University of Louisville Research Foundation Inc

NSF Awards · FY 2026 · 2026-09

Solid waste from construction, agricultural, industrial, and municipal sectors accumulates rapidly, straining landfill capacity, degrading ecosystems, and hurting human health. Converting this waste into sustainable chemicals and materials would simultaneously reduce environmental burdens and supply new raw materials to the chemical and construction industries. However, doing so requires a trained workforce that does not currently exist on a large scale. This REU Site at the University of Louisville addresses that gap. Over three years, the program will engage 24 undergraduate students in hands-on, faculty-mentored research on waste-to-value conversion, equipping them with the technical skills, professional preparation, and career awareness needed to contribute to a sustainable materials economy. The site is open to all Americans. By connecting undergraduate education to one of society's most pressing environmental challenges, this program advances NSF's mission to promote scientific progress and national welfare. This REU Site at the University of Louisville will host eight undergraduate participants annually in a 10-week summer research experience with a focus on converting solid waste, including lignocellulosic biomass, waste plastics, and rubber tires, into sustainable materials and chemicals. The program pursues four objectives: (1) building student understanding of waste-to-value science and its technological, social, and economic dimensions; (2) developing research competencies through the SODOTO (See One, Do One, Teach One) pedagogical framework; (3) strengthening critical thinking, problem-solving, and communication skills through use-inspired research projects with faculty mentors; and (4) preparing students for STEM careers in academia, industry, and government. Program activities include weekly research seminars, professional development workshops, and industrial site visits to local chemical and materials companies, and poster and manuscript preparation. Students are also encouraged to present their findings at regional and national conferences. These design features advance student self-efficacy, critical thinking, project management, and long-term retention in STEM. 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

Artificial intelligence (AI) systems built on machine learning have inherent limitations due to training and data quality, and may appear surprisingly unintelligent when their behavior does not match what people expect from an intelligent system. Despite these limitations, imperfect AI can still be very helpful and has been widely adopted, raising concerns about being left behind as these technologies advance. This project seeks to support non-AI experts in living and working effectively with imperfect artificial intelligence tools by studying the workarounds people generate when encountering imperfect AI, what affects their ability to adapt these AI tools to their needs, and what knowledge and skills should be prioritized when equipping non-AI experts to work with such tools. The insights gained can inform the design of human-AI interfaces and the development of training programs, enabling the general public to better utilize AI tools. The project will also identify essential competencies for AI literacy, helping equip the future workforce with the ability and confidence to explore, question, and critically assess new AI tools they encounter. To support non-AI experts in living and working effectively with imperfect AI, the project includes four research tasks that combine mixed-methods studies and controlled experiments. The first task is to develop and validate a general framework of AI-related workarounds by collecting a diverse sample of non-expert users adapting to imperfect AI tools. The second task focuses on studying factors that influence workaround generation. It involves exploring how users’ mental model, sensemaking, and trust calibration relate to workaround generation. It also investigates the influence of task effort and time constraints. The third task focuses on designing and testing interventions to support effective workarounds. It compares three types of training, namely machine learning knowledge, failure cases, and workaround strategies. It also examines whether just-in-time hints can inspire and encourage effective workarounds without requiring prior training. The fourth task explores whether and how skills and insights gained in the context of one AI tool may transfer across different tasks and tools. Together, these tasks will advance knowledge in human-AI interaction and AI literacy. The project establishes workarounds as an important lens for understanding human adaptability and resiliency in navigating imperfect AI. It also contributes to defining essential competencies for AI literacy and has implications for human-AI interface design that supports resilient human-AI partnerships. 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 immune system is the primary defense animals have against infectious diseases. A key component of this defense, the adaptive immune system, generates antibodies that recognize and neutralize bacteria, viruses, and other pathogens. The repertoire of antibodies an animal can produce is determined by a set of genes that vary considerably across mammalian species, yet this variation is almost poorly describe outside of humans, mice, and cattle. This project will conduct the first systematic comparison of immune genes and antibodies across 60 species of mammals, using state-of-the-art DNA and RNA sequencing technologies. The research team will generate high-quality genome assemblies focused on immune-related genes and expressed antibody repertoires, develop new computational tools to analyze them, and identify patterns that explain why species differ in their capacity to respond to infection. All data, genome assemblies, and software produced by this project will be made freely and publicly available, providing a foundational resource for immunology and disease research for years to come with the potential to facilitate biotechnological advances. The project will also provide meaningful research training for graduate and undergraduate students in computational biology, genetics, and immunology. Graduate students will gain hands-on experience with both laboratory sequencing methods and advanced computational analysis, while undergraduate students will participate in paid summer research positions. The team will also host a working group to establish community standards for comparing immune gene data across species, laying the groundwork for a broader research consortium that will expand this work well beyond the lifetime of this grant and further facilitate biotechnology advances. This project will generate paired datasets of antibody repertoires and germline immunoglobulin (IG) loci across approximately 60 mammalian species. For each species, expressed antibody repertoires will be profiled using long-read bulk RNA sequencing (PacBio Iso-Seq, a technology enabling full-length transcript recovery) of whole blood samples, enabling unbiased identification of V(D)J recombinations, the combinatorial gene rearrangements that generate antibody diversity, across all antibody chains. In parallel, high-quality genome assemblies will be generated using long-read whole-genome sequencing (PacBio HiFi), and IG loci will be assembled using state-of-the-art assemblers followed by targeted quality evaluation. New computational methods will be developed to integrate repertoire and genomic data to: (i) improve detection and annotation of germline IG genes, including highly divergent and previously unrecognized gene families; (ii) identify non-canonical antibody features such as ultralong or structurally atypical antigen-binding sites; and (iii) characterize structural variation and gene organization within IG loci. Using these data, the project will quantify species-level variation in germline gene content, gene usage frequencies, and V(D)J recombination features. Phylogenetic comparative models will then be applied to test hypotheses linking variation within antibody repertoires to ecological and life-history variables (including lifespan, diet, and population dynamics) and to reconstruct the evolutionary history of IG gene families. Finally, the project will analyze relationships between germline IG gene copy number, genomic organization, and expression bias to assess how the molecular evolution of IG loci shapes antibody repertoires. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2026 · 2026-01

The primary objective of the research supported by this award is to inform real-time adaptive automation interventions in safety-critical system operations by leveraging cognitive workload predictions and integrating operator emotional state information. The research seeks to address two critical limitations in current cognitive workload modeling: (1) unreliable ground-truth labeling; and (2) classification model dependence on extensive offline training. By capturing multi-source physiological signals and context-dependent emotional state information, the project looks to formulate a new cognitive workload model to significantly enhance the accuracy of human state assessment. This work seeks to produce an integrated cognitive-emotional state assessment framework that links operator cognitive and emotional states to specific system automation responses. The research plan involves high-fidelity driving simulator experiments to generate a multimodal dataset that captures driver performance, central and peripheral physiological signals, cognitive workload responses, emotional states, and driver feedback during challenging scenarios. Statistical analyses intend to identify how cognitive and emotional states interact, providing a quantitative and probabilistic basis for decision rules to trigger timely, context-aware driver assistance. In parallel, nonlinear deep learning models will be trained to the simulator dataset to predict optimal timing and forms of automation interventions. The primary technical outcome intends to be a novel systems design framework for advanced driver assistance systems, achieving more accurate real-time state assessment and contextually appropriate automation interventions. The research looks to generate fundamental insights on the dynamic interplay of cognitive and emotional states in safety-critical tasks, representing a paradigm shift in how human-centered automation systems can enhance operator safety and trust. The project will also have an educational impact by training graduate students in human factors engineering and intelligent systems design. Finally, research findings will be disseminated broadly through peer-reviewed publications, conference presentations, and other outreach efforts to benefit both the research community and industry practitioners. This award has been funded by the Engineering Design and Systems Engineering, EDSE and Mind, Machine and Motor Nexus, M3X programs in the Division of Civil, Mechanical, and Manufacturing Innovation. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-09

This project will develop mathematical models that will aid in the understanding of animal migration. Migration is a widespread phenomenon that occurs seasonally as animals shift their locations in response to changing conditions. Oftentimes these changes involve spatial variation in resources that serve as cues for animals to track, resulting in wave-like population expansions. This research will use a series of novel mathematical modeling approaches to explore such seasonal, wave-like migratory dynamics, with a specific focus on understanding how the quality and quantity of resources interact to shape the pace and pattern of migration for varied theoretical scenarios. In addition, a pre-existing dataset of GPS tracking data for the critically endangered scimitar-horned oryx (Oryx dammah) will be analyzed to characterize when, where, and how well the animals track seasonal changes in resource availability in a resource-poor landscape. The project will support the training of undergraduate and graduate students who are developing skills and knowledge at the interface of mathematics and biology. Consumer tracking of transient resources occurs worldwide in a wide range of systems and taxa. The 'green wave surfing' hypothesis is a recent conceptual advance in understanding such resource tracking that is now widely discussed with regard to seasonal migrations of ungulates, birds, and marine species. According to this hypothesis, migrating consumer species living in seasonal systems should closely track the progression of the highly nutritious plant green-up wave that moves across the landscape as the growing season begins. Empirical data demonstrates that such tracking does occur for some individuals, populations, and species; however, 'surfing the green wave' is not universal, and instead some taxa either jump ahead of the green wave or lag behind it as it seasonally translates in space. The project will develop hybrid dynamical system models involving reaction-advection-diffusion equations with reaction and diffusion coefficients and growth governed by the quantity and quality of the resource green-up wave. Model variants including Allee effects, shifting habitats, and population structure will bring added biological realism. Research will address the impacts of sex- and age-specific migratory behaviors, predation, and mating success on migratory dynamics. Methods from differential equations, integral equations, and dynamical systems will be employed to identify conditions under which populations can persist in the long run. Existence of equilibrium solutions, traveling wave solutions, and oscillating solutions in time and density will be established to understand how 'surfing the green wave' promotes population growth and develops spatiotemporal patterns in population persistence on bounded domains. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-09

This research project seeks to study astronomically important metal-bearing free radicals, specifically magnesium-bearing carbon chains and clusters, through a combination of laboratory spectroscopic measurements, quantum chemical calculations, and theoretical spectroscopic modeling. Free radicals generated using laser ablation of metals followed by reactions with organic precursors will be cooled in a supersonic molecular beam yielding conditions analogous to those in the interstellar gas. State of the art laser techniques will then be used to record high resolution optical and infrared spectra of the free radicals, enabling their identification in space by means of sensitive spectroscopic surveys done with large powerful telescopes. This project will train a graduate student and a postdoctoral scholar in methods of advanced laboratory astrophysics. The research team will develop novel methods to produce magnesium-bearing carbon chains and clusters containing an odd number of carbon atoms, which are hypothesized to exist but await detection in the circumstellar envelopes of evolved carbon stars. After conducting vibrationally resolved spectral surveys using nanosecond pulsed lasers, the team will conduct rotationally resolved measurements using continuous-wave lasers to achieve unprecedented spectral detail on the target molecules. The team will then use sophisticated Hamiltonian models they developed to gain insight into non-Born-Oppenheimer interactions between the lowest energy electronic states of these molecules, particularly pseudo-Jahn-Teller and spin-orbit interactions that influence the energy level structure and intramolecular dynamics. The project would characterize fundamental molecules which play a central role in optical cycling, enabling advances in precision spectroscopy, low-temperature chemistry, tests of fundamental physics, and quantum information science. 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

With the support of the Chemical Mechanism, Function, and Properties Program of the Division of Chemistry, Professor Craig Grapperhaus of the Department of Chemistry and Professor Gautam Gupta of the Department of Chemical Engineering at the University of Louisville are developing immobilized zinc complexes for the reversible binding of carbon dioxide. The goal of this research is to understand the fundamental molecular mechanisms of carbon dioxide binding and release, with the longer-term potential for capturing carbon dioxide from dilute sources for delivery as a production feedstock for applications that currently require concentrated, high purity carbon dioxide. The fundamental studies include two different strategies to immobilize zinc complexes on solid substrates, materials characterization, and measuring the effects of immobilization on the electronic structure and acid/base properties of the complexes. Evaluation of carbon dioxide capture and release includes optimizing reaction conditions, confirming product identities, and quantifying equilibrium binding constants and loading capacities. Broader impacts include the improved molecular-level understanding of carbon dioxide interactions with small molecule metal complexes, workforce development at the interface of chemistry and chemical engineering, and curriculum development. The reversible capture of carbon dioxide from dilute sources is a critical challenge to collect and deliver this ubiquitous but gaseous carbon feedstock for use in catalytic reactions and other applications requiring high purity, concentrated carbon dioxide. A key innovation in the proposed work is the use of metal-ligand cooperativity in complexes with a Lewis acidic metal center and non-coordinating Lewis base in close proximity. The Lewis acid-base pair facilitates carbon dioxide capture via insertion into a metal-alcohol bond, which balances the thermodynamics to provide for facile capture and release. Immobilization of the complexes on solid substrates will allow translation of the homogeneous, solution reactivity to heterogeneous conditions. A major advantage of this approach is that a large number of active sites can be incorporated per volume of material to increase the carbon dioxide capacity. Immobilization will be achieved through chemisorption via axial ligand exchange and/or covalent bonding via chelate functionalization. The reversibility of the carbon dioxide capture/release process will be demonstrated using pressure swing adsorption, which avoids the heating employed for many other carbon dioxide capture systems. This may improve durability and will reduce energy consumption during the release process. 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

With the support of the Chemical Catalysis program in the Division of Chemistry and the Established Program to Stimulate Competitive Research (EPSCoR), Professor Andrew Wilson of the University of Louisville is studying the electrochemical reduction of oxygen. The reduction of oxygen is an important reaction in the storage and conversion of energy as well as in the synthesis of organic chemicals. Limitations of the electrochemical oxygen reduction reaction include high energy costs and transport of oxygen to the reaction site. Professor Wilson and his team of researchers will address these limitations by studying how the properties of nonaqueous solvents influence the ability of metal nanoparticle catalysts to convert visible light into electrical energy and heat to improve the oxygen reduction reaction. Understanding how solvent can impact the conversion of visible light into electrical or chemical energy on metal electrodes will have broad impacts including new approaches in the synthesis of sustainable fuels and organic molecules and new strategies to improve the efficiency and capacity of metal-oxygen batteries, driving forward the pursuit of energy security. The research will be complemented with an educational plan to recruit and retain a diverse population of students in chemistry by engaging underrepresented minority and first-generation students in research at the beginning of college and pre-college time periods. Engagement will be accomplished through the development of an introductory research course, integrated workshops, and pre-college outreach and education. With the support of the Chemical Catalysis program in the Division of Chemistry and the Established Program to Stimulate Competitive Research (EPSCoR), Professor Andrew Wilson of the University of Louisville is studying how the external medium impacts the plasmonic enhancement of the electrocatalytic reduction of molecular oxygen. The primary scientific goals of this research are to obtain a mechanistic understanding of how plasmon excitation and decay effects electrochemically-driven oxygen reduction in aprotic solvents and to understand how the peak energy of plasmon resonances and excitation across a plasmon band effect oxygen reduction. Using electroanalytical chemistry, surface spectroscopy, and finite-element simulations, a systematic investigation of how solvent properties influence plasmon-enhanced oxygen reduction in aprotic electrolytic solutions will be conducted. The research outcomes are expected to provide an understanding of how solvent attributes influence the availability of light-generated charge carriers and dissipation of heat to reduce the kinetic overpotential and concentration polarization of the oxygen reduction reaction. Understanding the mechanism of plasmon-enhanced electrochemistry in nonaqueous solvents is expected to open new synthetic pathways for fuels and chemical commodities that do not occur in aqueous electrolytes or in the absence of light, as well as potentially unique ways to control surface and interfacial chemistry. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-07

Non-technical summary Innovations in materials are at the center of recent technological advances. The ability to modify and control material structures, and understand their structure-property relationships can lead to exciting scientific discoveries and new technologies. In addition to pushing the boundaries of science, such advances lead to the improved prosperity of society. An important family of oxide materials are perovskite oxides. The structure and functional properties of these materials can be modified by different synthesis methods. With support from the Solid State and Materials Chemistry program in NSF’s Division of Materials Research and the Office of Strategic Initiatives, Prof. Ramezanipour at the University of Louisville, investigates the incorporation of oxygen-vacancies into perovskite oxides, where some of the positions that would typically be occupied by oxygen atoms are vacant. The distribution of these vacant sites in the material structure can be either random or ordered. In this project, several hypotheses are evaluated in an effort to understand how the ordering of oxygen-vacancies can be induced and controlled. Several chemical principles are used, such as the effect of electronic properties and geometric stability. Based on these criteria, a wide range of oxygen-deficient perovskites with different vacancy-arrangements and ordering patterns are synthesized, helping to establish guidelines for rational design of vacancy-ordered materials. Broader impacts of this project include training student researchers, enhancing undergraduate students’ laboratory skills, and outreach activities aimed at motivating high school students to pursue higher education, particularly in STEM fields. Technical summary This project, supported by the Solid State and Materials Chemistry program in NSF’s Division of Materials Research and the Office of Strategic Initiatives, establishes guidelines for rational design of vacancy-ordered structures in oxygen-deficient perovskites and their derivatives. These studies are motivated by remarkable properties of this family of materials, which are different from those of stoichiometric systems. The research is built around several hypotheses, based on fundamental chemistry considerations, to explore the formation and arrangement of oxygen-vacancies. Methods of tuning the concentration of oxygen-vacancies are studied to obtain perovskite-based oxides with different degrees of vacancies. Several types of ordering can be realized in oxygen-deficient perovskites, each leading to different types of coordination, such as tetrahedral, square-pyramidal, and octahedral, for metal cations within the material lattice. In this project, these different ordering-types are investigated, and the principal investigator and his research group explore several factors as potential parameters that affect the ordering of oxygen-vacancies. A series of experimental techniques are used to obtain the target materials and investigated by an array of state-of-the art characterization methods. Overall, the researchers use a combination of compositional and synthetic considerations to develop design principles for the realization of each type of ordering in oxygen-deficient perovskites, which might enable new materials for various technological applications, thereby increasing the economic competitiveness of the U. S.. 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 grant provides funding to organize and execute the 2025 National Science Foundation (NSF) Foundational Research in Robotics (FRR) / National Robotics Initiative (NRI) Annual Meeting. This meeting will convene investigators of active awards of the NSF FRR and NRI Programs for the tenth time since the NRI Program began in 2011 and the FRR program began in 2020. This meeting serves as a conference bringing together a community of robotics researchers whose work is specifically relevant to the FRR and NRI programs. The agenda includes talks from selected projects, poster sessions, workshops, panel discussions, multiple keynote talks, an outreach event, and Federal agency program updates from leaders in the research community. This meeting will also support participation by a cohort of potential future researchers to the FRR program. The FRR/NRI PI meeting promotes dissemination of research ideas and collaboration amongst robotics researchers. This award is for the meeting organization. The annual PI Meeting is a national event for the NSF FRR and NRI research communities and serves as an annual forum where investigators have opportunities to meet and share their research and best practices, discuss new research opportunities, explore new ideas and partnerships, and interact with Federal agency representatives, industry, and other stakeholders interested in NSF robotics research. NSF PI meetings have played a major role in community-building across a broad range of robotics topics, sectors, and technologies, and serves to bring together researchers who have interest in learning about the program and participating as future proposers, transition partners, or sponsors. Invitees to the meeting will include, among others, all PIs with active NRI/FRR grants. 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 · 2025-04

This I-Corps project is based on the potential commercialization of a novel concrete material. By utilizing industrial waste materials and incorporating an energy-efficient curing method that recaptures water, this project reduces water consumption in the construction sector. The applications are relevant to potential industry sectors ranging from home construction and community infrastructure to space exploration. Notably, the technology's net-zero water aspect can be used in water-stressed regions in the U.S. and worldwide. Additionally, the technology can support U.S. efforts in space exploration by enabling on-site extraterrestrial construction materials. This I-Corps project utilizes experiential learning coupled with first-hand investigation of the industry ecosystem to assess the translation potential of an alternative concrete material. This concrete substitute utilizes alternative cementitious materials and carbon nanomaterials for the production of building materials with an integrated water recapture system. This technology utilizes geopolymerization, a reaction that results in a concrete-like product using industrial byproducts and water; Unlike conventional concrete's hydration reaction, which consumes water, geopolymerization does not consume water after solidification. This solution provides a cement alternative that is more cost effective and can be used in construction locations where resources are scarce. 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 · 2025-01

The broader impact of this I-Corps project is based on the development of a novel sensor for detecting free insulin. The sensor will provide its users with critical insights into their metabolic status and health. Such a device has broad-ranging healthcare applications, including early screening for metabolic disease, types I & II diabetes care management, and gestational diabetes screening. The ability for physicians to detect the early stages of metabolic disease may reduce the strain on the healthcare system, as diabetes care management accounts for one of every four healthcare dollars spent in the U.S. and costs over $400 billion annually. The advanced insight gleaned from preventive screening for metabolic disease can improve quality of life, minimize the complications associated with diabetes, and reduce the financial burden of metabolic disease. This I-Corps project utilizes experiential learning coupled with a first-hand investigation of the industry ecosystem to assess the translation potential of the technology. The solution is based on the development of a minimally invasive, wearable biosensor for continuous detection of free insulin. The sensor will quantify the user’s metabolic health (namely, insulin resistance). Currently, the diagnostic criteria for prediabetes and type II diabetes rely on elevations in blood glucose, a late-stage marker present when the disease has become difficult to manage. Emerging evidence suggests that insulin resistance precedes glucose dysregulation by nearly a decade, but there are currently no widely available diagnostic and monitoring technologies. Metabolic dysfunction and disease progression begin with insulin resistance, which is first apparent in the minutes and hours that follow a meal (“post-prandial insulin”). In this context, continuous insulin monitoring provides a unique opportunity to detect diabetes at its earliest stage, when it is most amenable to intervention. 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 2024 · 2024-12

This grant will support student attendance at the 2025 Annual Conference & Expo of the American Society of Engineering Education (ASEE), which will be held in Montreal, Canada, 22-25 June 2025. The ASEE Annual Conference & Expo is the society’s flagship event which attracts thousands of participants from academia, industry, and government. The conference provides a forum for networking, professional development, and dissemination of cutting-edge research and educational practices. At the ASEE Annual Conference, the Manufacturing Division organizes technical sessions, panel discussions, workshops, and hands-on activities covering cutting-edge topics such as automation, additive manufacturing, smart manufacturing, robotics, and Industry 4.0. The Manufacturing Division also recognizes excellence in manufacturing education by presenting awards for best papers, outstanding educators, and exemplary teaching and research contributions. The financial costs associated with the conference expenses represent a challenge for students, especially those from underrepresented groups, minority-serving institutions, and institutions with limited research funding. Many students, despite their potential and commitment, are unable to attend due to these financial constraints. This grant will directly address these challenges by providing financial support to ensure that a diverse group of students can attend and benefit from the ASEE conference. This funding will help cover the costs of registration, travel, and lodging for 25-30 undergraduate and graduate students from US institutions to attend the ASEE Annual Conference. The availability of travel funding will be announced through the ASEE Manufacturing Division website and shared with relevant communities. The selected students will participate in the ASEE Manufacturing Division sessions and activities. The key objectives of this grant are to (1) promote student engagement in state-of-the-art manufacturing innovation and education, with an emphasis on digital manufacturing, advanced manufacturing systems, and sustainable manufacturing practices, (2) increase participation of students from underrepresented groups in manufacturing engineering, thereby promoting diversity and inclusion in the field, (3) provide students with opportunities to network with experts and professionals in manufacturing for future collaborations and career development, and (4) contribute to the growth and development of U.S. manufacturing workforce by exposing students to the latest research and technological advancements in manufacturing education. 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 2024 · 2024-12

This grant is in partial support of a three-day symposium to identify research gaps and needs for ambient energy buildings. The symposium is targeted to be held in the fall of 2025 at Ghost Ranch, New Mexico. Ghost Ranch is a historic and fitting location for this ambient energy focused event, as the site includes passive solar cabins. Registration will be limited to approximately 55 in-person and 30 virtual participants from all facets of building research, education, policy, and industry. Ambient energy for buildings offers promising contributions for handling energy challenges. Much of the energy use in homes is directed towards maintaining comfortable temperatures, such as heating and cooling, and ambient energy approaches address both these functions. Moreover, regular household activities like hot water, lighting, refrigeration, and cooking can be powered more sustainably with ambient approaches. The abundant energy offered by the natural environment—sourced from the sun, wind, sky and earth—holds untapped potential. Further, by weaving in natural light and designs inspired by nature, living spaces can be transformed into more comfortable and health-promoting environments. Ambient-conditioned buildings can serve not only as a cornerstone for architectural advancement but can also significantly contribute to environmental sustainability, societal well-being, and educational development. The symposium aims to be a hub of innovation, bringing together luminaries from diverse fields such as architecture, design, engineering, and education. Anticipated key outcomes of the symposium include (1) a roadmap for integrating ambient energy and passive solar into modern building practices, (2) cross-disciplinary collaborations formed to spearhead new projects and research in the field, (3) a collection of best practices, case studies, and recommendations to guide policymakers and industry leaders in adopting sustainable practices, and (4) a website that will serve as a hub that houses the items just listed. The 21st century presents a dual mandate: to counteract climate change and ensure the wellbeing of the global populace. A pivotal player in this scenario is the building sector. The startling fact is that in the US, buildings dominate energy consumption charts, utilizing a significant proportion of electricity and natural gas. Their contribution to carbon emissions is also high. To tackle climate change effectively, there must be a focus on the building sector. While there has been enthusiasm about turning buildings "green" and achieving net-zero standards, the transition is moving more slowly than expected. Projections suggest that by the mid-century, a substantial portion of electricity will still be tied to fossil fuels. And, with urban sprawl and increasing infrastructure, building footprint is set to expand, potentially exacerbating the fossil fuel conundrum. This necessitates a rethinking of the current approach, expanding it to more fully embrace ambient energy for buildings. 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 2024 · 2024-12

This project establishes the Louisville Academic Research Compute Cluster (LARCC) at the University of Louisville (UofL). LARCC is a Graphics Processing Unit-enabled High-Throughput Computing infrastructure (HTC) that supports distributed parallelization of campus-wide computational tasks across both local and global computing resources. Connected to the OSG, LARCC can seamlessly scale across dozens of collaborating institutions, accelerating innovation in UofL’s priority areas, including Artificial Intelligence (AI), chemistry and molecular dynamics, health sciences and cell biology, bioinformatics, psychology and brain sciences, and materials science. LARCC overcomes the limitations of UofL’s existing cyberinfrastructure, facilitates cross-disciplinary research collaborations, and supports many outreach efforts including those to local K-12 students, African Americans, Hispanics, Native Americans, and Research Experiences for Undergraduates sites. LARCC’s HTC design optimizes large-scale computational tasks by enabling parallel execution across distributed computing resources, efficiently handling independent tasks including parameter sweeps and model optimizations. It advances research in energy-efficient data centers, high-throughput sequencing analysis to uncover underlying biological mechanisms, and the development of high-performance materials for nano-electronics and energy storage. In health sciences, LARCC supports the development of new machine learning models for cancer chemotherapy, digital pathology, and non-invasive cardiovascular diagnostics. It also facilitates new AI models in autonomous navigation and the development of digital therapeutics for conditions such as eating disorders, integrating 3D modeling and AI-driven diagnostics. By providing scalable and collaborative computational power, LARCC addresses UofL's campus-wide scientific computing needs, fosters new breakthroughs, and drives interdisciplinary innovation. This project is jointly funded by the Office of Advanced Cyberinfrastructure and the Established Program to Stimulate Competitive Research (EPSCoR). 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 2024 · 2024-11

This Computational and Data-Enabled Science and Engineering (CDS&E) collaborative research project will contribute to the progress of science and the advancement of national prosperity by developing a framework for the inverse design and fabrication of multiphase composite materials with tailored mechanical properties. Despite recent advances in the deployment of machine learning techniques to materials science, the creation of materials with desired mechanical properties in multiple loading directions remains a significant challenge. This research plans a new data-driven framework to understand the relationship between material architecture and mechanical behavior, facilitating the design of nonlinear materials for a wide range of applications such as lightweight structures, shock absorbers, and aerospace components. This research will be integrated with educational and outreach programs aimed at attracting underrepresented groups to engineering and improving undergraduate and graduate learning in data-driven science and engineering. High school students and the public will be introduced to data-driven material design and applications in collaboration with a local museum and science center. This collaborative research will create and test a new physics-informed deep learning (PIDL) framework to tailor the multidirectional or multi-objective mechanical properties of exotic composite materials. It will utilize the principles of PIDL to build a data-efficient and physically interpretable surrogate model of structure-property relationships for multiphase composite materials. This research will formulate constitutive equations for constituents in voxel-based composite materials and incorporate them into a forward physics-informed convolutional neural network model. A novel multi-objective inverse physics-informed conditional diffusion model will be developed to reveal the property-structure correlation between a multiphase composite material’s bulk mechanical properties and its architecture, combining macroscopic and microscopic data to enhance model accuracy and robustness. Finally, the designed materials will be additively manufactured and tested, with validation through advanced additive manufacturing, X-ray imaging, and multiaxial testing. 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 2024 · 2024-10

This project aims to automate the quantification of students’ engagement in early engineering course work, using non-invasive, non-intrusive and non-stigmatizing means. Engagement is captured in terms of three strands: a) Emotional engagement describes the students’ feelings about learning, the learning environment, the teachers, and their classmates. Operationalization of emotional engagement includes expressing interest, enjoyment, and excitement, all of which can be captured and interpreted from facial expressions. b) Behavioral engagement reflects internal attention and focus, and can be operationalized by body movement, hand gestures and eye movement. c) Cognitive engagement describes the extent to which the student is mentally processing the information, making connections with prior learning, and actively seeking to make sense of the key instructional ideas. This project expands on the research of an IUSE Level 1 funding which created a prototype of a biometric sensor network (BSN) that captures and interprets levels of behavioral and emotional engagement. This IUSE Level 2 project plans to make the BSN portable and scalable to various class sizes and settings, to explore novel machine learning (ML) methods for further quantifying students’ engagement and extend the automated quantification of engagement to include cognitive engagement. Proposed validation of the extension to include cognitive engagement to be operationalized by incorporating cognitive probes seamlessly into the lectures. These probes are scheduled to be designed to align with the cognitive hierarchy captured by the Interactive, Constructive, Active, and Passive (ICAP) framework, and student responses to the probes should permit these responses to serve as independent external cognitive engagement validation against which the automation algorithms’ interpretations can be compared. The NSF IUSE: EDU Program supports research and development projects to improve the effectiveness of STEM education for all students. Through the Engaged Student Learning track, the program supports the creation, exploration, and implementation of promising practices and tools. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-10

Reducing carbon dioxide levels in the atmosphere is essential for mitigating climate change, protecting human health, and ensuring a sustainable future with a healthy planet. Therefore, state-of-the-art technologies are needed that can maintain practical performance while efficiently decreasing atmospheric carbon dioxide levels. One such promising method is electrochemical carbon dioxide reduction in which CO2 electrolyzer instruments use electricity to facilitate a chemical reaction with carbon dioxide to convert it into chemical by-products that can be an alternative to fossil fuels. The coupling of eliminating CO2 by converting it to beneficial products is immensely promising as an economically viable strategy to mitigate climate change. However, several challenges prevent CO2 conversion by CO2 electrolyzers from being a practical large-scale option, including limitations in usable CO2 sources and the reaction being inefficient. This project aims to improve electrolyzer design to make CO2 conversion more efficient, which leads to electrolyzers being able to use commercially practical CO2 sources and enables new technological applications for converting CO2 to useful products, helping to decarbonize the chemical industry. The educational objectives of this project are to enhance the diversity in the STEM workforce by engaging students of historically underserved backgrounds from the partner HBCU institution in research internships, to advance research entrepreneurial education through product innovation boot camps and subsequent mentoring of a senior undergrad engineering design team, and to develop hands-on electrochemistry module targeting high school students. The vast majority of CO2 reduction (CO2R) research has utilized aqueous media and has had a small number of low-carbon products that can be synthesized with reasonable selectivity. The proposed research seeks to advance the field by designing a gas diffusion electrode (GDE) that is super-repellent to even low surface tension fluids to enable a generalizable porous cathode platform for gaseous reactant flow electrolysis without flooding that is applicable in nearly any non-aqueous solvent. Via the careful design of microstructures with high aspect ratio and overhanging tip geometry (i.e., re-entrant) features, electrode interfaces will be rendered superomniphobic by leveraging surface tension to maximize the energy barrier to transition between a suspended liquid droplet state (i.e., Cassie-Baxter) and a wetted surface state (i.e., Wenzel). Methods will be developed to incorporate the re-entrant microstructures on a porous electrode surface. The interfacial and applied potential parameters that affect charge transfer and control electrowetting of the surface will be unraveled as a function of microstructure geometry and solvent properties to determine critical threshold parameters for successful non-aqueous CO2R operation. Studies of the triple-phase interface under active flow electrolysis with in-situ high-speed fluorescence imaging of the GDE will elucidate the effects of pressure, flow regime, and potential on the cathode. Lastly, superomniphobic porous cathodes will be leveraged for high-performance non-aqueous electrolysis in CO2R tandem cathodic reactions for ester production and ionic-liquid mediated CO2R in an aprotic solvent. 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 2024 · 2024-09

Although the benefit of qualitative research and design methods is clear in social sciences and other fields, little is known about the need and impact of adding qualitative methods training for engineering students and professionals. The curriculum for undergraduate engineering students is heavily focused on developing quantitative skills that are inherent to the engineering discipline. Engineering professionals may need to expand their expertise and training to also include qualitative methods based on the interdisciplinary and evolving workplace. This project will introduce qualitative methods training into an existing engineering curriculum so that students acquire both quantitative and qualitative skills (i.e., "mixed methods"). This mixed methods approach may better prepare engineering professionals for interdisciplinary work. This research initiation proposal will include human-centered design (HCD) as an example. Qualitative methods, such as ethnography and interviews, can capture the complexity and preserve the context of the work environment within which a product that follows an HCD process is implemented. Quantitative methods, in addition to allowing for precise measurement and structured design principles, also allow for the manipulation of experimental conditions and measurement of dependent variables in a controlled setting. Thus, an ideal HCD process is a mixed-methods approach that leverages the advantages of both qualitative and quantitative methods and integrates them. This research will help enable future engineers to more systematically craft designs to better meet the needs of a wide diversity of clients. This project aligns with the National Science Foundation's Professional Formation of Engineers initiative; expanding engineering students' training to better prepare them for interdisciplinary work will contribute to creating and supporting an innovative and inclusive engineering profession for future engineers. This project will investigate the potential benefit of a mixed-methods approach (quantitative and qualitative methods) to engineering design within the realm of HCD. This research is guided by three questions: (1) What are current mixed methods scenarios that are used in the practicing engineering community? (2) What is the impact of introducing qualitative methods training for engineering students using the HCD example? (3) What mixed-methods models can be developed using an HCD process? Investigating these questions has the potential to substantially advance our knowledge for preparing engineers as interdisciplinary professionals. Activities for the proposed research will include an assessment of rich and relevant mixed methods scenarios in the practicing engineering community. We will build from the relevant literature to understand specific problems and context, and the extent to which qualitative methods are helpful. We will then perform an experiment to understand what differences the inclusion of qualitative methods instruction has on engineering students' design solutions to HCD problems. We will then develop plans to weave qualitative methods training into the existing engineering curriculum, initially, within the University of Louisville's Department of Industrial Engineering. This work will engender future research that will formalize qualitative methods training for engineering students to equip them as mixed-methods professionals to engage in interdisciplinary endeavors. We will work with regional industry partners on using the results of this project to strengthen the application of qualitative methods in the practicing engineering community. Findings from this project can be used to develop a mixed methods workshop for engineering professionals, as well as other engineering educators for teaching purposes. 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 2024 · 2024-09

Flash floods impact communities throughout the US each year, causing loss of life, property, and livelihoods. Rural communities, especially those in the Appalachian region, are particularly vulnerable to flash floods. This, in part, is due to the limited infrastructure to understand, predict, and prepare for flash floods in these regions. To address these challenges, the project will bring together civil engineers, environmental scientists, and social scientists to work alongside community research partners from the region. A key outcome will be an improved ability to understand, predict, and prepare for flash floods under different conditions. This will be achieved with new models, strategically placed sensors, regional flood analyses, and insight from those most affected by flash floods, community members. Researchers and community members will work together to identify specific issues related to flash floods, such as flooding knickpoints and locations where models may perform poorly. By integrating engineering, environmental science, and social science, this project will create solutions tailored to community goals, serving as a model for resilience planning in vulnerable communities across the US. The project's workforce development plan will guide over 500 middle and high school students in the Appalachian region through college and into their careers. Activities will include field experiences, tree plantings, and environmental sensor trainings.. This plan will be put into action with the help of community partners throughout Appalachia, including local citizens, non-profit organizations, and watershed associations. Flash flooding has caused the highest number of fatalities of any flood type in the last two decades. Communities in central Appalachia are especially vulnerable to flash floods. The goal of this project is to gain fundamental knowledge of flash flooding under a variety of weather events and mitigate its impacts in vulnerable rural communities by advancing research capacity, interdisciplinary collaboration, and scientific literacy across Kentucky and West Virginia EPSCoR jurisdictions. Using increased hydrologic research infrastructure and an evidence-based community engagement model, the project will integrate three research tasks to meet this goal: 1) advance the hydrologic sciences to understand controls of flash floods in disturbed and forested stream systems; 2) facilitate community-engaged research to increase resilience and flash flood technology uptake; and 3) develop a community-led science model for increasing knowledge of flash floods. The project will couple catchment-scale hydrologic models (process-based, machine learning), on-the-ground data collection, regional flooding analysis, and hydrologic sensing technology with evidence-based participatory action research to co-create new flash flood knowledge, tools, technology, and subsequently, tailored solutions. The project will provide insight on heavily disturbed landscapes across the US; how to measure, monitor, model, and predict flash flooding with sufficient time for communities to respond in understudied and infrequently monitored headwater systems; and what current and future flash flood risks look like in stream-adjacent communities. 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 2024 · 2024-09

This project engages stakeholders to drive research and workforce development on equitable design and implementation of nature-based solutions (NbS). The research will be conducted around three living hubs in New Hampshire (NH), Rhode Island (RI), and Kentucky (KY). The project will make major contributions to decision-making about NbS over the next decade. Given the project's location, it will have a direct impact on disproportionately affected populations in the three living hubs. The project's community-engaged, transdisciplinary approach will empower community members as change agents for increased climate resilience. The project has the potential to change the national and international paradigms for designing and implementing socially equitable NbS. The research will contribute to improved decisions about NbS in the three jurisdictions and help to address the urgent global need for improved decision tools for climate resilience. This research will also build human and social capital through transdisciplinary knowledge transfer, as well as training and mentoring of undergraduate and graduate students, post-docs, and early career faculty. A recruitment program for underrepresented students and mentorship plans for all early career personnel will significantly develop capacity for climate resilience research across the three jurisdictions while developing leadership skills and collective efficacy. The overarching goal of the Equitable Nature-based Climate Solutions (ENACTS) project is to understand and quantify the influence of NbS on social equity and to conceive design principles and best practices that promote equity, alongside sustainability, resilience, and practicality. ENACTS builds capacity through new research infrastructure to identify novel methods to center equity in NbS for climate resilience. The research, supported by local knowledge, coupled with integrated social and natural sciences, engineering, art, and design, will create an ecosystem of academia, government, and communities inclusive of underserved and Indigenous groups to support more informed and equitable NbS decisions. ENACTS will make transformative advances in our understanding and capacity in designing and implementing socially equitable NbS, leading to increased community resilience against the ongoing climate crisis. This work will generate new co-produced knowledge in 1) the risk-scapes and the NbS environmental, social, and economic effect-scapes across the three living hubs and their influence on distributional equity; 2) the level of process-based equity in past and current NbS projects and its influence on NbS decision-making; 3) the preferred NbS landscape designs for different social groups; 4) the optimal siting, sizing, and timing of NbS implementations in the three living hubs; and 5) the effectiveness of various behavioral interventions, including visual aids (e.g., augmented reality tours), scientific knowledge about the optimum solution (e.g., optimization model), understanding of the cultural background of the marginalized communities (e.g., Indigenous stories, photovoice, GIS story maps), and consensus building exercises (e.g., concept mapping, COPEWELL, serious gaming). 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 2024 · 2024-08

Fungi are a large and diverse group of organisms, estimated to contain 2.2-3.8 million species. Although the vast majority are not associated with disease, roughly 300 fungal species are known as human pathogens, and an even greater number are plant pathogens. In both cases, such pathogens pose major risks to human health via morbidity and mortality, and potentially devastating effects on agriculture, including both livestock and crops. While several models have developed for human fungal pathogens, important understanding of the fundamental biology and mechanisms of pathogenesis have come from plant pathogenic models, especially the group known as “smuts,” which can serve as a model for study of 1) nerve development; 2) DNA repair mechanisms similar to those of the BRCA2 human gene, strongly associated with risk for breast and ovarian cancers (absent from traditional yeast models); 3) mitochondrial dysfunction; and 4) evolution of disease mechanisms, with potential to predict emerging infectious disease via host shifts. This last point is the focus of the funded research project and explores genetic differences in plants and their responses to different fungal pathogens. The work has broad implications for society, especially by providing a better understanding of why some plants are more susceptible to severe infections, while others are better able to fight off the same pathogens while displaying no or milder symptoms. Moreover, this project will expand the pool of trained scientists at all levels (undergraduate, graduate student, Postdoctoral), thereby providing the next generation(s) of individuals prepared to address new threats to human health and agricultural security. The smut fungi are a large, diverse, and non-monophyletic group of plant pathogens. Among these are fungi with agricultural importance, and whose prominence has expanded due to (1) their facility of manipulation and extensive molecular genetic toolkits, or (2) their economic and international diplomatic impacts. By contrast, the Microbotryum violaceum complex of fungal plant pathogens continues to be important in ecological/population genetics/evolutionary studies and, more recently, has become a useful model of emerging infectious diseases through host shifts. Each fungal species in this complex of “anther smuts” is limited to successful infection of one or a limited number of host species in the Caryophyllaceae (Pinks). However, some Microbotryum species (e.g., on Dianthus hosts or Lychnis hosts) are more “Generalists,” having a broader range of host plant species. In this project, comparison is made between infections of host plants by specialist Microbotryum species on preferred vs. non-hosts; similar comparisons will be made with generalist Microbotryum infections on different host plants as well. Data will be collected throughout the lifecycle of the fungus/plant interaction via microscopy (Confocal fluorescence, SEM), proteomics of fungal/host protein complexes, transcriptomics, and analysis of RNA editing of fungal transcripts. Cases of blocked infection may suggest a switch to enodphytism, particularly when presence of the fungus has been confirmed in stem and flower bud stages. In such cases, co-infection of plants with better-adapted pathogenic Microbotryum species and “endophytic” Microbotryum species in the same host will test whether any protection to the host is provided by the endophyte. 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 2024 · 2024-06

The broader impact of this I-Corps project is the development of a device for any vehicle with an electronic ignition to improve safety by detecting cognitive driving risk due to Alzheimer’s disease and related dementias. Cognitively impaired drivers place themselves and others in danger due to predictable errors in a variety of skill maneuvers compared with cognitively “normal” drivers. In addition, older adults with late mild to early moderate Alzheimer’s disease and Alzheimer’s disease-related dementias may have an unrecognized lack of self-regulation to accurately self-assess driving risk. To address this challenge, this technology is designed to provide a rapid and objective way to quantify the predictive cognitive risk of driving. The technology will support autonomy and avert Alzheimer’s disease-related dementias driving-related incidents and potential tragedies and property losses. The prevention of cognitively impaired driving may benefit family caregivers of those with Alzheimer’s disease and related dementias, commercial and mass transportation, and those who are cognitively impaired due to illicit drugs not detectable by breathalyzer technologies. This I-Corps project utilizes experiential learning coupled with a first-hand investigation of the industry ecosystem to assess the translation potential of an adaptive cognitive driving risk device to prevent driving and prompt mitigation strategies if cognitive driving risk is detected. This artificial intelligence (AI)-powered technology allows the identification of the least number of tests from seven neurocognitive domains to predict cognitively-at-risk driving with the best accuracy. In addition, the device provides real-time, predictive assessments of cognitive driving risk to support driving autonomy along with a mechanism to halt driving for those who refuse to cease driving after being deemed cognitively unsafe by a healthcare professional. A lab prototype has established the principles for driving prevention with a failed cognitive test. This technology may be used to support the Infrastructure Act of 2021, which mandates that all auto manufacturers integrate the ability to indirectly identify impaired drivers beginning in 2026. 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.