Michigan Technological University

NSF Awards · FY 2026 · 2026-08

Epoxy thermosets are widely used in engineering applications due to their superior adhesive strength, structural integrity, and chemical resistance. However, their permanently crosslinked molecular structure prevents reshaping or recycling after use, creating significant sustainability challenges. The emergence of covalently adaptable thermosets, known as vitrimers, offers a promising pathway toward recyclable thermoset materials. Yet, the same dynamic bond exchange mechanisms that enable vitrimer reprocessability also make them susceptible to long-term degradation in harsh environments. Prolonged exposure to elevated temperatures, oxygen, and moisture can alter their chemical structure and compromise mechanical performance. Despite their promise for applications in aerospace, naval systems, and other demanding environments, a fundamental understanding of how environmental aging affects vitrimer durability and recyclability is lacking. The objective of this project is to establish a comprehensive framework to elucidate how aging processes influence the mechanical integrity, bond exchange dynamics, and long-term reliability of vitrimers, enabling their sustainable deployment in high-performance applications. The project integrates experimental characterization, theoretical modeling, and computational simulations to investigate the coupled effects of hydrolytic and thermo-oxidative aging on vitrimer behavior. Systematic experiments across multiple length scales will quantify the evolution of mechanical properties under controlled environmental exposures, establishing direct links between degradation mechanisms and macroscopic performance. Concurrently, a diffusion–reaction framework will be developed and coupled with a transient network theory-based constitutive model to capture the interplay between irreversible chemical degradation and dynamic bond exchange reactions. These efforts aim to uncover the mechanistic pathways governing vitrimer aging and to provide predictive capability for long-term material performance in extreme service conditions. The outcomes will include design guidelines for enhancing durability, maintaining recyclability, and optimizing performance under environmental stressors. Educational activities will involve training graduate and undergraduate students in polymer mechanics, chemistry, and materials science through research activities in the PI’s laboratory. 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

The complexity and heterogeneity of diseases such as sepsis and allergy arise from diverse pathophysiological mechanisms and variable patient responses to treatment. Traditional diagnostic approaches relying on clinical phenotypes fail to capture the underlying molecular variability, leading to ineffective therapies and high healthcare costs. Host response-based diagnostics, driven by endotype classification, offer a promising solution by enabling disease stratification and personalized treatment through biomarker-based profiling. However, current biosensing platforms lack the scalability, affordability, and multiplexing capacity necessary for widespread clinical implementation. This research program aims to revolutionize biomarker-based diagnostics by developing a cost-effective, scalable, and multiplexed biosensing platform based on Molecularly Imprinted Polymers (MIPs). MIPs, synthetic receptors mimicking antibody recognition, offer superior stability and lower production costs compared to conventional immunosensors. The PI's lab has already demonstrated MIP-based electrochemical sensors for small molecules and protein targets, including SARS-CoV-2 spike protein and stress biomarkers. Building on this foundation, the next five years will focus on three key objectives: 1) Establishing an adaptable imprinting platform for the rapid synthesis of MIP-based biosensors tailored to amino acids, peptides, and proteins, inspired by nature’s antibody generation system. This approach will integrate computational modeling, machine learning, and experimental validation to predict and optimize imprinting efficiency in physiological environments. 2) Advancing multiplexed biosensing technology by integrating novel transduction mechanisms with scalability, such as redox- integrated MIP sensors and extended-gate field-effect transistors (EG-FETs)-based MIP sensors, coupled with machine learning-driven signal deconvolution for multiplex biomarker detection. 3) Developing a seamless sample-to-answer platform incorporating electrospun nanofiber-based whole blood separation and passive fluidic delivery to facilitate biomarker detection directly from complex biological samples without extensive preprocessing. Our research will initially focus on two key applications: (1) an amino acid biosensor array targeting branched-chain and aromatic amino acids, which are biomarkers for metabolic and neurological disorders; and (2) a multiplexed biosensor for sepsis biomarkers, integrating cytokines, acute-phase proteins, and metabolic indicators for early diagnosis and patient stratification. By integrating expertise in materials science, computational chemistry, electronics, and machine learning, our program will establish a next-generation biosensing framework that is cost-effective, scalable, and adaptable to diverse disease applications. The proposed work will bridge fundamental molecular recognition with real-world clinical translation, accelerating biomarker-based diagnostics, improving patient outcomes, and advancing precision medicine.

NSF Awards · FY 2026 · 2026-05

The Advanced Construction Image Dataset suite is one of the most well-known artificial intelligence (AI) dataset resources in the construction engineering domain. It provides construction-specific AI datasets (i.e., object detection, image captioning, and instance segmentation), annotation guidelines, and benchmark analyses to over 700 active users. This project seeks to further support and grow the dataset, broaden the userbase, and increase its open-source contributors. The ecosystem will establish a decentralized, self-sustaining hub that provides datasets, models, algorithms, documentation, and learning materials for the construction domain. It will also foster collaboration among academia, industry, government, and professional societies to accelerate AI innovation in the construction engineering domain. The project conducts an in-depth systematic analysis of the current technological landscape of construction data, evaluates construction-specific gaps in capabilities, and broadly surveys the community to understand potential avenues of future growth for the Advanced Construction Image Dataset. It also evaluates the viability of a hierarchical consortium governance model for the open-source ecosystem, with the goal of ensuring long-term quality, ethical compliance, and contributor engagement. The open-source ecosystem develops a security-by-design framework where artifacts (e.g., datasets, models, applications, and documentation) can be validated and shared across distributed contributors/users under well-defined data quality guidance and privacy compliance. The project pursues community building plan through workshops, competitions, and international collaborations. 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

Plastics are everywhere; from the micro to the macro scale you’ll find them in oceans, in food, and even in your body. Plastics have untold environmental impacts, and mismanaged plastic waste represents a loss of valuable resources. The Caribbean has three times the mismanaged waste compared to other regions. Most research on waste management has focused on large, wealthy European and US systems. Those approaches are not applicable for the many small island nations of the Caribbean as the small island nations of the Caribbean import copious amounts of goods, including plastics, but have limited means or space for recycling and recovery. A circular economy for plastics could reduce global GHG emissions and fuel a more sustainable economy in the Caribbean. This project establishes an international network of Caribbean and US collaborators that will connect the various components of the plastic ecosystem to design sustainable solutions to the plastic economy of the Caribbean. We will collaborate with BioGals, a nonprofit empowering women of color to create sustainable solutions for communities, to support broadening participation in our project. The team will improve understanding of the impacts of microplastics, recover the resources lost when plastics are mismanaged, develop solutions based on local needs with local researchers, and improve understanding of the scale of plastic wastes in conjunction with their environmental impacts. The team will use a strategic planning process to develop a plastics network strategic plan and research roadmap that at its core ensures sustainability and supports a circular economy. The activities include a workshop and a network event that is part symposium and part design charrette. These events will develop operational links among networks; design collaborative approaches that would engage students, early-career researchers, and leaders in the identification of knowledge gaps and development of professional skills; and identify knowledge gaps and research needs. This project will prepare U.S. science and engineering students, postdoctoral scholars, and early-career researchers for success in conducting and leading multi-team international collaborations to address the pressing problem of plastic sustainability around the globe, particularly for the Caribbean. It will enable strategic linkages between U.S. and international research networks to identify knowledge gaps, accelerate the process of scientific discovery, and develop a research roadmap that will stimulate and foster future research advances for a sustainable plastics circular economy. The Accelerating Research through International Network-to-Network Collaborations (AccelNet) program is designed to accelerate the process of scientific discovery and prepare the next generation of U.S. researchers for multi-team international collaborations. The AccelNet program supports strategic linkages among U.S. research networks and complementary networks abroad that will leverage research and educational resources to tackle grand scientific challenges that require significant coordinated international efforts. This project is funded by the Office of International Science and Engineering (OISE). 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

The NSF ECR Building Capacity in STEM Education Research (BCSER) program contributes to the NSF mission (42 U.S. Code Chapter 16) by building the capacity of the US STEM education research workforce to design, propose, and implement high quality STEM education research. The BCSER Institutes for Methods and Practices in STEM Education Research (IMP) track supports institutes that provide participants with training and support to advance the participants' knowledge, skills, and competencies in STEM education research including in the use of cutting-edge methodological techniques. Institute participants include investigators at any stage in their career development. This institute's focus is on building capacity in STEM education research by sustaining and expanding a novel, problem-led, and quality-focused approach to interpretive research design. This project extends the impact of the first ProQual institute by training 48 scholars and providing web-accessible case study examples of key elements of the ProQual approach. The ProQual approach reconceptualizes research design as a structured, design-based process, helping STEM scholars overcome epistemological and methodological barriers in educational research. This BCSER IMP project is providing training to approximately 48 STEM faculty interested in retooling to become STEM education researchers during the lifetime of the institute. The participants engage in a suite of activities to learn how to approach STEM education research as a design problem and to gain qualitative and mixed-methodology skills to undertake their own research project. Through an innovative 4-step program, participants develop research competence while engaging in a community of practice that fosters long-term knowledge exchange. The incorporation of "ProQual-in-a-Box" resources further extends these benefits beyond direct participants, enhancing dissemination and adoption. This expansion of the first ProQual Institute will strengthen STEM education by increasing the quality of interpretive STEM research that is designed and conducted by faculty and postdocs with technical backgrounds in STEM 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

By improving the efficiency and effectiveness of the nation’s research support infrastructure, the National Science Foundation's GRANTED program is working to promote the progress of science, for the benefit of society. While the numerous funded GRANTED projects around the United States are making progress towards this program goal, no coordinated mechanism has yet been created to share outputs across projects and with broad audiences. Many solutions to administrative challenges in the U.S. research enterprise are being explored; these results need to be organized and shared to ensure wide and sustained impact. This two-year GRANTED planning grant works to explore how to address this need. The project will 1) build a team that will plan strategy for long-term knowledge management for GRANTED project outputs, 2) work with the GRANTED community to ensure the project execution meets community needs, 3) work with an external contractor to create a pilot knowledge management system where project outputs will be shared, and 4) develop a plan for a subsequent GRANTED proposal to support the full-scale development of a long-term, virtual knowledge management system for project outputs and related resources. This project is expected to pave the way for a system that will enable a wider audience to have access to the results of the GRANTED project community, broadening the impact of these projects across the nation. The field of research administration in the U.S. is still young. The U.S. has few research administration degree or certification programs; thus, few research administrators enter the field without the need for training. While professional associations have been developed in nearly every subfield, few associations have been in existence for more than a couple of decades. Professional development programs are growing but remain limited. Knowledge sharing across domains remains a challenge and is often cost-prohibitive. This project aims to fill this gap in knowledge and access by designing an online knowledge management system that will efficiently connect and share the project outputs of the breadth of highly innovative National Science Foundation GRANTED award projects widely to a range of stakeholders, regardless of financial resources. Studying community needs will enable understanding of research administrator resource needs for training and capacity building. Developing a shared community of practice via the collection, sharing, and analysis of the types of outputs created by GRANTED projects will enable an understanding of which resources already exist and are being shared, as well as identify gaps in publicly available resources. Results from a survey and project output content analysis will be submitted to the Journal of Research Administration. Resources of the type that this project will design can foster multi-institutional collaborations that can, in turn, lead to the creation of new teams focused on development of new research administration capacity. Overall, planning a coordinated, easy-to-use, accessible knowledge management system that makes GRANTED project resources widely available and accessible will be a key step forward to addressing the needs of the nation's research enterprise. This knowledge base can be expanded in future work to coordinate resources from various sources to create a single point for knowledge sharing and professional development across research administration subfields. 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

Plastic waste is contaminating oceans, waters, and soils at an alarming rate. Microplastics are plastics so small that they may be barely seen by the human eye. Sometimes microplastics are engineered to be tiny, such as when they are used in cosmetic products, and sometimes microplastics are formed from the breakdown of larger, macro plastics. Microplastics pose a growing but uncertain threat to human health and the environment. They have been found everywhere, in food, in humans, and in drinking water. While it is known that microplastics are being found everywhere, currently it is not well tracked where they’ve come from. It is not known who or what are the biggest sources of microplastics for humans and the environment. Without an accurate picture of where plastics of all sizes come from and how they move through systems and the environment, it is not possible to design effective solutions. This research will collect new data on the amount and type of plastics found in different geographic environments. These data will be used to create a model of plastic flows through engineered systems, products, and the environment and will experiment with potential solutions to minimize plastic flows to humans and the environment. The project will engage and educate US and Caribbean students in learning how to collect and measure microplastics in the environment. This project will build a Material Flow Analysis (MFA) for plastics in the US and Caribbean. The research aims to evaluate whether secondary micro and nano plastic concentrations can be correlated to macro-plastic concentrations. If correlations can be made, then the research will explore whether satellite and aerial data can be used to estimate the presence of plastics in aquatic environments. This research will identify where plastics move through and accumulate in the environment via MFA, as well as identify opportunities for resource recovery or mitigation of their release to the environment. To answer these research questions, this project will build a probabilistic MFA that includes macro, micro, and nano plastics. Subsequently, solution spaces will be identified via scenario analysis conducted with expert stakeholder input. First, the MFA will enable understanding of where the largest flows, losses, and accumulation of plastics occur. Second, and perhaps most important, is that this information will then enable researchers to identify and experiment with potential solutions that support sustainable management of plastics. Data mining and data estimation techniques will be used to populate data for the MFA. Field measurements of micro and nano plastics will be taken from 6 different geographic locations (CA, CO, MI, NY, Dominican Republic, Belize) to supplement mined data. Field measurements will follow analytical procedures outlined in the literature and by ASTM for sampling and analysis of microplastics. Once the MFA is constructed, model refinement, validation, and scenario analysis will be conducted to evaluate potential innovative solution scenarios with expert stakeholders. This project will curate a plastics database and create an MFA that investigates different geographic locations, the intersection of macro plastics with primary and secondary micro and nano plastics, flows into unique compartments such as food and humans, and an assessment of possible solutions and waste management practices leveraging a stakeholder network of over 200 people. 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

Around 2011, residents, tourists, and researchers began noticing unprecedented amounts of sargassum seaweed on and offshore of the southern U.S. coasts and Caribbean islands. Sargassum quickly began to disrupt fishing, tourism, nearshore ecosystems, and even caused health problems for populations exposed to rotting sargassum seaweed. Sargassum arrives on shore mixed with plastic trash and can be difficult and costly to clean up. An estimated 5-13 million tons of plastic enters the ocean each year, and sargassum blooms are estimated at over 20 million tons each year. While there have been increased efforts to track, collect, and create valuable products out of sargassum, research and development has vastly ignored the connection between sargassum and ocean plastics. This IRES project investigates plastic-sargassum interactions in the ocean and develops valuable products from sargassum-plastic pollution, such as concrete and composite lumber, for the building and construction industry. Simultaneously, this project trains U.S. students in innovation and international collaborations through mentored research experiences in the Dominican Republic. This research progresses through three of the most prominent challenges in creating valuable products from sargassum-plastic pollution: tracking, collection, and product development. One of the main challenges facing management of sargassum is tracking seasonal flows: when will the seaweed blooms reach shore, where will they arrive, and in what quantities? This project uses satellite and aerial images to map and measure sargassum flows and then correlates this data with plastic information taken from field measurements. Collection is usually slow and costly, as it is mostly performed manually and with small-scale equipment. This project designs and tests new methods for collecting and processing that are best suited to delivering sargassum-plastic material in the quantities and condition best suited for development of value-add construction and building products. Finally, product development ensures profitability and feasibility by designing technologies that do not require extensive preparation of sargassum-plastic waste. This project investigates the use of pyrolysis -a process that uses high temperatures to break down sargassum-plastic pollution- to upcycle sargassum-plastic wastes. Pyrolysis creates waxes, chemicals, and char that can then be used to make composite lumber and concrete. The development of pyrolysis as a feedstock agnostic recycling technique that results in high value products is considered of utmost importance for plastic recycling, since sorting, transportation and impurities are often barriers in the recycling industry. This research develops new methods and techniques to produce concrete and composite lumber products, which will help drive the economics for collection and management of harmful sargassum-plastic reaching US and Caribbean shores. Research is primarily conducted by U.S. collegiate student trainees; as such this project trains these students to be globally aware and internationally capable of collaborating and innovating to support the U.S. to remain at the forefront of science, technology, engineering, and mathematics. The student training program includes topics such as advanced research methods, science communication, and collaborating on international teams. 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

Michigan Tech’s biomedical research enterprise has seen sustained growth since 2008, when our efforts focused on creating an upward trajectory for biomedical research at our institution. Since then, strategic investments have nearly tripled the number of biomedical researchers on campus, led to the establishment of a rapidly growing Health Research Institute (HRI), and provided millions of dollars of resources for faculty startup, seed funding, and new infrastructure. HRI is the ideal transdisciplinary home for an NIGMS Transdisciplinary T32. The deep expanse of HRI-led research across engineering and the sciences will provide a broad training in working across departmental lines and increase communication between the disciplines. HRI members already heavily collaborate and are well poised to take on the challenge of an NIH T32. The mission of the NIH Transdisciplinary T32 Traineeship in health-integrated technologies is to increase the quality and workforce readiness of graduate trainees at Michigan Tech who have broad understanding of the biomedical sciences and can effectively communicate and integrate their knowledge across disciplines. The training program is designed to achieve the following four objectives: Objective 1: Increase the quality and quantity of pre-doctoral trainees in Integrated Health Technology. Objective 2: Retain pre-doctoral trainees through to graduation. Objective 3: Provide a broad biomedical sciences education that prepares pre-doctoral trainees for future career attainment (major program elements are outlined below). Objective 4: Consolidate and disseminate best practices for recruiting, retaining, and training of pre-doctoral trainees. To that end, the program will test effective, evidence-based biomedical research training practices and apply them to a small, technology-focused university. Results will be disseminated across campus and to other universities. Program design will focus on providing trainees comprehensive technical, operational, and professional training. Specific activities will include in-depth preceptor training, recruitment of cohorts of biomedical predoctoral trainees through proven evidence-based and novel strategies, a comprehensive orientation, and “bootcamp” that includes intentional activities to foster a sense of belonging. Mentoring of trainees will be structured by multiple mentors. Individual development plans will track each trainee to provide the best individualized professional development to enhance non-technical professional skills. Trainees will receive up to two years of funding before being transitioned to other support. The program will lay a foundation for high-quality predoctoral training from a technology-focused biomedical program with a proven track record of workforce development located in a unique rural setting that offers novel perspectives on healthcare and the need for interventions in health-outcomes.

NSF Awards · FY 2025 · 2025-09

This project will support the development of the first global emissions inventory for volcanic hydrogen sulfide (H2S). By improving estimates of natural sulfur emissions to the atmosphere, the uncertainty in the estimates of the global radiative forcing of anthropogenic sulfate aerosols will be reduced. The investigators of the project plan to: (1) make an initial inventory of global volcanic H2S emissions based on current carbon dioxide and sulfur dioxide emissions measured during passive degassing; (2) measure H2S emissions from select volcanoes; (3) use these observations along with previous work to construct a global emissions inventory of H2S from passive volcanic degassing; (4) implement the new volcanic H2S emissions inventory into the GEOS-Chem global chemical-transport model; and (5) examine the global chemical and radiative implications of the volcanic source of H2S. This work will partially support two graduate students, one each at the University of New Mexico and the University of Washington. 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

Universities need to learn how to identify threats to research, what research needs to be protected, and how to protect it. Many universities have recognized they are at risk and have hired Chief Research Security Officers (CRSOs) to take the lead in ensuring that research being conducted at, or for, the university is secure. This proposal will fund a workshop to gather CRSOs from newly minted R1 institutions and rising R2 institutions across US universities. These institutions are rapidly expanding their research portfolios and securing new funding, which can introduce research security challenges and provide a unique opportunity to lay the foundation for robust research security as they grow, serving as a model for others in best practices. A key aspect of this workshop will be to explore how smaller universities, or those transitioning to higher research activity, can effectively collaborate and share resources to establish and maintain a robust research security infrastructure comparable to that of established Tier 1 research institutions. The workshop’s broader impacts contribute to national security, economic competitiveness, and the integrity of the U.S. research enterprise. The proposed 2.5 day workshop will bring together CRSOs from new R1 and rising R2 institutions as a systematic method to capture and synthesize their firsthand experiences and concerns regarding research security challenges to direct future research needs. By bringing together key stakeholders, we aim to (1) understand institutional concerns, (2) identify key issues and future research directions, (3) generate actionable insights and enhance frameworks, (4) create a roadmap/guidelines for peers, (5) produce and disseminate a white paper about the research needs for university research security, and (6) build a community of practice and sustain momentum post-workshop by developing an interconnected community of research security professionals and enabling ongoing collaboration. The workshop will facilitate the identification of research gaps and the development of research questions leading to future research in cybersecurity, research security, and research administration. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2025 · 2025-09

Project Summary Familial hypertrophic cardiomyopathy (HCM) is the most common genetic heart disease with an estimated prevalence of 1:500 individuals in the general population. Sarcomere gene mutations result in the characteristic features of HCM include left ventricular (LV) hypertrophy, myocyte disarray, interstitial fibrosis, diastolic dysfunction, and high risk of cardiac arrhythmias and sudden death. Indeed, HCM is the number one cause of sudden cardiac death in young, otherwise healthy adults. Non-invasive treatments for HCM target symptoms and do not prevent or slow disease progression, underscoring the need for new therapies. Recently, a new small molecule negative ionotropic agent has shown promise in treating HCM, but long- term benefits remain to be reported. HCM research has focused primarily on genetics and molecular mechanisms impacting cardiomyocyte and LV structure and function. Hypercontractile sarcomeres accompanied by metabolic dysfunction and energy depletion are considered causal mechanisms. We propose that the aberrant mechanical and metabolic signals produced in HCM hearts are detected by cardiac sensory nerves, with the resulting abnormal reflexes contributing to acute cardiovascular instability, chronic and selective increases in sympathetic nerve activity to the heart, decreased parasympathetic activity, cardiac inflammation, fibrosis, and diastolic dysfunction. We hypothesize that cardiac spinal afferents (CSAs) in HCM exhibit increased chemosensitivity, reflecting increased [H+] produced by myocyte energy depletion/ischemia and increased neuronal expression of acid-sensing ion channel 3 (ASIC3). An alpha tropomyosin mouse model of HCM with cardiac-targeted mutation known to cause HCM in humans will be studied in male and female mice. Specific aims of the project are: 1) Test the hypothesis that HCM mice demonstrate a selective increase in cardiac sympathetic tone mediated by the cardiac spinal afferent reflex and dysfunction of the neuronal norepinephrine transporter, and 2) Test the hypothesis that increased activity of CSAs is mediated by ASIC3 and targeted cardiac ablation of CSAs will improve cardiac and autonomic function in HCM mice.The proposed studies using innovative, targeted, and complementary approaches will close gaps in knowledge regarding mechanisms causing sympathovagal imbalance in HCM prior to heart failure, the influence of cardiac innervation on the HCM heart, and the quest for disease-modifying therapies in HCM. Pursuant to the goals of the Academic Research Enhancement Award (R15), this project will also provide research opportunities in neural-cardiovascular physiology to undergraduate students studying human health at Michigan Technological University.

NSF Awards · FY 2025 · 2025-09

This Engineering Research Initiation (ERI) award supports research to investigate the micromechanical behavior of sand grain pairs bonded with different cementing agents. Bonded granular materials, where particles are bound by cementing agents, are widely present in applications such as infrastructure rehabilitation, ground improvement, liquefaction resistance, and erosion control. Conventional geotechnical laboratory tests often exhibit uncertainties in the loading and strength responses of bonded soils due to variations in cementation type and content. They also lack the resolution needed to capture the underlying micromechanical responses that govern material behavior. This project seeks to address these gaps through systematic laboratory experiments and microscopic observations at the scale of individual grains, focusing on the effects of cement type, particle characteristics, and loading conditions on influencing bond strength and failure mechanisms. Improved understanding of grain-scale mechanics in bonded soils will advance fundamental knowledge, support the design of effective soil stabilization strategies, and contribute to the resilience of the nation’s civil infrastructure. The research also looks to support education and outreach through curriculum development, demonstrations of cementation-based soil treatment, and outreach activities through Michigan Tech’s Summer Youth Programs. Data produced from this project will be archived and made publicly accessible through the NSF-supported Natural Hazards Engineering Research Infrastructure (NHERI) Data Depot (https://www.DesignSafe-ci.org). The effectiveness and durability of bonding, as well as the macroscale mechanical behavior of bonded granular materials, are primarily governed by inter-particle cementation. This research seeks to advance understanding of the fundamental grain-scale loading response through systematic experiments on bonded grain pairs. A custom-built inter-particle loading apparatus will be used to assess the loading behavior and bond strength of both natural and artificial sands bonded with a range of cementing agents, including Portland cement, gypsum, and biologically induced calcium carbonate precipitation. This work will specifically (1) investigate the effects of particle size and morphology on grain-scale tensile and shear strength of the bond between particles, (2) compare the role of different cementitious agents on bond strength and deformation response for individual grain pairs, and (3) analyze inter-particle friction and stiffness degradation at the interface of bonded grains following bond failure. This will facilitate the development of a crucial dataset aimed at enhancing the fundamental understanding of the contact mechanics of bonded sands and addressing uncertainties related to their macroscale responses. This work will also lay the groundwork for developing evidence-based numerical modeling strategies, specifically for use in discrete element method (DEM) based simulations of bonded granular materials. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-09

A workshop is planned to promote research data management (RDM) in the construction research community. The construction industry is experiencing an array of challenges including low productivity, issues in safety performance, and shortage of skilled workforce. To address these challenges, the construction research community has put much effort into integrating automation technologies such as artificial intelligence (AI), robotics, and Internet of Things (IoT) to support infrastructure management, on-site safety control, and structure health monitoring. Through collaboration between construction engineering and cyberinfrastructure experts, this project team will organize and hold a two-day workshop in May 2026 at the Michigan Tech University campus to identify challenges and opportunities of RDM in the construction research community. The workshop will invite leading scholars from government, industry and academia, including early-career researchers, and journal editors from construction engineering, open science, and cyberinfrastructure venues. The workshop facilitates the digital transformation of the construction industry by building the scientific connection between construction and open science communities, which will support construction community involvement and contribute to the Nation’s cyberinfrastructure ecosystem. This award by the Office of Advanced Cyberinfrastructure is jointly supported by the Directorate for Engineering. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-09

The High-Altitude Water Cherenkov Observatory (HAWC) is an array of hundreds of detector stations located on a very high plateau near the highest mountain in Mexico, Pico de Orizaba, also known as Citlaltépetl, which is Nahuatl and translates to star mountain. These detector stations capture the signals of the strongest form of light in the universe, gamma rays, as they zoom through the Earth’s atmosphere. The gamma rays, which are invisible to the human eye, reach Earth from parts of the Milky-Way Galaxy that are filled with gas clouds, exploding stars and their leftover subatomic material, cosmic dust, or black hole environments that shoot out jets of subatomic particles that move superfast, almost at the speed of light. The subatomic particles from these environments are called cosmic rays. The gamma rays the HAWC Observatory measures come from cosmic rays. The nature of cosmic rays prohibits observing them directly from a distant source. The goal of this project is to better understand how the cosmic rays are propelled to superfast speeds by studying the gamma-ray traces they leave behind. In this project, HAWC data will be used and combined with data from observatories that measure light from space at other frequencies and other space messengers like neutrinos. This will improve the understanding of cosmic-ray dynamics in the Milky-Way Galaxy by tracing cosmic-ray interactions with the interstellar medium that produce gamma rays. Existing software tools proven to work with HAWC data will be used as well as new machine learning algorithms explored. The focus will be on supernova remnant/molecular cloud complex surveys in the field-of-view of the HAWC Observatory, novel microquasar modeling, and the study of very extended gamma-ray emission phenomena like the galactic diffuse emission, the Northern Fermi Bubble, and passive molecular clouds. The ultimate goal is to obtain a more complete picture of cosmic-ray acceleration, propagation, and distribution in the Milky Way Galaxy. This project advances the objectives of "Windows on the Universe: the Era of Multi-Messenger Astrophysics", one of the 10 Big Ideas for Future NSF Investments. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2025 · 2025-09

Project Summary/Abstract Coronary artery aneurysms (CAA) and ectasia (CAE) describe abnormal local dilatations of the coronary artery that typically exceed 1.5 times the neighboring artery diameter. The pathogenesis of CAA and CAE is not well understood; however, several factors come into play such as certain vasculitic and connective tissue diseases such as Kawasaki disease (KD), a pediatric acquired heart disease. CAA and CAE are usually found incidentally, sometimes without symptoms and other times accompanied by acute coronary syndrome. Clinical symptoms can appear due to the presence of local thrombosis among others causes, leading to angina and myocardial infarction, which carries a substantial health and economic burden. Currently, anticoagulation therapy is the recommended approach used in CAA and CAE despite several conflicting studies. The thrombotic risk of CAA and CAE in KD, and recommended anticoagulation therapy, are currently defined by ONLY the largest diameter (through the z-score metric), without taking other morphologic features into consideration. However, studies including our preliminary data showed that for the same z-score, flow patterns and indices related to thrombosis were different. Literature has shown that blood flow stagnation and sluggish flow are correlated with thrombosis. The occurrence of blood flow stagnation and therefore thrombosis risk is correlated with low velocities, low time averaged wall shear stress (TAWSS), high oscillatory shear index (OSI), and elevated relative residence time (RRT). Several patient-specific computational fluid dynamics (CFD) and experimental investigations to analyze the flow and risk level of CAA cases in KD based on hemodynamic indices were performed and studies showed that for the same z-score, flow patterns were different. So how can we complement the z-score with flow indices and other geometrical factors beyond just the diameter and without running extensive and time-consuming computational simulations to facilitate the decision-making process for cardiologists? The goal of this proposal is to address this question through developing simple predictive hemodynamic indices that can be used to refine the z-score metric so in addition to the dilation diameter, more geometrical parameters and hemodynamic parameters can be included to obtain a more robust stratification of thrombosis risk and determination of anticoagulation therapy in KD. This will be done through using patient-specific datasets of Kawasaki Disease (KD) patients from the Nationwide Children’s Hospital (Columbus, OH) and Chicago Lurie’s Children’s Hospital registries. These datasets include patients with documented magnetic resonance imaging (MRI), computed tomography (CT) and echocardiographic data.

NSF Awards · FY 2025 · 2025-09

Electronic-structure methods have a profound impact on several disciplines, especially materials research, as demonstrated by extensive studies in this field and the discovery of numerous advanced materials and devices with widespread applications. However, large-scale electronic structure calculations are prohibitively expensive. Machine learning models can accelerate these simulations, but current models often lack one or more of the following: uncertainty quantification, preservation of symmetries, incorporation of physics, generalizability, accuracy, efficiency, or scalability. This research aims to address all these challenges within a single machine-learning framework. To achieve this goal, this project focuses on gaining fundamental insights into atomic configurations and corresponding electronic structures by developing a machine-learning model to predict electron density for a wide range of materials. The machine learning model facilitates the design of complex materials, which require simulations of larger systems. This fundamental research is expected to have broad applicability beyond materials science in areas where both quantifying uncertainty and respecting rotational-translational symmetry are crucial, such as biomedical imaging and continuum physics problems. The goal of this project is to enable machine learning-based electron density prediction for ultra-large systems and diverse compositions, thereby accelerating materials design. This will be achieved by developing a machine learning framework and data pruning schemes and gaining insights into atomic configurations and electron density. To accomplish this, a Bayesian convolutional neural network model will be developed that is rotational-translational symmetry-equivariant, physics-informed, chemically accurate, generalized, efficient, and scalable. Therefore, the machine learning model will achieve greater generalization and uncertainty quantification capabilities through a Bayesian approach while ensuring rotational and translational symmetrical equivariance. The physics-based volumetric input and output of the model will simultaneously improve both accuracy and efficiency, addressing a key gap in the field. To overcome the lack of generalization in data, a novel technique will be developed to explore the space of thermo-mechanical variables during data generation effectively. Additionally, data pruning techniques will be developed to enhance efficiency in data generation and training. The broad applicability of the machine learning model will be demonstrated for various metals, alloys (with and without defects), and molecules. Ultimately, it will be extended to a wide range of transition metals and their alloys. The uncertainty quantification capability of the machine learning model will be leveraged in a Bayesian Optimization framework for the design of high-entropy alloys. The project also involves various educational and outreach activities aimed at promoting machine learning-based materials design and increasing participation from underrepresented groups. These activities include developing a cyberinfrastructure tool for the machine learning model, releasing code through public repositories, providing education through a summer youth program and cyberinfrastructure, curriculum development, engaging underrepresented students in undergraduate research, conducting seminars at historically Black colleges and universities, and collaborating with industries. This project is jointly funded by the Office of Advanced Cyberinfrastructure and the Division of Civil, Mechanical and Manufacturing Innovation (CMMI). 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 · 2025-08

This proposal aims to advance the detection of viscosity changes and protein aggregation in live cells through the development of sophisticated, self-calibrating near-infrared fluorescent probes. These innovative probes will quantitatively assess microviscosity variations and monitor protein misfolding and aggregation within essential organelles, including mitochondria, the endoplasmic reticulum, and lysosomes. To ensure optimal performance, our probes will be engineered for enhanced water solubility, stability, and cell permeability, thereby maximizing biocompatibility and selectivity for detecting both viscosity changes and protein aggregation. Utilizing near- infrared imaging (650–860 nm) will facilitate deep tissue penetration, minimize cellular damage, and reduce biological autofluorescence, resulting in clearer and more precise imaging. The implementation of ratiometric imaging, which capitalizes on pseudo-Stokes shifts and dual emissions, will further mitigate errors related to excitation light and scattered fluorescence, yielding reliable quantitative data. A pivotal innovation of this system is its self-calibrating ratiometric responses, designed to overcome the limitations inherent in intensity-based probes, such as fluctuations in excitation light, sample heterogeneity, and uneven probe distribution. This feature will enable accurate diagnosis and monitoring of diseases associated with altered cellular microenvironments and protein homeostasis. Our probes will specifically target biomarkers pertinent to neurodegenerative diseases and cancer. In neurodegenerative contexts, they will be responsive to oxidative stress, pH changes, and protein aggregation associated with conditions such as Alzheimer’s and Parkinson’s diseases. For cancer applications, the probes will detect hypoxia, acidic environments, oxidative stress, and increased viscosity, allowing for real- time imaging of critical disease markers. Additionally, this project will explore microviscosity dynamics, protein folding, and aggregation in live cells under various conditions. We will investigate how glycerol concentrations and protein aggregation influence the probes’ optical properties, emphasizing their sensitivity and specificity. Molecular dynamic studies on proteins amyloid-β (Alzheimer’s) and α-synuclein (Parkinson) and computational studies on the ratiometric fluorescent probes will provide a theoretical understanding of the experimental results and suggest the nature of the conformational changes. The study will also assess the effects of chemical interventions, including cancer therapeutics and oxidative stressors, while evaluating the roles of protein chaperonins and co-chaperones in ensuring proper protein folding and preventing aggregation. Ultimately, this research aims to uncover valuable insights into the diagnostic potential of abnormal cellular viscosity dynamics and protein aggregation in various disease processes. By enhancing early diagnosis and therapeutic monitoring of conditions such as cancer and neurodegenerative diseases, our pioneering approach will make significant contributions to the fields of cellular imaging and disease diagnostics.

NSF Awards · FY 2025 · 2025-08

Winter weather events pose increasing threats to America’s electric power infrastructure, with storms like Winter Storm Uri demonstrating catastrophic consequences. This project focuses on planning electric power systems in cooperative and municipal utilities in the Midwest for maximum reliability and resilience in the winter. Winter resilience in electric power requires understanding future winter weather (annual average snowfall, temperature, ice, and their extremes), as well as changes in electricity supply and demand during the winter (electric heat pump adoption, electric vehicle demand, and inverter-based resources). In regions like the Midwest, where the security of heating and power systems in winter is key to human health and welfare, it is especially important to use the best available Earth system science and engineering tools in planning. This project engages with electric utilities in the Midwest in order to understand the industry’s needs for science tools to plan for winter resilience in the future, designing tools that will benefit electric power resilience in all communities. The project uses stakeholder engagement tools to connect Earth system science with practitioners (municipal and cooperative utilities in the Midwest), to apply Earth system modeling and engineering tools to real systems, and to identify how investments in training, the standardization of toolkits, and customized resilience analysis can support these institutions. This project aims to establish community needs and develop a framework for translating regional Earth system science to maximize winter resilience of electric power systems for small utilities in the Midwest region. The intellectual merit of this project is to address combined uncertainties in future winter severity and future electricity operation at local-to-regional scales using Earth system modeling. The initial focus of this work is on municipal and cooperative electric utilities because smaller, resource-limited organizations may not have the staff, time, or tools to incorporate Earth system science into their resilience work. The project will conduct a series of workshops and surveys engaging a team of practitioners and professionals from industry, research institutions, and academic institutions to identify industry needs and disseminate results. The aim of the project is to use a generative, collaborative process to define the exact needs of small municipal and cooperative utilities for planning for winter electricity resilience such as identifying specific data gaps or requirements for new tools and models. The project will create a plan for future activities, resources, and personnel needed to close the identified gaps and create a sustainable plan to deliver cost-effective resilience solutions. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-08

Accurate inertial navigation in underwater autonomous systems is critical for long-range operations. For example, the disappearance of airplanes or ships at sea is a tragic event that often leads to long and costly search operations, sometimes lasting months or even years covering thousands of square kilometers. Underwater vehicles, including autonomous underwater vehicles (AUVs) or robots, remotely operated vehicles (ROVs), and human-occupied vehicles (HOVs), play vital roles in these missions. However, due to inertial navigation drift and the complexity of the underwater environment, these vehicles can become lost. This Engineering Research Initiation (ERI) award will develop a method to enable the autonomous underwater robot to effectively estimate the flow around it and use this information along with novel localization algorithms to determine its position relative to the environment. The successful outcome would enable improved inertial navigation systems for autonomous operations, benefiting applications in aquatic monitoring, oil and gas exploration, and search and rescue operations. The award will also support education of students and engineers through courses, tutorials, online materials, seminars, workshops, contests, and research opportunities. The objective of this research is to accurately localize the position or dead reckoning accuracy of an underwater robot by addressing fundamental challenges in visualizing the flow dynamics around it. Two major factors that reduce dead reckoning performance are sensor noise and flow current estimation error. This research will overcome these challenges by efficiently representing the flow field around the robot using reduced-order dynamic flow modeling, estimating the flow current/rate by leveraging physics-informed neural network, and developing precise relationships between sensor noise, flow estimation error, and localization error. Ultimately, the research will provide quantitative insights on the influence of sensor accuracy and flow conditions on drift in inertial navigation, efficient real-time calculation of the flow filed under limited computational capacity, and the efficacy of physical information, such as the Navier Stokes equations, in estimating the flow field. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2025 · 2025-07

PROJECT SUMMARY Metal-containing enzymes or metalloenzymes represent more than 40% of all enzymes and perform a wide variety of chemical transformations in cells with great biomedical importance. The unique functionalities of the metalloenzymes result from the amazing synergy between the properties of the metal center, the coordinated biological ligands, and last but not least, the unique effects of the second coordination sphere (SCS) and long-range (LR) interactions with the protein and its dynamics. Among the diverse classes of metalloenzymes, the Zn(II)-containing matrix metalloproteinases (MMPs) and Fe(II)/2-oxoglutarate (2OG)-dependent oxygenases are in special interest due to their intriguing structure-function relationships and biomedical importance. Although both sets of metalloenzymes have been intensively studied and important aspects of their structure-function relationships have been elucidated, there are a lot of missing points that demand comprehensive investigation in the directions of revealing their catalytic mechanisms, understanding the effects of the SCS and LR-interacting residues and elucidating the correlation between the dynamics and catalysis. Providing the missing knowledge on the two groups of metalloenzymes applying computational chemistry methods in correlation to experiments is the long-term focus of the proposed research. The outcomes will contribute to understanding the mechanisms of metalloenzymes, will provide missing elements to the enzyme redesign protocols, and will help for the design of effective and specific enzyme inhibitors.

NSF Awards · FY 2025 · 2025-07

Past hybridization among closely related species can leave traces of genetic variation from endangered or even extinct species in the DNA of present-day animals. This phenomenon, known as ghost introgression, is often overlooked but is a reservoir of preserved genetic variation from endangered or extinct species found in present-day genomes of the related common species. These hidden reservoirs could be essential for conserving adaptive potential in the future. This project re-envisions the conservation value of ghost introgression and how it can be leveraged to support endangered species recovery. The project will characterize the ecology and population dynamics of Gulf Coast canids that carry varying amounts of red wolf ghost ancestry in their coyote genomes and inhabit a broad geographic range. First it will develop a non-invasive genetic tool to monitor and assess the ecological conditions that promote the persistence of red wolf ghost ancestry. Further, the tool will be used to identify individuals of high conservation value, as measured by their degree of unique red wolf ghost ancestry and thus have the greatest potential to resuscitate endangered red wolf ghost genetic variation. The conservation partner, the Endangered Wolf Center, will then implement a short-term breeding experiment to enhance ghost ancestry based on a careful pairing design in a captive breeding facility. The project integrates information and efforts across communities and organizations to pioneer new options for endangered species recovery programs in the future. The project will also involve public outreach and education, and engagement with managers with a focus on resolving human wildlife conflicts and conservation of key predators. Canids along the American Gulf Coast carry signatures of red wolf ghost introgression, yet little is known about the factors that support the persistence of such. The project will combine in- and ex-situ studies and develop a framework for evidence-based conservation in a natural landscape using population ecology and empirical genomics. First, canids will be captured, genetically sampled, and radio-monitored across a gradient of mortality risk and available resources to quantify the functional linkage between ghost introgression and ecology. Morphometrics and individual-level fitness correlates will also be considered to develop a landscape prioritization tool to identify areas for future conservation efforts. Second, a SNP panel will be developed to non-invasively monitor large landscapes for ghost introgression of red wolf DNA and behavioral ecology traits. The application of this technology will be for large-scale, cost effective, long-term, non-invasive monitoring and continued identification of conservation priority individuals. Third, an optimization framework will be developed to identify and rank individuals that maximize ghost genetic variation while prioritizing the genomic architecture of red wolf ancestry, noting that longer block lengths of endangered genetics are preferred for maintaining genome integrity. Finally, the project will attempt to revive ghost variation through an innovative short-term captive breeding experiment, challenging the existing endangered species conservation tenets to include ghost variation as a trailblazing method to protect imperiled species and diversify their genomes. This project will serve as a model, evaluating the potential of leveraging ghost introgression to preserve the genomes of endangered species that face the immediate threat of extinction. This project is jointly funded by the Divisions of Environmental Biology and Integrative Organismal Systems through the Partnership to Advance Conservation Science and Practice Program. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-07

This project supports research that advances national security and economic prosperity by seeking to enable the manufacture of high-performance composite structures through processes that are faster and less energy intensive. Thermoplastic polymers can be reheated, reshaped, welded, repaired, and recycled at end-of-life, leading to lighter aircraft, automobiles, and medical devices that consume less fuel and generate less waste than counterparts made from conventional thermosetting polymers. By seeking to deliver the fundamental science required to predict and optimize the manufacturing of thermoplastic composites, this award addresses the steep learning curve currently limiting their industrial adoption. An international team from the United States, Germany, and the National Aeronautics and Space Administration (NASA) will openly share data, simulation codes, and validated processing methods, accelerating innovation across multiple industrial sectors. The project will also strengthen the science and engineering workforce by providing research-driven training for undergraduate and doctoral students and offering a free public short course on integrated computational materials engineering, with materials available online for self-learning. In these ways, this effort directly serves the National Science Foundation’s mission to promote scientific progress and enhance the welfare of the United States. The central focus of this research project addresses a fundamental question: How do polymer morphology, interdiffusion, crystallization, and residual stresses during processing influence the interlaminar strength and fracture toughness of carbon fiber-reinforced thermoplastic composites? To answer this, the project looks to develop a physics-based, multiscale modeling framework that links processing conditions to interfacial mechanical properties in thermoplastic composites, enabling predictions and optimization of interlaminar strength and fracture toughness. Molecular dynamics simulations quantify polymer-chain interdiffusion, crystallization, and residual stress evolution at ply-to-ply interfaces under processing conditions representative of automated fiber placement, induction welding, and stamp forming. These interfacial properties inform micro-scale finite-element models that resolve heterogeneous crystallinity and coupled thermo-mechanical fields during consolidation. At the structural scale, cohesive-zone elements embedded within continuum damage mechanics capture interface bonding and subsequent debonding under service loads. To efficiently explore the extensive parameter space defined by time, temperature, and pressure, the team looks to develop a machine-learning surrogate model, identifying optimal processing windows that maximize mechanical performance while significantly reducing computation time. Advanced experimental characterizations across multiple length scales will validate the model predictions. This project is a collaboration between University of California-San Diego, Michigan Technological University, the National Aeronautics and Space Administration (NASA) and University of Wuppertal in Germany, which broadens modeling and experimental capabilities, ensuring the robustness of the developed toolset for certifying next-generation thermoplastic composite structures. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-07

This NSF ERI project aims to develop new algorithms and solution approaches to optimize demand response strategies of wastewater treatment plant operators under uncertainty, focusing on the benefits and barriers of using on-site biogas generators in these programs. Wastewater treatment is an energy-intensive process, and biogas produced through anaerobic digestion is often flared but can be used to provide demand response by reducing or shifting the energy consumption of wastewater treatment plants. This project focuses on the key challenges of managing uncertainties within and the variability across treatment plants and demand response programs. The project will bring transformative change by providing wastewater treatment operators with optimal demand response strategies. Increasing demand flexibility will improve power grid reliability, particularly during periods of network stress. This will be achieved by developing an approach for characterizing the uncertainty in wastewater treatment and introducing novel optimization techniques to manage these uncertainties within a demand response scheduling optimization framework. The intellectual merits of the project include the development of new demand response strategies under uncertainty and quantifying the trade-offs between cost and feasibility for using biogas within wastewater treatment plants to provide demand response. The broader impacts of the project include reducing costs in both the electrical energy and wastewater sectors by demonstrating and applying effective strategies for using biogas. Collaboration with utilities and water quality researchers throughout the project will support the adoption of these strategies by improving stakeholder understanding of demand response program requirements, financial incentives, and operational impacts. This project will also integrate findings into new curriculum materials on industrial and utility-scale demand response and provide research experiences for graduate and undergraduate engineering students. This project is structured into two main tasks. The first task will develop system models to accurately capture the necessary timescales, uncertainty, and performance of biogas production and demand response programs. Using utility data, data-driven uncertainty sets will be constructed and refined to maintain robustness but avoid excessively conservative solutions. The second task will focus on solving for optimal demand response strategies for biogas use under uncertainty and evaluating trade-offs in providing demand response in terms of costs and feasibility. Together, these tasks will enable an estimate of the economic flexibility potential of biogas generators for demand response within the United States. Utility data will be leveraged from both small and large wastewater treatment plants to evaluate the proposed approach. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-06

This NSF ERI project aims to develop real-time short-circuit (fault) diagnosis tools to enhance the stability, security, and resilience of electric power grids. Modern electric grids face challenges due to the increased number of various kinds of energy resources with added uncertainties. These include low fault currents, bidirectional power flow, and non-linear electrical behaviors that limit the effectiveness of traditional protection systems. Furthermore, the increasing frequency of extreme weather events poses significant threats to the reliability of the power grid. To address these challenges, this NSF ERI project will develop a novel real-time digital signal processing theory, offering a transformative new approach to capturing and analyzing rapid electrical phenomena that conventional techniques cannot resolve. This new theory will result in advanced diagnostic tools capable of identifying and responding to faults in complex power systems with unprecedented speed and accuracy. The intellectual merit of this project includes: development of wavelet theory with the ability to eliminate time delays in real-time signal decomposition and reconstruction, as well as sensitivity to small signal changes; integration with machine learning to enable classifying faults with microsecond-level precision. Additionally, the proposed framework introduces a unique capability to predict faults and enable early corrective actions, thereby improving system resilience against cascading failures. The broader impacts of this project include educational and outreach components that engage students and the public through hands-on learning, YouTube-based educational series, and summer youth programs, supporting workforce development in a rapidly evolving energy sector. This project will advance the mathematical foundation of real-time wavelet transform theory to overcome limitations in conventional digital signal processing techniques. Methods based on this new theory will be developed to improve the detection of high-impedance faults, fault-induced transients, and low-frequency harmonic distortion, particularly in inverter-based resource (IBR)-dominated and high-voltage DC systems. The research will span two interrelated thrusts: (1) the development of an innovative real-time wavelet transform theory capable of handling dynamic, multi-scale electrical signals with no time delay, and (2) the design of an integrated fault diagnosis framework that leverages this theory to achieve rapid fault prediction, classification, and location in both AC and DC systems. Algorithms will be tested through simulations, real-world data, and experimental validation using a unique laboratory testbed with renewable integration. The outcomes will include open-source tools and scalable methodologies suitable for real-time deployment in protective relays and control systems. These innovations will advance the state-of-the-art in real-time signal processing and power system protection and contribute to improved grid reliability under extreme operating conditions. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.