/ABSTRACT: St. Louis encephalitis virus (SLEV) is a mosquito-borne flavivirus that causes febrile illness and rare fatal encephalitis in humans. SLEV became endemic in California (CA) in the 1940’s, but between 2003 and 2014, no human cases nor mosquito isolations were recorded in CA, demonstrating an SLEV regional extinction. In 2003, a closely related flavivirus, West Nile virus (WNV), entered CA and other western states leading to 7,597 cases in humans (2003-2022) and fatal neurologic disease in horses and wild birds. Later in 2015, contemporary SLEV strains (cSLEV) traced to South America emerged in the western US and has since been detected in humans and Culex mosquitoes throughout CA in every subsequent year despite continued WNV presence. The reasons for the 11-year hiatus of historical SLEV strains (hSLEV) from CA and cSLEV expansion since 2015 remains uncharacterized. Our experimental studies show that house sparrows (Passer domesticus), a known avian reservoir host species for WNV and SLEV, will not mount hSLEV viremia after developing neutralizing antibodies for WNV, suggesting that hSLEV was displaced primarily by WNV avian herd immunity. Additionally, our preliminary data show that serum from birds inoculated with WNV poorly neutralize cSLEV strains in vitro. Given these findings, our central hypothesis for this proposal is that hSLEV strains were likely “out-competed” in avian reservoir hosts by cross- protective WNV derived humoral (antibody-mediated) immunity, and that cSLEV strains escape WNV avian host immunity such that infection with WNV does not confer cross-protection against cSLEV infection. To understand drivers of SLEV reemergence, the goal of this project is to identify how changing SLEV-vector- avian host interactions and cross-protection by WNV promote SLEV reemergence. To test our hypothesis, we propose a set of experiments to investigate 1) transmission competence and fitness of cSLEV versus hSLEV in Culex mosquitoes, and 2) avian fitness, antibody kinetics, and antibody-mediated protection for and between cSLEV, hSLEV, and WNV in house sparrows. The first aim will use two primary vectors in CA, Culex quinquefasciatus, and tarsalis for assessing viral titers in tissues and saliva, and time to dissemination and transmission in mosquitoes that ingest different doses of SLEV-spiked bloodmeals. Next, mosquitoes will be challenged via mixed infection with contemporary SLEV and an infectious clone competitor made from historic or contemporary SLEV to assess relative fitness. In the second aim, we will inoculate wild-caught house sparrows with WNV, cSLEV, and hSLEV serially in varying orders or concurrently in order to determine interviral infection kinetics (viremia and antibody responses) and host fitness. Lastly, passive transfer of SLEV (contemporary or historical) or WNV antisera from previously inoculated birds will be given to naive sparrows with subsequent challenge by heterologous virus. The significance of this proposal lies in its application to better predict and mitigate future human epidemics for both WNV and SLEV.

NSF Awards · FY 2026 · 2026-01

Models used to simulate weather and climate rely on sophisticated algorithms to represent the physics of the atmosphere, ocean, land surface, and cryosphere. These models have been quite successful but they have two important shortcomings: first, they are computationally intensive, typically running on world-class supercomputers and generating terabytes of data which are challenging to host and serve. Second, they do not take advantage of the large amounts of observational data collected over decades using satellites, weather balloons, ocean moorings, and other observing systems. A new approach addresses these shortcomings by developing "climate emulators" which use machine learning to extract statistical relationships from observations and various types of physics-based computer simulations. Climate emulators have tremendous potential but it is unclear how well they capture the underlying physics of weather and climate and are thus able to generalize beyond their training sets. For instance an emulator which has learned statistical relationships in a cold climate might not perform well in a warmer climate or vice versa. Work performed under this award uses multiple emulators to simulate the response of the climate system to patches of warmer surface temperatures in different regions. The patch methodology is well established and thus allows evaluation of emulators against traditional physics-based climate models. The work also addresses long-standing questions in coupled climate dynamics, such as the effect of surface temperature fluctuations in one region on surface temperature in other widely separated regions, the effect of regional surface temperature variations on the global energy balance, and the extent to which precipitation in the Southwestern US can be predicted from knowledge of surface temperature variations over the tropical Pacific Ocean. A key issue in the work is the ability of climate emulators to conserve energy, as energy conservation would dramatically increase their value and adoption for both scientific and practical 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 · 2025-11

This project supports travel for graduate and undergraduate students from US institutions to participate in programs coordinated with the 2025 International Conference on Automated Planning and Scheduling (ICAPS 2025), which will be held in Melbourne, Australia, from November 9 to 13. ICAPS is a premier conference in Artificial Intelligence (AI), focusing on planning and scheduling, which are critical components for optimizing complex decision-making across various domains. The methods developed in this community have been applied in robotics, transportation, healthcare, and many other fields. As part of the program, the students will participate in the Doctoral Consortium and the Launchpad workshop. The ICAPS 2025 Conference Doctoral Consortium is geared toward doctoral graduate students to present their research and receive feedback from leading AI researchers, while the Launchpad workshop is designed for undergraduate and master's students to get involved in the field of automated planning and scheduling. All the students will have the opportunity to present their work, discuss, and network with leading AI researchers. There will also be separate mentoring sessions to provide guidance that could help the students achieve further success in their research and professional careers. Through these activities, this project will help the students build professional connections, learn more about cutting-edge research in AI, and provide opportunities to further engage in research. In this way, the project seeks to nurture and promote the new generation of AI scientists. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-10

Large Language Models (LLMs) show great promise for generating source code and automating programming tasks. But these models are error-prone and can produce code with subtle bugs. This poses a risk for deploying LLMs in industrial settings for software engineering tasks - the subtly erroneous code generated by LLMs can expose vulnerabilities that compromise system security. It has been shown that the weakness of LLMs for code generation primarily stems from not accounting for the semantic properties of programs when training, using, and evaluating these models. This project aims to improve LLMs’ ability to generate high-quality code by deeply integrating program analyses with all the stages in the life cycle of LLMs: training, code generation, and evaluation. This project develops novel quantitative program analyses techniques to provide feedback to LLMs during training and decoding. First, the project leverages symbolic execution and Bayesian program analyses to design meaningful metrics to evaluate LLM-generated code. This project then uses program scores to train a differentiable reward model that can assess the quality of partial or complete generated code. At training time, inspired by Reinforcement Learning with Human Feedback (RLHF), this project uses the reward model for fine-tuning LLMs to generate high-quality code. To improve code generation at decoding time, this project leverages the reward model and similarity-based program ranking techniques to constrain and prune the decoding tree. Finally, this project develops semantics-guided metrics and collects new benchmarks consisting of realistic coding tasks for training and evaluating code LLMs. 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

Nontechnical Description The rapid rise of generative artificial intelligence (AI) has ushered society into a new era of supercomputing-driven data exploration. This tipping point is intensifying the gap between the exploding size of AI models and the limited computing throughput available today. A major bottleneck lies in data movement, specifically, the limitations of current interconnect technologies, further constrained by the aging von Neumann architecture. To address this, this project will employ a co-designed approach spanning architecture, packaging, and device innovation to meet the demands of next-generation interconnects, including high bandwidth, low energy use, low latency, scalability, and reliability. Supported by the ASCENT program, this project introduces a novel 3.5D integrated photonic interconnect solution that combines breakthroughs in network architecture, photonic devices, and advanced packaging. By leveraging the complementary expertise of academic and industry collaborators across several ECCS clusters, this effort drives interdisciplinary innovation, trains the next generation of engineers, and enables more powerful, efficient, and scalable computing systems that benefit society. Technical Description This project advances the field of integrated photonics by introducing a co-designed solution across architecture, packaging, and devices to meet the demands of next-generation computing. It proposes a transformative photonic interconnect-switching architecture based on a novel wavelength-mode division multiplexing scheme, enabled by athermal, energy-efficient, high-speed modulation and advanced hybrid Cu-Cu bonding techniques in 2.5D/3.5D integration. The goal is to achieve terabit-per-second data transmission with dynamic AI workload optimization. Key research tasks include the design of a reconfigurable and resource-aware photonic interposer network, exploration of 2.5D/3.5D network architectures with AI workload analysis, fabrication of heterogeneous athermal capacitive modulators and switches, development of microring-based transceiver and switch testbeds, and advancement of hybrid Cu-Cu bonding technology. This tightly integrated, interdisciplinary effort will drive innovation across multiple fronts, directly aligning with the core mission and priorities of the ASCENT 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 · 2025-10

This project is aimed to provide partial support for student participation in the 57th North American Power Symposium (NAPS 2025), to be held in Hartford, Connecticut, October 26–28, 2025. NAPS is a student-focused conference that brings together undergraduate and graduate students, faculty, and professionals from academia, industry, and national laboratories to share emerging research in power and energy systems. The symposium provides an important platform for students to present technical papers, receive constructive feedback, and engage with experts in the general field of energy and power systems. To remove financial barriers to participation, the project will fund travel-related expenses including lodging and discounted registration, along with access to technical tours and young professional networking sessions. The intellectual merit of this project lies primarily in its support for a national platform where students are introduced to advanced research topics and contribute to technical dissemination within the domain of power and energy systems. The broader impacts of this project include broadening access to meaningful educational opportunities, facilitating inter-disciplinary and inter-institutional collaborations, supporting the progress of science and contributing to the nation’s social and economic wellbeing by preparing participants to address complex infrastructure challenges and promoting innovation in energy technologies. NAPS 2025 will present a broad set of research and education topics, including coordination between transmission and distribution systems, artificial intelligence applications for grid operations, modeling of distributed energy resources, energy system cybersecurity, power electronics, transportation electrification, and optimization techniques for modern power systems. All accepted papers will be subject to peer review and will be published in the IEEE Xplore digital library to ensure both quality and visibility of student-authored work. In addition to technical sessions, the symposium will provide competitive student paper awards and guided mentoring activities. By enabling student attendance through financial support and creating direct engagement opportunities with leading researchers and practitioners, this project reinforces academic achievements across multiple disciplines and institutions. Through these coordinated efforts, NAPS 2025 will strengthen national expertise in power engineering and contribute to the advancement of scientific understanding in the power and energy field. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2025 · 2025-09

PROJECT SUMMARY The muscular dystrophies (MDs) are rare disorders that consist of over 30 genetic diseases that cause progressive muscle weakening and deterioration with Duchenne muscular dystrophy as the most common and most debilitating form, affecting about 1 on 3,500 live male births per year. Respiratory problems are a common symptom in persons with MDs due to weakness of inspiratory and expiratory muscles. However, the onset and pattern of respiratory symptoms varies according to the specific muscular dystrophy and stage of progression. Across all of the dystrophies, the most common cause of death is respiratory failure. To support respiration as the disease progresses, patients eventually receive non-invasive ventilatory support (NIV). However, the decision as to when NIV should start is subjective and is informed by pulmonary function tests (PFTs) and sleep studies. Unfortunately, pulmonary function tests cannot always be performed due to cognitive or physical disability or age. Sleep studies have the disadvantages that they are obtained in artificial environments, patients may not tolerate the measuring equipment, and patients may not sleep. Thus, there is a need for an objective surrogate for sleep studies to inform the decision as to when NIV is recommended. Electrical impedance tomography (EIT) is a noninvasive, non-ionizing real-time functional imaging technique with no harmful side effects, suitable for patients of any age. We will study the effectiveness of EIT as a measure of lung function longitudinally and as a surrogate for metrics of ventilation obtained during sleep lab studies in patients with muscular dystrophy. We will compare the EIT measures between dystrophies to determine whether their outcomes have the same meaning across the dystrophies. To accomplish this, we will carry out the following specific aims: (1) Develop a 3-D algorithm and a new method of segmentation to improve accuracy of EIT images and derived measures of ventilatory heterogeneity. (2) Assess lung function and heterogeneity longitudinally in patients with MD using EIT. (3) Determine the correlation of EIT measures with ventilation metrics obtained during a sleep study in patients with muscular dystrophy.

NSF Awards · FY 2025 · 2025-09

This I-Corps project investigates the commercial potential of a modular, low-cost, flow reactor system that enables chemical reactions to be performed more safely, efficiently, and affordably. The system, which features components that can be manufactured using three-dimensional printing, addresses a critical market need for affordable and customizable chemical synthesis tools, particularly for small to mid-sized companies. Traditional flow reactors are often prohibitively expensive and designed for large-scale operations, leaving early-stage pharmaceutical and specialty chemical companies without viable, affordable options. This innovation aims to reduce financial and technical barriers to continuous flow chemistry, a process known for improving safety and reproducibility in laboratory settings. By making sophisticated chemical processing equipment more accessible, the project supports national interests in economic competitiveness, scientific advancement, and public health through safer laboratory practices and more efficient chemical manufacturing of chemical building blocks and materials for the pharmaceutical, agrochemical and organic electronics industries. 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 a customizable photochemical flow reactor built using three-dimensional printing techniques, allowing for rapid prototyping and cost-effective production. The reactor system enables fast adaptive changes of various parameters such as reaction volume, flow rate, temperature control, and wavelength tuning for light-driven reactions. The technology also integrates in-line monitoring and purification modules, including ultraviolet and infrared spectroscopy as well as chromatographic components. Unlike conventional glass or metal reactors, the modular and printable design enables on-demand fabrication of replacement parts, streamlining maintenance and customization. The technology simplifies traditionally complex processes, enabling users with minimal training to conduct advanced reactions, thereby lowering the expertise threshold required for modern chemical continuous flow synthesis. 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

Low oxygen, as is common at high altitudes, poses major challenges to the body. These effects are well-described with respect to activities like exercise; however, low oxygen also challenges reproduction. Across mammals, low oxygen found at high altitudes leads to greater risk for pregnancy complications, especially low birth weight. Even though understanding how this happens could help us predict and prevent these poor outcomes, the processes by which low oxygen influences pregnancy and fetal growth trajectories remains poorly understood. This research will test the hypothesis that maternal metabolism and its response to low oxygen are important factors that determine pregnancy outcomes and fetal growth at high altitudes. The research uses a rodent model, the North American deer mouse, for which some populations have resided at high altitude for many generations and no longer experience birth weight reductions at high altitude. The research aims will compare how low oxygen alters the maternal physiology of high-altitude deer mice (that don’t experience birth weight reductions) versus low altitude-resident deer (which do experience birth weight reductions). The proposed experiments will also use advanced genetic and genomic tools to determine how the expression of key genes in the placenta influence maternal metabolism. This work will advance our basic understanding of how reproductive biology contributes to mammalian adaptation while also providing new perspectives on some of the most common pregnancy complications that are linked to low oxygen, like pre-eclampsia. This research applies experimental approaches to determine how reproductive physiologies evolve and adapt to cope with challenging environments (specifically, low oxygen). The experiments aim to test two main hypotheses: (a) that adaptation to high elevations modifies or prevents hypoxia-dependent adjustments to maternal metabolism and glucose regulation, and (b) that these protective maternal metabolic changes are directed by adaptions in imprinted gene networks found in the placenta. To test these hypotheses, North American deer mice of high- and low-altitude ancestry will be experimentally acclimated to high elevation during pregnancy. The maternal and fetal contributions to the physiologies that shape gestational metabolism will be parsed by measuring maternal energy allocation, fuel use, and the metabolic gene networks that interact with genomic imprinting in the. The integrated, whole-organism approach will enable the identification of links between specific gene expression patterns in the placenta and the fetal and maternal metabolic traits that are associated with growth outcomes under environmental hypoxia. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2025 · 2025-09

Project Summary. Despite intensive efforts, outcomes for patients with osteosarcoma (OS), the most common primary malignant pediatric bone tumor, have not improved since the advent of multi-drug chemotherapy in the 1980s. This clinical failure is attributable to our inability to overcome intrinsic or adaptive resistance in the >30% of OS patients who fail frontline chemotherapy and develop tumor recurrence, nearly always in the form of lung metastasis. The tumor microenvironment (TME) is recognized as a key barrier to therapeutic efficacy in solid tumors. Our overall goal is to understand how cell intrinsic molecular drivers regulate immunologic mechanisms that shape a metastasis permissive environment in the lung to inform development of both primary anti- metastatic drugs and co-targeting approaches to potentiate other cancer therapies in OS. We previously developed a strategy for depletion of metastasis associated macrophages, via blockade of monocyte migration with oral dosing of a repurposed CCR2 antagonist (losartan), combined with a multi-kinase inhibitor (toceranib), which resulted in significant clinical benefit (50% response rate) in dogs with spontaneous metastatic OS. Based on this canine data, this drug combination is currently in a Phase I trial in pediatric OS (NCT03900793). Our preliminary data suggest focal adhesion kinase (FAK) signaling 1) is an OS cell intrinsic dependency, 2) promotes fibroblast enrichment and pro-tumorigenic function in the OS TME, and 3) that fibroblasts mediate OS cell resistance to platinum therapy. Therefore, the objective of this proposal is to define the mechanisms by which FAK signaling in OS tumor cells and lung fibroblasts shapes immune composition of the lung metastatic TME, and to test whether molecular targeting of FAK improves standard-of-care platinum chemotherapy (SOCC) response. We hypothesize that a combination of tumor cell intrinsic and lung fibroblast FAK signaling drives a chemokine secretion profile that mediates a feed-forward loop of increased macrophage recruitment, fibroblast proliferation, and subsequent T cell exclusion to promote OS metastasis. In Aim 1, we will use a combination of wild-type and FAK-/- syngeneic and patient-derived OS cells, and fibroblast specific FAK knockout mouse metastasis models, in parallel with clinically annotated human OS tumors, to define the contributions of tumor intrinsic and lung fibroblast FAK signaling on chemokine expression, TME composition, and progression of OS lung metastasis. In Aim 2a, we will leverage dogs with spontaneous OS, the most faithful model of a pediatric solid tumor, to determine if dual compartment OS cell and TME targeting of lung fibroblasts with a FAK inhibitor significantly improves the response rate to SOCC. In Aim 2b, we leverage bronchoalveolar lavage, a novel lung TME monitoring approach developed in our lab and uniquely employable in dogs, and primary tumor multi-omic profiling, to identify mechanistic predictors of canine patient response to this immunotherapy. Completion of these studies will inform basic immuno-oncology research in pediatric OS and generate readily translatable data leading to deployment of this orally administered and clinically well tolerated immunotherapy in a human OS trial.

NSF Awards · FY 2025 · 2025-09

With the support of the Macromolecular, Supramolecular, and Nanochemistry Program in the Division of Chemistry, Professor Amy Prieto of Colorado State University is studying an efficient synthetic toolkit for making nanocrystals of semiconducting materials. A significant challenge in building new devices for modern applications is having better materials for those devices. While there have been major advances in the scientific community’s ability to predict new materials that would be useful for a range of important applications, even the most accurate predictions don’t come with clear, fool-proof recipes for how to make those compounds. The nanomaterials to be made in this project are composed of non-toxic, earth-abundant elements that could all be sourced in the United States and have the potential to offer tunable properties that could be exploited in photovoltaic devices. Professor Amy Prieto and her team will involve high school, undergraduate, and graduate students in this research, which results in excellent training for these students as they enter careers in chemistry. With the support of the Macromolecular, Supramolecular, and Nanochemistry Program in the Division of Chemistry, Professor Amy Prieto of Colorado State University is studying a synthetic toolkit that would result in atom-economical reactions to make phase pure ternary semiconductor nanoparticles. The main goal of this work would build off the initial synthesis of one member of the Cu/P/Se phase diagram to develop the synthetic parameters needed to controllably access phosphorous deficient metastable C/P/Se nanoparticles as well as analogous compounds on the Ag/P/Se phase diagram. This research will utilize a diverse range of tools to identify solution species and crystalline products under both in-situ reaction conditions and post-synthesis in order to identify and understand the stoichiometries of these reactions. By developing this toolkit for the synthesis of semiconductor nanoparticles, reaction pathways for pure phase multinary nanoparticles with tunable composition, structure, and surface chemistry are expected and these could be exploited in future applications utilizing semiconducting compounds. This type of research is a powerful tool for recruiting and training students from a range of ages, with the goal of preparing them for careers in materials chemistry. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-09

This project supports fundamental research on shape-morphing structures that looks to dynamically transform their physical shapes into desired configurations in two or three dimensions. Such morphing structures can potentially revolutionize various fields by enabling materials, systems, or devices that actively adapt their form to suit different needs. For example, such structures could lead to new materials that adjust their stiffness in response to changing demands, robotic systems that reconfigure themselves to move through complex environments, or wearable devices that alter their shape for improved fit and comfort. By advancing the scientific understanding and engineering capabilities of such systems, this project directly promotes the progress of engineering science and supports national interests in health, security, and manufacturing. The research activities will also provide educational opportunities for undergraduate students, enhance engineering curricula, and inspire the next generation of scientists and engineers through outreach to K-12 students. The technical focus of the project is to develop a rigorous framework for modeling, planning, and controlling high-dimensional morphing structures composed of interconnected morphing rods. Each morphing rod combines a thermally driven artificial muscle with a variable-stiffness shape memory polymer to enable large, reversible deformations. The project will begin by designing modular rod geometries and mechanical connectors that enable flexible and reconfigurable assemblies. It will then formulate physics-based models that integrate reduced-order rod mechanics, artificial muscle dynamics, and connector constraints. To enable scalable and robust control, the project looks to develop data-driven models based on Koopman operator theory, enhanced by deep learning to automatically discover system observables. These models will be made robust by incorporating uncertainty in the learned representations and scalable through the use of graph neural networks that capture the connectivity of complex structures. The project seeks to then leverage these models to enable real-time control and optimal planning, determining which elements should be actuated or softened to achieve desired shapes. The integration of model-based design, data-driven modeling, and real-time control seeks to establish a new paradigm for shape-morphing systems, enabling them to operate with precision, versatility, and autonomy in a wide range of 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

In terrestrial plant communities, woody plants are overtaking their herbaceous (non-woody) relatives. This is happening in plant communities globaly. In tundra ecosystems, the environmental causes of this have been studied extensively, but less is known about the potential influence of biologically-influenced interactions on woody plant abundance and growth rates. This project evaluates the extent to which associations between plants and root-soil fungi contribute to shrub expansion in alpine tundra. Studies will take place at the Niwot Ridge Long-Term Ecological Research (LTER) site. This project integrates field and modeling experiments across different spatial scales to understand plant-soil feedbacks that may result in shrub expansion into alpine ecosystems. This project will engage K-12 students and educators in partnership with "CU Science Discovery" at the University of Colorado, train undergraduate students through project-based research, mentor and train a graduate student on the practice and science of running ecosystem-scale simulations using computers. This project will investigate the role of mycorrhizal associations in shaping density-dependent threshold dynamics and how these feedbacks shift across topographically heterogeneous terrain. The main objectives include (1) quantifying heterogeneity in rates of woody plant expansion using remote sensing and supervised image classification, (2) assessing biotic and abiotic drivers of woody expansion, including mycorrhizal colonization rates, communities, and soil biogeochemistry by pairing remote sensing analysis with field surveys, (3) investigating the role of mycorrhizal symbionts in mediating shrub growth rates and gross nitrogen transformations by transplanting individual shrubs with known mycorrhizal symbionts across a shrub density, and (4) integrating field surveys and experiments into modeling experiments to quantify changes in shrub growth and expansion rates under future scenarios of environmental change, and examine how density-dependent feedbacks shift across topographically complex alpine landscapes. 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 majority of buildings use energy-intensive Heating, Ventilation, and Air Conditioning (HVAC) systems to maintain healthy and conformable spaces inside. The goal of this project is to develop lichen-inspired surfaces that are energy-efficient, capable of removing indoor pollutants, and maintain comfortable indoor humidity levels. In nature, lichens are complex communities of microbes that can absorb moisture and contaminants in the air with sunlight as their primary energy source, making them an ideal candidate for reducing the energy cost of maintaining indoor air quality. Yet, natural lichen is very slow-growing and is difficult to grow indoors. This work uses synthetic biology to engineer industrial microbes to create lichen-inspired surfaces on various building materials (wood, stone, brick, concrete). The project will study how these lichen-inspired surfaces remove pollutants and control humidity levels to enhance indoor air quality. Indoor environmental quality (IEQ) is a central determinant of human health and quality of life in the modern world, and maintaining it consumes 40% of the energy in the US. The goal of this project is to determine fundamental design principles for sustainable bioactive surfaces that improve IEQ. This work is inspired by lichens, a symbiotic consortium of cyanobacteria, fungi, and other microbes. Their resilience to environmental fluctuations and capacity to colonize building materials without exogenous inputs make them a promising material to generate sustainable bioactive surfaces. Additionally, their inherent capacity to buffer moisture and accumulate pollutants in the air makes them well suited to improving IEQ. However, their slow growth and the inability to engineer their biology have limited both the understanding of their material properties and bioactivity, as well as their application as a tool to enhance IEQ. This project will develop lichen-inspired consortia using engineered co-cultures of experimentally tractable and fast-growing microbes to address these challenges. The capacity of these consortia to generate surface coatings that can enhance indoor air quality will be determined by engineering the bioactivity and material properties of lichen, characterizing the capacity of lichen-inspired consortia to colonize nutrient-free materials, and characterizing the bioactive functions of these consortia. 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

With this award, Professors Linda Broadbelt and Eugene Chen of Northwestern University (NU) and Colorado State University (CSU), respectively, are studying data-driven design of recyclable plastics using artificial intelligence and machine learning (AI/ML). A large majority of today's commodity polymers were invented in the 1930s – 1950s and further developed for performance, durability, profitability, scalability, and disposability, rather than for efficient reuse of resources at their end of life. The linear economy framework of the "mine, make, use, dispose" model not only accelerated depletion of finite natural resources but also brought about enormous material value loss to the economy. The NU-CSU team proposes to address these challenges by using AI/ML coupled with experimental approaches to design chemically recyclable polymers, materials that can be selectively and rapidly depolymerized back to their monomers for virgin-quality polymer reproduction, improving energy efficiency and use of domestic resources. The project will build on the AI/ML tools to develop new approaches and open-source software that can be applied to effectively design and realize next-generation reusable polymers in real-world applications and marketplaces. To realize the potential of AI/ML applied to the design of plastics, an informed and educated workforce is critical. The project will train multiple graduate students and postdoctoral researchers in the AI/ML approach that will be developed, and students at the undergraduate and K-12 levels will also be educated through research and educational opportunities, including disseminating software for public use. With this award, Professors Linda Broadbelt and Eugene Chen of Northwestern University (NU) and Colorado State University (CSU), respectively, are studying data-driven design of polymers using artificial intelligence and machine learning (AI/ML) coupled with experimental design of new materials. There are three main elements of the project. The first aim will address critical fundamental knowledge gaps facing data-driven polymer design for reuse by combining AI/ML-guided theory and experiment at all stages, from exploratory synthesis of bio-based monomers designed for intrinsically circular polymers through polymer characterization and performance testing to polymer end-of-life management, including closed-loop recycling and biodegradability. The second aim will establish biodegradable polyester circularity through modeling cyclic oligomers, achieve polyurethane and nylon circularity through ML-guided monomer design, and predict environmental lifetimes of polymers based on their experimental (bio)degradation kinetic profiles. Such an approach will create insightful links across traditional interfaces along the polymer manufacturing chain. Finally, the research team will develop design principles for intrinsically circular polymers with tunable and advanced properties and create new fundamental understanding of de/polymerization mechanisms via density functional theory, kinetic modeling, and AI/ML. This Molecular Foundations for Sustainability: Sustainable Polymers Enabled by Emerging Data Analytics (MFS-SPEED) award is co-funded by the NSF through the Division of Chemistry (CHE), the Directorate for Mathematical and Physical Sciences (MPS), and the Division of Innovation and Technology Ecosystems (ITE) in the Directorate for Technology, Innovation, and Partnerships (TIP). Additional MFS-SPEED funding is provided by Procter & Gamble, PepsiCo, Dow, BASF, and IBM. 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.