University Of Illinois At Urbana-Champaign

NIH Research Projects · FY 2025 · 2025-09

Project Abstract The University of Illinois (UI) Veterinary Diagnostic Laboratory (VDL) is a full service, AAVLD accredited, all species, reference veterinary diagnostic laboratory. As a member laboratory of the FDA Vet-LIRN, UI VDL is actively involved in several VET-LIRN activities including iSeq-sequencing Listeria project as a lead lab, providing isolates for MiSeq WGS sequencing project as a source lab, participation on antimicrobial resistance pilot project, and evaluation of automatic nucleic acid extraction methods as a lead lab. Old MiSeq System has a longer turnaround time (19-56 hours) and complexed process for sequencing and equipment maintenance; by contrast, the new MiSeq i100 System has much shorter turnaround time (4-15.5 hours), simple integrated fluidics without additional equipment wash and library denaturation, and more versatile amplifications. Therefore, the new MiSeq i100 System add more diagnostic values to clinical case investigation of foodborne pathogen related outbreaks and other zoonotic diseases affecting both animals and humans. This funding support is crucial for maintaining UI VDL capacity in molecular testing and critical response to animal food/drug emergency or disease outbreak coordinated by FDA Vet-LIRN.

NIH Research Projects · FY 2025 · 2025-09

Project Summary/Abstract Retail raw meat and seafood pose risks of foodborne bacterial pathogen transmission to humans. Meat products like chicken, beef, ground turkey, and pork are major sources of foodborne pathogens including Escherichia coli, Salmonella, and Campylobacter. Seafood products at retail venues, including shrimp, tilapia, and salmon, often carry Vibrio and Aeromonas. These foodborne pathogens from retail food products frequently show resistance to multiple antimicrobials, posing a serious public health risk. As antimicrobial resistance (AMR) threatens food safety and public health, monitoring AMR in these bacteria is essential to prevent the spread through the food chain. The National Antimicrobial Resistance Monitoring System (NARMS), a U.S. public health surveillance system, tracks AMR in humans, animals, and retail meats. Illinois—with densely populated urban centers and agricultural regions—plays a key role in the national food supply chain. This project will enhance AMR surveillance by supporting the FDA-NARMS program in Illinois through standardized testing of retail food products. Fresh meat (including chicken, ground turkey, ground beef, and pork chops) will be collected twice monthly from retail outlets randomly selected from NARMS-designated locations within Illinois, with microbiological testing for Salmonella, Campylobacter, Escherichia coli, and Enterococcus. Seafood samples (shrimp, tilapia, and salmon) will be tested for Enterococcus, Aeromonas, Vibrio, and other lactose fermenting bacteria. The isolates will be serotyped and subjected to whole-genome sequencing (WGS) and molecular testing (PCR) following NARMS protocols. All sequences will be submitted to the National Center for Biotechnology Information (NCBI) within 2 months, and isolates will be sent to the FDA. Our work will follow NARMS protocols and data agreements, participating in NARMS calls and working groups. This project will track emerging AMR trends in foodborne bacterial pathogens through data derived from phenotypic and genotypic analyses, enabling public health agencies to take appropriate measures. The specific aims are: 1) Initiate and strengthen Illinois-wide AMR surveillance through strategic retail sampling of poultry, meat, and seafood products; 2) Elucidate phenotypic resistance profiles and perform WGS of priority foodborne bacterial pathogen isolates; 3) Leverage integrated genomic and metadata analyses to assess AMR trends and track dissemination patterns across retail food sources. The long-term objective is to strengthen national AMR surveillance and protect public health by identifying risks from resistant foodborne bacteria.

NIH Research Projects · FY 2025 · 2025-09

Abstract Access to frequent, accurate, and highly sensitive HIV viral load monitoring is a critical component of early infection diagnosis, HIV antiretroviral therapy, and routine diagnostic testing to keep people informed and help them maintain their HIV viral load status. The prevalence of co-infection with other viruses that present with similar symptoms, such as Hepatitis B virus (HBV) and Hepatitis C virus (HCV), drives the clinical need for multiplexed testing from the same blood sample. Although extensive research and product development has been applied to point-of-care (POC) viral load testing, the current paradigm of nucleic acid tests and antigen assays is inherently complex, lacks robustness, and is costly, which prohibits adoption in settings that serve key populations seeking care in lower stigma locations (e.g., community health centers vs. hospital settings). We seek to address this societal need, and technological gap, by advancing testing methods through the development of a microfluidic cartridge, molecular biology method, biosensor, and detection instrument that will simultaneously detect HIV, HBV, and HCV viral loads. This POC test will require minimal whole blood volume (100 L), take <30 minutes for sample-to-answer, and have superior sensitivity to PCR. Our nucleic acid test performs sample pre-processing in a microfluidic cartridge followed by virus-specific activation of CRISPR/Cas enzyme that rapidly releases large numbers of gold nanoparticle reporters that are subsequently captured and detected on crumpled graphene impedance-based biosensors. Our handheld, inexpensive (<$50) POC platform communicates its measurements to a mobile device (smartphone or tablet) to process current/voltage measurements from a custom circuit board. The affordable estimated cost per test (~$5) is based on our experience using the Additive Manufacturing approach for cartridge fabrication, combined with the low microfabrication cost of resistive sensors on silicon. Our preliminary data demonstrate the effectiveness of nucleic acid extraction from whole blood, the sensitivity/selectivity of the assay approach, and the sensitivity of the biosensors. The system will be evaluated using a rigorous tiered approach by spiking target nucleic acid sequences and viruses into buffer and whole blood for initial characterization of detection limits. We will validate the POC platform’s ability to detect HIV, HBV, and HCV in whole blood using clinical samples provided by our hospital collaborators in Illinois. Throughout the development and testing of the POC test, we will conduct rigorous human factor engineering and acceptability research with our behavioral scientist collaborators, who will mediate this iterative process to reflect the needs of clinicians, scientists, healthcare workers, health technicians, and potential end users represented by a scientific advisory group. The resulting platform will make a significant impact upon public health to those who lack easy access to sensitive, inexpensive, and triple rapid viral testing outside of traditional clinical settings.

NIH Research Projects · FY 2025 · 2025-09

Summary: Our modern environment contains many industrially produced chemicals that can impact human health, including molecules classified as endocrine disrupting compounds (EDCs). One type of EDC with potentially profound implications for neurodevelopment is the environmentally ubiquitous family of phthalates. Phthalates serve as stabilizers in plastics and are found in vinyl flooring, children’s toys, personal care products, and medical devices. Pregnant women are exposed to phthalates throughout pregnancy and metabolite concentrations of phthalates is greatest in children 6-11 years old. Recent studies suggest that individuals exposed gestationally to phthalate metabolites show higher scores of autistic traits including increased social anxiety and impaired communication. Despite these important developments, very little is known about how phthalate exposure alters the nervous system to contribute to changes in social behavior or communication. Our preliminary data indicate that exposure to phthalates during development leads to reduced social investigation and social communication in mice, which coincides with large-scale changes in anatomical and functional connectivity in the brain. It also results in changes in dopaminergic gene expression in limbic regions important for processing social stimuli. This is important as atypical social behavior and reduced communication are key diagnostic criteria for autism spectrum disorders (ASD). Therefore, the goal of this proposal is to test the novel hypothesis that exposure to phthalates during development leads to alterations in whole-brain connectivity, gene expression profiles, and physiological signaling across limbic regions important for the processing of social reward. Specifically, we will test: (1) whether phthalate exposure results in reduced anatomical and functional connectivity of frontal, sensory, and limbic regions essential for motivated social engagement; (2) whether phthalate exposure results in decreased social investigation and reduced dopamine signaling in pathways essential for processing of social reward; (3) whether phthalate exposure alters the molecular profile of hormone, dopaminergic, and cellular signaling markers across limbic regions in juvenile and adult animals. Explicit tests of these hypotheses will be accomplished by using whole brain magnetic resonance imaging, projection specific fiberphotometry during behavior, and multi-region high-density transcriptomics. The proposed research plan integrates the diverse technical skills and scientific knowledge of a MPI team and a strong cast of supporting toxicology, imaging, and bioinformatics collaborators, to generate an innovative and comprehensive approach for linking clinically-relevant features of autistic behaviors to phthalate exposure during development. This will be the first study to examine the effects of phthalate exposure on changes in social behavior, while innovatively and quantitatively assessing alterations across the entirety of limbic structures essential for contextual modulation of behavior. As such, it will launch a novel direction of research and is critical for determining the risks of environmental phthalates on atypical neural development in regions that regulate social behavior.

NIH Research Projects · FY 2025 · 2025-09

Project summary/abstract The University of Illinois Urbana-Champaign (Illinois) proposes to expand its Microscopy Core Facilities to establish an Advanced Microscopy and Diagnostics Facility to create a regional center for sophisiticated microscopy instruments, a facility to develop and test diagnostics tools to detect circulating biomarkers, and a clinical research suite for sample collection. By doing so, this facility, located in a premier interdisciplinary research institute, the Carl R. Woese Institute for Genomic Biology (IGB), will provide a centralized location for Illinois campus researchers, regional partners in universities, non-profits, and companies to have access to tools and instrumentation that help address important research topics including cancer, neurodegenerative diseases, and women’s health. Importantly, it will reduce the burden on smaller institutions to obtain high-end instruments and will increase collaboration across Illinois and the Midwest. The clinical research suites will provide added capacity on the Illinois campus to meet the clinical research needs, and the diagnostics lab will provide a shared central location for the scattered labs across campus. The current microscopy facility has exceeded capacity limits, and instruments are housed suboptimally in shared or repurposed rooms. The proposed remodeling will focus on a targeted expansion to accommodate the three above needs to increase capacity for biomedical research. The 4830 sq. ft. expansion requires renovating an existing space and new construction of an undeveloped space north of the IGB. The IGB’s central location on campus is particularly beneficial for Illinois trainees and post-docs, offering them 24/7 access and proximity to the expanded facilities. Our goal is to increase access and establish Illinois as a Midwest hub for advanced microscopy by providing best-in-class training with state-of-the-art equipment to better enable biomedical researchers to address research grand challenges, both within our university and across a diverse set of regional partners.

NSF Awards · FY 2025 · 2025-09

The increasingly connected and technologically mediated world both gives learners unprecedented access to data about complex systems, and simultaneously requires increasingly sophisticated STEM reasoning skills to grapple with those complex systems. This project will develop analytical tools, simulations, and online learning materials to help adult learners make sense of the complex systems involved in Type 1 Diabetes (T1D). The project will use the context of T1D to develop a model for how people engage in complex systems sensemaking with real-world incomplete data, and will examine how learners make use of the simulation tool to develop the STEM skills of data interpretation, understanding relationships between variables, and the role of uncertainty. Lessons learned from this project will both inform the further development of supports for this specific learner community, as well as inform supports that other adult learner communities might need to learn about complex systems in their day-to-day lives. The project will develop the sensemaking model by exploring the following research questions: (1) how people with T1D make sense of uncertain, real-time data along with dozens of other factors to help them learn the STEM knowledge and skills related to their chronic illness; (2) what features online materials need in order to best serve the STEM knowledge and skill gaps that people with T1D face when learning about their chronic illness; and (3) what ways a simulation can help people with T1D practice necessary skills and improve their understanding of the multifaceted relationship between relevant variables involved in their chronic illness. These questions will be addressed via a design-based research method, making use of survey data, focus groups, and computer log data gathered from T1D community members. The model will be used as an initial design plan for online learning materials and a simulation to help people with T1D learn about the necessary STEM knowledge and skills and then provide an opportunity to practice using that knowledge and those skills. The project will also deepen partnerships across T1D-focused non-profits and academics engaged in this work and leverage that to build and study the online instructional resources and supports. All of the materials will be made available online for free to all, including people with T1D, their caregivers, and health care professionals. This Integrating Research and Practice project 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 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.

NSF Awards · FY 2025 · 2025-09

The Earth’s magnetic field is critical for sustaining our planet’s habitability, deflecting harmful solar radiation. However, the processes generating this field deep within the Earth’s core remain mysterious, hindering our ability to predict its future behavior – a concern given recent observations of relatively rapid changes in the field. This project addresses this fundamental challenge by developing a “digital twin” system for a large-scale laboratory experiment that mimics conditions inside the Earth. The digital twin will allow researchers to operate and optimize complex experiments remotely, explore scenarios inaccessible through physical experimentation alone, and ultimately improve our understanding of the forces shaping the geodynamo – the engine driving Earth’s magnetic field. By creating extensible tools for laboratory science, this research advances computational mathematics, supports training for a new generation of scientists and engineers, and has potential benefits for diverse fields including medical device design and external aerodynamics. This translational science collaborative project between University of Maryland (UM) and University of Illinois creates a digital twin consisting of the 3-meter liquid sodium geodynamo experiment at UM, coupled with advanced numerical modeling schemes based on high-order spectral element methods (Nek5000/RS) and data assimilation techniques including Ensemble Kalman Filters. The research team will develop Reduced Order Models ROM incorporating Deep Learning Neural Networks to enhance predictive capabilities and enable flow control strategies. By synchronizing the model with experimental observations, researchers aim to achieve a 1:1 correspondence between geometry changes and simulation results, ultimately allowing for bidirectional interaction between the physical experiment and its digital counterpart. This work will leverage high-performance computing resources to advance computational mathematics and provide a framework extensible to other laboratory science domains. 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

Artificial intelligence (AI) and computing hardware (HW) are rapidly reshaping industries and research fields, but their advancement is increasingly interdependent and facing growing challenges. As AI models become larger and more complex, they place significant demands on computing infrastructure, leading to rising energy consumption and performance bottlenecks. At the same time, new hardware technologies offer exciting but underutilized potential. This project brings together top experts from universities, government, and industry in a high-impact workshop to chart a national vision for the co-evolution of AI and hardware over the next decade. By identifying pressing research questions and long-term opportunities, this effort will help guide future investments in science and technology that advance national prosperity, security, and innovation. The proposed workshop will also strengthen cross-disciplinary collaboration and help educate a new generation of researchers working at the intersection of AI and hardware. This workshop, titled “AI + HW 2035: Shaping the Next Decade,” will define a 10-year research roadmap for the integrated advancement of artificial intelligence and hardware. The workshop will address critical technical challenges, including energy-efficient model architectures, the design of scalable and specialized accelerators, the integration of emerging devices such as analog and quantum hardware, and the automation of model-hardware co-design workflows. Specific research topics to be explored include reasoning-capable AI systems, neuro-symbolic computing, sparse and domain-specific architectures, reconfigurable dataflows for next-generation accelerators, and chiplet-based system scalability. Participants will engage in invited talks, panel discussions, and structured working sessions to collaboratively draft a comprehensive vision paper. This paper will synthesize cross-domain insights, identify strategic priorities, and recommend actionable research directions that can inform coordinated funding efforts by federal agencies and stakeholders. The workshop also aims to establish a lasting platform to bridge the AI and hardware communities, enabling sustained cross-disciplinary dialogue. 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

Hot flashes are transient periods of intense heat in the upper body, arms, and face that are often followed by flushing of the skin, profuse sweating, chills, palpitations, and anxiety. Hot flashes pose a significant public health concern because they are the most common perimenopausal symptom reported by women in the U.S., affecting 4-5 million women each year. For more than half of these women, hot flashes last more than 7 years and some have hot flashes for up to 30 years. In addition, hot flashes negatively impact quality of life and lead to increased health care expenditures. Despite the importance of hot flashes in women's lives, little is known about their risk factors. Other than a few studies on cigarette smoking and one study on organic solvents, no studies examined the association between environmental chemical exposures and hot flashes in women. Thus, we conducted preliminary studies to examine whether exposure to phthalates is associated with an increased risk of hot flashes in a cohort of 782 midlife women. We focused on phthalates because they are one of the top contaminants present in human tissue and are used in a myriad of consumer products. Our data indicate that phthalate exposure is significantly associated with an increased risk of having recent and frequent hot flashes in midlife women. In fact, the degree of association is similar to that observed with cigarette smoking, one of the best known risk factors for hot flashes. Although our data clearly indicate that phthalate exposure is associated with a higher risk of hot flashes, we do not understand the underlying mechanisms. Published studies indicate that phthalate exposure increases oxidative stress in animal models and a few small epidemiological studies suggest that oxidative stress increases the risk of hot flashes. Thus, the overall goal of the proposed study is to test the hypothesis that a cumulative mixture of phthalates is associated with increased risk of hot flashes via oxidative stress pathways. We will test this hypothesis by completing the following specific aims: 1) prospectively determine if cumulative exposure to a mixture of phthalates is associated with validated biomarkers of oxidative stress in midlife women, 2) prospectively determine if validated biomarkers of oxidative stress are associated with hot flashes in midlife woman, and 3) explore whether observed associations between urinary phthalate metabolite concentrations and hot flashes are mediated by biomarkers of oxidative stress. This work may lead to the development of better strategies to prevent and treat perimenopausal hot flashes, a condition that goes untreated in the majority of women experiencing symptoms despite seeking medical treatment. Given the known risks associated with current treatments for hot flashes (e.g., cancer, blood clots, stroke), new treatments could be critical for this prevalent health outcome.

NIH Research Projects · FY 2026 · 2025-09

Project Summary/Abstract The overarching goal of this application is to identify targetable transplacental immune signaling pathways for optimizing antenatal therapies that improve maternal and offspring health outcomes during seasonal influenza outbreaks. Influenza viruses pose ever-present threats to the health of pregnant women and escalate the risk of neurodevelopmental disorders in their offspring. Yet, how and which immune signals propagate across the maternal-fetal interface to impact fetal brain development during gestational influenza virus infection are unknown. The objective of this proposal is to assess the extent to which transplacental immune signaling drives fetal brain pathologies during maternal respiratory influenza virus infection. The central hypothesis is that gestational influenza causes leukocyte and lymphocyte infiltration into the placenta and enhances transplacental inflammation that is ultimately sensed by fetal microglia and border-associated macrophages, leading to subsequent fetal brain pathologies. The premise for this proposal is that influenza-driven immune signaling must cross the maternal-fetal interface before reaching the fetal brain. Surprisingly little is known about the contribution of transplacental immune signaling in directing aberrant fetal brain development during gestational influenza virus infection. This is significant because noninvasive antenatal therapies directed at curbing inflammation at the maternal-fetal interface could mitigate fetal neuroinflammation and subsequent neurodevelopmental disorders. The current proposal will use cutting-edge spatial transcriptomics to define time-dependent phenotypic and functional shifts in immune and non-immune cells at the maternal-fetal interface during maternal respiratory influenza infection. Rates of placental vascular permeability and leukocyte diapedesis as well as their dependence upon IL-6 signaling will be defined throughout mid-to-late gestation. The extent to which fetal brain microglia and macrophages shift their proliferation, activation, and colonization patterns in response to transplacental IL-6 signaling will help determine whether this cytokine could be targeted in clinical settings. Overall, these studies will illuminate transplacental immune mechanisms by which gestational influenza predisposes individuals to neurodevelopmental disorders and will provide insights for mitigating fetal neuroinflammation during related viral pandemics.

NIH Research Projects · FY 2026 · 2025-09

PROJECT SUMMARY Atherosclerosis has presented an enormous burden to public health and economy with increasing prevalence. Cholesterol and triglyceride metabolism play crucial roles in atherogenesis. Genetic, epidemiologic, and clinical studies have revealed the causal role of LDL in atherosclerosis. The development of LDL-lowering drugs, including statins and those targeting PCSK9, has greatly improved the prevention and outcome of atherosclerosis. Despite advances in controlling LDL-C levels, a considerable residual risk remains for atherosclerotic cardiovascular diseases. In recent years, increasing evidence has linked triglyceride-rich lipoproteins (TGRLs), in particular VLDL, to atherosclerosis. The liver plays a central role in regulating triglyceride and cholesterol homeostasis. VLDL is synthesized in hepatocytes and secreted to the circulation. Their metabolism involves several steps: TG synthesis, lipoprotein particle assembly, intracellular trafficking from the ER to Golgi and secretion from Golgi. Although we have gained much knowledge about the whole process over the years, the regulatory mechanisms governing VLDL lipidation and transport from the ER to Golgi have not been fully understood. VLDL secretion is closely connected to lipid droplet (LD) metabolism. Excess lipids will be stored in LDs when lipid influx or synthesis surpasses VLDL secretion, while lipids in LDs can be delivered to apoB for VLDL biogenesis. However, the regulator mechanisms governing these interconnected processes remain elusive. Our preliminary data suggested that SEC16B, a scaffold protein located at the ER, mediates both VLDL and LD metabolism in the liver. The overall goal of this proposal is to define the roles of SEC16B in VLDL and LD metabolism using a series of in vitro and in vivo experiments. Aim 1 will examine how SEC16B regulates lipid droplets formation in hepatocytes. In Aim 2, we will elucidate the function and mechanisms of action of SEC16B in VLDL metabolism in the liver. The results of this work will define the novel functions of SEC16B in VLDL and LD metabolism, and provide insight whether SEC16B could be a novel therapeutic target for atherosclerosis.

NSF Awards · FY 2025 · 2025-09

This project aims to advance how machines interpret video content by developing new capabilities for analyzing extended video streams; that is, ranging from several minutes to multiple hours, which is far beyond the short clips most current systems are designed to handle. As videos continue to dominate digital communication and information sharing, the ability to understand video over extended timescales is becoming increasingly essential. This research will support both live and recorded formats and encompass a broad spectrum of video sources, including footage from wearable, mobile, and fixed cameras. By equipping intelligent systems with the capacity to comprehend complex, time-varying visual information, the project is expected to drive progress in real-world applications such as interactive assistance, autonomous navigation, augmented reality, and content summarization. The primary technical challenge addressed by this project is the extreme data volume inherent in long video sequences, which can produce millions of representational units -- known as tokens -- when processed by modern vision-language models based on transformer architectures. This exceeds the context length limits of current models and hinders effective reasoning over long time horizons. To overcome these limitations, the project proposes a novel framework centered on token selection and context-aware representation. Instead of encoding entire video streams, the system will prioritize a small, highly informative subset of tokens that are dynamically selected based on both video content and user intent. The research plan integrates three core components: (1) a multi-resolution encoding strategy that adjusts token granularity to balance detail and efficiency; (2) a content- and intent-aware selection process that filters out redundancy while preserving relevance; and (3) a reasoning module that enables iterative exploration of video content to support long-term, multi-step analysis. Together, these contributions will deliver a scalable and adaptable foundation for long video understanding and support a range of emerging multimodal tasks, including temporal reasoning, object grounding, and open-ended question answering. 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

Material fractures present a major obstacle to safety and economic efficiency across a wide range of industries, including construction, manufacturing, transportation, and aerospace. This highlights the urgent need for new material systems that offer fracture resistance far beyond the capabilities of traditional materials. This award supports fundamental research to enable the design and creation of a new class of fracture-resistant mechanical metamaterials (MMs). These materials exhibit extraordinary mechanical properties due to their structural geometry rather than chemical composition. Despite their promise, how these MMs break and how to design them to resist fracture remain underexplored. This project will address this gap by developing a new, hierarchical approach to analyze and optimize fracture resistance in MMs across global and local scales. The research is expected to generate new insights and design principles for fracture-resistant MMs, reduce economic losses resulting from material failure, and enable the development of safer and more durable technologies. In addition, the project will contribute to national educational goals by developing interactive educational tools and training students in a multidisciplinary environment that spans mechanics, materials science, and computational design through university programs and courses. This project aims to develop a hierarchical framework for understanding and designing multi-scale MMs with enhanced fracture resistance. The key hypothesis is that local constitutive behavior can serve as an intermediary, linking local structural deformation to global fracture behavior. This conjecture decouples the complex problem into three manageable tasks: fracture of general networks, fracture of local structures, and their integration. At the global network level, the project investigates fracture behavior in homogeneous, defected, and heterogeneous networks under diverse loading conditions using theoretical and computational tools. At the local level, it explores and reveals the geometric characteristics that achieve specific nonlinear constitutive behaviors for superior fracture properties using topology optimization and data-driven generative models. These components are integrated into a design pipeline for creating multi-scale MMs with unprecedented fracture properties. The resulting framework will be validated through simulation and experiment. The outcomes will contribute to the fundamental understanding of fracture mechanics in architected materials and enable the design and fabrication of MMs with unprecedented toughness for real-world 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

With the support of the Macromolecular, Supramolecular and Nanochemistry Program in the Division of Chemistry, Professors Smith and Schleife of the University of Illinois Urbana-Champaign are studying a new approach to manufacturing nanocrystals. Nanocrystals are an important part of the next generation of electronics, visual displays, solar cells, and medical tests. However, they are expensive because they require new manufacturing procedures that are not yet scalable, and their production generates new types of waste products that are difficult to process. The new approach is based on “alkoxy reactions” which replace oil-based liquids used in traditional nanocrystal reactions with liquids that are more similar to water. As a result, methods can be used that have already been industrially scaled and do not generate complex waste products. The new approach can also be performed safely and at low cost in classrooms and educational labs. The project will focus on understanding alkoxy reactions, including how the nanocrystal attaches to molecules within the reactions that cause the products to have long-term stability, how the liquids influence the quality of the products at high reaction temperatures, and how the nanocrystal products can be joined to biological molecules for use in medical applications. Both experimental and theoretical approaches will occur in tandem in order to reach both a fundamental and applied understanding of alkoxy reactions. With success of the project, it is expected that low-cost nanocrystals will become more readily available for advanced devices and medical tests. With the support of the Macromolecular, Supramolecular and Nanochemistry Program in the Division of Chemistry, Professors Smith and Schleife of the University of Illinois Urbana-Champaign are studying a new synthetic method for diverse classes of nanocrystals. Current compound nanocrystals are normally synthesized in nonpolar alkane-based solvents with high boiling points but they require extensive processing for applications in polar solvents such as water. In a new approach, nonpolar solvents will be replaced with ones that are polar to enable immediate dispersion in both nonpolar and polar solvents including water. The primary focus is on the use of solvents and reagents with alkoxy functional groups such as ethers, esters, and alcohols. The project aims to understand (1) how ligands in these reactions bind to the nanocrystal surface to elicit long-term stability, (2) how solvent properties (dielectric constant and reactivity) determine the colloidal stability of the nanocrystals at high temperatures needed for the production of high-quality products, and (3) how chemical reactions occur on terminal functional groups of the ligands to allow covalent conjugation to biomolecules. The reactions are expected to allow scalable manufacturing using minimal unit operations to generate colloidally stable products in polar solvents. The reactions are also expected to generate benign waste products due to processing and purification in aqueous solvents. With success of the project, low-cost and scalable manufacturing of diverse nanocrystals should be more accessible to non-experts and in educational settings. 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

Over nearly two centuries, molecular synthesis has transformed the world by creating new molecular entities that have improved quality of life around the globe. This enterprise currently requires access to highly sophisticated equipment and extensive training. Established in 2020, the NSF Molecule Maker Lab Institute (MMLI) is dedicated to developing AI tools, automated workflows, and educational resources for the next generation of molecular innovators that will accelerate molecular discovery and broaden access to the expertise and sophisticated mechanics of molecular synthesis. The second phase of the institute (MMLI 2.0) focuses on the development and application of new foundational AI methods and generative AI models for molecule discovery and synthesis, as well as the dissemination of these tools to a broad research community. Synergistically, these activities are advancing the frontiers of AI with data requirements unique to molecular synthesis. MMLI2.0 further seeks to train a next-generation workforce with a unique set of skills in AI, chemistry, and robotics. This new generation of molecule makers represents the vanguard of innovators who will enable more efficient manufacturing and discovery of molecules with important functions and broaden access to the small molecule making process, achieving a powerful impact on the U.S. research community. Functional molecules play a critical role in addressing many grand challenges facing society today. However, the process of discovering and manufacturing such molecules has remained slow, expensive, and highly specialist-dependent. To address this grand challenge, the second iteration of the Molecule Maker Lab Institute (MMLI) seeks to advance AI-driven approaches such as foundational AI agents, large language models, and generative AI methods to accelerate the discovery and synthesis of functional molecules required for societal advance. Building upon the successes of MMLI1.0, the second phase of the institute (MMLI2.0) is addressing predictive challenges in catalysis, drug design, and material discovery by integrating new AI methodologies within closed-loop experimentation platforms, emphasizing human-in-the-loop systems, and making progress towards fully autonomous molecular discovery. In parallel, the MMLI 2.0 seeks to enhance workforce development and expert training in this emerging field through their integrated MATRIX program and continues to engage industrial collaboration through their partnership 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 · 2025-09

This project aims to develop and test a novel human-cognizant decision-making framework for bridge maintenance that integrates predictive modeling and institutional trust, behavioral adaptation, and participatory insight to improve infrastructure resilience and expand long-term economic opportunity. Bridge maintenance decisions have wide-reaching societal consequences – affecting public safety, job accessibility, freight mobility, and economic productivity – yet current models often overlook the human and institutional dynamics that shape those outcomes. By embedding decision-maker trust in machine learning models for bridge deterioration prediction, modeling how individuals and businesses adapt to disruptions, and incorporating stakeholder input into maintenance prioritization, this research transforms how infrastructure decisions are made. The project supports the progress of science and engineering and serves the national interest by informing infrastructure strategies that enhance safety and deliver greater economic impact for the public. Technically, the project advances four key innovations: (1) it reframes trust as a measurable design element of machine learning models by testing how model explainability, data quality, and uncertainty influence adoption by public-sector agencies; (2) it introduces modeling of behavioral adaptation among commuters and businesses in response to bridge maintenance disruptions, drawing on theories of risk, habit, and loss aversion; (3) it develops formal participatory models that assess how public input can enhance maintenance prioritization under budget constraints; and (4) it integrates these insights into an opportunity-sensitive planning framework that quantifies access and economic impacts alongside engineering performance. The research uses multimodal bridge deterioration models, stakeholder interviews and experiments, behavioral simulations, and scenario-based evaluation to test its framework. By combining civil engineering, behavioral science, and decision theory, the project advances the analytical foundations of infrastructure planning and delivers tools to support more adaptive and economically effective decisions for transportation agencies and communities nationwide. 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

It is now a well-established fact that nucleons, protons and neutrons—particles that make up the vast majority of the visible matter in the Universe—are made up of more elementary particles called quarks. The main physics program supported by this grant is to measure how quarks are distributed in nucleons. In this quark form of matter, energy and mass are traded back and forth on short timescales according to the famous E = mc2. Particularly intriguing questions regarding this essential feature of quark matter include: What is the role of anti-matter in the nucleon? What happens when the temperature inside a nucleon is increased to a high value? The goal of these experiments is to compare with theoretical predictions from the so-called Standard Model of Particle Physics, both to better understand it, as well as to look for signs of new phenomena that are not described by it. These research efforts will contribute to the education of postdocs, graduate students, and undergraduates in a broad array of skills needed in the advanced high tech workforce. The research program also includes a number of outreach activities aimed at young students and the general public. The prevailing theory of the strong force, quantum chromodynamics or QCD, is a generalization of the highly successful QED, yet we are hard-pressed to provide truly QCD-based quantitative or intuitive descriptions of nucleons. This project will focus on investigating key properties of the proton, targeting the poorly measured sea quarks and the unknown orbital motion of the quarks and of the quark-gluon plasma (QGP) which existed at the high temperatures in the early universe before the strong force confined the quarks into bound states. This fleeting state of matter has been recreated in the laboratory and found to possess extraordinary properties, such as a viscosity so low that it may be at its theoretical minimum. This grant enables continued study of the QGP’s properties using fully reconstructed jets at ATLAS and sPHENIX. The QCD research will support work on SeaQuest at Fermilab, ATLAS at CERN, and sPHENIX at Brookhaven. The ATLAS Reaction Plane Detector is calibrated using machine learning to provide the resolution needed for the physics program. Additionally, we are investigating using machine learning to identify rapidity gaps in ultra-peripheral collisions at ATLAS. This award also supports a search for a permanent electric dipole moment (EDM) of the neutron at LANL as well as UCNtau+ and the new, complementary neutron beam lifetime experiment, BL3. 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 research security project aims to protect AI workloads, supercomputing cyberinfrastructure, and embargoed research data from sophisticated threats. Recent attacks have shown that these threats can be subtle, often disguised as routine maintenance or accidental system failures, challenging security operators. Despite having subtle traces, these attacks can significantly disrupt research momentum, exfiltrate sensitive data, corrupt scientific findings, and ultimately undermine public confidence in the critical infrastructure driving the AI innovation engine. Traditionally, mitigating such evolving threats requires significant efforts to curate historical attack traces and discover out-of-distribution lateral movements. By leveraging high-performance computing for accelerated analytics, this project will develop a self-securing AI infrastructure protected by AI agents. Additionally, this project will rigorously educate students, research administrators, and scientists about insidious cyber-threats, allowing them to mitigate risks associated with future AI workloads powering research innovations. The study focuses on uncovering improper uses of resources in supercomputing cyberinfrastructure, leveraging the National Center for Supercomputing Applications (NCSA) as the main vantage point. The technical approach involves deploying a federation of AI agents to process unstructured logs, including high-speed interconnects, login hosts, and GPU nodes from a data lake such as AICyberLake. This approach provides statistical insights into utilization, job completion, energy consumption, temperatures, and graphs of scientific workflow metadata using job schedulers, e.g., SLURM or PBS. The aim is to pinpoint uncertainties and uncover new research security violations exploiting emerging technologies such as AI-driven malware and quantum-resistant cryptography communications. A product of the study will be a standardize knowledge base of stealthy attack/abuse techniques on graphics processing unit (GPU)-accelerated systems, working with the National Institute of Standards and Technology (NIST), to provide a blueprint of such activities and corresponding mitigations. Successful implementation will yield novel graph-based AI agents inference methods and provide concrete attack case studies to inform future research. The project will also contribute to course materials on research security that will be broadly applicable to other research computing centers, ultimately unleashing a trustworthy AI innovation engine. 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 Internet of things (IoT) cloud is one of the key pillars of the foundation upon which modern IoT systems rest (Smart Home, Industrial, Smart City, Retail, and Health applications, etc.). Newer IoT devices are taking advantage of the managed Platform-as-a-Service (PaaS) and Infrastructure-as-a-Service (IaaS) IoT cloud services (e.g., AWS IoT Core, Azure IoT Hub), which offload much of the security responsibilities and deployment burden from device manufacturers to the public cloud providers. IoT clouds must manage trust for hundreds of millions of IoT devices and users, and provide device manufacturers reliable and usable tools for secure IoT deployments. In the IoT cloud systems, compromised security or improper deployments can cause hazardous and deadly consequences. The outcomes of the proposed work will (1) establish the foundational scientific theory, security principles, and practices that define the field of IoT cloud security and (2) protect PaaS and IaaS IoT clouds that underlie the wide array of Smart Home, Health, Industrial, Smart City, Retail, and critical infrastructure from cyberattacks. Techniques and tools to be developed in this project will be used by IoT developers, security analysts in industry, academic researchers, and a wide range of students (system security, formal methods, and engineering). IoT cloud systems have specific challenges imposed by their requirements of large-scale distributed trust management and secure support of emerging IoT computing paradigms such as IoT interoperability, which preclude direct application of solutions devised for general-purpose systems. The project will characterize these challenges while addressing three key, novel research thrusts. The first thrust is to formalize the threats concerning the emerging paradigms of IoT interoperability to conduct novel attacks, and formally verify their security in IoT cloud systems and protocols. The second thrust is to explore and understand emerging cyberattacks leveraging misconfiguration of cloud IoT policies by device manufacturers, and develop innovative formal modeling and verification approaches to elevate security assurance of policy specification and cloud-based IoT deployments. The third thrust is informed by the first two thrusts, which identify the threats and challenges, and is to fundamentally address the threats by developing a systematized IoT-cloud security framework with a set of innovative techniques, including secure clean-slate design of IoT interoperability protocols, a novel in-device channel control framework, and hardened supply chain for IoT brokers (a core component of IoT clouds). Through these thrusts, this project will produce new foundational understanding and methods to safeguard modern and the next generation of IoT cloud 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

Measuring the mass of the supermassive black holes (SMBHs) found at the center of nearly every massive galaxy is essential to our understanding of the cosmic evolution of these exotic objects in the Universe. However, at great cosmological distances, it is extremely challenging to measure SMBH masses. A primary technique, called "reverberation mapping" (or echo mapping), is to measure the light echo from the brightness flickering of the accretion flow around the SMBH and the response of the gas cloud emission further out. When combined with the speed of these clouds, a black hole mass can be derived as these clouds are moving under the gravitational influence of the black hole. This project will measure the masses of a sample of distant SMBHs with reverberation mapping and advance our understanding of their accretion processes. The program includes related educational opportunities and public outreach activities. This project (Gemini Echo Mapping — GEM) will obtain cadenced, multi-year, optical spectroscopy from the two Gemini telescopes for a sample of twelve accreting SMBHs across a wide range of cosmological distances (from redshift of 0.2 to about redshift 2). Combining intensive monitoring data from other ground-based facilities, in particular, the multi-epoch spectra from the Sloan Digital Sky Survey, GEM is promising to enable accurate measurements of the masses of these SMBHs by resolving the detailed velocity structure of the gas clouds echoing the luminosity flickering in the vicinity of the black hole. GEM will allow accurate measurements of black hole masses and a systematic investigation of the structure and kinematics of the gas clouds around accreting SMBHs. GEM will also establish a pathway to developing robust recipes of mass estimation for SMBHs across cosmic time. 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 Chirality, the property of being non-superimposable on its mirror image, plays a pivotal role in biological systems. Homochirality, the exclusive use of one chiral form, shapes the structure and function of vital biomolecules like proteins and DNA. This uniform chirality raises profound questions about life’s nature and origins. However, current research faces challenges in bridging molecular and cellular understanding. Existing methods, while high in resolution, often lack the capacity to provide a comprehensive view at larger cellular scales. Conversely, tools designed for cellular-to-tissue analysis typically overlook finer molecular details. This gap hinders our full understanding and manipulation of chirality in complex biological contexts, highlighting the need for versatile tools capable of comprehensively exploring chirality across these varying scales. Central to this effort is the development of cutting-edge nanophotonic technologies tailored for probing and manipulating chirality. These advancements will enable high-resolution, label-free analysis of molecular and cellular chirality, potentially revolutionizing our comprehension of chirality in biological systems and impacting drug development and disease treatment. This research aims to provide groundbreaking insights into the interplay between molecular and cellular chirality, specifically how they affect cellular functions and disease mechanisms. It will tackle key questions about the fundamental connections between cellular and molecular chirality, define and measure molecular chirality at extremely low concentrations, and explore how altering cellular chirality could impact molecular- level mechanisms and cellular functions. The anticipated outcomes include the creation of novel diagnostic tools, safer chiral drugs, and non-invasive treatment methods, significantly advancing chirality research and bridging the divide between molecular intricacies and cellular complexity.

NSF Awards · FY 2025 · 2025-09

Today's technologies, including videoconferencing software, video games, and virtual reality, have become integral to education. These technologies allow individuals to represent themselves through virtual identities. However, these virtual identities can inadvertently trigger stereotype threat--a psychological state where individuals feel at risk of confirming negative stereotypes related to their social group and can underperform educationally as a result. Thus, addressing stereotype threat in virtual environments is crucial. This project aims to create computational interventions and virtual representations that reduce stereotype threat in online educational settings, thereby contributing to the development of a diverse and globally competitive workforce. The specific objectives of this research within the context of educational settings are: 1) Investigate the impact of different visual and auditory virtual identities on stereotype threat, and 2) Develop avatar customization systems that mitigate stereotype threat by leveraging insights from the first objective. Through controlled studies, this project generates new insights into several areas: 1) The effects of different visual and auditory virtual identities on stereotype threat among diverse online learners, 2) How to develop avatar customization systems aimed at reducing stereotype threat in online educational settings, 3) The effectiveness of these systems in improving performance for diverse learners, and 4) Generalizability of the findings across two distinct educational settings: gaming platforms and virtual meeting platforms. The work will be integrated into a number of courses and learning platforms, reaching a large audience of diverse learners. 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

Recent advances in data science and statistics have revolutionized how researchers uncover cause-and-effect relationships from complex, real-world data. Many pressing questions—such as whether flu vaccination reduces infection rates, whether sanitation programs improve children’s health, or whether educational policies enhance student outcomes—cannot be answered through randomized experiments alone. Observational data, while abundant, often pose serious challenges due to hidden biases, unmeasured factors, or interconnected influences among individuals. For example, a person’s risk of flu depends not only on their own vaccination status but also on whether people around them are vaccinated, while unmeasured behaviors such as health-seeking habits can distort results. This project tackles these challenges by developing advanced statistical methodologies that improve the reliability of causal conclusions. In particular, it enhances a class of techniques known as distributional balancing methods, which create fair, comparable groups across the full range of observed variables. By extending these methods to account for complex data structures and unobserved confounding, the project will equip scientists and policymakers with more trustworthy evidence for decision-making. The research outcomes will impact healthcare, education, economics, and environmental policy, while also contributing to science through open-source software, user-friendly resources, and the training of students in cutting-edge statistical methods. Technically, the project focuses on two complementary innovations. First, it develops a novel framework for distributional balancing in settings where data exhibit dependency structures, such as patients treated within hospitals, students nested within schools, or individuals connected by social networks. The proposed methodology constructs balancing weights by aligning the joint distribution of covariates between treatment groups while explicitly accounting for clustering and network effects, which pose major challenges for current balancing methods. The approach includes diagnostic procedures for assessing covariate balance under dependence and robust sensitivity analysis for evaluating the stability of causal conclusions. Second, the project introduces a new integration of instrumental variable (IV) techniques with reproducing kernel Hilbert space (RKHS)-based distributional balancing. This extension allows researchers to address unmeasured confounding by leveraging valid instruments and estimating balancing weights with respect to flexible, nonparametric distributional distances. The resulting IV-balancing methods provide both theoretical guarantees and computational efficiency, expanding the toolkit of modern causal inference. Together, these methodological advances fill critical gaps in existing frameworks, enabling robust causal analysis in complex observational studies and yielding immediate applications in healthcare policy evaluation, biomedical research, and other domains where confounding and dependency are inherent. 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

Understanding the forest understory is essential for effective forest management, biodiversity conservation, wildfire prevention, and environmental monitoring. However, traditional satellite and aerial remote sensing technologies are fundamentally limited in their ability to observe vegetation beneath the forest canopy due to occlusion and signal attenuation. As a result, critical indicators of ecosystem health, such as aboveground biomass carbon pools, combustible understory fuel loads, and other measures of biodiversity in the understory, remain largely unmeasured at scale. This project addresses this critical gap by envisioning a low-cost, scalable sensing system that uses radar and wireless communication technologies to detect and characterize the forest understory, complementing existing orbital and suborbital remote sensing systems. The project advances radar sensing and physics-aware modeling by demonstrating an IoT-powered forest observatory in real-world scenarios, and creates new educational and outreach materials, including open-source software and student training modules, to broaden the impact and accessibility of the research. The project outlines a new approach that bridges radar sensing and backscatter communication for environmental sensing via a novel channel modeling approach through vegetation and generalizable physics-aware models that characterize the effect of biomass on radar signals. The key intuition is that vegetative dielectric and moisture content will alter the RF signatures in both frequency and time domains as they penetrate through the vegetation medium. These signatures can be learned using complex physics-aware models as long as the RF reflections that carry this signature can be reliably separated, modeled, and interpreted. The project team will realize this vision through four inter-connected tasks: (i) formalizing the radar backscatter through multi-layer forests, which results in a radar forest synthesizer (ii) proposing physics-aware radar backscatter models that offer spatial characterization and mapping of forest understory biomass (iii) introducing an RF-coded tag design that can pair up with off-the-shelf radar platforms and serve as ground references for accurate understory characterization; (iv) Fully evaluating the end to end system in wide-area testbeds and demonstrating the accuracy of characterizing forest understory. 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

Geoscientific computational models are used to predict a wide range of natural processes such as weather, water supply, air pollution, eruptions, floods, and tsunamis. Progress in geo-modeling is held back by the need for geoscientists to also acquire expertise in computer science. The project will introduce a novel modeling workflow to overcome this hurdle. This project supports the development and community adoption of a symbolic equation-based modeling system. This system separates automates the numerical processing optimization so geoscientists can focus on the equations that describe natural processes. This system also advances the use of artificial intelligence in geosciences for simplifying model reduction and parameterization. The proposed work consists of three thrusts involving 1) community organization, 2) model development, and 3) de-centralized model management and education. A series of workshops at major international geoscientific meetings domestically and abroad will help define project priorities. Models will be documented on a dedicated website, run by a decentralized governance system and supported with interactive educational experiences to transition to a user-supported network for long-term growth. Geoscientific computational models simulate natural processes such as weather, water supply, and air pollution; for analyzing risks of volcanoes, floods, and tsunamis. The proposed project will introduce a new process of geoscientific model development, where model components and their interrelationships are specified as a system of equations that a compiler automatically transforms into a computer model. By separating the model design (the equations) from model implementation (the code compiler), geoscientists can focus on building equation systems that represent their areas of expertise, greatly increasing the participation in geoscientific modeling. This system will also provide an ideal base for integrating AI model reduction and parameterization into the geosciences. Project activities are divided into three thrusts. Thrust 1 will convene a series of workshops to create a shared roadmap for model development at major international geoscientific meetings domestically and abroad. Thrust 2 will expand on the types of systems that can be studied with equation-based models by implementing model components and capabilities as prioritized by project members and workshop participants. Model capabilities will be documented on a dedicated website. Thrust 3 will implement a decentralized governance system and interactive educational experiences to transition to a user-supported network of equation-based modelers. 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.