University of Cincinnati Main Campus

NSF Awards · FY 2026 · 2026-09

This award supports the study of nutrient runoff and its effects on American river systems. Nitrogen and phosphorus are known to cause harmful algal blooms and other ecological impacts in rivers. However, knowledge about the causes of variability in nutrient loads across river networks remains limited. Through the application of artificial intelligence to data from satellite remote sensing, this project will generate a detailed map of nutrients in rivers across the United States over time. This map will then be analyzed to understand human and natural factors affecting nutrient variability. This research integrates with education for high school, undergraduate, and graduate students. Project outcomes will support water management, ecosystem protection, and public health. This project will pursue three objectives. (1) A novel modeling framework that integrates remote sensing and deep learning will be developed. This framework will be used to estimate daily, reach-level total phosphorus and total nitrogen concentration in American rivers over the past five decades. (2) Major drivers and controlling mechanisms for nutrient variability across space and time will be identified. (3) Relationships between riverine nutrients and harmful algal blooms across various settings will be quantified. Outcomes of these analyses will reveal spatial patterns of nutrient sensitivity and eutrophication risk. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2026 · 2026-03

This award supports student travel for undergraduate and graduate students to attend the 69th IEEE (International) Midwest Symposium on Circuits and Systems, Cincinnati, on Aug. 9-12, 2026. The MWCAS is a premier international conference in the field of circuits and systems. Other than tutorials, keynote and plenary lectures by leading experts in the field, the conference will be attended by academic and industry researchers and students to present their recent work both in lecture sessions and poster sessions. The conference will host a student paper competition to encourage the best students to keep abreast of the most modern technologies. Recipients of this grant will be selected from the pool of students presenting papers and will be paid part of their expenses for travel and registration at the conference. The experience gained by the students in the process is going to be an invaluable component of their education. Due to a good number of industrial participants in the meeting the experience will also help them to secure positions in industry, thus contributing to the much-needed technological workforce of the United States. The selection committee will provide support from this grant for Education and Work Force Development (EWD) in the area of microelectronics, which is a major focus of MWCAS 2026. The conference will extensively cover technical areas in Circuits and Systems, including, but not limited to: Analog and Mixed Signal Circuits and Systems, Digital Integrated Circuits and Systems, Power and Energy Circuits and Systems, Sensory Circuits and Systems, Signal, Image, and Multimedia Processing, Communications Circuits and Systems, RF and Wireless Circuits and Systems, Biomedical Circuits and Systems, Neural Networks and Neuromorphic Engineering, Beyond CMOS Circuits and Architectures etc. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-11

The US requires more highly trained engineers to meet the problem-solving needs of the next century. Improved graduate education systems are one way to increase the degree completion of graduate engineering students. About half of engineering graduate students do not complete their degree. Students leave graduate degree programs for many reasons including conflict with faculty or peers, financial or academic difficulties, or family concerns. Additionally, well-paying industry positions in many sectors of engineering also provide attractive alternatives to continued education. However, some students who leave would have preferred to complete their degree. These partially trained graduate students represent a missed opportunity for future innovation and development. Changing research labs during doctoral study is one avenue doctoral students use to resolve serious conflicts. The research will measure how often students change research labs and identify obstacles, processes, and solutions needed in the graduate education systems to support students in addressing academic, personal, or social reasons for changing research labs. The research will inform strategic recommendations to improve engineering graduate education systems to facilitate students changing research labs as an opportunity to retain skilled and partially trained students. Engineering education requires creative solutions to the continued attrition of talented and well-qualified doctoral students who choose to leave without a doctoral degree. Over 70% of engineering doctoral students consider departing their programs and many, 40-60%, leave due to conflict with advisors and peers, financial or academic difficulties, and personal or family concerns. Some students choose to remain in doctoral engineering by changing their research lab or advisor, program, or university. In a recent preliminary survey, 60% of doctoral engineering students seriously considered changing or had changed research labs or universities during their doctoral training. Lab change provides the opportunity to retain partially trained and qualified engineering doctoral students. However, the costs for the individual, programmatic barriers, and advisor conflicts complicate changing labs. The research will describe and contextualize the lived experience of changing research labs during doctoral engineering training through quantitative, qualitative, and mixed methods. First, longitudinal data from a new survey will capture the frequency, predictors, and career outcomes of lab change. The second phase will use qualitative data from students planning, who are currently or have recently changed research labs or universities during doctoral engineering studies. A third phase will combine longitudinal quantitative data (Phase 1) with the qualitative interview data (Phase 2) to construct timelines for mixed-methods analysis of the process of changing research labs. Increased persistence of students with the experience, knowledge, and interest to be admitted for doctoral education impacts the industries and communities they participate in post-graduation. In particular, the persistence of underrepresented groups represents an opportunity to profoundly impact marginalized communities through the socioeconomic benefits of advanced engineering training and attention to marginalized research questions unasked by current engineers. Marginalized groups are focused on due to the disproportionately higher attrition rates. Findings from this research will be developed into proposed guidelines and considerations for lab-change policies to be adapted and integrated into existing department and college policies. The research will advance knowledge about why, how, when, and who changes research labs during engineering doctoral education. Outcomes include the identification of considerations to retain students in graduate study while addressing their academic, personal, or social needs that require changing research labs. While focused on engineering disciplines, the outcomes, and research methods have potential applications across fields in STEM, providing a research framework and direction for future research. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-10

The STEM workforce needs to be well-equipped with knowledge and competencies to ensure our nation’s global competitiveness. Experiential learning experiences like cooperative education (co-op) offer an apprentice-style approach to preparing the next generation of engineers and STEM professionals. Understanding how these authentic learning experiences fundamentally shape students’ learning and professional development is essential. This project will investigate how cooperative education (co-op) experiences influence engineering students’ readiness for the workforce by comparing various types of co-op experiences from the lens of transformative learning theory. Grounded in Mezirow’s transformative learning theory, this research will examine how first-time co-op experiences change students’ perspectives, learning gains, behaviors, and sense of professional identity. A variety of co-op experiences will be investigated – industry-drive, research-based, international, remote or virtual, etc. This research will be conducted at the University of Cincinnati, who is an institutional leader and nationally recognized for co-op experiences that engineering students integrate as part of their academic and professional journey. As part of this research, data will be collected from over 1,200 engineering students who participate in a variety of co-op experiences. Co-op pathways will be investigated across a variety of variables, and student comparisons will enable the investigative team to understand how co-op timing and diverse co-op experiences shape the student experience. The team will use surveys and interviews to identify patterns of change and determine how students’ experiences and co-op contexts influence student development. The use of Mezirow’s transformative learning theory will enable new knowledge to be gained around learning, challenges, dilemmas, reflection, and student development. This new knowledge to be generated as part of this project will inform academic practices around co-op experiences and inform industry co-op practices as well. This new knowledge will also enable STEM educators to understand the pathways to better prepare engineers and other STEM professionals for the workforce and support authentic student development. The findings, which will be disseminated widely across engineering and STEM education communities, will benefit universities by informing co-op structures, practices, and curricula, as well as support employers improve mentoring practices and strengthen co-op experiences. This project is supported by NSF's EDU Core Research (ECR) program. The ECR program emphasizes fundamental STEM education research that generates foundational knowledge in the field. Investments are made in critical areas that are essential, broad and enduring: STEM learning and STEM learning environments, broadening participation in STEM, and STEM workforce development. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-10

This NSF IUSE:EDU Level 1 project aims to serve the national interest by developing design principles by which educational tools powered by generative AI can support the goal of personalized learning in STEM education. Generative AI models, such as large language models, have presented educators with a tangible step toward addressing the National Academies of Engineering's grand challenge of personalized learning. In particular, chatbot-style AI tools (e.g., ChatGPT) have gained significant attention as mechanisms to enhance learning environments within and outside the classroom. Much effort has been placed into developing specific chatbots for disciplinary contexts, which lean heavily on the purported problem-solving abilities of modern large language models. This project plans to question design processes when creating these systems and aims to develop and test design principles for developing AI-empowered educational tools, focusing on providing personalized and humanized interactions and feedback grounded in theories of social presence and self-regulated learning. Computational thinking is recognized as a core 21st-century skill and a vital part of STEM education. As a context for testing the design principles, the project team intends to focus on courses aimed at developing computational thinking. The project team plans to build a generative AI tool called the "coachbot" utilizing the proposed education-informed design principles and will be tested in two complementary computing-focused courses at the University of Michigan and University of Cincinnati. The project aims to synthesize current literature to document how current educational AI tools are currently developed to extract preliminary design principles and gather insights related to their efficacy using a combination of learning analytics, student interviews, and pre-post surveys. The goal of this project is to develop design principles for AI-driven synchronous tutoring systems, focusing on computing education, by emphasizing broader, theory-based frameworks rather than niche, one-off chatbot solutions. Grounded in self-regulation theory and social presence theory, the initiative seeks to enhance students' metacognition and motivation in STEM while fostering acceptance of human-like digital tools. The resulting principles and AI model are intended to guide the development of future intelligent tutoring systems that prioritize educational outcomes beyond technological innovation. The project focuses on three research questions: (1) What design principles have been used to create educational AI tools? (2) How do students' experience interacting with the generative AI-powered tool called a “Coachbot” align (or not) with the elements of social presence? (3) How does the Coachbot support students' learning to solve computational problems? The project intends to address challenges in creating effective personalized educational experiences. To help to answer these questions, the project plans to: (1) conduct a scoping review to propose a set of evidence-based design principles for AI educational tool creation, (2) refine and implement the Coachbot as a specialized, personalized, principle-informed intelligent tutoring system for computationally focused courses, and conduct a case study centered on students’ use of the Coachbot of the proposed design principles from the perspective of students’ user experiences and learning outcomes; (3) revise the design principles based on the findings synthesized in Phase 2 and disseminate feedback from the greater design, AI, and education communities. 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 2025 · 2025-09

Non-technical abstract: Topological materials are a unique set of materials that conduct electrons differently on their surfaces than they do throughout their bulk. This leads to interesting and unique transport properties and potential to unlocking a new generation of efficient solid-state energy conversion devices. Results of this project will help gain fundamental understanding of the properties of topological materials, allowing their synthesis with most useful properties and usable forms while preserving their unique attributes. This approach is likely to ultimately enable a new class of efficient energy conversion devices based on strategically designed topological materials. This project also humanizes the fundamental concepts of charge carrier transport through extensive set of outreach and education activities. The research team uses a physical embodiment of charge carrier motion to explain it to a broad general audience and uses creative ways of conveying these concepts and inspiring interest in science. Technical abstract: While transport properties of topological materials have strong potential for use in efficient solid-state devices, little progress has been made moving towards their use due to challenges in designing and synthesizing topological material in useful forms. Crucial to this progress is understanding the critical length scales over which topological transport dominates, eliminating stray magnetic fields and reducing externally applied magnetic fields, and understanding transport across magnetic phase transitions. This project addresses these gaps using magneto-thermoelectric transport as a probe to gain deep insight into the motion of charge carriers. Magneto-thermoelectric transport measurements uniquely interrogate the interplay between heat, charge, and spin with the electronic structure of the material. The principal investigator uses magneto-thermoelectric transport to tease apart the intertwined transport signatures due to the topological band structure itself from the transport signatures stemming from physical length scales of samples, parabolic bands, and magnetic textures. Results of this work determine the fundamental mechanisms underlying and controlling charge carrier transport in topological materials, informing targeted materials design for enhanced device applications using topological 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 group’s way of life shapes its gene pool over many generations by selecting for or against different traits. But, because genetic changes require time, a quick switch in lifestyle can lead to a gene-environment mismatch, where genes that were previously advantageous become neutral or harmful. A more rapid adaptive response to such challenges can emerge through epigenetic modifications, which do not alter the DNA sequence, but modify gene expression. To understand how humans respond to rapid lifestyle changes, this study compares individuals with shared ancestry that have and have not undergone such process. The study assesses the regulatory effect of lifestyle on the epigenome, the impact of the gene-environment mismatch on biological aging, and can inform research about how future populations may adapt to lifestyle changes. The study builds upon existing collaborations and provides training opportunities for students. Applying biotechnology methods, the study collects gene expression data and estimates genome expression values. Differential genome-wide methylation is assessed, and methylation differences are quantified. Differentially expressed genes, and differentially methylated sites and regions are identified. All analyses control for relatedness between individuals, population admixture, and differential coverage. Biological aging is estimated using four methods: an elastic net regression model, and three epigenetic clocks. Biological age is assessed in relation to differences in gene expression and methylation patterns. 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 new collaborative REU Site hosted by California State University San Bernardino and the University of Cincinnati aims to recruit junior-level university students and sophomore-level community college students into geosciences educational and career tracks through year-long, cohort-based, authentic geologic research experiences that also include professional development training. The research activities center on resolving the movement history of the southern San Andreas fault (SAF) system, providing a unique opportunity to both further scientific understanding of this significant and hazardous fault and increase awareness of the societal importance of geoscience research and careers. Although much is known about the recent history (past 10,000 years) and the origin of the SAF system ~18-20 million years ago, less is known about its evolution during the intervening time. The goal of this REU is to help fill this knowledge gap by applying a combination of field-based and lab-based techniques in two-week field and lab components. This unconventional schedule will facilitate participation by students for whom a traditional summer research experience is not feasible. The project plan as well as student recruitment activities will broaden awareness of geoscience research and careers and their significance for society, while strengthening connections between 2-yr and 4-yr geoscience programs in southern California and the Ohio-Kentucky-Indiana tristate area. The research will also contribute to a better understanding of the fault system and its influence on earthquake hazards and landscape evolution in southern California. The overarching theme of the REU site is investigating the middle Miocene to latest Pleistocene spatial and temporal evolution of the southern San Andreas fault (SAF) system in the greater San Bernardino region. The SAF system in this region provides a natural laboratory for exploring the evolution of fault systems and their influences on landscapes, erosion, and sedimentation. Mentored team projects will apply modern field data collection and laboratory analytical techniques (including LiDAR-based mapping and morphometric analysis, facies analysis, detrital zircon provenance analysis, and Quaternary dating methods) to displaced sedimentary basins, deposits, and geomorphic surfaces, providing students with a range of research and training experiences that cultivate abundant mentor-student and student-student interactions. The project also implements structured virtual workshops throughout the academic year to sustain cohort engagement and to provide professional development training in such areas as finding and securing geoscience internships and employment, applying for graduate school, communicating effectively, and data visualization and analysis. The resulting data and analyses will contribute to an improved understanding of the space-time evolution of strain within the SAF system, which is important for understanding fault dynamics, kinematic partitioning, seismic hazards, and drivers of deformation and landscape evolution. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-09

With the support of the Chemical Catalysis Program, Professor Yujie Sun of the University of Cincinnati and Professor Christopher Elles of the University of Kansas are developing advanced photocatalysts that can be activated by near-infrared (NIR) light. This collaborative research effort is harnessing the unique properties of two-photon absorption (TPA) of designed chromophores to drive various chemical transformations under the irradiation of NIR photons, which can penetrate deeper into media and tissues with minimal interference. The project is also expanding the fundamental understanding of NIR light-driven chemistry and enabling applications ranging from sustainable polymer production to targeted biomolecule modification. In addition to its scientific impact, this project is providing interdisciplinary training for students, broadening STEM participation through outreach programs, and contributing new content to chemistry education. Conventional ultraviolet/visible-light-driven photocatalysis is limited by light penetration and competing light absorption in biological environments. To address these challenges Prof. Sun and Prof. Elles and their research team are developing molecular TPA photocatalysts that can be activated by NIR light excitation. The specific aims of this research program are focused on the design, synthesis, and photophysical characterization of novel molecular chromophores with enhanced TPA cross-sections in the NIR region. Experimental and computational studies are guiding the molecular design and enabling structure–function correlations for improved photocatalytic performance. Together, these efforts are establishing a transformative platform for NIR photocatalysis with broad implications for synthesis, materials science, and chemical biology. 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

This project focuses on two types of mathematical problems: tomographic problems and isoperimetric problems. Tomography concerns the retrieval of information about a geometric object based on limited information arising from its cross-sections or shadows (projections). For example, think of trying to determine the volume of a mountain based on the size of its shadows at different times of the day. Isoperimetric problems arise in geometry and optimization and have been of interest for thousands of years, dating back to ancient Greece. In a modern context, isoperimetric problems can involve studying the regularity of high-dimensional information. In complex data sets, high dimensionality often results in a regularizing effect. Tomographic and isoperimetric problems have been highly influential in many scientific disciplines, including physics, engineering, and computer science. Beyond their historical significance and applicability, these problems appeal to a wide audience because they are often intuitively stated and explained, while their solutions are difficult and require sophisticated mathematical techniques. The principal investigator of this project seeks to address problems arising naturally from geometry and harmonic analysis by employing techniques involving the Fourier transform, Radon transform, and other tools from various mathematical fields. Among these proposed problems are new affine invariant estimates for mixed-Sobolev norms, estimates for Radon and Cosine transforms (each abstractly represents the cross-sections and projections of an object, respectively) and their connections to long-standing problems including the recently resolved Bourgain slicing problem and the Busemann-Petty problems, and the very illusive Petty’s isoperimetric conjecture and Schneider’s difference body conjecture. The principal investigator will also seek interactions between these problems and other mathematical disciplines, including calculus of variations, partial differential equations, and probability theory. 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

The functioning of neurons depends on interactions between many different proteins and the cell cytoskeleton; problems with these interactions lead to diseases such as Alzheimer’s disease or cancer. Microtubules, filaments of tubulin subunits and the largest structure in the cytoskeleton, have long been viewed as passive tracks, along which motor proteins transport cargo. A more recent view focuses instead on the plasticity of the microtubule lattice within the cell, including changes in lattice stability that are induced by the binding of microtubule-associated proteins (MAPs), and an active response to that binding by the microtubule filament. Mechanistic understanding of these interactions between MAPs and microtubules, and the changes in microtubule conformation that result and then modulate the action of other cytoskeletal proteins, are key challenges of molecular biology. This project uses multi-scale computational modeling and machine learning, complemented by experimental testing of the models, to provide the first, quantitative insight into how changes in tubulin subunits that are induced by MAP binding in neurons influence the function of molecules designed to either enhance or block that MAP binding. This research will address a critical gap in our understanding of dynamic properties of microtubules and MAP-microtubule complexes in both healthy and disease states. The project will also provide education and training for undergraduate and graduate students in computational biophysical chemistry and machine learning in chemistry by involving them in interdisciplinary science, and in outreach at the Cincinnati Museum Center. The results of the project will be disseminated to the public by the investigator and students through publications, and conference presentations. This project will elucidate the microtubule allosteric response resulting from interaction between microtubule lattices composed of various tubulin isotypes and a set of three MAPs crucial for microtubule function in neurons: tau, MAP7, and doublecortin. Importantly, these three MAPs cover both positive and negative allosteric effects and leverage the newly solved cryo-EM structures of each MAP on microtubules. The project will use state-of-the-art atomistic molecular dynamics simulations on very large biological systems to build graph networks and train explainable machine learning approaches to determine allosteric regions in proteins. Modeling complex problems such as microtubule allostery is crucial because simulations can provide insight into processes that are inaccessible to experiments. Modeled changes in the microtubule lattice induced by binding of MAPs, and the influence of those changes on interactions between the MAPs will then be tested experimentally. This combination of coarse-grained modeling and experiments will provide insight into factors responsible for formation and disassembly of MAP-based envelopes on microtubules. These insights will impact directly our understanding of how microtubules function in cells, e.g., maintaining neuronal polarization in both axons and dendrites, and driving intracellular transport. 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

NON-TECHNICAL SUMMARY Aluminum-Magnesium (Al-Mg) alloys are commonly used as a lightweight alternative to steel in transportation applications and exposed infrastructure due to their excellent strength-to-weight ratio, weldability, and some of the highest resistances to corrosion damage amongst aluminum alloys. Prolonged exposure to elevated temperatures, however, leads these alloys to develop a serious problem known as sensitization that can eventually result in catastrophic failures. This project investigates the fundamental mechanisms controlling the sensitization process in Al-Mg, which are governed by complex atomic interactions with defects in the material’s internal structure created during manufacturing. These findings will ultimately guide the development of new material processing strategies that mitigate the sensitization process and extend the lifetime of these materials by several times their current limit. As Al-Mg alloys are already used extensively in critical transportation and energy infrastructure, this research presents immediate potential for large economic and sustainability improvements. The education and outreach efforts included in this project introduce materials science education in venues and contexts classically associated with the humanities. In an ongoing collaboration with the Cincinnati Art Museum, educational Science of Art programming is being introduced via QR-code links located alongside select objects within the museum, and a new interdisciplinary undergraduate course is being offered that uses the unique and publicly intriguing lens of sword-making to introduce fundamental concepts in physical metallurgy and materials science to undergraduates outside science and engineering majors. TECHNICAL SUMMARY This project tests three major hypotheses regarding the fundamental mechanisms by which microstructural features control the nucleation and growth of grain boundary precipitates in 5xxx series Al-Mg alloys, a defect-controlled precipitation process governed by complex diffusion pathways. Precipitation of the anodic Mg-rich beta-phase along grain boundaries in these super-saturated Al-Mg alloys leads to increased susceptibility to intergranular corrosion in a process known as sensitization, which can compound over time at environmental temperatures and result in catastrophic failures. Carefully designed experiments are employed that take advantage of novel characterization techniques to make statistically relevant direct observations of the nanoscale precipitates along the grain boundaries, addressing uncertainties regarding the underlying mechanisms and diffusion modes dictating the nucleation and growth, along with the precipitate distribution’s consequent impact on the sensitization response. Establishing the contributions that specific microstructural features play in the progression of this type of precipitation, notably as a function of temperature and configuration, enables the development of new processing strategies that lower long-term sensitization response in these alloys and increase service life. This research answers pertinent questions inherent to many systems possessing detrimental nanoscale precipitates that nucleate preferentially upon defect structures, and provides critical new experimental studies exploring the interplay between microstructural elements and multi-stage diffusion pathways. 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

Nonlinear wave formation and propagation are central to many physical phenomena across science and engineering, from ocean swells and rogue waves to plasma dynamics in fusion reactors and signal transmission in optical fibers. Despite arising in distinct systems, nonlinear waves often exhibit recognizable patterns such as rapidly oscillating wave trains, persistent localized beams, and rogue waves, under asymptotic regimes such as long-time evolution or small-dispersion. In many cases, these structures are universal, meaning they arise independently of conditions such as initial data and, in some cases, even of the governing equations. The project will develop and apply mathematical tools to identify and characterize emergent asymptotic phenomena in nonlinear wave models, with a particular focus on non-generic or anomalous behavior. The outcomes will advance the understanding of wave propagation involving large amplitudes or heavy tails, with broad applications in hydrodynamics and optical telecommunications. The project will also provide research training for graduate students and early-career scientists, broadening its impact beyond technical discovery into education and scientific workforce development. The project focuses on obtaining quantitative information on nonlinear wave behavior in asymptotic regimes not accessible through current inverse-scattering transform (IST) methods. Key goals include identifying mechanisms behind anomalously slow decay in solutions to integrable wave equations, extending IST techniques to broader classes of initial data, and establishing long-time behavior and other properties of weakly localized wave formations on modulationally unstable backgrounds using continuum Darboux transformations. To achieve these goals, the investigator will combine and further develop tools from the theory of integrable systems, complex and asymptotic analysis, and numerical methods. Anticipated outcomes include the identification of slowly decaying solutions beyond the predictions of the soliton resolution theory; novel IST methods for weakly localized data; and an expanded universality theory for the nonlinear stage of modulational instability. 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 aims to serve the national interest by improving students’ understanding of how social, cultural, economic, and political systems can inform their engineering designs. Traditionally, engineering design courses have focused primarily on the technical specifications and requirements of a design without full consideration of the wider context in which they will be used. There is also a lack of engineering design courses in the middle two years of a typical engineering curriculum. This leads to a gap in learning design principles and skills for students between their first design course and the final capstone design course. The project plans to develop, implement, refine, and evaluate engineering design courses for second and third year students at the University of Illinois-Chicago and the University of Texas-San Antonio. The courses should help students learn how to conduct in-depth analyses of the implications of design choices for society using historical and current examples of engineering design. This project intends to develop an instructional framework for the middle years of engineering programs' courses along with instructional materials for those courses. Using interviews and student surveys, the project will assess the impact of the courses on students. Project results will be disseminated to engineering educators through professional development workshops. The goal of this project is to help students learn how to consider social, cultural, economic, and political factors during the engineering design process. This project is based on Critical Consciousness theory which suggests that students need to develop an awareness of inequitable societal conditions that can occur due to engineering design decisions. A new teaching framework for engineering design in the middle years will be developed and refined so that engineering design includes a contextual perspective. The goals for the new engineering design course are: (1) to empower students to be more socially and critically driven engineers, (2) to help students learn about the engineering design process from a Critical Consciousness perspective, and (3) to help students become validated in their aspirations to pursue an engineering career. An inter-group dialogue approach will be used in the course to establish communication relationships, facilitate dialogue, and encourage collaborations between students. This study will address two research questions: (1) What teaching strategies are most helpful in developing students’ critical consciousness through an engineering design course? (2) How and in what ways do the programmatic features of the project impact students’ learning of engineering design and engineering identity development? The project team will use quantitative and qualitative methods to analyze data from classroom observations, course artifacts, and student surveys. The NSF IUSE: EHR 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 2025 · 2025-07

Semiconductors are foundational to the technologies that power modern life, from artificial intelligence and telecommunications to healthcare and the automotive industry. U.S. leadership in these sectors depends on advancing domestic semiconductor research and manufacturing. While the U.S. pioneered the field, it now produces only about 10% of the global semiconductor supply and relies heavily on Asia, especially Taiwan. Taiwan leads the world in semiconductor manufacturing, supported by top-tier research institutions, leading companies, a robust supply chain, and strong government investment. The International Research Experiences - Pathway for Research and Innovation in Semiconductor Manufacturing (IRES-PRISM) program creates a summer research opportunity for U.S. undergraduate and graduate students to engage in cutting-edge research on semiconductor devices and manufacturing in Taiwan. Over five years, the program will support 40 student researchers from the U.S. to gain hands-on experience, immerse themselves in Taiwan’s vibrant semiconductor ecosystem, and build lasting research partnerships. Working with mentors from both the U.S. and Taiwan, students will be well-positioned to advance U.S. innovation and global leadership in the semiconductor industry. Built on established partnerships with leading institutions in Taiwan across multiple engineering disciplines, the IRES-PRISM program advances frontier semiconductor research in thermal management, IoT sensors, and data-driven, sustainable manufacturing. Leveraging Taiwan’s global leadership in semiconductor manufacturing, the program positions participants at the forefront of U.S. semiconductor innovation. Students engage in undergraduate co-ops, graduate research, and dual master’s degrees in semiconductors, immersing themselves in a rich, industry-relevant research environment supported by Taiwan’s strong industrial base, advanced facilities, and supply chain. IRES-PRISM also fosters sustained collaboration through faculty exchanges, promoting long-term partnerships and transformative technological advancements. The program offers robust technical and professional skill development through workshops in research, leadership, career development, team building, and global awareness. Participants share their research findings to raise public awareness of advancements in semiconductor research and manufacturing. Leveraging the project team’s ongoing semiconductor workforce development initiatives, IRES-PRISM also generates educational materials to amplify its broader societal and technological impact. 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 · 2025-03

As technology advances across aerospace, biomedical, and semiconductor fields, the demand for high-performance engineering materials continues to grow. Ceramics like sapphire and zirconia excel in extreme environments such as hypersonic flights, medical applications, and high-speed semiconductor devices. However, as these applications evolve, ceramic component designs become more complex and difficult to produce using conventional manufacturing methods such as grinding and polishing. Ultra-precision machining, which manipulates cutting tools at small length scales, has been developed to create complex geometries without costly post-processing. However, the tool-workpiece interactions and material removal mechanisms at this scale are not fully understood, limiting productivity. By combining precision cutting experiments with computer simulations, this project aims to investigate the interactions between various deformation and fracture mechanisms and develop a predictive model to enhance machining efficiency and advance the manufacturing of cutting-edge technologies. Beyond immediate applications, the findings will advance materials science by providing insights relevant to other high-performance materials. Project results will be shared through publications, conferences, and graduate courses. Moreover, this project will provide research opportunities for undergraduate students, fostering a broad talent pipeline in various science and engineering fields. Selecting optimal process parameters for ultra-precision machining of difficult-to-cut materials often relies on trial-and-error, leading to wasted resources and require extensive post-processing. Addressing these challenges requires predictive models based on a comprehensive understanding of material deformation in crystalline structures during machining. Current models primarily focus on single dominant deformation modes, which limits their ability to capture the complex interplay of slip, twinning, and fracture mechanisms involved in machining ceramics. This research aims to elucidate the interactions among various deformation and failure mechanisms during ultra-precision machining of single-crystal materials and their influence on machining performance. The study focuses on sapphire, examining how specific types of defects impact machining outcomes. Artificial defects will be introduced using focused ion-beam milling, enabling controlled investigations of individual deformation modes. To complement experimental efforts, atomistic simulations will be conducted to analyze the temporal evolution of deformations and the role of defects, such as dislocations and twinning, which are difficult to replicate experimentally. Insights gained will enhance existing material deformation models, moving beyond single-mode analyses. These improved models will support the development of machining strategies that optimize the machinability of advanced engineering ceramics, reducing costs and improving efficiency. 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 · 2025-03

This award provides participant support for the 2025, 2026, and 2027 editions of the Ohio River Analysis Meetings (ORAM), the first of which will be held on March 29-30, 2025 at the University of Cincinnati. The subsequent meetings will be held in Spring 2026 at the University of Kentucky and in Spring 2027 at the University of Cincinnati. ORAM is an annual conference in mathematical analysis organized by faculty at the University of Cincinnati and the University of Kentucky. It provides a venue for mathematicians to learn about the latest developments in the field, to disseminate their own results, and to collaborate with other researchers. ORAM supports the development of early-career researchers through speaking opportunities and travel support to attend the conference. ORAM will be held for the 14th time in 2025. Each year it features a robust scientific program, with five plenary talks by distinguished mathematicians, approximately 35 contributed short talks in parallel sessions, and approximately 70 participants. The conference welcomes researchers in all areas of analysis, with a particular focus on partial differential equations, geometric analysis, and harmonic analysis. Anticipated plenary speakers for the 2025 event and additional information can be found on the conference website at https://sites.google.com/view/oram14/home. 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 · 2025-02

Deuterated molecules play important roles in pharmaceutical research because the replacement of hydrogen atoms by deuterium atoms can provide unique properties, such as altered metabolic pathway, longer stability, increased half-life, and reduced clearance. However, the utilization of deuteration in pharmaceutical research still faces many challenges because of the limited versatile and inexpensive deuteration methods. This project will investigate novel electrocatalytic methods for the effective deuteration of organic molecules under ambient conditions (room temperature and atmospheric pressure). This project will not only advance the integration of electrochemistry in organic synthesis but also build the talent pool of future research scientists in the emerging field of organic electrocatalysis. The research work will also be leveraged with a myriad of outreach activities to educate the public about the emerging transition to a greener chemical industry. The outcomes of the research will also enrich a new class in Green Chemistry and Sustainability. The Sun group at the University of Cincinnati will design, develop, and understand innovative electrocatalytic systems for the efficient deuteration of organic model compounds using deuterated water (D2O) as the deuterium source. Different from conventional electrocatalytic deuteration which takes place at the cathode while reactants and products are both dissolved in the electrolyte, the synthesis strategy will enable the deuteration reactions to take place in a separate chamber outside of the electrochemical cell, taking advantage of the unique deuterium absorption and permeation property of palladium membrane electrodes. Deuterated water will be split in an electrolysis cell. The deuterium species will then permeate through the palladium reactor to the exterior surface of the membrane. By developing various co-catalysts coated on the palladium membrane electrodes, the project will aim to realize the efficient deuteration of a large group of organic molecules, ranging from alkenes, aldehydes, ketones, halides, to amino acids. The proposal's reactor concept takes advantage of the catalytic hydrogenation and hydrogen absorption properties of the palladium membrane. The main strength is the reactor concept which takes the electricity driven splitting of D2O to provide the deuterium source under low voltage input and under ambient 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.

NSF Awards · FY 2025 · 2025-01

The Global Problem Solvers in Sustainable Living (GPS-SL) is a co-op initiative that aims to facilitate the development of a world-class U.S. STEM workforce, equipped to address complex engineering problems of sustainable living within tropical regions. By 2050, over half of the world’s population is projected to reside in the tropics. The tropics, however, face significant challenges in sustainable living – and are essential for surplus heating driving global circulation. Safeguarding and preserving these tropical areas is paramount to securing a sustainable future globally. The GPS-SL aims to uphold U.S. strategic leadership in international science and engineering by cultivating a diverse and globally competitive STEM workforce. The initiative generates invaluable knowledge, innovative practices, and profound understanding for affordable, cost-effective sustainable living in the tropics. Acquired skills enhance students' career perspectives, benefiting a global technical workforce. Collaboration with foreign students contributes to cross-cultural skills development, fostering appreciation for global engineering and diverse research perspectives. The GPS-SL is grounded in the best practices for experiential learning effectiveness to facilitate a transformative experience. The GPS-SL facilitates this transformative experience for 24 U.S.-based students through two core pillars: (i) Engagement in semester-long virtual Research Preparation and Professional Development activities, (ii) Collaborative Advanced Research in Sustainable Living, and Educational Research on Capacity Building in the following connected research foci: (1): Soilless Fresh/Brackish Water Solar-Powered AI-controlled Aquaponics System: Develop sustainable, low-tech aquaponic that reduce overall cost, and educational workshops to empower farmers to design and maintain aquaponic. (2): Low-Cost Technique to Immobilize Heavy Metals and Recycling Plastic Waste in Coastal Communities: Develop and test innovative sustainable approaches to remediate toxic metal contamination. (3): AI-Based Agric-Product Price Monitoring Systems: Develop a low-cost AI-based system to empower farmers with market insights, enabling fair pricing for produce and fostering a more equitable agricultural ecosystem. 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 serve the national interest by promoting equal learning opportunities and success for all students, irrespective of race, gender, or first-generation college status. Educational attainment is widely recognized as a key determinant of economic success, yet studies show that many students who discontinue college do so due to feeling a sense of belonging uncertainty. Research shows that students who feel that they belong in their courses are more likely to seek and utilize resources and exhibit higher levels of self-efficacy, engagement, and achievement. However, many instructors do not promote a sense of belonging in their courses, possibly due to a lack of understanding of its importance or limitations such as inadequate training or lack of class time. This project seeks to address this gap by leveraging the power of established psychological interventions to develop four 15-minute interactive online interventions (IOIs) for cultivating students' sense of belonging. The IOIs are innovative in that they will be designed to offer instructors flexible and user-friendly methods for incorporating them into their courses with minimal effort, thereby encouraging widespread adoption and maximizing impact. The IOIs are expected to enhance students' sense of belonging, similar to in-person interventions, ultimately leading to improved academic success and retention, particularly in courses that embrace evidence-based active engagement (EBAE) pedagogies. This is particularly significant in the context of STEM, where persistent challenges related to representation underscore the urgent need for interventions aimed at fostering inclusivity and support for all students. The project’s research plan aims to investigate the effectiveness of online psychological interventions compared to in-person methods in promoting students' sense of belonging and achievement in introductory physics courses. To achieve this, students will be randomly assigned to receive interventions through either in-person or online delivery (IOIs) at various points throughout the semester. Students' sense of belonging will be assessed using self-report surveys administered at the beginning and end of the term, while academic success will be measured using final exam scores. Linear regressions will be conducted to analyze the effects of interventions (IOI vs. face-to-face) on students' sense of belonging and final exam performance, while controlling for students' baseline levels of belonging, high-school GPA, race, gender, first-generation college status, and instructor use of EBAE teaching strategies. In addition, we will test for two- and three-way interactions to explore the extent to which the relation between intervention format (IOI vs. in-person) and students’ sense of belonging and achievement are moderated by factors such as race, gender, first-generation college status, and the use of EBAE. Although the research focuses on introductory physics courses, the broad applicability of the interventions across domains suggests potential benefits for student outcomes across disciplines. The IOIs and best practices guide will be freely accessible to instructors through ComPADRE, a national digital library of educational resources. Research findings will be disseminated through presentations at national conferences, workshops, and peer-reviewed publications, adding to the limited research on the use of psychological interventions in introductory physics courses, particularly interventions administered online. The NSF IUSE: EDU Program supports research and development projects to improve the effectiveness of STEM education for all students. Through its 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

Landslides are a major geo-hazard causing significant casualties, infrastructure damage, and economic losses worldwide. With the increasing frequency of extreme weather events, many communities are facing increasing risk of landslides. However, many rural, isolated, and small communities (RISC) communities lack the knowledge and tools to prepare for and mitigate landslide impacts. This project seeks to establish robust community partnerships, engage community members through collaborative workshops and focus groups to identify their needs and priorities related to landslide risks, co-produce research questions and solutions. These foundational efforts are crucial for creating a comprehensive, community-driven research initiative that leverages advanced earth system science to enhance resilience and provide actionable solutions for mitigating landslide hazards. This project will help enhance community resilience in areas disproportionately affected by landslide hazards. It will also provide valuable research and educational opportunities for local citizen scientists from underrepresented backgrounds for building capacity in the community. This project aims to address significant knowledge gaps in landslide risk propagation, perception, communication, and mitigation for underserved rural, isolated, and small communities. This project will engage RISC communities in Southwestern Puerto Rico and establish robust community partnerships. The primary objectives of this project are: (1) Establish new collaborations with essential federal and local partners. These partnerships will facilitate access to critical data, resources, and local knowledge to support coordinated responses to landslide hazards. (2) Co-produce research questions and solutions. This project will engage community members through surveys, interviews, and collaborative workshops, and focus groups to identify their needs and priorities related to landslide risks, co-produce research questions and actionable solutions. This participatory approach will ensure that the research questions and solutions developed are grounded in real-world experiences and directly address community concerns. (3) Establish feedback channels and protocols to continuously gather input from stakeholders and community members. By building the necessary connections to confront landslide hazards and build resilient communities, this project will lay the foundation to advance the scientific understanding of risk propagation of landslide-community systems and support effective community-centric risk mitigation strategies customized to the local social and cultural context. The project results are expected to inform the adaptation and application in other regions facing similar hazards, amplifying its impact beyond Puerto Rico. 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 focusses on how the hormone aldosterone functions to facilitate aquatic to terrestrial transitions in vertebrate animals. The processes of birth in humans, hatching in birds, and metamorphosis in frogs all involve a critical transition from an aquatic environment (womb, egg, pond) to a terrestrial existence with vastly different physiological, locomotor, and feeding demands. All vertebrates use the same hormones to control the transition, which must be carefully orchestrated to ensure survival of life on land. The project fills a gap in knowledge about function of the role of aldosterone in the timing of metamorphosis in frogs. The research results will help evaluate how man-made environmental conditions, such as climate change and the presence of endocrine disrupting chemicals, may pose health threats to the processes of birth, hatching, and metamorphosis. In addition, this research will open new avenues to study why individuals may respond differently to changing environmental conditions, identify novel genes and hormone interactions, and develop new assays for endocrinology studies. During these studies, graduate and undergraduate students will be trained in basic research with applications relating to amphibian declines and climate change, which advances the National Science Foundation's goal to create a well-prepared, innovative scientific workforce. In addition, an undergraduate laboratory course will be developed to teach hands-on skills in gene disruption technology and bring research to high school students. The complex endocrine regulation of vertebrate developmental transitions has been challenging to unravel. Many hormones act and interact simultaneously on multiple cell types at different time points throughout the organism. The goal of the current proposal is to reveal how one such hormone, the corticosteroid aldosterone, regulates frog metamorphosis. Aldosterone is implicated in amphibian development, but lack of appropriate experimental tools has previously precluded conclusions about its precise role in the process. The project’s central hypothesis is that aldosterone is the primary mineralocorticoid in tadpole plasma and acts through the mineralocorticoid receptor to regulate genes required for normal developmental progression through metamorphosis. To address this hypothesis, the approach is to: 1) measure the levels of 11 corticosteroids in plasma and tail tissue throughout the larval period, 2) compare growth and development at molecular and morphological levels in tadpoles wild-type and mutant for the mineralocorticoid receptor, and 3) use RNA-sequencing to identify mineralocorticoid receptor response genes in tail, kidney, and brain. Knowledge of the identity and quantity of steroid hormones and induced genes during development and the consequences for development in the absence of aldosterone signaling will advance the long-term goal of explaining how hormones modulate developmental processes that shape adult health and fitness. This project is co-funded by the BIO-IOS Physiological Mechanisms and Biomechanics and Animal Developmental Mechanisms programs. 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

There continues to be increasing demand for more comprehensive assessments of the impacts of STEM education programs, policies, and practices. One stumbling block to such research is the need for large sample sizes for studies to be able to make the intended claims. This research develops, and tests new estimation procedures for cross-classified structural equation models for the statistical analysis of small to moderate sample size studies of teaching and learning in mathematics. These new tools reduce the burden of large sample sizes. The team will develop new shareable software tools (in an open-source software application) and deliver training opportunities to researchers. Further, these estimation models and research tools will have broad impact as they are usable for similar study designs in all STEM content learning settings. This research bridges the gap between current research methods and the scale with which most STEM education studies are conducted. The team will develop new estimation methods for small to moderate sample cross-classified structural equation models that are tightly attuned to the features of teaching and learning research but broadly applicable across areas of STEM education. The research integrates four components. The research study (a) derives a new statistical framework and estimators, (b) develops empirical guidelines for study design with the proposed methods using a rich survey of teaching and learning programs and teacher development processes, (c) develops free and accessible software and tutorials, and (d) conducts highly accessible workshops to improve the capacity of the field. This research project is supported by NSF's STEM EDU Core Research (ECR) program. The ECR program emphasizes fundamental STEM education research that generates foundational knowledge in the field. Investments are made in critical areas that are essential, broad, and enduring: STEM learning and STEM learning environments, broadening participation in STEM, and STEM workforce development. The program supports the accumulation of robust evidence to inform efforts to understand, build theory to explain, and suggest intervention and innovations to address persistent challenges in STEM interest, education, learning and participation. 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

Particle and nuclear physics (PNP) are fundamentally probabilistic due to quantum mechanics. Both fields rely on complex Monte-Carlo (MC)-based simulators that use random number sampling to make predictions for nearly all aspects of experimental design and data interpretation. In fact, most branches of science and engineering rely heavily on MC simulations for solving difficult problems, from modeling traffic flow to predicting weather patterns; in the rapidly emerging fields of machine learning and quantum computing, MC methods are essential. Progress in these areas requires developing, validating, and deploying novel and efficient MC algorithms. However, many university computer science programs focus on deterministic methods, with MC techniques covered only in passing, leading to a gap between knowledge and required skills for junior researchers. This project fills the knowledge gap by training graduate students and junior postdoctoral researchers in the development of MC models with traineeships and schools focused on real-world PNP problems. The project has three main goals. The first is to develop summer-school curricula as well as organize summer schools to train graduate students and junior postdoctoral researchers in MC generator algorithms and their applications. The second is to build on summer school material and produce online tutorials for self-guided study. The third goal is to create and run a 2-year pilot program of focused, short-term traineeships for graduate students and postdoctoral researchers, which could in the future be scaled up to include more nodes and mentors in the training network. This award by the Office of Advanced Cyberinfrastructure is jointly supported by the Division of Physics within the Directorate for Mathematical and Physical Sciences. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-09

Abstract: Probing the Standard Model with the LHCb Detector at the Large Hadron Collider Fundamental particles and their interactions are described by the Standard Model (SM) of elementary particle physics. Although the SM has been remarkably successful in its ability to describe observations of elementary particles to date, there are good reasons to believe that this model is incomplete, and that some new physics should exist beyond the Standard Model. Proton-proton collisions at the Large Hadron Collider (LHC) can probe the Standard Model at the highest energies ever achieved in the laboratory. This award will support a research program at the University of Cincinnati that takes advantage of the unique capabilities of the LHCb experiment at the LHC to probe SM predictions, and may reveal evidence for new physics beyond the SM. The force-carriers of the Weak interaction are known as W bosons. This award makes the first measurements of the helicity of W bosons and of the production of pairs of W bosons in the forward region at LHC. These measurements will be important tests of the SM theory predictions at the energy frontier. This award will also study rare decays of B and other mesons and contribute to the future upgrade of the LHCb electromagnetic calorimeter, taking a leading role in development of the calibration system. Broader impacts of this research program will arise through efforts to broaden participation in physics, at the high school, undergraduate and graduate levels. The work will seek to inspire more high school students to choose a future in physics by providing students and their teachers with experience of high-energy physics research through participation in the QuarkNet teacher training program. This will also provide opportunities and mentoring for undergraduates to participate in research at the LHC, and we will strive to support students from under-represented groups to pursue graduate degrees in physics. 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.