Kansas State University

NSF Awards · FY 2025 · 2025-04

The International Centre for Mathematical Sciences in Edinburgh, United Kingdom will host the workshop Diophantine Equations, Combinatorics, Analysis in Number Theory on June 23-27, 2025, and a related two-day conference aimed at early-career researchers, Diophantine Equations, Combinatorics, Analysis in Number Theory: Emerging Researchers, will be held June 19-20.  This grant will support participation in these programs by early-career researchers from the United States, which will enhance the U.S. pipeline of mathematical talent by providing a unique opportunity for networking and collaboration. This event will be the first of its kind to bring together researchers in Diophantine equations, arithmetic combinatorics, and harmonic analysis. Recent breakthroughs surrounding Vinogradov's Mean Value Theorem and three-term arithmetic progressions have led to significant progress in these areas. Moreover, the associated techniques are frequently intertwined and generate unexpected insights when applied to problems that may at first seem unrelated. A striking example is the relationship between decoupling in harmonic analysis and efficient congruencing in number theory. Connecting experts from these cognate fields in a workshop-style environment will foster cross-disciplinary collaborations and lead to seminal research papers.   https://www.icms.org.uk/DiophantineEquations   https://www.icms.org.uk/DiophantineEquationsEmergingResearchers 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

This EArly-concept Grant for Exploratory Research (EAGER) award will contribute to the prosperity and welfare of the nation’s defense and commercial industrial base by developing an innovating framework for strengthening supply chain of critical minerals. The US is 100 percent reliant on imports for at least 12 key minerals deemed critical by the US government, increasing exposure to a number of natural and geopolitical risks. These minerals play a significant role in developing products important to renewable energy and defense technologies, among other important sectors. Using the supply chain for manganese as a test case, this project will study strategies for companies in a distributed supply chain to form alliances to ensure continuity of product flow by promoting supplier stability and decreasing single source dependencies. Currently, supply chain participants act in self-interest for profit maximization, leading to potential inefficiencies in ensuring functionality and performance of the entire supply chain. By engaging in alliances via shared costs, the potential exists to reduce exposure to demand and price fluctuations and raw material supply disruptions and to improve supply chain performance. Such strategies place a premium on stable sourcing as opposed to low-cost sourcing and incentivize all supply chain participants to ensure end market product availability. The project develops new models and utility functions to quantify the value, benefit and cost associated with companies engaging in strategic cooperation. Additionally, the project will offer mechanisms to control commodity price volatility using financial instruments such as offtake agreements and minimum guaranteed purchase prices. The results of this research will offer a blueprint for companies to form associations and alliances with each other that encourage risk sharing among supply chain partners and offer a fair way of allocating the cost of resilience among them. This award will support the involvement of a graduate student and undergraduate student to advance the research agenda. This project will develop a framework wherein companies in a supply chain are viewed as players in a cooperative game. Collaboration among two or more players of groups are called coalitions where the assertion that the maximum benefit of cooperation can be realized by partaking in the grand coalition (group of all players) will be assessed. Notably, the study will quantify the cost of resilience defined as additional capacity built into the supply chain network by means of chaining and containment that offers benefit of stability to all entities in the network. The project focuses on developing fair cost allocation schemes using proportional allocations methods and advanced game-theoretic methods such as Shapley Value and Least Core. Novel optimization approaches are employed to enforce fairness as constraints of the model during cost allocation with the goal of maximizing the incentive to participate in a grand coalition for each company. Profit and Loss functions for players are developed using method of least squares comprising of linear and non-linear regression. These cost functions will predict commodity prices based on the value of the refined product in the open market, which in turn will serve as an indicator of viability in cooperation among players in the game. The project will validate methods by conducting a case study on purchase and sale of manganese and its refined products with its largest application area in the steel 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.

NSF Awards · FY 2025 · 2025-01

This award is funded by NSF Global Centers program, an innovative program that supports use-inspired research addressing global challenges through the bioeconomy. It is co-funded by the Office of International Science and Engineering, the Directorate for Technology, Innovation and Partnerships, and the Directorate for Geosciences. It supports U.S.-based researchers developing global international partnerships and building multi-stakeholder engagement to advance use-inspired research, in the aim to develop their project toward a large-scale international effort. The BioSenseInnovations project is dedicated to tackling global challenges in nitrogen management, greenhouse gas (GHG) emissions, and their interactions in relation to sustainable agriculture. It develops cutting-edge sensors that can detect nutrients, chemical compounds, soil microbiomes, and GHGs. Collecting these data in real time is essential for advancing precision agriculture and promoting sustainable practices. By focusing on how soil, microbes, and plants interact, innovative technologies are conceptualized and generated to help farmers manage their crops more effectively and sustainably. The transdisciplinary team includes experts in chemistry, computational biology, omics, microbiology, and metabolic engineering. They develop and translate to the agricultural sector new nitrogen management strategies and GHG emission mitigation technologies. The research takes place in key agricultural regions with primary test sites in the U.S. (corn research sites in Kansas). The mid-term goal is to extend this effort to potential partner countries, Canada (wheat sites in Quebec) and the UK (wheat sites in Sheffield), and ultimately globally. This project integrates research, education, stakeholder engagement and outreach. It provides support and training to undergraduate and graduate students, notably from underrepresented groups in STEM, as well as outreach toward K-12 students, farmers and producers, and industry partners. The researchers focus on the overall understanding of fertilizer, plant, and soil interactions. They prioritize three research thrusts: (1) creating innovative sensors to reliably sense and measure key nutrients (ammonium and nitrate) and GHG emissions (nitrous oxide and methane) by actuating processes within the soil-microbe-plant-environment continuum; (2) designing greenhouse gas (GHG) modeling systems and advanced machine and statistical learning techniques; (3) establishing and testing a biosensor network to monitor plants with Biological Nitrification Inhibitors (BNI) to support sustainable agriculture. Additionally, they engage in crosscutting activities that include (4) stakeholder engagement and collaborative research initiatives to strengthen the global science and technology community, and (5) workforce development and educational programs. With collaborators in Canada and the UK, the team develops alternative nitrogen fertilizers including biofertilizers, biostimulants, and fertilizers derived from waste recovery. It assesses how the complex soil-microbe-plant system and the environment (GHG emission) can be studied by the development and optimal deployment of low-cost sensors. These sensors measure nutrients’ presence in the fertilizers and the soil, microbial activities, GHG emission, pH, and moisture, as well as their spatial and temporal variations in real field applications. This award is funded by NSF Global Centers program, an innovative program that supports use-inspired research addressing global challenges through the bioeconomy. This award support U.S.-based researchers developing global international partnerships and building multi-stakeholder engagement to advance use-inspired research, in the aim to develop their project toward a large-scale international effort. 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 project will test how fire affects biodiversity in fire-prone ecosystems. Disturbance is important in maintaining species diversity in most ecosystems. Fire is a common disturbance that occurs in many systems such as longleaf pine savannas. Although research has tested the effects of fire on biodiversity, little is known about how climate change might alter these effects. For example, longer or more intense droughts might reduce population recovery after a fire. This project will test how fire frequency affects biodiversity and how climate change might modify fire effects in the future. Conservation of threatened ecosystems such as longleaf pine savannas requires the ability to predict how populations will change in the future. Longleaf pine savannas are home to many threatened plants and animals like the Venus flytrap and red-cockaded woodpecker. This project will provide recommendations for prescribed burns in longleaf pine savannas and will create a web-based tool for land managers in the southeastern U.S. that will help them make conservation plans. Additionally, the project will train graduate and undergraduate students in ecology and conservation. The project will explicitly test predictions that the optimal fire management strategy for Venus flytraps will differ in a future climate. These predictions were constructed using data collected under ambient variation in climate and fire regimes, rather than extreme values of climate consistent with future climate change, or experimental manipulations of fire that disentangle correlations between historical fire frequency and current fire effects. Using demographic data on Venus flytraps collected from almost-factorial manipulations of fire frequency, drought, and warming conducted across a broad geographic area, the PIs aim to construct a climate- and fire-driven integral projection model that explicitly includes site-specific effects. We will validate the model using independently collected abundance data on fire and climate effects. The proposed work will also estimate the degree to which Venus flytraps can be used as an indicator species, where a good indicator is one that accurately predicts changes in abundance of other species in response to fire and climate, rather than simply the presence of other species or of high levels of biodiversity. Assessing the indicator potential of Venus flytraps will help conservation managers identify fire frequencies that could bolster biodiversity or abundances of species of concern. These efforts will culminate in generalizable insights underlying disturbance management in a future climate and the development of a framework for assessing the utility of indicator species. This project is jointly funded by the Divisions of Environmental Biology and Integrative Organismal Systems through the Partnership to Advance Conservation Science and Practice Program. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2026 · 2024-12

PROJECT SUMMARY Carbapenem-resistant Klebsiella pneumoniae (CRKP) causes Gram-negative lung infections and fatal pneumonia-derived sepsis (or pneumonic sepsis) for which minimal treatment options are available. Importantly, CRKP-mediated pneumonia and sepsis is associated with immune suppression, rapid bacterial dissemination, and high mortality rate (20-40%) among the hospitalized patients. Host targeted alternative therapeutic approaches are thus necessary for pneumonic sepsis. The respiratory tract is densely innervated by nociceptor sensory neurons that mediate cough and bronchoconstriction and release of neuropeptides in the lungs, including calcitonin gene-related peptide (CGRP). Further, the released CGRP acts on its receptor complex (RAMP1/CALCRL) expressed in immune cells for immunomodulation. However, it is yet unknown the role of nociceptor neurons and CGRP in host defenses to Gram-negative pneumonia and pneumonic sepsis. Specifically, this research project will address the following two key questions: 1) Do nociceptor neurons and their subsets play role to alter the host CRKP clearance abilities and survival in pneumonic sepsis 2) Does neuropeptide signaling involve in driving pneumonic sepsis? Using both ‘loss and gain of function’ neuronal manipulating strategies in mice and using the nociceptor-targeted pharmacologic approach and neuropeptide- and neuropeptide receptor-deficient mice, this study will determine the role of neuroimmune interactions in pneumonic sepsis to address these questions. The preliminary in vivo and in vitro data demonstrate the host deleterious effects of nociceptor neurons and the CGRP signaling pathway for the defense against CRKP-induced pneumonic sepsis. Furthermore, the nociceptor-depleted mice showed higher CRKP clearance abilities and recruitment of neutrophils and inflammatory monocytes (Ly6Chi) at primary site of infection as compared to the control littermates. However, Ly6Chi monocytes were only observed to be critical for controlling CRKP dissemination. The proposed studies are significant and innovative because they identify neuroimmune crosstalk between nociceptors and innate immune cells as a novel mechanism to promote sepsis at both cellular and whole animal levels. Targeting the nervous system directly, or through downstream receptor signaling pathways in immune cells, will inform about the host-based strategy as a treatment modality for lethal Gram-negative infection and pneumonic sepsis.

NSF Awards · FY 2024 · 2024-12

This project aims to revolutionize how chemical synthesis and composite material discoveries are made. Traditionally, scientists have relied on one-variable-at-a-time experimentation, which is time-consuming and often overlooks the complex interactions among various components and processing conditions. In contrast, this project introduces an innovative approach called adaptive design of experiments, which allows for the simultaneous evaluation and optimization of multiple variables in a dynamic process. This method can significantly enhance the quality and efficiency of chemical synthesis, leading to better product properties while saving time and resources in the laboratory. The project focuses on developing advanced computational modeling tools that integrate experimental data with first-principles modeling, machine learning, and process optimization. This integrated approach will be primarily applied to synthesizing perovskite oxides, materials critical for energy-related applications such as fuel cells and catalysis. The outcomes of this project will not only accelerate material discovery and manufacturing optimization but also serve broader societal needs by advancing energy technologies and providing a systematic framework for various manufacturing systems. Moreover, the project will foster interdisciplinary student training and engagement, aiming to produce graduates with high-level expertise in mathematics, computation, and data science, thereby contributing to a skilled workforce in modern chemical manufacturing. The primary objective of this project is to address the complex multivariable problem inherent in chemical synthesis by developing and applying a physics-informed machine learning-based adaptive design of experiments framework. The project will integrate first-principles modeling, machine learning, process optimization, and experimental data to create a hybrid model that predicts and optimizes synthesized materials’ microstructure, stoichiometric variation, and composition. This hybrid model will serve as the foundation for an autonomous chemical synthesis framework, optimizing the decision variables to enhance the overall characteristics of the product. Specifically, the project will focus on synthesizing perovskite oxides to manipulate product microstructure and compositional variations to tune the electronic structure and oxygen vacancies. The proposed approach will identify optimal operating temperature dynamics, annealing times, and other synthesis variables to improve product properties. By automating the experimental exploration process, the research will significantly reduce the cost, time, and effort typically associated with material discovery and optimization. The outcomes will enhance the synthesis process and provide a versatile platform applicable to various manufacturing systems, including chemicals, advanced materials, pharmaceuticals, and biological probes. The project will also contribute to educational and outreach efforts, promoting interdisciplinary training and broadening participation in STEM fields. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-10

This project serves the national interest by preparing a qualified engineering workforce with important technical and professional skills for the health-based point-of-care (POC) additive manufacturing (AM) industry. Health-based POC-AM is a non-traditional form of manufacturing referring to the just-in-time creation of anatomical models, surgical instruments, prosthetics, scaffolds, etc., based on medical imaging data and need at the place of patient care. The growth of POC-AM requires the collaboration of medical, engineering, and social science professionals in that engineers must be trained to be socially adept and communicative about additive manufacturing specifically for healthcare applications. Despite the exponential growth in POC-AM market value and scholarly activities, the needed education and training components are underdeveloped, especially for undergraduate students in public engineering schools. This IUSE Engaged Student Learning Level 2 project will bridge this talent gap by creating an undergraduate engineering course that is broadly accessible and will be able to define, cultivate, and assess students' technical and professional skills needed by the booming POC-AM industry. This project features a project-based learning plan to develop students' theoretical and hands-on skills to create a broad range of medical objects from non-patient-specific personal protection equipment and anatomical models to patient-specific prosthetics, tissues, and implants. This project will strongly emphasize the development of students' reflective communication skills, both written and verbal, with colleagues in both engineering and in healthcare. The project will also design a protocol for assessing and developing those communication skills using objective and subjective metrics. Thus, the goal of this project is to remove barriers between POC-AM research and education while interconnecting key concepts in multiple related sub-disciplines through teaching this unique skillset to undergraduate students at two large public universities. The innovative course that focuses on students' technical and communication skills development will train holistic and well-rounded engineering students who can solve complex problems that require a broad integration of technical knowledge and communication skills. The combination of cutting-edge learning about POC-AM and a targeted and efficient communication skills development targeted to the needs of the post-COVID student population makes this project highly effective for undergraduate education. The developed instructional and assessment materials will be publicly available as this project can be a model for other similar upper division engineering courses, especially in an emerging and practical field. The NSF IUSE: EDU Program supports research and development projects to improve the effectiveness of STEM education for all students. This project is jointly funded by the Established Program to Stimulate Competitive Research. This project is jointly funded by IUSE and the Established Program to Stimulate Competitive Research (EPSCoR). This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-10

This planning project takes a unique multidisciplinary cyber-physical systems (CPS) perspective in the control of complex system-of-systems. Learning, estimation, and control of sparsely observed spatiotemporal processes/complex system-of-systems in the presence of multiple sources of variabilities/uncertainties, while offering performance guarantees is a fundamental problem that is encountered in many CPS domains. While this planning project will work towards developing new monitoring and control paradigms, the underlying fundamental CPS research has potential to make a significant impact on diverse CPS arenas such as agriculture, chemical/biological systems, smart communities, smart grid, and transportation systems. This planning project involves activities designed to enable the creation of a multi-university consortium that will not only advance the state of the art in applied CPS research but also host innovative educational, workforce development, outreach, and industry engagement programs. The CPS perspective is empowered by foundational advances in (1) multimodal sensing and detection; (2) system state assessment and tracking with limited spatiotemporal data; (3) control and modulation of system states along with optimal plans for meeting specified objectives. While machine learning tools are being increasingly used to support the foundational tasks of sensing, detection, diagnostics, planning, and control, humans will always remain an integral part of safety-critical CPS systems. Advances in modeling and quantifying the performance of this human-AI interactive decision space will be an integral and unique aspect of this project. 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 will contribute to the national need for well-educated scientists, mathematicians, engineers and technicians by supporting the retention and graduation of high-achieving, low-income students with demonstrated financial need at Kansas State University, working in partnership with Dodge City Community College (DCCC). Over its 5-year duration, this project will fund scholarships to 36 unique full-time students who are pursuing bachelor’s degrees in Agricultural Technology Management, Architectural Engineering, Biological Systems Engineering, Biomedical Engineering, Chemical Engineering, Civil Engineering, Computer Engineering, Computer Science, Construction Science and Management, Cybersecurity, Electrical Engineering, Industrial Engineering, and Mechanical Engineering. Returning sophomore students and incoming transfer students from DCCC and other partners will receive the scholarships for up to 4 years. This project will provide student support that includes academic coaching, additional advising, and research opportunities. Research and evaluation will also be conducted to determine the effectiveness and student perceptions of the project support activities. This research may help to improve student services in the future and improve outreach and use of services by low-income students. The overall goal of this project is to increase STEM degree completion of low-income, high achieving undergraduates with demonstrated financial need. In addition to scholarships, students will be provided evidence-based, context-specific interventions including academic coaching. Academic coaching is a one-on-one meeting with a professional to improve skills and performance in areas such as goal setting, study skills, and time management. Coaching has some similarities to intensive advising but is more focused on personal development and goals outside of a student's academic discipline. The mixed-method project research plan will explore the impacts of academic coaching on retention, academic standing, and graduation among engineering students from low-socioeconomic backgrounds. Project practices and outcomes will be disseminated across fields, primarily through journal and conference publications. This project is funded by NSF’s Scholarships in Science, Technology, Engineering, and Mathematics program, which seeks to increase the number of low-income academically talented students with demonstrated financial need who earn degrees in STEM fields. It also aims to improve the education of future STEM workers and to generate knowledge about academic success, retention, transfer, graduation, and academic/career pathways of low-income students. 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

The vulnerability of older adults to financial exploitation has raised serious concerns. Effective prevention strategies must consider the intersection of technology and the psychosocial challenges faced by older adults, making them susceptible to exploitation. Smart assistive technology uses data-driven algorithms and generative AI to provide proactive feedback to users or caregivers about fraud warning signs. Unlike existing fraud-detection technologies focusing on individual instances, such as an email or a social media request, the proposed technology tracks all interactions from the same soliciting party, providing a comprehensive safeguard. This project integrates social psychological factors into smart assistive technology design, enabling researchers to study ergonomics, privacy, loneliness, perceptions and attitudes toward technology, and fraud susceptibility. The project will benefit smart in-home product designers in creating practical solutions to mitigate financial exploitation among older adults. It will also benefit society by improving the quality of life of older adults and enhancing their ability to age in place, thus significantly reducing healthcare and long-term care costs. The proposed project will be conducted in five counties in Kansas, impacting over half a million adults aged 60 and older, with a scalable framework beyond Kansas. This aligns with the NSF’s mission to advance national welfare by protecting a vulnerable population from financial fraud and scams. By integrating social science research into smart assistive technology, this planning project pushes the boundaries of new knowledge. It aims to understand how generative AI models can be tailored for different user profiles (e.g., married vs. single, urban vs. rural, high vs. low socio-economic status) and incorporate various forms of communication (e.g., emails, social media, financial records, phone calls), social-emotional experiences, and socio-demographic factors to combat financial exploitation. The proposed Large Multimodal Model (LMM) framework will expand knowledge on generative AI and fraud prevention, accommodating images and voices in addition to text. The models will reside on users’ mobile devices to address privacy concerns and preferences. This project will also explore how fraudulent solicitations, social-emotional, and socio-demographic factors are linked to susceptibility to financial exploitation among older adults. By examining these relationships, the research aims to develop more effective and tailored interventions to protect this vulnerable population from financial fraud. 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 award supports participants at the 2024 and 2025 editions of the Prairie Analysis Seminar. The Fall 2024 event will be held in October 2024 at the University of Kansas; the Fall 2025 event will be hosted by Kansas State University (date to be determined). The Prairie Analysis Seminar is an ongoing collaboration between the mathematics departments at Kansas State University and the University of Kansas. Since its inception in 2001, the conference has showcased the research of a diverse group of mathematicians working in analysis and partial differential equations. The event provides participants in early career stages with the opportunity to present their work via contributed talks, to get advice from experts, and to expand their professional networks. In addition, the event promotes the participation of underrepresented and underserved groups in mathematics, in particular, researchers from smaller colleges and universities in geographical proximity to the host institutions. Invited speakers at the Prairie Analysis Seminar are leading scholars well known for their contributions to active areas of research within analysis and partial differential equations and for their ability to communicate with a broad mathematical audience. Each event features an invited principal speaker, who gives two one-hour lectures, accompanied by two invited speakers who each give a one-hour lecture. An important component of the seminar is the time reserved for short talks by early career participants, including advanced Ph.D. students and postdocs. The conference also includes a session for discussion of open problems suggested by conference participants. https://www.math.ksu.edu/research/centers-groups/group/analysis/prairie_seminar.html This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2025 · 2024-09

Project Summary Antibiotic resistance stands as a formidable challenge in both human and veterinary medicine, demanding comprehensive strategies to monitor and regulate antimicrobial usage. This FDA proposal brings together the esteemed Food Animal Residue Avoidance Databank (FARAD) and the pioneering data analytics prowess of the 1DATA consortium to confront this urgent issue head-on. With a dual focus, the project aims to (AIM 1) extract antimicrobial use data for major livestock and poultry species and (AIM 2) extend data collection efforts to encompass minor species and companion animals. FARAD, a stalwart institution with over four decades of experience, serves as the bedrock of evidence-based withdrawal recommendations in veterinary practice. Through a collaborative network spanning prominent veterinary colleges nationwide, FARAD has cultivated databases and tools to meticulously curate and analyze antimicrobial usage data across diverse animal demographics. Harnessing FARAD's reservoir of expertise, this project endeavors to birth the Long-term Antimicrobial Use with AI web-crawler (LAMU-AI), a revolutionary platform poised to bridge existing data lacunae. LAMU-AI emerges as a beacon of innovation, amalgamating data streams from FARAD's secure case repository, regulatory bodies, veterinary medical teaching hospitals, and online repositories to furnish real-time insights into antimicrobial utilization trends. Armed with cutting-edge data analytics, machine learning, artificial intelligence methodologies, and a large language processing model, LAMU-AI promises scalable and multifaceted visualization of antimicrobial deployment patterns. This groundbreaking approach empowers stakeholders—be it veterinarians, producers, regulatory agencies, or researchers—with the ammunition to make judicious decisions regarding antimicrobial stewardship and public health. Central to this initiative is the integration of disparate data sources, including FARAD's databases and regulatory testing datasets, to furnish a complete view of antimicrobial utilization practices. The advent of a sophisticated Big Data Dashboard and Visualization system promises to democratize the analysis and interpretation of intricate datasets, fostering collaboration and knowledge propagation across sectors. Furthermore, robust data security protocols will safeguard the sanctity and confidentiality of sensitive information, assuring stakeholders of the integrity of the data ecosystem. In summation, this project represents a paradigm shift in veterinary medicine—a concerted effort to confront critical data lacunae through the fusion of advanced data analytics and artificial intelligence. By marrying FARAD's unparalleled expertise with the avant-garde technology of the 1DATA consortium, we aspire not only to redefine antimicrobial surveillance but also to catalyze global endeavors aimed at combating antibiotic resistance at its core.

NSF Awards · FY 2024 · 2024-09

This project applies behavior science research approaches to investigate how people self-organize to manage the use of natural resources. There is a pressing need for understanding and improving collective action participation in native habitat conservation and restoration in grassland ecosystems. This planning project is for a research center that will use knowledge from psychology research to build communities and networks that help maintain vital natural resources for the common good. The project focuses on native watershed habitat rehabilitation and maintenance in urban/rural settings as a test topic. The research team investigates the social, environmental, and information infrastructure that tend to lead to sustained natural resource governance arrangements. This work is developed in partnership with local stakeholders to build sustainable community-involved research opportunities (social infrastructure). The deterioration of collective action for maintaining natural resources as common goods is an ongoing, complex, and global problem. Collective action can under some conditions sustain these natural resource governance arrangements. However, lack of participation and free-rider problems are two of just many challenges that pose barriers to sustained common-pool resource management. Previous work has identified the conditions that tend to support sustained governance of common pool resources. This project specifically investigates how to build and maintain those characteristics. The research project focuses on the context of urban/rural native watershed habitat rehabilitation and maintenance. A key challenge in these systems is how to translate existing insights about the features of existing systems that successfully maintain common goods into ways to effectively change social, political, economic, and environmental contexts that will bring common and public goods situations into full collective action participation. This project addresses this need by developing multi-site, community engaged research labs that a) test the application of techniques from cognitive and social psychology to guide changes to people’s experienced environment, and b) build the infrastructure of people, resources, and equipment needed to support common goods in at-risk grasslands ecosystems. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2025 · 2024-09

Development and validation of pain models in food animals Project Summary Each year, more than 65 million piglets, 1 million goats and 15 million calves in the U.S. undergo painful procedures such as tail docking, disbudding, and castration. Livestock also commonly develop painful conditions such as lameness during the production cycle. Misalignment between industry guidelines and on-farm analgesic use threatens consumer confidence in livestock production practices. One factor contributing to the low adoption rate of pain mitigation protocols on farms is the lack of FDA-approved analgesic drugs. Our long-term goal is to improve on-farm animal welfare by controlling pain in a manner that is safe for the animal and the consumer, and compliant with U.S. regulations. The specific objective of this proposal is to develop models which reliably and consistently evaluate the efficacy of analgesics in food animals in support of neww drug approvals. A central hypothesis of this proposal is that there are interactions between measuring current standard behavioral outcomes and measuring analytical biomarkers which affect how we perceive pain in animals, and therefore how efficacy of an analgesic is evaluated in the approval process. We also hypothesize that more advanced analysis of analgesic pharmacokinetics and pharmacodynamics in food animals will improve modeling of optimal regimens. Painful processes addressed through the 5 specific aims of this proposal include footrot and castration in goats, dehorning and castration in calves, and castration and tail docking in piglets. In goats and calves, first we seek to establish the optimal pain biomarkers (behavioral and analytic) and evaluate the effect of collecting concurrent behavioral and analytic biomarkers on the values determined for each. Secondly, the efficacy of two doses of transdermal flunixin meglumine are evaluated for controlling pain associated with the indicated pain process, and then the pharmacokinetics/pharmacodynamics in relation to pain biomarkers are characterized. In swine, the first phase seeks to create a pharmacokinetic/ pharmacodynamic model to describe the analgesic efficacy of flunixin meglumine in castration and tail docking of swine. The second swine phase validates predictions made by this model. This novel proposal will advance our understanding of measuring pain and create a more standardized process to support investigation and approval of existing and candidate analgesic drugs for food animal species. The novel pharmacokinetic/pharmacodynamic components of this project advance our understanding of modeling analgesic drug efficacy and support optimal regimen development prior to clinical studies.

NSF Awards · FY 2024 · 2024-09

Microbial symbionts help plants acquire resources and thereby improve plant productivity. Many plant species simultaneously interact with multiple symbionts. Notably, legumes, including economically important crops such as beans, associate with both soil fungi called arbuscular mycorrhizal fungi (AMF) and rhizobia bacteria, which improve uptake of soil phosphorus and nitrogen, respectively. Legumes can realize greater benefits from associating with both symbionts than expected from the effect each symbiont has on its own. That is, plants can derive synergistic benefits from multiple symbionts. However, when legumes will receive synergistic benefits is not understood. Understanding the contexts under which legumes are likely to receive synergistic benefits will help land managers and restoration ecologists manipulate environmental conditions to help establish desirable legumes in managed and degraded systems. In agricultural systems, understanding these context dependencies could help improve food production while potentially lessening the need for fertilizer inputs on legume crops. The goal of this project is to develop and test a predictive framework for synergistic benefits within the legume-AMF-rhizobia system. The investigators will also develop educational modules using legumes, rhizobia, and AMF for use in middle and high school classrooms, and train students within a collaborative environment. Although individual studies of legume-AMF-rhizobia interactions have shown synergism, synergism is not the general result according to meta-analyses. This suggests that the impact of simultaneous interaction with AMF and rhizobia on legume performance may depend on environmental context or plant and microbial characteristics. The investigators predict that synergism can occur when symbionts provide plants with complementary, limiting resources required for growth (phosphorus and nitrogen). This stoichiometric complementarity hypothesis suggests that synergism will occur when nitrogen and phosphorus are co-limiting and when the symbionts are effective at providing complementary resources. Moreover, synergism may require sustained reinvestment in symbionts over time and consequently be more likely in perennial than in short-lived annual legumes. The investigators will test these predictions by evaluating synergism in legume-AMF-rhizobia interactions while manipulating soil nitrogen and phosphorus availability, symbiont quality, and legume life history. The investigators will couple the experimental work with the development of theoretical models to extend mechanistic understanding of these symbioses to predictions of synergism. This work will improve understanding of the role that symbionts play in plant ecology and evolution and guide development of regenerative agricultural systems that maximize the value of plant microbiomes. 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

Antimicrobial peptides and proteins (AMPs) play a critical role in enhancing food safety, livestock health, and agricultural productivity. However, traditional methods for discovering and optimizing AMPs are inefficient, costly, and technically demanding. The integration of artificial intelligence (AI) and bioinformatics has revolutionized this process, but the reliance on large, often unverified datasets introduces significant cybersecurity risks. Manipulated or erroneous data can lead to costly and time-consuming setbacks. This project develops an automated framework for the security assessment of training data in bioinformatics, particularly focusing on AMPs. The framework evaluates both the sequence and functionality of AMPs, considering the costs associated with laboratory validation experiments. This effort enhances the reliability of computational predictions, reduces the need for costly wet-lab validations, and fosters a culture of security-mindedness within the scientific community. Additionally, it creates an open-source dataset focused on AMPs functionality security and an online platform for dataset evaluation and security education. The project has two key tasks: 1) model-driven low-quality data filtering and 2) data poisoning vulnerability exploration and defenses. By developing a novel graph model for analyzing AMPs structures and compiling an open-source dataset for AI training, this project automates security assessments, significantly enhancing data integrity and security against both inadvertent and malicious data vulnerabilities. This research advances bioinformatics by providing robust cybersecurity tools for AMPs research and other peptide/protein research, bridging gaps in data security, and fostering safer, more reliable scientific collaborations. The open-source dataset and automated data verification framework democratizes data access and innovation in bioinformatics. The project also emphasizes community engagement and education on cybersecurity in cyberinfrastructure to promote the advancement of health, prosperity, and welfare through scientific 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.

NIH Research Projects · FY 2024 · 2024-09

Project Summary Enterobacter cloacae complex (ECC) are a natural component of mammalian gastrointestinal microbiota (Davin-Regli and Pagès, 2015; Ramirez and Giron, 2020), but also found in a wide range of foods and can potentially serve as foodborne pathogens (Healy et al., 2010; Shaker et al., 2007;Capita et al., 2020; Gwida et al., 2014; Kilonzo-Nthenge et al., 2013). A recent study has identified multi-drug resistant ECC in raw tomatoes, lettuce, carrots and other fresh vegetables that were intended for direct human consumption in Spain (Pintor-Cora et al., 2023). ECC and E. aerogenes are recognized as the most predominant nosocomial clinical pathogens within the Enterobacter genus, often linked to infection outbreaks (Davin-Regli and Pagès, 2015; Mezzatesta et al., 2012). Over the past three decades, they emerged as highly significant opportunistic, and multi-drug resistant pathogens in hospital settings (Gaston, 1988). These infections, ranging from urinary tract infections, pneumonia, bacteremia, and sepsis, pose significant risks particularly for immunocompromised individuals (Annavajhala et al., 2019; Girlich et al., 2021; Intra et al., 2023). Antimicrobial resistance (AMR) in Enterobacteriaceae represents a global public health concern (John Jr et al., 1982; Ramirez and Giron, 2020). Klebsiella species and Enterobacter species, including ECC isolates, are recognized as the most prevalent carbapenem-resistant Enterobacteriaceae (CRE) in the United States (Annavajhala et al., 2019; Lutgring, 2019; Mezzatesta et al., 2012). In recent years, many antibiotics commonly used to treat Enterobacter infections, including ECC- associated diseases, have been alarmingly less effective(Sanders Jr and Sanders, 1997). This trend is attributed to the intrinsic β-lactam resistance observed in ECC species, primarily due to expression of low levels of ampC genes encoding for an inducible AmpC-type cephalosporinase (Annavajhala et al., 2019; Seeberg et al., 1983). This unique chromosomal β-lactam mechanism enables ECC species to resist the bactericidal effect of Penicillins and first- and second-generation Cephalosporins. In cases of prolonged exposure to β-lactam drugs, ECC species may even exhibit resistance to third generation Cephalosporins (Seeberg et al., 1983). The Vet-LIRN AMR monitoring program has collected and sequenced approximately 200 ECC strains, and determined their corresponding AMR phenotypes. KSVDL routinely receives ECC-positive diagnostic samples. The objectives of this project are to isolate ~20 ECC isolates from KSVDL submitted specimens, obtain genome sequences and determine AMR phenotypes, then perform a comparative analysis of AMR phenotypes of these ECC genome sequences alongside the 200 ECC strains from the Vet-LIRN AMR monitoring program.

NSF Awards · FY 2024 · 2024-09

Frost and ice formation negatively impact many industries including aviation, refrigeration and air-conditioning systems, consumer devices, clean water, and agriculture. The global deicing market for commercial aircrafts reached one billion dollars in 2019, and condensate freezing on external heat exchangers in air-source heat pumps can reduce performance by 35-60%. The main objective of this project is to better understand frost and ice formation to enable the engineering of surfaces and processes to prevent the negative effects of frost formation. The microgravity environment of the International Space Station will slow the growth of ice crystals, which will enable the visualization and study of frost and ice formation mechanisms. Videos of the surfaces will be analyzed using machine learning to automatically track and quantify condensation (i.e., the formation of liquid droplets) and frost characteristics. Another objective of the project is to share results with the public through a local science museum as well as summer camps for middle school and high school girls. The goal of this project is to use a microgravity environment to gain a deeper understanding of condensation, frost, and ice formation and use machine learning to develop models for predicting condensation and freezing behavior. The project has three main scientific objectives. The first objective is to investigate condensation, including droplet nucleation and droplet dynamics, in microgravity and gravity environments and use machine learning to understand droplet growth rates, and frequency of coalescence events. The suppression of natural convection in microgravity will enable the quantification of convection and radiation during condensation from moist air. The second objective is to investigate freezing front propagation in microgravity and gravity environments. This would provide fundamental new insights into the different mechanisms that propagate frost formation. Machine learning will enable the development of mechanistic models to predict frost propagation rates. The third objective is to investigate ice crystal growth from the initial ice nuclei in microgravity and gravity environments. Machine learning will be utilized to characterize crystal growth and create models to understand factors that impact crystal growth and dendritic formations. This would provide fundamental new insights into the role of water vapor dynamics and heat transfer on frost growth enabling the development of mechanistic models to optimize surface design for controlling ice formation. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-08

This award is jointly supported by the Major Research Instrumentation (MRI) and the Chemistry Research Instrumentation programs. Kansas State University is acquiring a a powder X-ray diffraction (PXRD) with high-temperature, pair distribution function analysis, in-plane grazing incidence X-ray diffraction, small-angle X-ray scattering, and laser/video microscope guided micro-focused PXRD to support the research of Professor Jun Li along with colleagues Daniel Higgins, James Edgar, Ganga Hettiarachchi, and Tendai Gadzikwa. This instrument facilitates research in the areas of nanomaterials, renewable energy, catalysis, separation and chemical/biochemical sensing, polymers, geology, and biology. In general, an X-ray diffractometer allows accurate and precise measurements of the full three-dimensional structure of a molecule, including bond distances and angles, and provides accurate information about the spatial arrangement of a molecule relative to neighboring molecules. The research facilitated by the instrument has profound impacts on a range of critical scientific and technological developments, including those defined as national priorities in the CHIPS Act of 2022 for semiconductor manufacturing in the USA. The instrument supports the training and professional development of ~ 65 young researchers, including postdoctoral associates, graduate students, and undergraduate students. The instrument supports a broad array of existing federally funded projects among the 40 participating research groups by providing new capabilities not presently available within the State of Kansas. Its acquisition will greatly enhance the competitiveness of proposals for new federal and industrial funding from these and other investigators. It will also help Kansas institutions recruit and retain world-class researchers/educators, particularly those from under-represented groups in STEM disciplines. The research groups include scientists from groups underrepresented in STEM and early-career researchers. The instrument helps maintain a diverse, globally competitive STEM workforce within the nation. The award is aimed at enhancing research and education at all levels, especially in areas such as nanomaterials, renewable energy, catalysis, separation and chemical/biochemical sensing, polymers, geology, and biology. Specifically, the instrument enables research focusing on four cluster areas: (i) developing nanomaterials for renewable energy, catalysis, separation and chemical/biochemical sensing; (ii) exploring highly crystalline, precisely controlled bulk and epitaxial thin films (including large defect free 2D materials) for novel electronics, optoelectronics and next-generation computing; (iii) characterizing material’s nanostructures in colloids, semicrystalline polymers, bio-based construction composites, and supramolecular assemblies in broad hard, soft, and bio-based materials; and (iv) understanding the bioavailability, bio accessibility and stability of nutrient elements and potentially toxic trace elements in minor mineral phases of highly heterogeneous soils and geological samples. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-08

Reproducible, adjustable, and massive liquid droplet generation techniques are pivotal in wide applications, such as emulsion production, digital chemical and biological assay, single cell analysis, drug delivery, and nanoparticle synthesis. However, the state-of-the-art droplet generation systems heavily rely on mechanical components, including pumps, centrifuges, or mixers, which is bulky and difficult to control the droplet uniformity. In this project, a novel electrical method, electrowetting bursting, will be studied to generate controllable droplets with significantly reduced system volume and weight, potentially applicable in point-of-care scenarios for disease diagnostics, drug synthesis, and vaccine manufacturing. The success of this integrated analytical, experimental, and numerical study will enhance our understanding, surpassing the long-standing limit of electrowetting set since its discovery in 1875. Education components such as undergraduate and graduate education and class teaching are integrated in this project. Demonstration of electrowetting and electrowetting bursting is included in outreach events to promote public interests in STEM. The goal of this project is to delineate electrowetting bursting into three fluid conformation stages, namely fingering, shedding, and propagating, and thoroughly investigate these three stages using analytical models (Objective 1), experiments (Objective 2), and numerical simulations (Objective 3). Through the exploration of electrowetting with parallel electrodes sandwiching a water mother drop surrounded by a surfactant-supplemented oil environment, the electrowetting instability is anticipated to occur consistently without causing damages to the device. This crucial improvement will ensure reliable and reproducible droplet ejection, achieving the novel phenomenon of electrowetting bursting. In addition to robust experimental design, for the first time, electrowetting instability and bursting will be comprehensively studied by incorporating the effects of shear stress with electrostatic pressure and surface tension in the modeling and simulation. The accomplishment of this study will not only advance the understanding of electrowetting, electrowetting bursting, and droplet generation but also lay the foundation for future investigations into droplet electrokinetics influenced by the interplay among shear stress, electrostatic pressure, and surface tension. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-08

Traditional methods for estimating causal effects from experimental data often assume that an intervention only affects the unit receiving the intervention and does not impact the behavior of any other unit in the experiment. However, experimental settings where this condition fails to hold are increasingly common. For example, in an experiment on a social media network, an individual receiving an intervention may engage with other users on the network, thereby impacting the other users' responses. When this occurs, we say that the experiment exhibits treatment interference. This project investigates a new model of treatment interference- the K-nearest neighbors interference model (KNNIM)-in which a unit's response may be affected by the intervention given to its 'K' closest connections. Notably, this model allows for interventions given to closer connections of a unit to have a greater impact on that unit's response. This project also provides research training opportunities for graduate students. The project will derive estimators for useful causal estimands, in particular, nearest neighbor treatment effects, which quantify the amount of influence that neighboring units have on a unit's response under KNNIM and relaxations of this model. Tests for determining whether these relaxed models are plausible will also be developed. Furthermore, the project will derive effective experimental designs for improved estimation of and inference of treatment effects under KNNIM. Finally, borrowing approaches for detecting communities in networks, this project will develop methods for simultaneously determining the correct KNNIM interference structure-i.e., the correct value of 'K'- and estimating treatment effects under repeated experimentation on the same set of units. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-08

With support from the Atomic, Molecular and Optical Experimental Physics Program in the Division of Physics, and co-funding from Office of Strategic Initiatives in the Directorate for Mathematical and Physical Sciences, and the Chemical Measurement and Imaging Program in the Division of Chemistry, Professor Daniel Rolles of Kansas State University intends to perform photoelectron spectroscopy of gas-phase molecules. The advent of high-repetition-rate light sources from the extreme ultraviolet to the X-ray regime is a game-changing opportunity for the fields of ultrafast atomic and molecular physics, and gas-phase photochemistry, since they enable experimental studies to be performed in a time-resolved manner with femtosecond resolution. The results will have a profound impact on our understanding of chemical reaction mechanisms and of the control of chemical reaction dynamics. Being able to record “molecular movies” of benchmark photochemical reactions will allow for a more direct comparison to quantum chemistry calculations and therefore not only result in a better understanding of the underlying reaction mechanism, but ultimately also help to develop and optimize schemes for controlling the dynamics and outcomes of such reactions. Students and post-doctoral researchers will greatly benefit from participating in the project. They will become familiar with many tools and techniques of ultrafast laser science while experiencing a wide variety of research environments ranging from a state-of-the-art laser laboratory at a university to large-scale user facilities such as free-electron lasers and synchrotron light sources. These opportunities will prepare them for their future career either in academic or high-tech industrial research. The research of this project also has significant technological implications with potential benefits to society, e.g., through the development of novel materials for light harvesting or through a better understanding and control of the environmental impact of certain chemicals in the atmosphere. This project focuses specifically on imaging nuclear and electronic dynamics during photochemical reactions by means of time-resolved photoelectron spectroscopy with X-ray free-electron lasers and high-order harmonic generation sources. The aim of these experiments is to study exemplary reactions in gas-phase molecules with the goal of clarifying their reaction mechanisms and pathways. Light-induced reactions in gas-phase molecules play a major role, e.g., in atmospheric chemistry and can also serve as prototypes for (bio-) chemical processes occurring, for instance, during the biosynthesis of vitamin D and for organic molecular switches. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-08

This Research Experiences for Undergraduates (REU) site award to Kansas State University, located in Manhattan, KS, supports the training of 9 undergraduate students for 10 weeks during the summers of 2024-2026. In this program, funded by the Division of Chemistry, participants will engage in interdisciplinary research projects covering analytical, inorganic, organic and physical chemistry. The overall theme of the REU Site is the application of fundamental chemistry to address biologically relevant issues. Participants will be mentored by individual faculty throughout the research experience. In addition to conducting research during the summer, the undergraduate students will also take part in numerous professional development activities including seminars introducing participants to research, laboratory safety, scientific presentations, and the nature of science and scientific inquiry. Through this program, participants will engage in the scientific research process that will better prepare them for graduate school and careers in the chemical sciences. The research projects conducted by the undergraduate researchers will be in chemistry areas of relevance to national biosecurity. The projects will include the development of (a) diagnostic devices for the detection of diseases and disease organisms, (b) synthetic routes towards organic molecules with potential impact on human health, (c) inexpensive energy resources and energy storage devices, (d) novel polymers for drug delivery and imaging abilities, and (e) bio-inspired catalysts. This REU site will recruit students from throughout the nation, particularly those from institutions with limited research opportunities. In addition to the research experience, undergraduates will participate in a seminar series and networking social functions that will aim to prepare them for graduate school and careers in the chemical 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 · 2024-08

Photoelectron emission is a fundamental light-matter interaction process. It occurs when light with an appropriate wavelength and intensity strikes a material object, interacts with the electrons in it, and gives some of them enough energy to eventually leave the material altogether. These emitted photoelectrons carry information about the dynamics of the process and the electronic properties of the target material. For more than a century, the measurement and analysis of their energy and momentum distribution has been one of the most prolific methods for determining the electronic structure of matter, importantly promoting the development of laser and detection technologies as well as accurate quantum-mechanical theoretical methods. Traditional energy-domain spectra image the sample's time-averaged internal electronic dynamics during the photoemission process, but do not resolve the ultrafast time-dependent electronic dynamics during the photoelectron-release process. The PI and graduate student’s theoretical modeling of time-resolved photoelectron emission from solid surfaces and plasmonic nanoparticles is motivated by extraordinary progress in ultrafast laser technology that enabled the generation of ultrashort light pulses and their accurate control and synchronization. These pulses allow for investigations of the electronic dynamics in isolated atoms and condensed matter systems with temporal resolution at the natural timescale of the electronic motion in matter and with atomic spatial resolution. In the same way as making a movie of a fast-moving object, such as a bullet in flight, requires the stroboscopic assembly of many frames, each constituting a momentary image of the object, time-domain spectroscopy is about to provide “electronic movies”, capable of displaying the motion of electrons in and their emission from matter with atomic spatiotemporal resolution. Attosecond (1 as = 10-18 seconds) time-resolved spectroscopy has led to impressive time-domain studies of ionization processes on isolated (gaseous) atoms and is anticipated to significantly advance our understanding of electronic properties of layered-semiconductor structures and nanoparticles. However, the physical interpretation of time-resolved photoemission spectra faces significant conceptual challenges and necessitates comprehensive theoretical investigations, even for simple atomic systems. For complex systems, such as plasmonic nanoparticles and solid surfaces, additional severe technical difficulties in describing the transiently photoexcited electronic dynamics must be overcome. The PI and graduate student’s work addresses these challenges. It focuses on the numerical modeling of time- and spatially resolved emission of electrons and the generation of up-converted high-harmonic (HH) radiation from adsorbate-covered metal surfaces and nanoparticles. It proceeds by developing and applying complementary quantum-mechanical methods, including numerical solutions of the time-dependent Schrödinger equation, and physically more transparent semi-classical methods. It will assess the fidelity with which time- and emission-angle-resolved photoelectron and HH spectra can reveal information on (a) electronic forces and dynamics in solids and (b) non-homogenous nano-plasmonic electric-field enhancements of incident light pulses. These investigations will advance our understanding of (i) single-electron and collective electronic excitations and (ii) the dynamics of electrons and fields in layered semiconductors, adsorbate-covered surfaces, and nanoparticles. It thus promotes emerging technologies, such as light-wave computing, nano-catalysis, and artificial photosynthesis, thereby contributing to the development of novel computers and catalytic devices for securing our energy supply and preserving our environment. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-08

The project supports travel of the US-based mathematicians to the international conference New Trends in Geometry, Combinatorics and Mathematical Physics, that will be take place October 21-25, 2024 at the CNRS center la Vieille Perrotine - Oleron, France. The goal of the project is to provide opportunities for early-career, US-based researchers and to boost the visibility and impact of US-based research. Early-career participants will benefit by acquiring new scientific knowledge from international experts and building long-term professional connections. Ultimately, participation of US-based researchers in the conference will have a positive impact on research projects conducted in the United States. The scientific foci of the conference are differential geometry and algebraic combinatorics, with applications to mathematical physics. More specifically, applications of cluster algebras in integrable systems and mathematical physics. These applications will be a main topic of the conference, along with interactions between cluster algebras and complex geometry. Further applications of cluster algebras in physics will also be highlighted. Participants from a wide variety of backgrounds will serve to boost the exchange of methods, applications and new ideas, and will form foundations for continuing collaborations. This project is jointly funded by the Algebra and Number Theory and the Combinatorics programs. The conference website is https://indico.math.cnrs.fr/event/11259/. 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.