University of Kansas Center for Research Inc

NSF Awards · FY 2026 · 2026-07

Essential products, including microelectronics, pharmaceuticals, and food, are often sourced from distributed and sometimes untrusted suppliers. Without reliable traceability, counterfeit or unauthorized items can enter systems and threaten public health, economic stability, and national security. For example, counterfeit chips in defense systems pose national security risks, a concern reflected in the CHIPS and Science Act. Similarly, counterfeit or mislabeled drugs in pharmaceutical supply chains can endanger public health. In human surveillance, rapid and accurate tracking supports public safety and cross-border investigations, with relevance to agencies including the Department of Homeland Security (DHS) and the Federal Bureau of Investigation (FBI). Current tracking systems face significant limitations: authentication degrades under data variability, scalability is constrained by storage and query latency, and systems remain vulnerable to spoofing and cloning attacks. A key gap persists in scalable architectures capable of handling variable, hard-to-clone identifiers under real-world noise. To address this national need, this project investigates a foundational framework, named AuthenTrack, for object tracking in large-scale applications such as supply chains to strengthen authentication, scalability, and security across critical sectors. The project embeds hands-on modules into computing curricula and disseminates open-source tools, benchmarks, and datasets to broaden educational and societal impact nationwide. This project advances data structure design through a unified probabilistic framework integrating hierarchical indexing, adaptive hashing, and time-aware querying to enable secure identity resolution under uncertainty. This project develops domain-agnostic architecture, enhances model capabilities, and strengthens the security through adversarial modeling. In this project, the researchers validate prototypes in real-world supply chains. These efforts advance probabilistic data structures and resilient system design while enabling precise, scalable authentication. 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

In modern healthcare, analyzing vast amounts of longitudinal patient data, such as diagnoses, prescriptions, and medical procedures, can help healthcare providers understand complex health conditions and make treatment decisions. Artificial intelligence (AI) has great potential to assist in this process by discovering hidden patterns in a patient's medical history to provide personalized care recommendations. However, current AI systems often struggle to capture the complex structure of medical records, may produce recommendations that conflict with established medical knowledge, and can become unreliable when faced with noisy or unexpected data. This project addresses these challenges by developing an integrated framework to make AI in healthcare more reliable and medically accurate. By improving the dependability of machine learning frameworks, the project serves the national interest by advancing health and welfare, and enabling more effective and personalized patient care. The project also supports education by training undergraduate and graduate students to prepare the next generation of healthcare technology innovators. This project will develop a novel, comprehensive machine learning framework to improve healthcare decision support systems using electronic health record data. The research will pursue three complementary activities spanning data representation, algorithm alignment, and system robustness. First, the team of researchers will design deep learning architectures that jointly model patient visit sequences and relational graphs of clinical events to create richer data representations. Second, the project will integrate medical knowledge graphs and large language models into the training process, ensuring algorithmic reasoning aligns with established clinical principles. Finally, the research will evaluate system vulnerabilities through adversarial testing and incorporate contrastive training to maintain predictive consistency under adverse conditions. The framework will be rigorously evaluated across diverse clinical applications. Project outcomes will be disseminated to the broader research community through open-source software releases and integrated into interdisciplinary academic curricula. 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

More than 6 million metric tons of polypropylene (PP) is produced annually in the U.S. It is the second most produced plastic in the U.S. Less than 1% of this PP is recycled because common recycling methods are inefficient and often produce low-quality material. Instead of recycling PP, a special chemical process with catalysts can break down PP into useful products, turning plastic waste into valuable materials. However, when PP melts, it becomes thick and sticky, which makes it hard to heat and mix evenly during processing. This CAREER project will study how carbon dioxide at certain temperatures and pressures (called subcritical and supercritical conditions) can help make melted PP less thick and easier to process. By examining how carbon dioxide changes the properties of different types of PP during these reactions, the research will develop better technologies for reducing plastic waste. The project will also include hands-on lessons for middle school students in Kansas to teach them about reducing plastic waste and inspire interest in science and engineering. This CAREER project will investigate how subcritical and supercritical carbon dioxide-containing media influence the catalytic conversion of structurally diverse polypropylene (PP) substrates, using catalytic hydrocracking as a model system to elucidate the underlying reaction and transport mechanisms. The study will systematically evaluate how carbon dioxide at varying temperatures and pressures modifies PP thermophysical properties and how these changes affect reaction kinetics, pathways, product selectivity, and coke formation and deposition within catalyst pores. In addition, the project will examine how variations in PP characteristics - such as molar mass, degree of branching, and tacticity - govern the extent to which subcritical and supercritical carbon dioxide alter reaction rates, selectivity, and catalyst recyclability. These insights are critical for rigorously defining the role of carbon dioxide as a reaction medium and for overcoming transport limitations in catalytic plastic upcycling. The resulting mechanistic and methodological framework is expected to be broadly applicable to other catalytic systems, including hydrogenolysis and pyrolysis, as well as to other highly viscous or structurally complex feedstocks that exhibit transport limitations, such as lignocellulosic biomass. 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 Heartland Mathematics Conference (HMC) is an annual graduate student mathematics conference to be held at Kansas State University (KSU), University of Nebraska-Lincoln (UNL), and University of Kansas (KU) in 2026, 2027, and 2028. The conferences are organized by graduate students at the three participating universities, under the guidance of Professors Dave Auckly (KSU), Alex Zupan (UNL), and Jeremy Martin (KU). The HMC provides a unique and valuable opportunity for graduate students to experience the benefits of presenting and taking part in a research conference and helps train organizing students in project administration. HMC is a continuation of the Kansas Mathematics Graduate Student Conference, which started in Fall 2021 and has run for four years as a joint project between graduate students at KU and KSU. With mostly graduate students and undergraduates in attendance, the conference provides a low-pressure environment in which graduate students can present their research and improve upon their presentation abilities. The conference introduces participants to current trends in multiple fields of research and provides an environment for interdisciplinary collaboration, with the hope of creating strong research groups moving forward. The networking possibilities and the experience gained from giving a research talk enable attendees to mature as mathematicians and as future faculty members or professionals outside of academia. The conference website is: https://sites.google.com/view/heartlandmathconference 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

This Research Infrastructure Improvement (RII) EPSCoR Research Fellows project provides a fellowship to an Associate Professor and training for a graduate student at the University of Kansas Center for Research Inc. This work is conducted in collaboration with Professor Rick Edmondson at the National Resource for Quantitative Proteomics at the University of Arkansas for Medical Sciences. Through the fellowship, the PI will advance the use of proteomic analysis to better understand how soil microbes degrade contaminants of emerging concern, including persistent “forever chemicals.” The proposed research will adapt advanced protein-based approaches, widely used in biomedical research, to environmental soil systems. This transdisciplinary approach integrates biochemistry and environmental engineering to accelerate discovery of biological pathways capable of degrading persistent contaminants. The knowledge gained about these natural degradation mechanisms may be of use in efforts to ensure access to safe, clean drinking water. Further, the project will promote partnerships between academic institutions, increasing Kansas’ research competitiveness. This project will develop alternative strategies deployed in biomedical research to advance proteomic analysis of environmental soil samples to better understand how microbes degrade contaminants of emerging concern. The project will expand the scientific frontier by introducing protein-centered approaches to environmental remediation research. The project provides training and access to state-of-the-art proteomic equipment to enable bottom-up proteomic approaches, including differential peptide analysis to identify proteins from environmental samples that interact with contaminants of emerging concern. Bioinformatical training will leverage open-access proteomic workflows to create aggregated databases necessary to identify novel, contaminant degrading proteins. The research will be integrated with the PI’s current leadership role in the Center for Undergraduate Research to boost STEM student engagement and workforce development. This project is supported by the EPSCoR Research Infrastructure Improvement Program: EPSCoR Research Fellows, which supports early- and mid-career investigators in eligible jurisdictions to develop collaborations at the nation’s private, government or academic research institutions. 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

This Research Infrastructure Improvement (RII) EPSCoR Research Fellows project provides a fellowship to an assistant professor and training for a graduate student at the University of Kansas (KU). This work is conducted in collaboration with the Department of Health Outcomes and Biomedical Informatics at the University of Florida (UF). Through the fellowship, the PI will advance the development of reliable machine learning (ML) methods designed to address pervasive data limitations in medical artificial intelligence (AI) systems. By integrating AI, ML, and clinical informatics, the research will investigate data challenges in clinical environments and build data-resilient learning methods validated on large-scale patient data repositories. The project results will improve the trustworthiness of AI-based clinical decision support and enable physicians to make more accurate, timely, and personalized treatment decisions. In addition, the project will provide hands-on training for graduate students in AI for healthcare and contribute to strengthening the future workforce in this critical domain. This project will address challenges of data quality in developing reliable AI models for medical applications such as disease prognosis, survival analysis, and treatment recommendation. It will investigate issues including data sparsity, domain shifts, and data noise in large-scale electronic health records, and will develop robust AI frameworks through algorithmic innovation, real-world evidence generation, and retrospective clinical validation. The project will strengthen research infrastructure at KU by supporting faculty professional advancement in AI for healthcare, establishing foundational research in data-resilient learning methods, and providing graduate students with hands-on training. The research activities will also strengthen KU’s partnerships with UF by fostering interdisciplinary collaboration that engages trainees from both computer science and medicine, while advancing workforce development in AI and biomedicine. In addition, the project will enrich KU’s computer science curriculum through the integration of trustworthy AI modules derived from the research outcomes. Finally, it will promote scientific dissemination and outreach within the broader research community. This project is supported by the EPSCoR Research Infrastructure Improvement Program: EPSCoR Research Fellows, which supports early- and mid-career investigators in eligible jurisdictions to develop collaborations at the nation’s private, government or academic research institutions. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2026 · 2026-04

The Great Plains Combinatorics Conference (GPCC) will be held May 2-3 2026 at the University of Kansas in Lawrence, KS. The GPCC is a regional conference in combinatorics. The conference has a tradition of showcasing the work of junior faculty from the Great Plains region in all areas of combinatorics. Invited speakers for 2026 include Torin Greenwood (North Dakota State), Pamela Harris (Wisconsin-Milwaukee), Reuven Hodges (Kansas), Carlos Martinez Mori (Colorado-Denver), George Nasr (Augustana University), and Shira Zerbib (Iowa State). The conference also features a poster session open to all graduate students and postdoctoral researchers. Many research universities in the western Great Plains have small groups (often just one or two faculty members) working in combinatorics, and the low concentration of universities leads to geographical isolation. Accordingly, the GPCC was started in 2014 to bring together combinatorialists from the region. A central goal of GPCC is to provide graduate students and junior researchers from the vast Great Plains region with an opportunity to meet leading researchers in the area, learn about recent results in combinatorics, share ideas, and start new collaborations. The website for the conference is at: https://great-plains-combinatorics-conference-2026.ku.edu/ 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

Manufactured homes are fundamentally different from other building types due to their configuration, installation, and size and weight constraints required to maintain transportability and affordability. Both recent and historical post-disaster reconnaissance have documented their poor performance under strong wind events. Despite the widespread use of manufactured homes, modern engineering research on their structural behavior remains limited. This Grant for Rapid Response Research (RAPID) seeks to address fundamental knowledge gaps on manufactured home performance under hurricane level winds. The outcomes from this research can improve the welfare, safety and protection of more than 20 million residents of manufactured homes across the United States. This project has a time-sensitive urgency to move three manufactured homes, to the Florida International University campus, that will be used as specimens for full-scale testing under wind loads, while the team still has access to these units. Findings from the full-scale testing will inform and validate retrofit strategies and design improvements for new manufactured homes, enhancing their resilience to future hurricanes and windstorms. The research team will disseminate results to technical and scientific communities through peer-reviewed publications, data releases, and conference presentations, thereby promoting continued research and informing updates to design and installation codes. The project will also provide engineering students with valuable training in experimental testing, computational modeling, and data science. This award will contribute to the NSF role in the National Windstorm Impact Reduction Program (NWIRP). This project will generate critical data to quantify wind resistance and characterize wind performance of manufactured homes through examining the initiation, propagation, and accumulation of damage during full-scale wind tunnel testing at the Natural Hazards Engineering Research Infrastructure (NHERI) Wall of Wind Experimental Facility at Florida International University. These full-scale experiments are essential for validating finite element models, which will support broader analysis under varying wind conditions and structural configurations. Experiments and subsequent modeling will facilitate the development of optimized design and retrofit recommendations. Full-scale testing will bridge the current gap in understanding between component- and system-level behavior for manufactured homes. The experiments will include (1) moderate-level wind speed testing for wind field evaluation and wind pressure measurements on exterior walls, floor underside, and roof surfaces, and (2) high-speed testing to measure displacements and strains, while capturing dynamic response, component performance, and progressive and accumulative damage through sensors and videography. Project data will be archived and made publicly available in the NHERI Data Depot at https://www.DesignSafe-ci.org. 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

Electricity demand is rising rapidly across the U.S., driven by the expansion of energy-intensive facilities such as battery plants and data centers. While demand-side programs offer a flexible solution to manage peak loads, many communities are often left out due to high costs and limited access to existing energy storage systems that support participation in demand-response initiatives and reduce grid dependence during power disruptions. Our goal is to strengthen U.S. energy resilience by developing an affordable, general-purpose alternative to existing storage systems. By repurposing retired electric vehicle (EV) batteries, we aim to build community-driven, sustainable energy storage systems that provide backup power and demand flexibility where needed. Our Battery-Second-Use Community Energy Storage (B2U-CES) model is unique in that it: (1) leverages community-donated EV batteries as a circular resource, fostering a sense of ownership and shared responsibility in supporting essential community facilities; and (2) engages local stakeholders in co-designing pilot systems that combine virtual testing with real-world deployment. This project will deliver broad societal, economic, and environmental benefits by improving energy resilience, reducing battery waste, and promoting circular energy practices. Through cross-sector collaboration and engagement with community partners, this research will develop a comprehensive, community-driven energy resilience solution that addresses the societal, technical, and economic challenges of B2U-CES. The integrative research - spanning batteries, power electronics, building systems, control strategies, decision-making, and machine learning - will advance adaptive, community-centered energy storage systems and generate key outcomes: (1) a stakeholder-informed dataset capturing the social and techno-economic challenges and drivers influencing B2U-CES adoption; (2) a high-fidelity virtual testbed for battery diagnostics, system architecture optimization, and integration with building energy systems, supported by a physics-informed, safety-aware reinforcement learning control strategy; and (3) a hierarchical market mechanism that models stakeholder decision-making to guide sustainable system planning and management. These insights will provide a robust foundation for a computational framework that enables safe, community-informed B2U-CES design and testing, laying the groundwork for future pilot implementation. 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

Resource scheduling is a critical component of high-performance computing (HPC) systems. Despite extensive literature on scheduling, new challenges continue to arise due to advancements in hardware, software, and evolving models, metrics, and performance demands. Today’s HPC systems operate on an unprecedented scale, presenting significant challenges for resource management, particularly when facing uncertainty introduced by emerging application characteristics and system-level complexities. Existing schedulers lack robust mechanisms to effectively handle uncertainty, limiting their ability to achieve optimal performance. This project takes on the grand challenge of scheduling HPC resources under uncertainty by introducing an integrated approach that combines algorithm and machine learning (ML). The approach leverages the rigor of algorithmic analysis to provide performance guarantees while utilizing ML’s predictive capabilities to manage uncertainty effectively. The anticipated outcome is a substantial enhancement to current HPC schedulers, enabling more efficient execution of a diverse range of scientific applications, such as neuroscience, medical research, climate modeling, and artificial intelligence. Additionally, the project includes a series of synergistic activities, including outreach programs, curriculum development, and student recruitment, aimed at engaging students from K-12 through graduate levels. These efforts focus particularly on underrepresented and underserved communities, offering research opportunities that foster success in STEM and CS education. Technically, this project aims to design, implement, and evaluate scheduling algorithms that integrate ML prediction models to enhance efficiency. The focus will be on addressing three primary sources of uncertainty: (1) inherent runtime variability of emerging applications; (2) resource contention in job co-scheduling; and (3) structural variations within dynamic workflows. These aspects represent uncertainties across temporal, spatial, and structural dimensions, all of which demand solutions due to their growing prevalence in modern HPC environments. Algorithmically, approximation and semi-online algorithms will be developed to provide performance guarantees relative to theoretical lower bounds for metrics such as job completion time and resource utilization. On the ML front, various models, including those based on regression and reinforcement learning, will be trained to deliver accurate predictions for job runtime, performance degradation, and structural variability. A key ambition of this project is to establish an incubation framework that enables the effective integration of heuristic-based algorithms and data-driven ML models. This approach aims to achieve a level of performance that neither paradigm could accomplish independently. The framework will offer a novel perspective on resource management and potentially set the stage for future HPC advancements. This project is jointly funded by Software and Hardware Foundations 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 2025 · 2025-09

This project establishes a new international research initiative that brings together scientists from the United States, Canada, and Latin America to collaborate on some of the most fundamental questions in the physical sciences. The focus is on Quantum Chromodynamics, or QCD, which explores how matter forms and evolves. Understanding how quarks and gluons create the particles that make up atoms is central to answering big-picture questions about the nature of the universe and have a broad range of interdisciplinary applications. The Inter American Network of Networks of QCD Challenges, or IANN QCD, will strengthen partnerships across the Americas and also serve as a platform for future collaboration with scientists from other regions, including Europe and Asia. The project supports coordination with major research efforts such as the Relativistic Heavy Ion Collider, Jefferson Lab, the future Electron Ion Collider, and the Large Hadron Collider at CERN. In addition to advancing fundamental science, this initiative supports a broad educational mission. The project will provide early career researchers, including graduate students and postdoctoral scholars, with training in both core nuclear physics and widely applicable skills such as scientific computing, data analysis, machine learning, quantum information science, and accelerator science. Through international exchanges, collaborative research planning, and open educational resources, participants will gain experience working in global scientific teams and build the knowledge needed to contribute to future scientific and technological advances. The project will also emphasize participation from a wide range of networks, network partners, institutions and communities across the Americas. Activities will be designed to foster collaboration, openness, and accessibility across the network. All participants will be supported in contributing to a professional research environment guided by shared scientific goals, mutual respect, and responsible conduct of research, leveraging resources within the network. By connecting scientific communities and sharing knowledge across borders, this initiative strengthens the United States role in global science, promotes the progress of research, and helps prepare the next generation of researchers to succeed in a collaborative and rapidly evolving scientific landscape. This project is jointly funded by AccelNet 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 2025 · 2025-09

Many physical phenomena in fields such as water waves and optics naturally occur in regions with spatial boundaries, presenting significant mathematical challenges. These challenges stem from the complex ways in which boundaries influence the behavior of the underlying partial differential equations (PDEs) used to model such systems. This project will yield a universal methodology for analyzing the dynamics of PDEs in bounded domains, across both one and higher spatial dimensions. It will address fundamental questions about the existence, uniqueness, and stability of solutions, and provide new analytical and computational tools to study boundary-influenced behavior. The project will support the training of undergraduate and graduate students, contributing to the development of the next generation of mathematicians. The project will advance three interconnected directions: (1) the study of nonlinear PDEs in bounded domains in two or more spatial dimensions; (2) the analysis of multi-component systems with nonzero boundary conditions; and (3) the development of a framework for equations with nonzero boundary conditions at infinity, which is closely associated with complex phenomena such as modulational instability and rogue waves. Many of the systems investigated in this project arise as approximations to the fundamental Euler and Navier-Stokes equations in fluid dynamics. As such, the results produced will be of value to the broader area of hydrodynamics and other areas of applied mathematics, including concrete real-world problems from control theory, particularly in stabilization problems, and physical/laboratory experiments, such as the wavemaker problem. 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 revolution in what scientists can measure about planets orbiting other stars (exoplanets) is underway: the elements present in their atmospheres can now be determined. Some of the material that forms a star settles into a disk, and as that material goes on to form planets, the physics and chemistry of that process depends on the relative abundance of each element. Life itself depends on certain elements in excess of typical stellar values, so the topic of extrasolar biosignatures depends on understanding the stellar elemental inventory. Measurements of the elemental abundances in the host stars are needed to interpret what we detect in exoplanet atmospheres. This project will make stellar and planetary abundances available to maximize the scientific payoff from the flood of ultra-precise exoplanetary measurements. The project will support a graduate student in the role of Planetarium Coordinator to bring astronomy education and outreach to under-served communities in the Kansas region. It will also expand an astronomy-themed crafting series called CubeWorlds, an interactive approach to STEM education. The project team pursue these goals using large, public spectroscopic data sets of thousands of planet-hosting stars of interest. The primary focus is the highest-priority targets of FGKM spectral types using data from the world’s foremost observatories. The project team aims to: (a) build a public, data-driven tool to efficiently and precisely derive stellar abundances from spectra, (b) measure elemental abundances for the target lists of leading observatories, and (c) produce a suite of exoplanetary atmosphere models that account for each system’s peculiar stellar abundance patterns. The results will guide future studies of these systems and support the interpretation of upcoming spectroscopy of these systems. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-09

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 · 2025-09

With the support of the Chemical Mechanism, Function, and Properties Program of the Division of Chemistry, Professor Tim Jackson of the Department of Chemistry at the University of Kansas is developing new types of manganese and iron complexes that mimic intermediates in biological metal-dependent enzymes. The goal of this research is to use these new complexes to improve proton-coupled electron-transfer reactions, which are ubiquitous in biological and synthetic catalyst processes. This project will thus lead to a better understanding of biological processes and contribute to the design of new synthetic catalysts. Both manganese and iron are abundant metals; their increased use in industrial catalysis could improve manufacturing by reducing costs and reliance on rare and precious metals. The project lies at the interface of inorganic chemistry, physical chemistry, and biochemistry and is therefore well suited to provide an outstanding educational and training experience for scientists at all levels. Graduate students working on this project will travel to National Labs to receive training and perform research on specialized instrumentation. This project focuses on structure-reactivity relationships for concerted proton-electron transfer (CPET) reactions of manganese(III)- and iron(III)- hydroxo complexes. Transition metal-hydroxo complexes are used in several biological enzymes to perform proton-coupled electron transfer reactions. The proposed studies involve strategies to tune the primary and secondary coordination sphere of manganese-hydroxo and iron-hydroxo complexes to control their physical properties and chemical reactivity, probing the relationship between structure and function. Comparisons of reactivity of manganese-hydroxo and iron-hydroxo complexes are hindered by the paucity of complexes with the same coordination sphere. This project will generate new manganese-hydroxo and iron-hydroxo complexes to understand how geometric, electronic, and thermodynamic factors combine to effect reactivity. In this project, a combination of synthetic, kinetic, spectroscopic, and computational methods will be used to address the following questions: 1) Can second-sphere hydrogen bonding acceptors be used systematically to tune the thermodynamic and kinetic properties of manganese-hydroxo complexes? 2) How does the geometric and electronic structure of iron-hydroxo complexes control their reactivity? 3) Can the reactivity of metal-hydroxo complexes be enhanced by incorporating weak oxygen-donors in the primary coordination sphere? 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 investigates the stability and dynamics of spatially periodic solutions to nonlinear partial differential equations (PDEs) that arise across physics, engineering, and applied mathematics. The principal investigator (PI) focuses on the dynamical stability of these patterns -- their ability to persist under small perturbations – which is crucial since unstable solutions are generally not observable in practical settings except as transient phenomena. Specifically, the project aims to study the modulational stability of periodic wave patterns, examining how their fundamental wave characteristics evolve under slow spatial and temporal variations. Insights from this research have important implications for many applications, including optical signal propagation, fluid flows, and plasma physics. The project also includes opportunities for both undergraduate and graduate students to participate in advanced research training. Building on the PI’s prior success in studying modulated signals in one-dimensional media, this research extends to multi-dimensional nonlinear wave phenomena. The goal is to develop new techniques and methodologies for studying the behavior of spatially periodic patterns under small modulational perturbations. Because periodic patterns can support multiple modulated signals simultaneously, their dynamics exhibit rich, multi-scale structures that are effectively infinite-dimensional. Analyzing these systems involves addressing many nonstandard and interesting issues in dynamical systems, bifurcation and continuation theory, Whitham modulation theory, and spectral and semigroup theory for linear differential operators. The PI’s work is expected to yield new analytical tools and techniques of general interest and applicability across the scientific community. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-08

Breakthroughs in materials science consistently drive progress in information technologies and beyond. Recently, there has been growing interest in materials with properties driven by macroscopic quantum phenomena. The University of Kansas partners with the University of Nebraska–Lincoln to harness the convergence of two such quantum phenomena: magnetism and the unique physics of two-dimensional (2D) materials. Confined to a 2D "flatland", materials can exhibit novel magnetic and electric properties enabling devices with unprecedented functionalities. Experts from two jurisdictions across physics, chemistry, materials science, and engineering will stack and twist atomically thin 2D layers to form artificial structures not found in nature. Insights gleaned from studying their magnetic and electronic behavior will inform the design of next-generation devices including energy-efficient memory and logic components. A key outcome will be the education of future materials scientists and building of a quantum-ready workforce to boost Kansas and Nebraska’s economy. Education and outreach efforts will raise public awareness and inspire the next generation of scientists and engineers. The collaboration between the University of Kansas and University of Nebraska–Lincoln will combine complementary expertise in synthesis, nanofabrication, characterization, sensing, and theory to advance both the science and device applications of artificial two-dimensional (2D) magnetic materials. 2D magnetic and ferroelectric materials, along with their layered and twisted heterostructures, unlock novel quantum states and enable applications that go beyond traditional silicon-based electronics. Realizing their potential requires understanding of structure including Moiré modulations, electronic and magnetic states, and emergent interfacial effects. The team will employ probes sensitive to structural, electronic, and magnetic properties across multiple length scales to establish an integrated cycle of novel synthesis, characterization, and device fabrication. Existing infrastructure, such as the Nebraska Center for Materials and Nanoscience, will benefit from the project through increase and broadening of the user base and utilization of unique instrumentation including the first commercial nitrogen vacancy low temperature scanning microscope in the US. The project is essential to educate a quantum-ready workforce, necessary for the economic growth of both jurisdictions, and to attract the next generation of scientists and quantum materials engineers. This project is supported by the EPSCoR Research Infrastructure Improvement Program: Focused EPSCoR Collaborations (FEC), which supports interjurisdictional teams of EPSCoR investigators to perform research in topics that align with NSF priorities, with the goals of driving discovery and building sustainable STEM capacity. 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 Cascadia subduction zone lies offshore Oregon and Washington. This region is known for slow-slip events, where the plates gradually slip past each other slowly over days, weeks, or months. Onshore instrumentation can detect slow slip, but seafloor instrumentation is needed to monitor the full subduction zone. This is important because slow slip may increase the chance of triggering a great megathrust earthquake and tsunami. This project will extend a time series of seafloor optical fiber strainmeters that detect slow slip off the coast of Oregon. The results could allow for more precise timing of earthquake forecasts for the region. The project supports two graduate students and a post-doctoral scholar. Two orthogonal seafloor optical fiber strainmeters were deployed off the coast of Oregon in 2022. Early results show a signal that is interpreted as an offshore slow slip event that is near simultaneous with onshore slow slip. This project will extend the deployment of the seafloor instruments for two additional years. Capturing more events should help clarify the relative timing of slip in the shallow and deep patches. A primary scientific question is how two slow slip zones, with a gap separating them, are mechanically linked so that they slip nearly simultaneously. This project will also further the real-world use of the seafloor optical fiber strainmeters, a relatively new type of seafloor geodetic instrument which has unique capabilities for capturing offshore slow slip. This project is the first to deploy these instruments over a multi-year time frame. Detecting and monitoring offshore slow slip at the base of the locked zone is important for understanding earthquake hazards in this coastal region. The project will provide training in seafloor geodesy techniques and instrumentation for graduate students and a post-doctoral scholar. 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

Since their invention four hundred years ago, optical telescopes have been the primary tools used in astronomy. Over the past 25 years, scientists have started to focus more on subatomic particles, along with visible light, that come from cosmic sources. Previous experiments have shown that exploring new aspects of the universe can lead to unexpected discoveries of astronomical objects. One particularly fascinating subatomic particle is the neutrino. Neutrinos can arrive on Earth from sources that are too far away to be seen with regular telescopes. As a result, several neutrino telescopes have recently been set up in remote areas around the world, and researchers are currently developing their scientific capabilities. This approach is similar to what Galileo did when he built his telescope; shortly after it was invented, he made the remarkable discovery in 1610 of the four moons orbiting Jupiter, rather than Earth. By improving the images captured by neutrino telescopes, we may uncover equally exciting and transformative cosmic sources that could reshape our understanding of the universe. Over the past three decades, our research group firstly demonstrated the feasibility of detecting Ultra-High Energy Neutrinos (UHEN) via in-ice radio-frequency (RF) methods, the characterization of the RF properties of polar ice has been since then an ongoing effort. Foundational measurements of the RF attenuation length and the depth-dependent refractive index (n(z)) has confirmed the suitability of cold polar ice as both a neutrino target and as an effective RF transmission medium. However, subsequent studies of RF propagation along both vertical and horizontal paths revealed unexpected ±6 dB variations in signal strength and even detected signals in configurations for which propagation should have been forbidden. Additional complexities emerged from 2018 deep pulsing experiments conducted from the SPICE borehole at the South Pole to the ARA radio receiver array, which revealed puzzling differences in amplitude and frequency content between direct (D) and refracted (R) signal paths. Aggregated calibration data highlight persistent discrepancies between theoretical models and observed behavior, particularly for receivers located in the upper 100 meters of the ice sheet—known as the firn—where density gradients are most pronounced. This region, while ideal for deploying radio receivers using existing drilling technology, presents significant modeling challenges. Moreover, to enable multi-messenger astrophysics, it is essential to accurately reconstruct the incoming neutrino’s direction to correlate it with known astrophysical sources. While a confirmed UHEN detection may be within reach by the end of the decade, our current understanding of RF signal propagation in ice remains insufficient for precise neutrino astronomy. The discoveries of new sources will be enabled by an extended, targeted calibration campaign of the telescopes to be conducted on-site in Greenland, and off-site in our domestic laboratories. 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 explores how macrodomain proteins have evolved to balance their different multiple biochemical activities. Macrodomains are ancient enzymes that bind to and remove ADP-ribose, an important post-translation modification that is critical in several cellular stress responses, including DNA damage, ER stress, and virus infection. These enzymes are conserved through all domains of life, including archaea, bacteria, eukaryotes, and viruses, indicating that they are critical for multiple cellular processes. However, the function of macrodomain enzymes in cell biology and microbiology are just now being uncovered. Furthermore, recent research indicates that macrodomains have evolved to properly balance their biochemical functions depending on if they are expressed from a virus, a bacteria, or from a eukaryotic cell. This project will evaluate how viral macrodomains have evolved to develop the ideal biochemical properties that allow them function in the context of a virus infection. This project also has strong educational and community outreach components. Most notably, students at all training levels will participate in this project and will learn how to evaluate the evolution of proteins through workshops in phylogenetics. This project will provide new insights into the fundamental biology of macrodomain enzymes and could lead to new insights into antiviral drug-development. This project aims to define how the coronavirus macrodomain has evolved to best function in the context of a virus infection using the mouse coronavirus, murine hepatitis virus (MHV), as a model. The use of MHV, which is unable to infect humans, for creating macrodomain mutations eliminates the potential for gain-of-function research. Research over the last decade has demonstrated that the coronavirus macrodomain blocks innate immune responses, is critical for viral pathogenesis, and is a potential drug target. Furthermore, the macrodomain uses both its ADP-ribose binding and hydrolysis activities to promote virus replication, and they must be balanced for optimal replication. The PI will use recombinant proteins and a panel of recombinant viruses, developed using a bacterial artificial chromosome based reverse genetic system, to better understand how highly conserved amino acids and selective pressure induced mutations impact Mac1 biochemical activities, virus replication, and pathogenesis using well-established assays and model systems. Additionally, macrodomains from across the evolutionary spectrum will be expressed in the context of MHV infection to define the evolutionary limits of macrodomain divergence that is tolerated in MHV. This work will provide a deeper understanding of how macrodomains have evolved to counter various ADP-ribose dependent antiviral responses. This project is jointly funded by the Genetic Mechanisms Program and the Division of Molecular and Cellular Biosciences. 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 Synthesis (SYN) program in the Division of Chemistry, Professor Zarko Boskovic of the University of Kansas is developing new photochemical reactions to create valuable molecules efficiently and selectively. Robust reaction conditions will enable access to diverse and structurally intricate compounds. Such compounds are important in drug-discovery research. Understanding how molecules behave after absorbing light may lead to the next-generation chemical technologies that avoid the harsh conditions and waste associated with traditional synthetic methods. The project will also train students in interdisciplinary science that combines synthetic chemistry, physical organic chemistry, and computational modeling. Educational tools such as interactive chemistry software and coding modules will be developed to improve chemistry education and promote data literacy among students. The project will also preserve and share a collection of thousands of previously synthesized compounds for future research and discovery. This research will explore n,π* excited states, which are special high-energy states populated by molecules after absorbing light. These states will be used to develop new chemical reactions. The project will focus on three main goals: (1) generating reactive ylides from strained rings through light-induced sensitized decarbonylation, (2) controlling the stereochemistry of nitrogen-containing molecules to make four-membered azetidine rings, and (3) studying short-lived radical intermediates using advanced spectroscopic techniques in conjunction with light sources. The team will use visible light activation, custom-designed light-absorbing groups, and chiral anions and auxiliaries to control chemical reactivity. Computational models will help explain and predict reaction behavior. These studies aim to expand synthetic photochemistry while providing a deeper understanding of the mechanisms of photochemical transformations thus enabling more facile access to the new types of molecules. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-07

With the joint support of the Macromolecular, Supramolecular and Nanochemistry program in the Division of Chemistry and the Established Program to Stimulate Competitive Research (EPSCoR), Professor Aaron Teator of the University of Kansas will develop new polymerization methods with enhanced control over molecular structure. While current synthetic approaches have led to incredible high-performance materials, the available structure space is limited to monomers designed to undergo chain growth by shifting electrons through existing carbon-carbon double bonds. In this research, a through-space transfer of electrons facilitated by functional group migration will be used to control the growth of polymer chains. This will allow for precise control over the number of carbons in each repeat unit based on monomer design. Careful optimization of this approach, coupled with a thorough kinetic and mechanistic exploration will provide access to a new class of synthetic polymers. This proposal has the potential to deliver fundamental knowledge in multiple areas of chemistry, including small molecule reactivity, mechanistic analysis, and polymer methods development. The associated education plan complements and enhances the project by integrating chemical research with education and using societally relevant plastics to generate excitement about organic synthesis. This project will focus on the development of chain-growth polymerizations centered on anionic silyl migration as a key propagation step. In the first objective, careful optimization of reaction conditions will establish through-space active site transfer as a viable approach for chain-growth polymerization. This will provide important fundamental scientific discoveries related to reactivity, reaction design, and structure that will serve as the foundation to expand the approach to a platform methodology. In the second objective, a thorough analysis of reaction kinetics will further mechanistic understanding of the polymerization and aid in the development of design principles to both improve the current approach and enhance future iterations. In the last objective, systematic characterization of thermal, mechanical, and physical properties will reveal important structure-property relationships. This research has the potential to establish a new platform method for the preparation of functional polymers with broad appeal to the organic and polymer 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 2025 · 2025-07

Unmanned aerial systems (UAS) are expected to see rapid growth in the coming decade with millions of autonomous flights annually. To fully unlock the potentials of UAS and advanced air mobility (AAM) technologies, most flights will need to operate in beyond visual line-of-sight (BVLOS) scenarios. Despite being autonomous, BVLOS flight operations require reliable communication links with ground stations and other aircraft to ensure safety and efficiency in the national airspace. However, there is currently no dedicated licensed spectrum for these safety-critical communications, leading operators to rely on unlicensed or experimental licenses without protection from harmful interference. Aligned with the National Spectrum Strategy Implementation Plan, this project aims to develop a new dynamic frequency management system (DFMS) tailored for UAS operations. The DFMS will allow interference-protected spectrum access for autonomous flight communications, supporting the safe and scalable integration of UAS into the airspace. This project advances spectrum science and engineering for aerial use cases and informs public policy on efficient spectrum utilization by sharing extensive spectrum measurement data. It also promotes STEM education by engaging graduate and undergraduate students in interdisciplinary research at the interaction of wireless communications, aerospace engineering, and economics. This project aims to design and validate a DFMS that enables adaptive, location- and time-based spectrum access for UAS operations, particularly in the 5030–5091 MHz band. The project combines theoretical developments in spectrum optimization, cooperative sensing, and decentralized spectrum markets with multi-vehicle real-world flight tests. Specific research tasks include (1) developing a comprehensive system and architecture design of the DFMS to enable dynamic spectrum allocation, (2) conducting multi-vehicle flight tests to gather extensive spectrum data, which are crucial for testing and refining spatiotemporal interference and channel models, (3) enabling spectrum situational awareness through adaptive machine learning (ML)-based algorithms trained on collected spectrum data, establishing joint cooperative sensing and access, and ultimately achieving automated spectrum monitoring and enforcement, and (4) developing a decentralized market system with advanced reservation, implementing the “pay-as-you-fly” concept for UAS operators seeking access to interference-protected aviation-grade spectrum in the 5030-5091 MHz band. The project results in extensive datasets of spatiotemporal channel measurements and interference characteristics to inform and support current and future spectrum policy efforts by the broader research community, regulatory agencies, and standardization committees. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-06

Hands-on training helps students bridge theoretical knowledge with practical application in cybersecurity. While remote platforms offer excellent opportunities for learning software security, no comparable resources exist for hardware security education. Key topics in hardware security require access to physical development boards, creating a significant barrier for learners without these resources. In this interdisciplinary project, a remote platform will be designed to provide open access to physical hardware for beginners to advanced-level hardware security experiments. By providing a scalable, accessible, and innovative educational resource, this tool will advance hardware security education, support the development of skilled practitioners in this field, and respond to national security needs. The remote platform will consist of an array of hardware security development boards, comprising microcontrollers, Field Programmable Gate Arrays (FPGAs), built-in power side-channel measurement and fault injection hardware. The software will include a user-friendly front-end based on JupyterLab and an open-source backend for C and FPGA development. The project will also develop extensions for collecting important educational benchmarks and usage statistics, enabling the evaluation of pedagogical strategies for remote hardware labs. Students will design and conduct complex hardware security experiments on remote physical boards using Python to control the hardware and analyze outputs. By combining innovative hardware tools with scalable software solutions, the platform will enhance both the accessibility and effectiveness of hardware security education and foster advancements with teaching and learning in the field. This project is supported by the Secure and Trustworthy Cyberspace (SaTC) program, which funds proposals that address cybersecurity and privacy, and in this case, cybersecurity education. The SaTC program aligns with the Federal Cybersecurity Research and Development Strategic Plan and the National Privacy Research Strategy to protect and preserve the growing social and economic benefits of cyber systems while ensuring security and privacy. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-06

This REU Site award to the University of Kansas, located in Lawrence, KS, will support the training of 10 students for 10 weeks during the summers of 2025–2027. The U.S. faces a shortage of skilled professionals in the biotechnology sector, and this program aims to strengthen the domestic STEM workforce by providing hands-on research experiences that will cultivate critical thinking, problem-solving skills, and scientific innovation. Students will work in faculty-led laboratories within the Departments of Molecular Biosciences, Ecology and Evolutionary Biology, and Computational Biology. In addition to gaining valuable research skills, students will also participate in workshops focused on scientific communication, career development, and professional networking. This program benefits the university and state by showcasing the region’s cultural and scientific opportunities. The program will conclude with students presenting their research findings in a university-wide scientific poster session. Many participants will also have the opportunity to present their work at scientific conferences after the program, further developing their skills in scientific communication and professional networking. The effectiveness of the program will be assessed through observations, in-person interviews, in-house evaluations, and NSF-approved surveys, together with tracking students’ career trajectories for five years, post-program. Students should apply to the REU site using NSF ETAP (Education and Training Application: https://etap.nsf.gov). The scientific focus of the program, The Stressed Life of Cells, explores how living systems respond to biotic and abiotic stressors at molecular, cellular, and organismal levels. Research projects will investigate stress responses in diverse biological contexts, integrating experimental and computational approaches. Students will engage in studies ranging from cellular signaling pathways, microbial stress adaptation, and genetic variation leading to either resilience or disease. This interdisciplinary training will equip participants with critical analytical skills and expand talent in emerging areas of biotechnology and environmental biology. The program also emphasizes ethical research practices and professional development, preparing students for careers in science and industry. 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.