University Of Notre Dame

NSF Awards · FY 2026 · 2026-09

This project is funded through the NSF Translation to Practice (TTP) program, which supports efforts to translate research discoveries into practical tools that benefit communities, industry, and society. For the TTP program, teams advance research results toward real-world deployment and adoption. This research team creates high-performance and low-cost thermoelectric devices that can both cool things and turn heat into electricity, using a low-cost printing process. These devices are used in cooling and refrigeration for electronics, data centers, automobiles and buildings without the need for harmful refrigerants. The devices also capture wasted heat and turn it into useful electricity to improve energy efficiency and power sensors and smart devices. An innovative and scalable ink-based printing method makes these devices much cheaper and easier to produce. The team turns recent research breakthroughs into real products, by making devices that are up to ten times higher in performance and ten times less expensive than current technologies, bringing significant economic and societal impacts. This work enables sustainable energy harvesting and cooling technologies to become accessible to broad communities, thus improving resilience and quality of life. Key technical challenges in designing, printing and integrating thermoelectric materials and metal electrodes into high-performance and low-cost devices are addressed in this project. A multi-physics design and modeling framework help to realize printed devices with fully optimized composition and property distributions, dimensions and form factors. A high-throughput printing and sintering process is utilized for large-scale device manufacturing, and the metal contact processing is established to achieve both low contact resistances and high bonding strength between the metal electrodes and thermoelectric materials. Both the cooling and power generation performances of the printed devices are validated by testing the power density, efficiency, cooling temperature, and thermal and mechanical stability under various operational conditions. In addition, this project generates new knowledge on the design, printing and integration of semiconducting and metallic inks and their interface properties, which is widely applicable to the printing of a broad range of electronics, optoelectronics, and devices for energy conversion/storage. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2026 · 2026-07

The immune system plays a critical role in protecting the body from disease. However, in the context of cancer, the immune system becomes ineffective at recognizing and eliminating tumor cells. One important and often overlooked reason for this failure is that tumors pose not only biological but also physical challenges. As tumors grow, they generate crowding and pressure that physically squeeze nearby cells and disrupt how they function. With brain tumors, the skull limits how much the tissue can expand and amplifies these forces. This Faculty Early Career Development Program (CAREER) project investigates how these physical forces interfere with the ability of immune cells to find and destroy cancer cells, ultimately allowing tumors to grow unchecked. By uncovering how mechanical forces disrupt the immune system, this work addresses a fundamental gap in understanding how biology and physics interact in human health. The outcomes have broad implications for improving our understanding of cancer and immunity, guiding the design of new therapies, and advancing national priorities in biotechnology, health, and biomedical innovation. The project also includes integrated educational and training programs that engage students across multiple levels, providing research experiences and fostering multidisciplinary skills. These efforts will help prepare a capable workforce to address complex biomedical challenges. This research will establish fundamental principles of how compressive mechanical forces regulate immune cell fate and function in the tumor microenvironment. The central hypothesis is that growth-induced compressive forces directly impair the ability of anti-tumor immune cells to recognize and eliminate cancer cells. To test this, the project will combine engineered model systems spanning multiple scales, including cellular systems, organotypic brain models, and animal models of disease. Mechanical compression mimicking tumor growth will be precisely applied while immune cell function is evaluated through measurements of antigen presentation and recognition, cytotoxic function, and inflammatory signaling. Advanced imaging and molecular profiling techniques, including single-cell and spatial analyses, will be used to quantify changes in cell behavior and gene expression, and data-driven and machine learning approaches will integrate these datasets to identify mechanical mechanisms. By linking mechanical forces to immune dysfunction, this work will generate new mechanistic insights into immunomechanics and how tissue-scale forces influence immune cell behavior. In parallel, this project integrates research and education by developing multidisciplinary training programs, research experiences, and outreach activities that will expand participation in engineering and biomedical sciences. These advances will contribute foundational knowledge to biomechanics and mechanobiology and support national priorities in biotechnology, health, and workforce development. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2026 · 2026-06

This project presents a groundbreaking approach to developing a large-scale terahertz (THz) reconfigurable reflectarray (an antenna focusing beams like a parabolic dish antenna but with an array of unit cells on a flat surface) for advanced beam steering capability in next-generation wireless communication networks. The approach can potentially revolutionize how data is transmitted and received, enabling faster, more reliable, and adaptive wireless networks that can meet the fast-growing demands of a digitally connected society. By using innovative optical control methods, the project aims to eliminate traditional limitations in high-frequency communication systems, such as signal loss and integration complexity. The societal and national benefits are substantial: improved wireless infrastructure can support smart cities, enhance connectivity in rural and underserved areas, and drive economic growth through new technology platforms. Beyond communications, the research has broad impacts across multiple scientific and engineering fields by offering new tools for medical imaging, security screening, and chemical/biological sensing, enabling new discoveries and new applications. The project also contributes to education and outreach by integrating its research findings into university courses, involving students at all levels in research, and promoting STEM engagement in local schools, thereby fostering the next generation of innovators and engineers. The research of this project aims to investigate and demonstrate a novel method for achieving extremely large-scale THz reflectarrays using photonically-driven unit cells based on enhanced spatially resolved photoconductivity modulation. The innovative approach utilizes a closely coupled micro-LED array to modulate the phase of each unit cell in a hybrid Au-Ge mesa-array semiconductor structure. By adjusting computer-generated light patterns, the system enables pseudo-continuous phase modulation across a full 360-degree range, allowing for real-time synthesis of arbitrary two-dimensional phase profiles to control reflected THz beams. This eliminates the need of electrical wires for biasing or control, mitigating parasitic effects and enabling highly scalable, dense array implementations. The project scope includes device-level design, fabrication, and characterization, as well as system/network-level analysis, simulations, and prototype demonstrations of adaptive high-speed THz wireless links. The approach overcomes limitations of conventional methods, which have been constrained by signal losses and design complexity at frequencies above 100 GHz, and enables advanced functionalities such as beam bending, curving, and multiple-input multiple-output (MIMO) operation. The international collaboration team consists of experts in semiconductor physics, THz technology, electromagnetics, antenna design, and wireless communications to carry out the research tasks and advance knowledge in both semiconductors and wireless technologies. This project was submitted under the United States-Ireland-Northern Ireland R&D Partnership and is a collaboration between researchers at the University of Notre Dame, University of Massachusetts Lowell, Tyndall National Institute, and Queen's University Belfast. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2026 · 2026-06

Non-technical Description: This project involves the acquisition of a modern helium recovery system to enable new quantum and physical science research at the University of Notre Dame. Modern research in physics and chemistry increasingly relies on helium, a scarce element required to reach the extremely low temperatures needed for many advanced scientific instruments. At the University of Notre Dame, a growing number of experiments in condensed matter physics, quantum science, nuclear astrophysics, chemistry, and biochemistry depend on liquid helium or helium gas. However, global helium shortages and rising costs threaten the sustainability of these research activities when helium is not recycled. The modern helium recovery system in this project will capture, purify, and reuse helium that would otherwise be lost. The project will establish a reliable and sustainable helium supply for the local research community and for external users of scientific facilities on the campus. By conserving helium and reducing operational costs, the project will support pioneering research while maximizing the effectiveness of federal investments. The system will also enhance education, workforce training, and outreach by enabling hands-on research opportunities and demonstrations involving low-temperatures. Technical Description: This project will acquire a modern helium recovery system for centralized helium recycling at the University of Notre Dame to enable new quantum and physical science research. The system will collect helium boil-off gas from distributed cryogenic instruments, purify and compress the recovered gas for re-liquefaction and reuse. The system will serve multiple individual research groups in the physics and chemistry departments and two core facilities including the Nuclear Science Laboratory and Magnetic Resonance Research Center on the campus. The recovery system will enable multiple research projects including the synthesis and characterization of superconducting, topological, magnetic, and low dimensional quantum materials and heterostructures, development of new scanning probe microscopy techniques, exploration of nucleosynthesis and physics beyond the standard model, and nuclear magnetic resonance studies of chemical materials and functional dynamics of molecules. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2026 · 2026-06

Project Summary Mycobacteria have hydrophobic cell envelopes which slow growth in liquid culture. Non-ionic detergents are included in the media to reduce clumping and promote growth in suspension. Tyloxapol is a non-ionic detergent that is used to support mycobacterial growth for metabolism and physiology studies. There is a robust classic literature supporting that tyloxapol is protective during M. tuberculosis infection in animal models. It was proposed more than 40 years ago that tyloxapol inhibits the interaction between M. tuberculosis and the macrophage phagosomal membrane. The mechanism underlying this inhibition is a gap in the field. The applicant’s long-term goal is to define the molecular mechanisms underlying mycobacterial disease. The ESX-1 secretion system is essential for mycobacterial infection because it lyses the phagosomal membrane, promoting mycobacterial cell to cell spread. The objective of this application is to determine how tyloxapol impacts protein secretion and ESX- 1-mediated lytic activity. The applicant’s preliminary data shows that growth in sub-critical micellar concentrations of tyloxapol inhibits ESX-1-dependent hemolytic activity of Mycobacterium marinum in a dose dependent man- ner. M. marinum is a non-tubercular mycobacterial species that is an accepted model for studying M. tuberculo- sis. EspE is a secreted ESX-1 substrate that is essential for hemolysis and phagosomal escape during infection. In the presence of low levels of tyloxapol, espE transcript levels are significantly reduced, resulting in less EspE production and a loss of secretion from M. marinum. These preliminary data and the literature supports the central hypothesis that tyloxapol signals changes to mycobacterial gene expression and secretion, impacting physiology and pathogenesis. To test this hypothesis, the following specific aims will be addressed. Aim 1 will define the mechanism of tyloxapol sensing by M. marinum. Unbiased genetic approaches will be coupled with hemolysis assays to identify a molecular pathway that promotes sensing and responding to tyloxapol. The impact of tyloxapol on ESX-1 activity during macrophage infection will be tested. Aim 2 will discover how tyloxapol impacts mycobacterial protein secretion. The approach will include quantitative proteomics followed by an anal- ysis of transcript levels using qRT-PCR and promoter fusions. It is expected that the successful completion of the proposed aims will reveal a signal transduction pathway that regulates ESX-1 protein secretion in response to tyloxapol and will determine how tyloxapol affects ESX-1 activity during infection and in the laboratory. The idea that sub-critical micellar concentrations of tyloxapol could specifically regulate ESX-1 gene expression and secretion, impacting pathogenesis is conceptually innovative. These contributions will be significant because they will inform how the field interprets fundamental data generated from in vitro growth of mycobacterial species, reveal tyloxapol as a useful tool to study the fundamental biology of ESX secretion systems and provide insight into the protective properties of tyloxapol against mycobacterial infection, positively impacting the field.

NSF Awards · FY 2026 · 2026-06

This award provides travel support for participants in the international conference "Singularities in Topology and Physics" to be held at the University of Notre Dame, Indiana, during August 3-7, 2026. This will be a satellite event of the 2026 International Congress of Mathematicians. The conference is designed to highlight the many ways singularities arise in topology, geometry, and mathematical physics and will bring together researchers who approach singularities from complementary perspectives. Alongside presentations of current research, the meeting aims to initiate conversations among diverse research groups and to seed new collaborations. The event will give junior researchers, postdoctoral scholars, and graduate students exposure to a broad range of ideas and will provide them with an opportunity to build connections among each other and with senior experts. Driven by recent fundamental advances, the program will focus on the following themes: (1) codimension-two embedding theory; (2) characteristic classes of singular varieties; (3) singularities in mathematical physics; and (4) stratified spaces in computational topology. These topics span a wide range of research areas, from pure mathematics to more applied domains, and the participant base will reflect this breadth. In recent decades, the foundational tools used to study singular spaces have expanded across numerous areas of mathematics, making it increasingly important to create opportunities such as this conference for interaction among researchers working in these related but distinct specializations. Additional details about the program can be found at: https://people.math.wisc.edu/~lmaxim/SingTopPhy.html This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2026 · 2026-06

Lymph nodes are vital components of the immune and lymphatic systems, playing a key role in filtering lymphatic fluid, facilitating immune cell transfer, and regulating protein content in lymph. This project aims to develop a novel mathematical model and computational framework for lymph node dynamics, capturing both lymph flow and the elastic deformation of the node, which remains largely unexplored in current models. By quantifying key parameters and processes governing these dynamics under various physiological conditions, this project will provide deeper insights into the basic function of lymph nodes. The project also supports educational development by training students who will work in a multidisciplinary environment, gaining intensive training in mathematical modeling, numerical analysis, and scientific computing, as well as developing a deep understanding of biological concepts. An outreach activity at a local K-8 school is planned, where topics related to this research will be used in hands-on workshops designed to present mathematics, modeling, and coding as exciting and accessible topics. This project addresses a critical gap in lymph node modeling by developing a multiphysics, moving-domain computational framework that couples lymph flow with the elastic deformation of the node. The model captures the interaction between an incompressible viscous fluid (lymph), a thin elastic membrane (capsule), and a poroelastic structure (inner compartments), taking into account both linear and nonlinear formulations. A novel partitioned numerical scheme based on the Robin interface conditions will be developed and rigorously analyzed for stability, convergence, and second-order accuracy. The method will be implemented in the high-performance programming language Julia and validated against published experimental data under physiological and pathological conditions. A parameter sensitivity study will be performed to identify critical model parameters and to guide estimation of coefficients that are not directly measurable. The framework will be further applied to modeling lymphadenopathy, investigating how dynamic changes in porosity, elasticity, and fluid density during immune activation alter intranodal flow patterns and lymph-blood fluid exchange. From the standpoint of biotechnology, the project will integrate an advection-diffusion transport model to study macromolecule transport through the conduit network under normal and inflammatory conditions. This novel computational framework can be integrated with in vitro models as an alternative method to validate cell therapies and drug screening. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2026 · 2026-06

This award will provide support for junior participants to attend the two-week thematic program "Algebraic Combinatorics and Applications" held at the Center for Mathematics at Notre Dame from June 29 to July 10, 2026. The first week consists of two summer schools held in parallel: one for undergraduate students, and another for graduate students and postdoctoral researchers. Attendees will participate in mini-courses by well-established researchers, including both lectures and problem sessions where they will have the opportunity to start new collaborations. It will also feature mentorship and social events. The second week consists of an international research conference presenting some of the latest advances in algebraic combinatorics, some of which are inspired by AI. The field of algebraic combinatorics applies combinatorial techniques to concretely understand and illuminate phenomena in abstract algebra. It is a rapidly developing field with broad applications. The thematic program will feature some of the most exciting and cutting-edge developments, including positive geometries, cluster algebras, webs, Coxeter groups, and Schubert calculus, with applications to theoretical physics, integrable systems, and representation theory. The event's website is at https://sites.nd.edu/cmnd2026-thematic-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 2026 · 2026-06

Understanding the earliest chemical reactions and molecules involved in the first living cells is a long standing question in biology. Early life is thought to have used RNA as its genetic material as well as its enzymes. Therefore, it is important to understand how RNA molecules could have facilitated the emergence of the first self-replicating cells. This project addresses this fundamental question by building simplified models of primitive cells, or protocells, in the laboratory. These protocells contain enzymes made of RNA that can help them grow and divide, and even compete with other protocells. This project will advance the understanding of the chemical events that led to the emergence of the first living cells. This research will also lead to the creation of self-replicating synthetic cells which could be used to manufacture sustainable materials, therapeutics, and other goods. In parallel, the project will also support workforce development through training of students and engage the public through an integrated education and outreach program that connects early life research with science communication and art. These efforts include science communication through a popular science blog, a series of popular science writing workshops, a new course on public engagement in science, and the creation of a full-length graphic novel on the early life. Together, these activities will reach local and global communities to advance scientific discovery, inspire public curiosity, and cultivate the next generation of scientists and science communicators. This project advances NSF’s priorities in Biotechnology and Advanced Materials and Manufacturing. The project aims to construct model protocells that exhibit life-like properties such as growth, division, competition, and ultimately Darwinian evolution – a defining feature of life. Because early life is thought to have been powered largely by catalytic RNA molecules, or ribozymes, this project will use in vitro evolution to generate a suite of ribozymes that provide evolutionary benefit to the protocells that contain them. First, ribozymes will be evolved to copy RNA sequences inside prebiotic lipid protocells. This will establish a protocellular system capable of sustaining ribozyme-catalyzed RNA replication using building blocks that enter from the extracellular environment. Second, ribozymes will be evolved that stabilize protocell membranes, testing whether improved protocell survival due to membrane stabilization can drive their competitive growth and division. Third, this study will examine how RNA evolution is influenced by the RNA’s immediate microenvironment, including variations in UV exposure, temperature, and pH, and the presence of minerals, molecular crowding, and confinement – conditions relevant to life on the early Earth. The expected outcome is a set of experimentally grounded models for how Darwinian evolution could have emerged in primitive cell-like chemical systems, along with new insights into RNA function, RNA evolution, and the physicochemical conditions that shaped early life. 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 Staphylococcus aureus, designated as a serious public health threat, causes >323,000 hospitalizations and kills >10,000 individuals annually in the United States alone. A number of antibiotic-resistant variants of S. aureus—the variants referred to as methicillin-resistant S. aureus (MRSA), vancomycin-intermediate S. aureus (VISA), and vancomycin-resistant S. aureus (VRSA)—are among the most serious bacterial pathogens. This grant application targets this nefarious pathogen for intervention by small molecules. We disclose that the integral-membrane protein BlaR is a suitable target for inhibition by small molecules, whereupon the b-lactam- resistant bacterium becomes sensitive to b-lactam antibiotics. The BlaR protein is a b-lactam sensor/signal transducer protein that serves as a sentinel for detection of b-lactam antibiotics in the growth medium. Once the sensing of the antibiotic takes place, signal transduction across the membrane initiates cytoplasmic events that culminate in derepression of the antibiotic-resistance gene, resulting in full-blown resistance response to b-lactam antibiotics. We document for the first time that a class of designer boronates covalently modify the sensor domain of BlaR. Compound 15, an advance lead, potentiates by 16- to 4096-fold the activity of oxacillin and of meropenem against a broad range of strains of MRSA (and variants) of distinct clonal origins, lowering the minimal-inhibitory concentrations (MICs) of the antibiotics to below their clinical breakpoint MICs. The combination of 15 with oxacillin or meropenem shows efficacy in MRSA-infected mice, validating the strategy. The grant application proposes to optimize the boron-based adjuvants imparted it with drug-like attributes both as a mechanistic tool in studies of S. aureus as well as a potential therapeutic agent (Specific Aims 1 and 2). Furthermore, we will explore how resistance to these agents could emerge (Specific Aim 3)

NIH Research Projects · FY 2026 · 2026-05

Abstract Infections caused by Clostridioides difficile (previously known as Clostridium difficile) are urgent threats resulting in more than 7-fold deaths in the United States than the other four urgent bacterial threats combined. The normal gut flora prevents colonization by C. difficile. However, usage of broad-spectrum antibiotics disrupts the gut microflora, allowing for C. difficile colonization. C. difficile spores germinate in the presence of host bile acids to vegetative cells, which produce toxins that damage gut mucosa, promoting inflammation and diarrhea. The vegetative cells produce more spores that are either shed/eliminated in feces or germinate to vegetative cells to start another cycle of infection. About 25% of patients have recurrent C. difficile infection. Spores can remain dormant for months and are not affected by antibiotics. Understanding spore germination is key to halting recurrent infections, which are the cause of significant morbidity and mortality. There are no antibiotics in the clinic or in development that inhibit spore germination. We have discovered a class of antibiotics called the oxadiazoles that kill C. difficile vegetative cells and inhibit spore germination. We showed that the target in spores is SleC, a lytic transglycosylase that turns over the spore cell wall for the onset of spore germination. We propose to elucidate the machinery of spore germination in Specific Aim 1, which will be the target of inhibition for prevention of spore germination. In addition, we propose in Specific Aim 2 a lead-optimization plan for the oxadiazoles for the discovery of pre-clinical candidates of spore germination inhibitors with improved attributes that include desirable safety profile, pharmacokinetic properties, in vitro activity, selectivity, and efficacy.

NSF Awards · FY 2026 · 2026-05

Tiny particles such as microplastics, pathogens, and other contaminants are increasingly found in groundwater, raising concerns for drinking water quality and ecosystem health. Predicting how these particles move underground remains a major scientific challenge because existing models do not accurately represent how particles behave in natural soils. This project seeks to improve understanding of how very small particles travel through soil and groundwater, which is essential for protecting water resources and improving cleanup strategies. The research will also support education, workforce development, and public awareness. By improving the ability to predict how contaminants spread, this project advances the national interest in safeguarding water quality, public health, and environmental sustainability. This project investigates how nanoscale surface features and repeated particle–surface interactions control particle attachment and transport in porous media. The research integrates laboratory experiments, surface characterization, and modeling to quantify how particles attach after multiple encounters with sediment grains. A new predictive framework will be developed that incorporates nanoscale attraction and interception history into transport models. The work combines theory, simulations, and machine learning to improve prediction of particle retention and mobility across scales. 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

SUMMARY Advances in immunology are revolutionizing medicine. The last 10-20 years have seen the rapid development of a range of transformative immunotherapies, with numerous others on the horizon, including new precision and personalized therapies for cancer and infectious disease. Advances in immunology are also powering new ways to address health conditions that emerge from immune dysfunction or dysregulation. Many immunotherapies center on the cellular arm of the adaptive immune system. In cellular immunity, T cells use their αβ T cell receptors (TCRs) to recognize small peptides (or “epitopes”) bound and presented by class I or class II major histocompatibility complex (MHC) proteins. While specificity is a hallmark of T cell recognition, TCRs are also broadly cross-reactive. The biological imperative for TCR cross-reactivity results from the limited size of an individual’s T cell repertoire compared to the vastly larger number of potential epitopes, as well as the need for T cells to recognize self for positive selection and homeostasis. However, TCR cross-reactivity also poses substantial risks for new and existing immunotherapies and leads to autoimmunity and cellular rejection of transplanted organs. TCR cross-reactivity can almost always be ascribed to the concept of molecular mimicry, where cross-recognized peptides share key structural and physical features. However, mimicry is frequently obscured behind structural and physicochemical complexities. These complexities have generally prevented the prospective identification of mimics of T cell epitopes, greatly complicating derisking and pre-clinical testing in immunotherapy and hindering our ability to address the underlying immunology of T cell driven autoimmunity and transplant rejection. Indeed, molecular mimics have traditionally only been identified and understood after cross-recognition – often in the form of a clinical presentation or complication – is observed. To address this major limitation in translational immunology and advance precision immunotherapy, we have begun developing a technology platform that, beginning with a known target epitope and its presenting MHC protein, uses data science, AI-driven 3D structural modeling, and structure-based scoring that incorporates advances in deep learning and structural analysis to prospectively identify molecular mimics within genetic databases. Our platform, termed MimicMaker, directly addresses the complexities of cross-reactivity in T cell recognition. It leverages our decades of experience in structural immunology, protein biophysics, and immunoinformatics. In two iterative Specific Aims, we will develop and refine MimicMaker, generating and using new data from our mouse model of virus-accelerated transplant rejection. Collaborations with industry and academia are in place to help test, optimize, and validate the platform and, with success, facilitate its adoption and eventual commercialization to improve outcomes in areas such as oncology, autoimmunity, and transplantation.

NSF Awards · FY 2026 · 2026-05

This project supports long-term research in rangeland ecosystem to better understand the relationships between livestock, wildlife, and other stressors impacting ecosystem resilience. Researchers will study the impact of multiple factors on competition and coexistence of livestock with wildlife, and the stability and resilience in a savanna rangeland community in the face of drought, fire, and other environmental stressors. This research provides a unique and essential baseline for the conservation, management, and restoration of rangelands including those in the United States, which lost most of its large herbivores more than 10,000 years ago, but where efforts are underway to reintroduce species similar to those lost. This project will fosters the career development of a strong research team of early career researchers and graduate students and outreach to stakeholders. The use of molecular techniques and remote sensing technology to evaluate the impact of herbivory, drought, and fertilization will improve rangeland management practices from targeted approaches to the landscape scale. This proposal is to support years 31-35 of the Kenya Long-term Exclosure Experiment (KLEE), a controlled replicated experiment examining the separate and combined effects of livestock, wildlife, and fire on each other and on their shared savanna landscape. Although it is becoming increasingly clear that loss of native fauna (“defaunation”) can have far-reaching effects on ecosystems, experimental studies to evaluate these effects remain rare. KLEE uses semi-permeable barriers to create six replicated treatments comprised of different combinations of 1) cattle, 2) meso-herbivore wildlife, and 3) mega-herbivores (elephants and giraffes). This project provides a unique opportunity to understand how interactions between defaunation and multiple pulse and press disturbances affect ecosystem stability and function. After 30 years, the six herbivore treatments support distinct (but still diverging) plant communities, providing powerful opportunities to 1) analyze long-term data in the context of community and ecosystem resilience and stability, and 2) analyze new experimental layers and additional response variables that, along with our previous core long-term data, allow us to assess community resilience under multiple disturbance stressors, including herbivory (three guilds), drought, fire, fertilization, heavy grazing, and termites. The project will continue to add to and explore this rich data set. The decadal proposal also included an ambitious plan to implement experimental reversals of several KLEE treatments in the second five years to test dynamics related to the efficacy of rewilding, the reversibility of rangeland degradation, and the stability of alternative ecological states in general. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2026 · 2026-05

The program, Integrative Cell and Molecular Biology, will immerse undergraduate students in hands-on research that explores how life works across many levels, from molecules and cells to whole organisms and their environments. This award is important because many of today’s biggest questions in biology require scientists to connect ideas and methods from different fields rather than study one level of life in isolation. By bringing students into an interdisciplinary research community, the program will help prepare the next generation of scientists for careers in research, biotechnology, health, and education. It will also expand access to research experiences for students from across the nation, especially those with limited opportunities at their home institutions. Participants will work closely with faculty mentors, develop research questions, carry out experiments, analyze results, and present their findings to the campus community. Weekly seminars and workshops will strengthen scientific communication, professional skills, and understanding of research careers. The program also encourages engagement with the broader community by providing opportunities for participants to mentor local K–12 students in science activities. Assessment of the program includes participant surveys, mentor evaluations, and long-term tracking of student educational and career outcomes. Students apply to the REU site using NSF ETAP (Education and Training Application: https://etap.nsf.gov). The training students will receive is aligned with the NSF priorities in Quantum Information Science and Biotechnology. The scientific focus of the program is integrative cell and molecular biology, with research opportunities centered in the Department of Biological Sciences and involving faculty from genetics, cell biology, physiology, neuroscience, evolution, ecology, biochemistry, and engineering. The program emphasizes comparative and interdisciplinary approaches that link molecular mechanisms to cellular function, organismal biology, and broader biological systems. Each student will work with a primary mentor and a secondary mentor from a collaborating field to encourage intellectual integration across disciplines. Example projects may examine gene regulation, cell signaling, neural function, metabolism, host-microbe interactions, evolutionary adaptation, or other fundamental biological processes using modern molecular, cellular, imaging, and computational methods. Professional development will include laboratory group meetings, scientific writing, the publication process, graduate school preparation, and training in the responsible and ethical conduct of research. Program assessment will evaluate student learning, mentor feedback, research progress, and longer-term persistence in STEM education and research careers. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2026 · 2026-04

The 11th Lake Michigan Workshop on Combinatorics and Graph Theory will be held at the University of Notre Dame on May 2-3 2026. The workshop will benefit graduate students and junior researchers in multiple subfields of discrete mathematics, working at institutions in the Great Lakes area and beyond. The workshop is built around three sets of two tutorial lectures, focusing on state-of-the-art techniques and results on which the speakers are particularly qualified to expound. There will also be short talks by students and early-career researchers. There will be ample unscheduled time during the weekend, allowing new research collaborations to commence and active collaborations to be continued. Junior participants will establish valuable connections with more senior colleagues and receive guidance from them in a relaxed and informal environment. Combinatorics and graph theory are two very active areas of research within the broader field of discrete mathematics, with important ties to disciplines such as statistical physics, probability theory, and computer science. The tutorial speakers for the 2026 workshop are confirmed to be Igor Pak (University of California, Los Angeles), Lutz Warnke (University of California, San Diego), and Fan Wei (Duke University). The tutorial speakers were selected with the goal of representing disparate subfields of combinatorics and graph theory, with Pak as an expert in algebraic combinatorics, Warnke an expert in probabilistic combinatorics, and Wei an expert in extremal combinatorics. The website for the workshop is https://sites.nd.edu/lmcgt/. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2026 · 2026-04

PROJECT SUMMARY Sand flies are the vectors of several disease pathogens that infect humans, the most devastating being leishmaniasis. Leishmaniasis, a neglected tropical disease, is a serious public health concern, particularly in resource-poor settings, and is endemic in over 99 countries both in the Old and New World, affecting more than 12 million people globally. In Asia, Africa, and Europe, leishmaniasis is spread by sand flies of the genus Phlebotomus, and in the Americas, the genus Lutzomyia. Treatments for leishmaniasis are either highly toxic, require long intravenous treatment regiments, or are expensive, and drug resistance is emerging. With no vaccine currently available to protect humans, the disease vectors pose a significant threat to 310 million of people worldwide. The ecological niche and natural breeding sites of sand flies are diverse, ranging from sylvatic landscapes in forests to urban areas that include the peridomestic environments. The increasing close human–sand fly associations are giving rise to greater risks of Leishmania transmission in the home environment; therefore, there is an urgent need to control the vectors during the immature and adult stages to reduce risk of disease transmission. Although sand fly chemical-based control is a key component of leishmaniasis control, the effectiveness and success of this intervention is limited due to insecticide resistance and non-target effects. Our research program has successfully demonstrated yeast RNAi-based insecticides as innovative, environmentally safe and highly effective for direct control of vector mosquitoes. Although both mosquitoes and sand flies are important blood- sucking vectors of human pathogens occurring in the peridomestic environment, the innovative new yeast insecticides have not yet been assessed in leishmaniasis vectors. The objective of the proposed research is to evaluate the potential for RNAi yeast-based technologies to facilitate the control of sand flies. The proposed investigation will test the hypothesis, which is supported by strong preliminary data, that interfering RNA produced and delivered in Saccharomyces cerevisiae can be utilized as insecticidal agents targeting sand flies. The specific aims are to 1) identify RNAi yeast formulations that can be used to target sand flies, 2) evaluate RNAi-based yeast attractive targeted sugar baits (ATSBs) for the control of adult sand flies, and 3) develop and assess sand fly female-specific RNAi yeast larvicides that can facilitate male sex-sorting. The identification of female-specific larvicides will permit the design of an RNAi yeast-based mass rearing diet that can facilitate production of fit adult males that can be deployed for mass release of sterile males to suppress sand fly populations in targeted areas of disease endemic countries. The aims will be pursued through laboratory and simulated field trials that are designed to achieve the expected outcome of identifying a new class of RNAi yeast-based insecticides to facilitate sand fly control for the reduction of leishmaniasis transmission.

NSF Awards · FY 2026 · 2026-04

This award will establish a device-ready wafer-based platform enabled by the controlled formation of large-area ultrathin crystals of gold. The work is motivated by an application space that is thriving in terms of state-of-the-art device demonstrations in nanophotonics and biosensing despite the use of ultrathin gold derived from tedious synthetic procedures and suboptimal nanofabrication processing techniques. Using a newly discovered gold microplate synthesis capable of realizing ultrathin gold at addressable positions, the research aims to fundamentally transform existing techniques with emphasis placed on the low-cost, high-throughput, and scalability needs of a manufacturing setting. The so-formed device platform will be validated through the fabrication and testing of first-of-their-kind digital biosensors. In doing so, the project will elevate the status of ultrathin crystalline gold as a technologically viable, high-performance nanomaterial. This research will also realize societal benefits through the training of graduate students, involving undergraduates in research projects focused on advanced manufacturing, and engaging K-12 students in activities designed to promote interest in STEM-related fields. Proof-of-principle device demonstrations in plasmonic and electroplasmonic circuitry, biosensing, metamaterials, nanolasers, and nanoparticle-on-mirror optical devices have definitively shown that single-crystal gold microplates are unrivaled in terms of the properties they offer and the device processing characteristics they enable. These advancements are, however, reliant on highly undesirable synthetic processes and, as such, provide no pathway for translating research excitement into a viable technology. This research aims to disrupt the status quo by (i) forwarding a new synthetic strategy that addresses the need for the precise placement of microplates on substrates surfaces at high yield in a manner that is amenable to batch processing, (ii) advancing the post-synthesis processing capabilities needed for scalable nanofabrication, and (iii) integrating these materials and processes into application-driven device platforms. Specific outcomes that will be targeted include (i) scaling microplate array fabrication to two-inch wafer sizes, (ii) establishing the first large-area processing route for nanoparticle-on-mirror configurations where the nanoparticles are arranged in periodic arrays with Rayleigh criterion separation, and (iii) demonstrating the first single-crystal microplate platform for digital biosensing with multiplexing capabilities. These new concepts, if successfully implemented, could provide a paradigm shift in the on-chip synthesis, processing, and application of ultrathin single-crystal gold. 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

Abstract With the progress in vascular bioengineering, three-dimensional (3D) bioprinting has emerged as a potential approach for generating physiologically relevant blood vessels with the ability to grow, remodel, and repair vascular structures in vitro and in vivo. Different 3D bioprinting techniques and strategies have been investigated and established over the past years to fabricate biomimetic blood vessels and are constantly being refined and improved. To this end, sacrificial bioprinting technologies developed in the past few years have provided a convenient solution for this problem due to its capability to fabricate interconnected microchannels of arbitrary geometries and connectivity. However, although sacrificial bioprinting provides a versatile method to create vessel-like structures, there are two major obstacles preventing its further growth, i.e., the patterns attainable are relatively simple, and the resolutions attainable for the perfusable vessels are insufficient in most cases, both due to the extrusion bioprinting method that has been exclusively utilized in sacrificial bioprinting so far. We accordingly propose to develop an innovative hybrid multi-material and multi- method autonomous printing (HM2AP) technology integrating aerosol jet sacrificial printing and extrusion bioprinting, which enables facile construction of 3D complex and hierarchical vascular structures with feature sizes ranging from 10-100 µm embedded within tissue microenvironments. Successful completion of this project will build a paradigm-shifting bioprinting technology using which complex and hierarchical vessels of varying dimensions can be readily generated.

NSF Awards · FY 2026 · 2026-02

The Midwest Numerical Analysis Day 2026 is a regional conference held at the University of Notre Dame that brings together researchers in numerical analysis and scientific computing, with an emphasis on early-career mathematicians from institutions across the Midwest. Numerical analysis underpins modern advances in science, engineering, and technology, including artificial intelligence, energy modeling, and large-scale scientific simulations that are important to many areas of research and industry. By providing an accessible and affordable forum for sharing ideas, the conference strengthens the research community, promotes collaboration, and supports the development of the next generation of computational scientists. Targeted support for students, postdoctoral researchers, and junior faculty broadens participation and contributes to a strong national research workforce. The project organizes a two-day meeting consisting of plenary lectures, minisymposia, contributed talks, poster presentations, and a panel discussion focused on current research in numerical analysis and scientific computing. Technical areas include finite element and high-order methods, computational fluid dynamics, machine learning for partial differential equations, model reduction, and inverse problems. Plenary lectures by leading researchers highlight recent advances and emerging challenges, while specialized sessions facilitate focused technical exchange. The conference design emphasizes participation by early-career researchers and encourages interdisciplinary interaction, supporting knowledge transfer and the development of new research collaborations within the numerical analysis community. 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 · 2025-12

Among all shapes that enclose a given volume, which ones have the smallest possible surface area? This classical question, known as the isoperimetric problem, dates back to ancient Greece and appears naturally in phenomena such as soap bubbles, crystals, and models of the universe in physics. This project aims to deepen our understanding of how geometric properties of a space -- especially curvature -- influence solutions to geometric optimization problems. The broader impact of the project includes mentoring undergraduate and graduate students and organizing seminars to engage the local mathematical community and foster the development of young researchers. The project uses tools from Geometric Measure Theory to study the isoperimetric structure of spaces with geometric constraints. A central theme is the development of a theory for spaces with curvature bounded below in a spectral sense, with applications to classical problems like the classification of stable minimal surfaces in Euclidean space. Another major goal is to analyze the existence and uniqueness of isoperimetric sets in nonnegatively curved spaces, including specific cases like the Euclidean unit cube and manifolds with nonnegative scalar curvature -- a setting that is also relevant in Mathematical Relativity. The project also addresses problems at the intersection of Algebra, Analysis, and Geometry, including the rectifiability of metric spaces with the same tangents almost everywhere, and the quasi-isometric classification of nilpotent groups. 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

Today, society faces critical issues of poverty-driven food insecurity and the opioid epidemic: in 2023, 36.8 million Americans lived in poverty, 47.4 million experienced food insecurity, and 8.9 million misused opioids. As these crises operate synergistically, addressing poverty-driven food insecurity while mitigating opioid-related harms presents a pressing and complex societal challenge. Although efforts have been made to tackle these issues separately and to explore their interconnections, research on developing effective, integrated interventions tailored to affected populations is lacking. To bridge this gap, by harnessing the big data revolution and advancing artificial intelligence (AI) technologies, the goal of this project is to design and develop a data-driven, AI-augmented paradigm to investigate the intersection of poverty-driven food insecurity and the opioid crisis and develop integrated, personalized interventions for affected individuals to address the intertwined challenge, and thus help enhance national public health, safety, and welfare. The project outcomes (e.g., open-source code, benchmark data, models, and findings) will be made publicly accessible and broadly distributed through demos, publications, and media presses, etc. This project will integrate research with education, including novel curriculum development, student mentoring, professional training and workforce development, and K-12 outreach activities. Tackling the nexus of poverty-driven food insecurity and the opioid crisis is an urgent societal priority. To achieve this goal, this project consists of three coherent research objectives. First, although the U.S. food assistance system (with 211 food banks and 26,000 pantries) serves millions, the specific distributed foods and their nutritional value remain unclear. To address this, the team will develop an adaptive multi-agent framework powered by large language models (LLMs) to automate analysis of free food supplies and reveal their nutritional contributions. Second, a critical gap exists in understanding how poverty-driven food insecurity and opioid misuse reinforce each other, and what the specific nutritional needs of vulnerable populations are. To fill the gap, the team will build an integrated graph from multi-source data across social, food, health, and nutrition domains, and advance graph prompt learning and graph retrieval augmented generation (GraphRAG) techniques to develop a novel causal analysis method that explores their intersection and informs targeted food demand strategies. Third, with the analyzed food supplies and informed demand strategies, optimizing personalized, food-secure, and nutrition-adequate interventions for affected individuals remains a key objective. To achieve this, the team will develop a novel multi-armed bandit algorithm integrating free food access, user budgets, and nutritional needs to close the supply-demand gap and enable effective, integrated interventions. The suite of novel AI-driven techniques developed in this project will benefit research communities in information integration and informatics (III). This AI-augmented paradigm can also be adapted to other crises - such as substance abuse, educational deprivation, and suicide risk in impoverished communities - and will benefit fields including economics, epidemiology, policy, and social sciences. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-11

This ExLENT Beginnings Tack project addresses a critical national need by developing, delivering, and assessing an experiential training program in the rapidly evolving field of marine technology. Coastal communities, home to 40% of the US population, are vital to the nation's economy and depend on the skilled marine technology workforce to monitor, maintain, and rebuild essential coastal infrastructure. Yet demand for these services is outpacing workforce growth. Importantly, advances in robotics and artificial intelligence (AI) are transforming the field. Smart deployment of autonomous underwater vehicles (AUVs) now enables real time decision making and complex, observation-dependent tasks. While these AI enabled technologies offer tremendous potential, existing professionals need opportunities to upskill, and students entering the field must be equipped for success in this new technological landscape. This initiative brings together a cross-sector partnership that includes colleges and universities, marine technology manufacturers, industry employers, and regional entrepreneurship leaders driving the water-based economy. Grounded in this strong foundation, the project immerses participants in hands-on learning, offering meaningful opportunities to deploy AUV projects for clients in authentic real-world settings. The project pursues two overarching goals: 1) create opportunities for skill development in emerging areas of marine technology, with an emphasis on artificial intelligence and smart deployment of AUVs, and 2) strengthen students' and early career professionals' confidence in their career preparation. To support these goals, the initiative offers a six-week hybrid course that integrates online and in-person instruction, emphasizing both the theoretical foundations and practical applications of smart AUV deployment. A team of collaborating researchers with expertise in marine technology, AI, autonomous systems, and aquatic ecology facilitates this experience for 72 participants over three years. Participants complete 4-weeks of foundational training followed by two weeks of hands-on project-based work aboard a research vessel. During the immersive phase, small groups plan and execute AUV deployments aligned with model client needs, such as lake bottom surface mapping, water chemistry analysis, and shipwreck investigations. An external evaluator collects both formative and summative data to inform continuous refinement of the project and to contribute new insights to the marine technology education literature. Dissemination occurs through local and national venues, including publications and professional conferences. Collectively, this work contributes to the development of a collaborative, adaptable model for advancing both technological innovation and workforce preparation in the field. The NSF ExLENT Program, supported by the NSF TIP and EDU Directorates, seeks to support experiential learning opportunities for individuals to increase their interest in and their access to career pathways in emerging technology fields. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-10

Many small- and medium-sized companies that manufacture construction materials face growing pressure to improve environmental performance and meet new sustainability requirements. However, they often lack the technical capacity or financial resources to do so. This research addresses a critical need by developing a low-cost, easy-to-use method for estimating the environmental impacts of building materials. By combining artificial intelligence with life cycle science, the project will enable manufacturers to evaluate and improve their production practices without relying on expensive consultants or specialized software. The screening tool developed through this research will support participation in emerging procurement policies that favor environmentally responsible products. In addition to its industrial applications, the research will contribute to workforce development and education by offering open-access instructional materials and research opportunities. The broader significance lies in making environmental performance assessment more accessible and affordable, thus supporting improved practices across the manufacturing sector. This research advances the science of environmental assessment in the construction industry by addressing major limitations in data quality, modeling methods, and practical usability. Life cycle assessment is a widely used method for evaluating the environmental effects of materials and products over their full lifespan, but existing tools often rely on incomplete data and require expert knowledge to operate. This research will develop a new screening method that integrates machine learning with public data sources to automate environmental impact analysis for building products. A pilot study with a wood product manufacturer will demonstrate and refine the method. A key outcome will be the creation of an interactive interface that enables users to enter product information and receive real-time environmental assessments. The research will also expand public life cycle inventory databases and demonstrate a scalable optimization framework for manufacturing assessment. Through partnerships with policy and industry groups, the work will contribute to evidence-based decision-making and broader adoption of sustainability practices in construction material production. 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 ExLENT Explorations Track project serves the national interest by creating and implementing a transformative experiential learning opportunity in data science and AI for undergraduate students and adult learners. Designed to close the widening skills gap and broaden entry points into STEM careers, the initiative engages non-STEM undergraduates and adults from various backgrounds in a year-long, hands-on, data-science focused curriculum. By combining technical literacy, domain-specific application, and industry-embedded internships, the project aims to prepare participants with both data acumen and essential “super skills” such as critical thinking, collaboration, and decision-making. Through partnerships with local governments, community organizations, and small-to-medium enterprises (SMEs) across Indiana, the project seeks to cultivate a multidisciplinary ecosystem that enhances regional economic resilience, fosters access to technology careers, and empowers young and adult learners alike to engage with emerging technologies. Rooted in interdisciplinary education, the initiative advances science and innovation for broad societal benefit. The primary goal of this project is to develop a transformative experiential education opportunity that unites participants from different fields and life stages to gain training in AI and data science. To achieve this, a structured curriculum integrates technical instruction, human-centered design thinking, and applied data science methods, tailored to the needs of a mixed-age cohort. Capstone projects, co-developed with industry and civil society partners, address real-world challenges and foster domain-informed problem solving. The experience culminates in an eight-week experiential internship with a local company. An external evaluator applies a mixed-methods framework to assess implementation fidelity, participant outcomes, and partnership quality, complemented by a longitudinal study to analyze graduates’ career trajectories and contributions. Dissemination targets academic, practitioner, and policymaking audiences through peer-reviewed publications and presentations that highlight new insights into mixed-age learning, cross-stage peer mentoring, and interdisciplinary training. By bridging education, workforce development, and community impact, the project contributes evidence for effective interdisciplinary and experiential learning models. The NSF ExLENT Program, supported by the NSF TIP and EDU Directorates, seeks to support experiential learning opportunities for individuals to increase their interest in and their access to career pathways in emerging technology fields. 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.