Purdue University

NSF Awards · FY 2026 · 2026-04

The third Algebraic Structures in Topology conference will be held July 10-17, 2026, in San Juan, Puerto Rico. This conference brings together mathematicians working in algebraic topology, a field that uses algebraic tools to study qualitative and quantitative properties of geometric spaces. A central theme of this iteration is the interactions between algebraic topology and fields of national priority, including quantum physics, quantum computing, and data science. The meeting will feature a mini-course session designed to introduce graduate students to cutting-edge research areas, followed by a research conference with talks given by leading researchers and short contributed talks offering early-career mathematicians the opportunity to present their work. By gathering researchers from both theoretical and applied backgrounds, the conference aims to spark new collaborations and open new research directions at the intersection of pure mathematics and real-world applications. Supporting the training and professional development of junior mathematicians, including graduate students and postdoctoral researchers, is a primary goal of the meeting. By exposing participants to the mathematical aspects of high-priority national research areas such as quantum information science, data science, and advanced computational methods, this conference aligns with NSF’s emphasis on strengthening the US STEM workforce and sustaining American leadership in emerging technologies. The conference will highlight recent advances in algebraic topology and its applications, with an emphasis on the use of higher algebra to illuminate problems in topology and geometry arising from physical systems. Featured research topics include homotopy theory, algebraic K-theory, moduli spaces and homological stability, knot theory, topological field theories, fusion categories, topological aspects of quantum computing, and topological data analysis. By bringing together researchers from across these areas, the conference seeks to foster the cross-pollination of ideas, support the development of the next generation of mathematicians, and leverage United States strength in algebraic topology to advance progress in applied fields. The conference webpage is: algtoppr.github.io 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 IEEE International Symposium on Performance Analysis of Systems and Software (ISPASS) is a premier forum for sharing advanced academic and industrial research focused on performance analysis of computer systems and software, and brings together researchers from the computer architecture, systems and applications software, and performance analysis fields. The conference will be held from April 26-28, 2026 in Seoul, South Korea. This award will offer a great educational opportunity for students attending ISPASS 2026. Students will have an opportunity to learn about the recent advances in their research field via attending technical sessions and tutorials. The main conference program generally consists of paper presentations, a poster session, and two keynote talks. There will also be two to four tutorials and workshops focusing on performance and power analysis tools and emerging applications. Through these sessions and discussions, students will gain exposure to cutting edge research ideas and methodologies. In addition, networking opportunities during workshops, tutorials, and social events will allow students to interact with senior researchers and industry practitioners, helping them develop professional connections and deepen their understanding of the field. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2026 · 2026-04

PROJECT SUMMARY Double-stranded RNAs (dsRNAs) are ancient molecules with essential roles in early and modern life. The proteins that bind dsRNA, dsRNA binding proteins (dsRBPs), are ubiquitous and crucial for various cellular functions, including RNA processing, localization, translation, stress responses, and sensing of viral infection. While dsRBPs have many essential and diverse functions, the RNAs they bind are often similar. This along with the general inability of dsRBPs to recognize specific RNA sequences makes it likely that many dsRBPs compete with one another. This competition is important for normal homeostasis, as changes to the expression of individual dsRBPs can alter the competitive balance, and in some cases cause activation of dsRNA sensors and cell death. For human cells that express dozens of dsRBPs and orders of magnitude more dsRNAs, controlling competition between dsRBPs is major regulatory challenge. Although there are examples of competition between dsRBPs in the literature, the mechanisms that regulate competition are not fully understood. Given the diverse and essential functions of dsRBPs, understanding how competition between them is controlled could inform our understanding of multiple aspects of human health, and may lead to new therapies to treat diseases caused by dysregulation of dsRBPs and/or dsRNAs. The cell likely uses several mechanisms to regulate competition between dsRBPs, for instance changing the abundance of specific dsRBPs or dsRNAs, or altering the localization of dsRBPs or dsRNAs. Furthermore, intrinsic differences between dsRBPs, like their affinity for dsRNA or specificity for structures like mis-matches, bulges and loops within dsRNAs, may also influence competition. However, information regarding the affinity, specificity, interactomes and localization of many dsRBPs is not known. Additionally, there are likely undiscovered dsRNAs and dsRBPs. Without those pieces of information, it is impossible to understand how global competition between dsRBPs is regulated, which makes it hard to develop therapies that target dsRBPs to treat disease, or interventions to correct dysregulation of competition caused by mutations in dsRBPs. To address these essential knowledge gaps, our laboratory will use a systematic approach with a combination of low and high-throughput methods to study competition between dsRBPs from multiple angles. We will map dsRNA-dsRBP interaction networks with subcellular resolution and evaluate the affinity and specificities of dsRNA binding domains and dsRBPs. For specific dsRBPs we will study how their localization to specific cellular compartments is regulated, and elucidate their function within those compartments. The information generated through this work will fill in the existing knowledge gaps, and eventually provide the means to model competition between dsRBPs.

NIH Research Projects · FY 2026 · 2026-04

Abstract The Purdue University Research Experience in Toxicology (PURE-Tox) is a mentored summer research experience for undergraduates to introduce them to toxicology. The program will broadly recruit 10 promising students from across the United States to Purdue University for a comprehensive 8-week training experience in the laboratories of world leading toxicologists. Recruitment and advertising strategies are employed to attract students from Undergraduate Colleges, Master’s Colleges and Universities and Doctoral Universities (R1, R2, and R3) with limited experience in research. Students are admitted to the PURE-Tox program based on review of scholarly achievements, personal statements, interest in toxicology, and letters of recommendation. Students will be matched with a mentor from the Purdue Toxicology Faculty that most closely aligns with their research interest. Mentors will then provide a hands-on research project with the student on a toxicology topic. Faculty serving as mentors have research excellence ranging from environmental exposure assessments, mechanistic toxicology investigations, community health, and computational approaches. All mentors have mentored successful undergraduate students in their laboratory and Purdue has an environment committed to undergraduate development. Throughout the program student progress and experience is evaluated through progress reports and weekly meetings with the mentor. Students will participate in weekly educational seminars introducing them to basic toxicology principles and disciplines. Seminars will focus on exposures, toxicity mechanisms, disease outcomes, and treatment strategies. These seminars combine didactic delivery of information, journal clubs, and experiential learning activities. Additionally, students will attend weekly career development seminars covering necessary professional and scientific skills. These include responsible conduct of research, graduate application preparation, communication skills, scientific literature comprehension, and careers in toxicology. Sessions are designed for active student participation. Throughout the PURE-Tox experience students will engage in structured social activities to build community and network with peers and future colleagues. Social activities were specifically selected for accessibility and various interests. At the end of the program, students will have produced a research abstract that can be used for attending local, regional, and national conferences. Students will provide an oral research presentation describing their research. Following the presentation there is a networking event for PURE-Tox student, mentors, faculty, graduate students and postdocs to engage with one another. This program synergizes with a successful summer undergraduate program that has resulted in undergraduate research awards, graduate student applications, and publications. PURE-Tox activities provide comprehensive toxicology training, ultimately aimed at preparation for future training opportunities in graduate programs and independent research careers. Our program is expected to be the critical first step in identifying and training the next generation of toxicologists who will ultimately improve human health.

NIH Research Projects · FY 2026 · 2026-04

The long-term goal of our team (Drs. Hill, Murgia and Kaur) is to revolutionize control of vector-borne diseases through innovations in autonomous, AI-driven robotics systems for precision pest management. On this two-year R21, we propose the development of a transformative autonomous robotic system for precise, real-time detection and targeted treatment of the Lyme disease tick, Ixodes scapularis in occluded natural environments. We will leverage our combined expertise in tick biology, AI, sensing, robotics and product development to introduce two novel technologies: (1) a mmWave-sensing system customized for detection and tracking of multiple nymphs and adult ticks in occluded environments and (2) AI-based algorithms to drive the autonomous, large-scale sampling of multiple land types, and subsequent detection of tick habitat and tick hotspots by quadruped robots (or alternatively, drones). In Aim 1, we will develop an AI- powered multimodal sensing-based system taking advantage of the Purdue Phenocosm, a versatile test arena for detailed assessment of tick behaviors over weeks-long study periods and involving various substrate types to mimic natural tick habitats. This work will combine multi-modal mmWave and traditional camera technology, together with neural network-driven tracking algorithms. Through this work, we expect to generate the first system capable of continuously monitoring multiple ticks in complex, occluded environments, representing a paradigm shift for surveillance. In Aim 2, we will develop sophisticated path planning, navigation, and control algorithms needed to support autonomous quadruped robot movement in outdoor settings, including a range of habitats such as deciduous forest/grass ecotones that typically support I. scapularis populations. The quadruped robot will have capability to (1) navigate in and adapt movements for uneven and complex terrains, (2) optimize movement for energy efficiency, (3) collect large environmental data sets and (4) detect ticks concealed by vegetation, leaf litter and other matter. The sensing and robotics technologies developed on this project will lay the foundation for future field studies to validate protypes for area-wide tick surveillance and targeted control. Once validated and fully developed, this autonomous system will greatly expand U.S. public health surveillance capacity, substantially reducing human-tick encounters, the incidence and burden of tick-borne diseases, pesticide use, healthcare and pest management costs, paving the way for a new era in fully remote pest and disease management. 1

NIH Research Projects · FY 2026 · 2026-03

PROJECT SUMMARY/ABSTRACT Angiosarcomas are aggressive soft tissue sarcomas arising in endothelial cells with a poor prognosis and inadequate treatment options. Due to the rarity of the disease, there are limited resources for studying angiosarcoma and therefore little progress in identifying novel therapeutics. MicroRNAs (miRNAs) are small non-coding RNAs that regulate gene expression and have emerged as potential therapeutic targets in disease. MiRNAs undergo a series of enzymatic processing steps culminating with DICER1 cleavage to generate mature miRNAs. The miRNAs are then loaded into Argonaute (Ago) silencing complexes where the miRNA direct the complex to target mRNA transcripts for post-transcriptional repression. Global miRNA repression is a hallmark cancer. However, most cells require some expression of DICER1 and miRNAs for survival. Studies in mouse models of angiosarcoma have demonstrated that the endothelial biallelic deletion of Dicer1 and loss of miRNAs leads to angiosarcoma development. While most cells require DICER1 expression for survival, endothelial cells can not only survive, but this leads to transformation and tumorigenesis. This implicates the importance of miRNAs in repressing tumorigenesis in endothelial cells. However, it is unclear how miRNA loss leads to transformation or which tumor suppressing miRNAs contribute. This finding motivated a CRISPR- CAS9 loss of function screen with a miRNA focused gRNA library to identify critical tumor suppressing miRNAs. Results from this screen identified novel miRNA candidates that have not been interrogated in angiosarcoma. With this preliminary data, this proposal addresses the hypothesis that miRNAs are critical tumor suppressors in angiosarcoma and understanding their roles in tumorigenesis will lead to the identification of novel therapeutic interventions. In Aim 1, the functional consequences of the novel tumor suppressing miRNA candidates will be evaluated in normal cells and angiosarcoma. Aim 2 will test the repurposed FDA-approved antibiotic enoxacin for its potential to enhance miRNA processing as a therapeutic in angiosarcoma and as a tool to understand further understand miRNA functions. Completion of these studies will provide insights into the critical miRNA involved in repressing tumorigenesis, define their regulatory network, and determine the therapeutic relevance of enoxacin enhancement of miRNAs in angiosarcoma.

NIH Research Projects · FY 2026 · 2026-03

Spatially-resolved analysis of multiple classes of biomolecules in animal and human tissues is critically important to understanding the structure and function of different tissue types in health and disease. Despite considerable progress in spatial omics, the inherent complexity of this analytical task drives the development of novel technologies aimed at achieving high chemical specificity, sensitivity, and subcellular spatial resolution. Among these technologies, mass spectrometry imaging (MSI) has emerged as an ideal tool for label-free simultaneous mapping of multiple classes of biomolecules in biological systems with high specificity and sensitivity. Nanospray desorption electrospray ionization mass spectrometry imaging (nano-DESI MSI) is an ambient ionization technique that enables highly sensitive quantitative imaging of hundreds of biomolecules in biological tissues. This technique has achieved a spatial resolution approaching that of the traditional histology imaging, sub-femtomole sensitivity, and circumvents the need for specialized sample pretreatment. This project is focused on the development of a highly sensitive platform for correlative and multimodal imaging of biomolecules in biological tissues. This platform will provide cell-specific spatially-resolved molecular maps of biological systems with unprecedented specificity and cellular spatial resolution. This will be achieved by developing new strategies for enhancing the extraction and ionization efficiency of important low-abundance and poorly ionizable analytes and establishing robust experimental and computational approaches for correlative and multimodal imaging of tissues. The proof-of-concept studies demonstrating and validating the performance of this enabling technology will be performed by examining the inflammatory response in streptozotocin-induced mouse model of type 1 diabetes mellitus (T1DM). This model is of a broad interest to understanding T1DM and presents analytical challenges that will be addressed in the proposed research.

NSF Awards · FY 2026 · 2026-02

Achieving national sustainability goals will require rapid adoption of more sustainable practices in many areas of society but transitions to sustainable practices are often slow. This project tests whether these transitions can be accelerated by (1) creating innovative ecological forecasts that predict where and when more sustainable practices would have the greatest benefits and (2) engaging impacted communities in the process of co-implementing forecasts and advocating for sustainability transitions. The study system is the proliferation of artificial lights at night (ALAN) and its impacts on migrant birds. ALAN is increasing rapidly worldwide, and its benefits are countered by pervasive negative consequences for biodiversity, ecosystems, and human health. A major ecological consequence of ALAN is disruption of bird migration – millions of birds die annually in collisions with well-lit buildings – which contributes to widespread bird population declines. The ALAN-bird migration system is ideal for this study because, like many wicked environmental problems, environmental concerns emerge as a product of complex social and cultural processes that have proven difficult to resolve using traditional approaches. This project employs a transdisciplinary convergence approach to integrating advances in ecological forecasting with those in the social and political science of community engaged scholarship. Experiments testing sustainability impacts of innovations in ecological forecasting will be co-designed and implemented with a coalition of convergence research partners. The project will generate an understanding of pathways by which sustainable practices are adopted for ALAN, this new knowledge can be used to help address other societal-environmental conflicts. The project focuses on testing a key prediction of sustainability transformations science theory – that innovations originate within advocacy coalitions then accumulate at the subsystem level to drive sustainability transformations (e.g., new policies). During phase one the investigation gathers detailed national survey information on the ALAN system and creates transformational technological improvements in existing bird migration forecasts specific to impacts of ALAN. This new social and ecological knowledge will then be used to engage with advocacy coalitions in specific urban testbed sites to co-implement sustainability transformation experiments during phase two. These experiments will use targeted messaging campaigns to foster ALAN mitigation. Experiments will be focused on sustainability-oriented coalitions because these advocates are predicted to have high leverage to affect radical transformation toward sustainability across the ALAN subsystem. Impacts of the experiments on ALAN, impacts of ALAN on migrant birds, and human behaviors and attitudes toward ALAN will be quantified. Through this two-phase approach this project will produce a new understanding of how innovations derived from a convergence research approach can be employed in a sustainability science and policy framework to accelerate transformations. These outcomes will contribute understanding of how communities and researchers can co-engage with wicked environmental problems more broadly to drive transformations toward sustainability. Results will create new, and potentially transformative, understanding of how ecological forecasting contributes to sustainability transformations. This project is jointly funded by the Growing Convergence Research Program 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.

NIH Research Projects · FY 2026 · 2026-02

PROJECT SUMMARY Even with small-molecule drugs and antibodies on the market, there remains a pressing need to develop more effective, affordable, and safer treatments for Alzheimer's Disease (AD) and related dementias (ADRD). To fill this need, this project enables generative artificial intelligence (AI) technologies to advance peptide drugs for AD and ADRD treatments. This approach is significant because peptide drugs can combine the key advantages of small-molecule drugs and antibodies, potentially leading to improvements in effectiveness, safety, biocompatibility, manufacturability, and delivery. However, peptide drugs face significant design challenges, particularly with limited permeability and stability. Our central hypothesis is that generative AI can address these fundamental challenges by designing more pharmaceutically relevant peptide candidates. With appropriate design and training, generative AI provides unmatched power to explore the vast chemical space of peptides and identify novel peptides with drug-like properties. Our goal is to develop an integrated platform that combines generative AI with cutting-edge high-throughput data generation and in vitro/in vivo assays for designing AD- targeting peptides. We have obtained strong preliminary data, including the development of generative AI models for peptide design (AMP-GAN and Pep-Diff), initial success in creating an innovative microdroplet-based peptide selection platform, and peptide candidates targeting AD/ADRD-related protein aggregates. We will create effective, transferable AI models for sequence- and structure-based peptide designs targeting AD and ADRD. These models build on our previous successes and are carefully designed to overcome the challenges of exploring the chemical complexity and to address the limitation of scarce AD-targeting peptide data. We will also develop multimodal AI models to predict peptide drug-like properties. Moreover, high-throughput selection methods and advanced mass spectrometry will produce extensive data to enhance generative AI models. Finally, in vitro and in vivo assays will be performed with the AI-generated peptides, providing insights for further enhancements of the AI models. The successful completion of this project is expected to provide new tools for academic and industrial researchers to discover peptides that intervene with various AD targets. Overall, our study will pave new paths for discovering peptide therapeutics for AD/ADRD and accelerate the transition from design to clinical application by shortening the design-development cycle. The resulting insights will be widely disseminated within the scientific community to advance AD/ADRD research and drug development, transforming the landscape of peptide-based therapeutics through generative AI-driven design for enhanced accuracy, diversity, and target selectivity. The methodologies and tools developed in this study will enable the discovery of novel peptide therapeutics, thus helping to meet the increasing demand for effective AD/ADRD treatments. The general platform technology developed for peptides inhibiting tau and α-synuclein aggregation will also apply to other less-studied AD/ADRD targets in the future.

NSF Awards · FY 2026 · 2026-02

Cavitation occurs when small vapor bubbles form in a liquid as the pressure drops. When the bubbles move into regions of higher pressure, they collapse and can create noise, vibration, and damage in engineering systems. Cavitation affects many technologies, including ship propellers, water turbines, medical ultrasound, and drug delivery devices. Yet it remains difficult to predict and control because it arises from complex links between fluid motion, pressure changes, and phase transitions. These gaps in understanding can reduce efficiency, shorten equipment life, and slow progress in healthcare and biotechnology. This award will develop a new method to study and control cavitation by combining advanced physical models with modern artificial intelligence. The goal is to improve cavitation prediction tools and support safer, more efficient, and more sustainable engineering designs. The award also advances the national interest by strengthening U.S. leadership in computational science and engineering and supporting economic competitiveness in marine, energy, and biomedical technologies. Educational and outreach efforts will help train the future science and engineering workforce. This award will develop a framework by integrating physics and artificial intelligence for predicting and suppressing cavitation in turbulent flows. High-fidelity simulations based on first-principles thermodynamics will be used to model the formation and evolution of vapor bubbles. These simulations will generate detailed data linking fluid motion to cavitation events. Explainable-deep-learning methods will then be applied to identify the flow patterns that most strongly influence cavitation. The award will establish causal links between turbulence structures and cavitation onset by systematically quantifying which features of the flow promote cavitation inception. Building on this understanding, the project will develop adaptive-control strategies using deep reinforcement learning. These controllers will act on the flow in real time, targeting the specific structures responsible for cavitation. The expected outcomes include improved predictive capability for cavitation; interpretable control strategies grounded in physics, and broadly applicable tools for managing complex fluid systems. These advances will contribute to more reliable engineering systems and deeper understanding of multiphase-flow phenomena. 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 · 2026-02

Fires at the wildland-urban interface damage property and infrastructure and release hazardous materials. After a fire, stakeholders must assess the safety of contaminated property and infrastructure. This project aims to improve assessment of post-fire property and infrastructure contamination and enhance the accessibility of these assessments for stakeholders. The project will advance fundamental understanding of contamination during wildland-urban interface fires, improve sampling and testing, and develop an AI-supported platform for access to testing data, thereby enhancing decontamination and economic recovery efforts. This project will address gaps in necessary post-fire property sampling and testing, along with better understanding the needs of residents. The project focuses on three research objectives: 1) examining the fundamental processes governing the fate and transport of wildfire contaminants in wildland-urban interface fires, and applying this knowledge to guide water and soil testing post-fire, 2) identifying residents’ needs, and 3) leveraging AI to assess how best to navigate multiple data sources and using this assessment to develop an interactive online platform to support decontamination and economic recovery efforts. Partners from fire-impacted communities will be engaged. Key project activities include analyzing contaminants generated during the burning of mixed household products and examining their transport into the plumbing system and through burned soils. Results will shed light on exposure risks after a wildfire. To capture community needs, residents impacted by specific fires will be interviewed to identify key factors influencing community priorities. Further, a tool combining large language model-assisted and rule-based methods will harmonize disparate data sources into a structured report for use by stakeholders. 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 · 2026-02

Wildfires pose a growing threat to communities across the US, particularly in areas where homes and natural landscapes meet, known as the Wildland-Urban Interface (WUI). Each year, more than 60,000 wildfires occur in the US, often resulting in tragic loss of life, destruction of homes, and severe economic disruption. Effective wildfire response and prevention efforts are hindered by a lack of detailed, up-to-date information about vegetation and buildings in these high-risk areas. To address these challenges, this project will develop a framework that creates detailed, 3D digital representations, referred to here as digital twins, of both natural and built environments. These digital twins will provide accurate, location-specific information needed to simulate how fires spread and enable the development of more effective mitigation strategies. The intellectual merit of this project includes the creation of a groundbreaking framework for generating scalable, multi-resolution digital twins that combine data from satellites, drones, and ground sensors. By integrating AI and advanced modeling techniques, the system will enhance the understanding of fire behavior in WUI environments and support more accurate fire-spread predictions. This research represents a convergent and interdisciplinary approach that combines geospatial science, wildfire modeling, and data analytics in novel ways still unrealized at this scale. The broader impacts of this project are significant. The digital twin framework will help improve community resilience and preparedness against wildfires by providing first responders, planners, and policymakers with the tools to make faster, data-driven decisions. The system is designed to scale nationwide and can be adapted to other types of natural disasters. The project will also prepare the next generation of experts through interdisciplinary education and training programs, including new courses, online learning modules, mentorship, and professional training developed in collaboration with industry organizations. These efforts will ensure a more knowledgeable workforce ready to confront the complex challenges of wildfire management. The “Community Resilience Assessment Framework Against WUI-Fires using Multi-Resolution Digital Twins – (CRAFT)” project aims to develop a scalable, dynamic digital twin framework to support wildfire modeling and mitigation in the WUI. CRAFT will integrate heterogeneous geospatial data from satellite, aerial, and ground-based platforms using AI-based super-resolution, generative modeling, and QA/QC protocols to create multi-resolution, continuously updated 3D models. These digital twins will feed into enhanced fire-spread models to improve prediction accuracy and investigate the trade-off among resolution, performance, and computational complexity. The improved models are then used to evaluate mitigation strategies. Validation will occur through testbeds with distinct characteristics in the U.S. Eastern and Western WUI regions. Educational components will support workforce development in geospatial and wildfire 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 2026 · 2026-01

Geospatial data has become a cornerstone of modern society, underpinning critical applications that range from navigation and logistics to emergency response, infrastructure management, agriculture, and environmental monitoring. As technologies such as smartphones, drones, satellite imaging, and connected sensors proliferate, the volume and variety of geospatial data continue to expand rapidly. However, the utility of this data hinges on one fundamental requirement: accurate geo-referencing. Without precise spatial georeferencing, the value of collected data and derived products is significantly compromised, leading to flawed analysis, poor decision-making, and even public safety concerns. The IUCRC Center for Accurate Georeferencing of the Environment (CAGE) at Purdue University (PU) in partnership with The Ohio State University (OSU), and Saint Louis University (SLU), is dedicated to advancing methods for accurate and reliable geo-referencing of diverse geospatial data sources. The center serves as a collaborative research hub uniting academic researchers, government agencies, and industry partners to address the technical and operational challenges in geo-referencing, sensor calibration, spatial data fusion, data quality assessment, data-driven and decision making. As artificial intelligence (AI) and machine learning (ML) tools are increasingly applied to extract insight from massive geospatial datasets, there is a growing need to ensure the accuracy, consistency, and traceability of the input data. Poorly referenced or misaligned datasets can lead to biased models, unreliable predictions, and degraded system performance. CAGE develops rigorous methodologies and frameworks to evaluate and certify the positional quality of geospatial data, ensuring that downstream AI/ML applications are built on trustworthy spatial foundations. Research efforts focus on innovations in sensor calibration, photogrammetry, LiDAR, GNSS/INS based positioning, SLAM (Simultaneous Localization and Mapping), and uncertainty quantification. Emphasis is placed on developing scalable, cost-effective approaches suitable for deployment across a wide range of platforms—from satellite systems and aerial drones to mobile devices and wearable sensors. By fostering strong industry partnerships, CAGE ensures that the research remains aligned with real-world needs, accelerates technology transfer, and supports workforce development in the geospatial domain. Ultimately, the center will enable a new generation of accurate, AI-ready geospatial products and services that are critical to smart cities, autonomous systems, precision agriculture, climate monitoring, and beyond. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2026 · 2026-01

This award supports research with the primary objective to establish an approach for Design for Product Evolution (DfPE) considering the dynamic nature of customers' preferences, and how they are affected by the information received through their social network. The central hypothesis is that a computational framework for DfPE can be accomplished by integrating dynamic models of decision-making at multiple resolutions, specifically, Decision-Field Theory (DFT) for individual level customer decision-making, and spreading dynamics at the population level to model the influence of social networks on the customer demand. The research objective will be achieved by pursuing three thrusts: (1) Extending the DFT-based model to a multi-decision framework, (2) Modeling the demand dynamics for evolving products inspired by networked epidemic models, and (3) Integration of multi-resolution models for optimal design for product evolution. The research outcomes will be validated using real data and through collaboration with industry partners. The computational methods developed in this project will enable concurrent design of marketing campaigns and agile product development strategy considering the dynamic nature of preferences, competition, changes in technology, and changes in supply chains resulting from new regulations. The project will equip engineering design practitioners and systems engineers with computational tools that reflect real-world complexities such as evolving user preferences, social influence, and rapid technological advancement. These tools will help engineers design more adaptable and user-responsive products—especially critical in industries where updates and iteration are continuous, such as AI-enabled autonomous systems, consumer electronics, and mobility solutions. Additionally, the project team will create an online game for learning how to design for product evolution. The game will simulate product design in dynamic markets, providing an engaging platform for both classroom learning and public science outreach. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2026 · 2026-01

As an award under the Research in the Formation of Engineers program, this project will investigate how to form software engineers for the era of Generative Artificial Intelligence (GenAI). We will study how generative AI tools (e.g., ChatGPT) are used by software engineers to understand what knowledge, skills, and dispositions are necessary to effectively use GenAI to develop software. GenAI is revolutionizing how software engineers do their work, but so far, there have been limited efforts to adapt software engineering education to account for these changes. The first step in this process is to understand how professional software engineers are using GenAI tools and identify the knowledge, skills, and dispositions they demonstrate, which we refer to collectively as prompt engineering competency. We will use two approaches to characterize prompt engineering competency in the context of software development. First, we will analyze a sample of prompts that are available through a public database of prompts from professional software engineers across industries and job roles. Second, we will conduct interviews and surveys will leaders in software engineering companies as well as practicing software engineers. By analyzing these data, we can understand what an effective prompt looks like and identify the skills necessary to craft such prompts. Our study will contribute to the formation of software engineers by developing a prompt engineering competency framework that can inform future efforts to develop software engineering courses and degree programs that prepare future engineers to use GenAI effectively in their software development processes. GenAI tools are being used by practicing software engineers in every industry and it is essential that software engineering education adapt to teach prompt engineering competency to improve the economic competitiveness of the United States. In this interdisciplinary project, we will characterize prompt engineering (PE) competency in the context of software engineering. We will address the following overarching research question: What knowledge, skills, and dispositions characterize prompt engineering competency in software engineering? We will build on our preliminary work, which found that these elements can be integrated into a PE competency framework using two existing frameworks: Socratic Questioning and the Goals-Operators-Methods-Selection Rules (GOMS). To build this novel PE competency framework, we will carry out three activities. First, we will use a hybrid coding approach to analyze an existing database of Developer-ChatGPT conversations to identify knowledge and skills needed for PE. Second, we will conduct a Delphi study with expert software engineers and educators to refine the PE competency framework. Our study will include multiple phases and collect perspectives via both interviews and surveys. Our project will contribute a PE competency framework grounded in theory and the research of software engineering practice. This framework can inform future research on PE practices and PE competency development in software engineering workplaces as well as in engineering education. This project is designed to deliver four societally relevant outcomes. Our PE framework, learning modules, and related rubric can be used to inform both (1) undergraduate STEM education, and (2) workforce development in software engineering. We also anticipate that our framework could be adapted or extended for use in other STEM disciplines for additional educational impact. The effective use of GenAI technologies has been shown to dramatically increase software engineering productivity, so our results will also (3) promote the economic competitiveness of the United States. Finally, (4) we expect increased academia-industry partnerships through the Delphi study and the new connections made via our advisory board. 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 · 2026-01

Project Summary/Abstract Difficulty using morphemes of tense and agreement (e.g., The girl sits, The dog barked) during the preschool years is a hallmark symptom of developmental language disorder (DLD). Children with DLD omit these mor- phemes more often and for a longer period of time than children with typical language development (TLD). The mechanistic factors driving the difficulty in acquiring consistent use of these morphemes in DLD are not well understood. Network science is a complex systems approach that models how simple relationships (known as edges) between items (known as nodes) in a network give rise to complex patterns of behavior. Network ap- proaches to language have indicated that the network structure of different language sub-systems (semantics, phonology, syntax) explains variability in language skills that traditional language measures cannot. Although difficulty using grammatical morphemes during the preschool years is a key indicator of DLD, network science has not yet been applied to examine the link between structural representations of morphosyntax and produc- tive morphosyntactic abilities. To address this gap, the current objective is to build morphosyntactic networks of preschool-aged children with DLD and TLD that explain differences in morphosyntactic productivity. The cen- tral hypothesis is that the networks of children with stronger language skills will have structural signatures driven primarily by linguistic skills that support morphosyntactic development. First, morphosyntactic networks (i.e., networks that model grammatical relationships between words and bound morphemes such as -s and - ed) will be built from spontaneous speech of preschoolers with TLD and DLD to determine structural properties of a group morphosyntactic network (Aim 1). The hypothesis is that the morphosyntactic network will have high connectivity like other networks of natural language systems. Next, regression analysis will be used to examine how within-group (TLD and DLD) individual differences in language skills known to support the development of morphosyntax predict the structure of the morphosyntactic network (Aim 2). The hypothesis is that the predic- tors of structural differences in the morphosyntactic networks will be different between TLD and DLD given that morphosyntax is a key area of difficulty in DLD during the preschool years. Whether the hypotheses are sup- ported or refuted, this work will advance the field’s understanding of how differences in productive morphosyn- tax in healthy and disordered language development may be related to the underlying structure of these repre- sentations. This work will advance the field by establishing a framework by which to study morphosyntax in a comprehensive way that fully considers the complexity of tense and agreement and how it develops. Addition- ally, the technical and professional research skills I will acquire through completion of these aims will enhance my clinical SLP training to position me for an impactful career as an independent language scientist program- matically focused on a mechanist approach to language acquisition and treatment in cases of disorder.

NSF Awards · FY 2026 · 2026-01

Lithium-ion batteries enable electric transportation, portable electronics, and grid storage, but fire safety remains a critical barrier to wider adoption. In rare but serious events, a battery can enter a self-accelerating heating process that drives rapid chemical breakdown and produces flammable gases, leading to ignition and cascading failures that threaten people, property, and critical infrastructure. This project advances a largely underexplored route to safer batteries: engineering non-flammable or low-flammability electrolytes using flame-retardant additives. The work will provide the scientific basis for electrolyte designs that raise the temperature at which decomposition begins and thereby reduce the likelihood of catastrophic failure, improving safety for applications spanning transportation, aerospace, consumer devices, renewable energy storage, and medical technologies. The project will also provide research opportunities for undergraduate and graduate researchers as well as for high school teachers. The research will pursue three integrated objectives. First, it will quantify how flame-retardant additives affect electrochemical performance, including lithium-ion transport and the formation and composition of the protective surface layer that develops on battery electrodes during early cycling. Second, it will determine how these additives change thermal stability and ignition probability, and it will map the key reactions, rates, and heat-release processes that govern the onset and growth of runaway heating. Third, it will evaluate safety under abusive conditions and use the resulting mechanistic insights to develop and validate new electrolyte formulations that improve both safety and performance. A multidisciplinary, quantitative approach will link electrochemical testing with materials and surface characterization and thermal/ignition measurements to identify additive-electrolyte combinations that suppress flammability while maintaining efficient operation. Results will be disseminated through peer-reviewed publications, conference presentations, and open resources, while simultaneously providing training and mentorship that strengthens the future workforce in battery technology and energy storage. 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 · 2025-12

With support from the Chemical Catalysis program in the Division of Chemistry, David Flaherty (Georgia Institute of Technology), David HIbbitts (University of Florida), and Ayman Karim (Virginia Polytechnical Institute) will examine the connections among the structure, dynamics, and catalysis of reactions with oxygen on bimetallic nanoparticles. The team will create, characterize, simulate, and test how atoms of distinct metals move and facilitate reactions upon the surfaces of nanoparticles comprised primarily of gold with small amounts (1-5%) of a second element such as palladium or platinum. These materials are commonly described as single atom alloy (SAA) catalysts. These materials offer high rates and selectivities for numerous reactions important for domestic production of energy carriers and platform chemicals (e.g., valorization of biomass, shale gas, operation of fuel cells and electrolyzers). SAA currently suffer from a distressingly low number of active sites per gram of precious metal used. The collaborative team aims to develop methods to create SAA nanoparticles with smaller diameters (< 2 nm) to remedy this problem, and then test if the emergent and beneficial catalytic properties of these SAA are preserved as the size of the nanoparticles decreases. Here, the team will combine cutting-edge methods in quantum chemical simulations and multiscale modeling, characterization of operating catalysts using synchrotron methods, and catalyst testing and spectroscopy to learn how the nanoparticles restructure in different combinations of reactive gases relevant for catalysis (e.g., oxygen, hydrogen, carbon monoxide). Subsequently, the team will assess how rates and selectivities for a testbed reaction (reduction of oxygen with hydrogen) depend on the spatial organization of the atoms on the nanoparticle surface. Methods that will be developed will be useful for other dynamic catalyst systems and will be integrated into graduate-level courses. The proposed work involves lab-based education of graduate and undergraduate students and focused efforts to increase participation of women in catalysis science, especially with NSF REU (Research Experiences for Undergraduates) opportunities and cross-training of researchers across the three partnering institutions. Under this award, the collaborative Flaherty/Hibbitts/Karim team aims to learn how the structure, dynamics, and catalytic properties of bimetallic and SAA materials depend upon mean particle diameters, composition, and support identity, all factors that impact the coordinative saturation of surface atoms and the identity of their nearest and next-nearest neighbor atoms. The team will couple precise synthesis, advanced characterization techniques (including n situ, operando X-ray absorption spectroscopy, microcalorimetry, infrared spectroscopy), and computational methods (simulations of full nanoparticles with density functional theory and kinetic Monte Carlo) to address the complexity and dynamics of SAA catalysts. A testbed reaction system with rates and selectivities proven to be structure-sensitive with respect to these materials (H2 + O2 → H2O2) will be used to probe the surface structures of active catalysts, a challenge as the high pressures and complex solvents used often render characterization difficult. First, Au-rich bimetallic alloy nanoparticles (i.e., M1Aux materials, where M = Pd, Pt, Rh) with mean diameters of 1-2, ~6 and ~10 nm will be created, their post-synthesis structures will be characterized, and then the influence of adsorption and reactions on their structures will be examined over extended periods. Second, the thermodynamic relationships among adsorption energies, active site motifs, and nanoparticle structure will be determined. Third, the fundamental connections surrounding elemental identity, mole fraction, and coordination of the reactive metal and reaction rates, selectivities and barriers for H2O2 and H2O formation will be examined. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2025 · 2025-11

PROJECT SUMMARY/ABSTRACT Bacteria Microcompartments (BMCs) are pseudo-organelles comprised of a highly organized, semi-permeable protein shell which self-assembles around components of specific metabolic pathways. BMCs offer bacteria an advantage to survive in harsh conditions, to organize the cytosol by separating reactive components from each other, or to increase efficiency of enzymatic pathways by tuning local concentrations. Genetic models suggest that the compact organization of the BMC loci is ideally suited for lateral transfer to other bacteria, increasing the potential for greater prevalence of this feature in the future. Understanding the mechanisms of BMC assembly, organization, and stability are important to developing new approaches involving human-bacteria interactions and symbiosis. The model BMC is a key component in the CO2 concentrating mechanism (CCM) found in cyanobacteria called the carboxysome (CB). CBs sequester Rubisco with carbonic anhydrase and CO2 increasing carbon fixing efficiency. Rubisco is the most abundant enzyme on earth and the protein responsible for carbon fixation in plants, bacteria, and algae. Rubisco has low selectivity for its substrate CO2 and has a slow enzymatic rate, resulting in an inefficient metabolic pathway. By sequestering Rubisco in CBs along with additional co-factors, cyanobacteria and many chemoautotrophs have enhanced carbon fixation. We propose studying shell assembly and cargo organization in vivo and in vitro using cryogenic electron tomography (cryoET) and live cell imaging using total internal reflection fluorescence microscopy (TIRFM) to understand internal and external factors that affect BMC stability, permeability, and function. Understanding how this model BMC organizes during assembly and throughout its life cycle will be critical for developing potential bioreactors with custom cargo for pharmaceutical applications or increasing carbon fixation in photosynthetic organizations for improved crop yields and decreasing atmospheric carbon.

NSF Awards · FY 2025 · 2025-11

This project aligns with NSF's priorities related to economic competitiveness, public welfare, STEM education improvement, and academia-industry partnerships in the United States. It seeks to examine how sustainability education aligned with the Engineering for One Planet (EOP) framework can support the ethical formation of globally competitive engineers—particularly their development of environmental and social responsibility—for the enduring economic competitiveness of the United States. The ethical dimensions of sustainability that enable long-lasting economic competitiveness and public welfare advancement are often underemphasized in engineering curricula. This project addresses this gap by leveraging embodied carbon (EC)—a measurable and design-relevant concept—as an entry point for students to critically engage with the environmental and societal impacts of engineering practice. Guided by the EOP framework developed by the Lemelson Foundation with input from industry, this project integrates EOP-aligned and EC-focused ethics modules into five engineering courses total across West Virginia University (WVU) and Purdue University, directly promoting the ethical formation of approximately 800 engineering students across two institutions. The findings will contribute to the scholarship of engineering education and inform national efforts to strengthen undergraduate STEM instruction by forging partnerships between academia and industry in the United States for a sustainable economic competitive future. This project directly supports the NSF Research in the Formation of Engineers (RFE) program by advancing research on how educational experiences shape students' professional and ethical formation. By situating EC within engineering education, this work contributes to knowledge regarding best practices in developing engineers who are not only equipped to design sustainable solutions but are also motivated to do so with a deep sense of responsibility to people and the planet. The project is guided by the EOP framework, which emphasizes comprehensive design thinking, material selection, professional responsibility, and teamwork in engineering education. It will (1) investigate how exposure to EC education affects students' understanding of environmental and social responsibility; (2) examine how students' ethical reasoning evolves in response to EC-focused learning experiences; and (3) explore how the impacts of EC modules differ across three implementation modalities (i.e., lecture-based, case-based, and project-based). Through specially designed modules in five engineering courses across WVU and Purdue, students will engage with real-world sustainability challenges delivered in varied formats to assess instructional effectiveness. A mixed-methods research design will combine pre/post surveys, group case study reports, and reflective writing to capture changes in students' knowledge, ethical awareness, and reasoning. Comparative analyses will identify patterns across modalities and institutions. This project will produce openly shared instructional toolkits and assessment instruments for use by many instructors across the U.S. Furthermore, by explicitly aligning the modules with the EOP framework and implementing them in multiple engineering disciplines, the project will generate empirical evidence and scalable models that support the broader goals of the Lemelson Foundation's initiative—to develop responsible engineers for a sustainable future through improved engineering education. In doing so, this project will help further operationalize the EOP competencies in real classroom settings and inform national efforts to prepare engineers for the future. 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

Modern artificial intelligence (AI) is inspired by the brain’s cognitive functions but relies on models that differ greatly from biological systems and consume substantial energy during training and inference. According to the Semiconductor Research Corporation, continued scaling of logic devices and increasing model complexity could push machine learning energy consumption beyond global energy production capacity—an unsustainable trajectory. In contrast, the human brain performs complex computations with vastly lower energy. To bridge this gap, this project proposes a novel three-terminal transistor that integrates interconnected long-term and short-term memory—an essential yet underutilized feature of the brain—within a single device to improve energy efficiency, simplify architectures, and enable new capabilities. The device will advance two computing paradigms: Spiking Neural Networks and Physical Reservoir Computing, supporting scalable, high-performance, energy-efficient hardware for temporal signal processing, neuromorphic computing, AI, and post-silicon technologies. It will also drive progress in fabrication methods, learning algorithms, and system architectures that leverage the unique properties of the proposed materials and devices. The interdisciplinary nature of this project—spanning engineering, physics, chemistry, neuroscience, nonlinear dynamics, and AI—will provide students with exceptional scientific training and prepare them to contribute across multiple fields. This project aims to develop the Diffusive Ferroelectric Field-Effect Transistor (DFeFET), a novel brain-inspired highly scalable memory device that integrates long-term (non-volatile) and short-term (volatile) memory in an interconnected manner. The DFeFET combines engineered drain contact metals and amorphous oxide semiconductor (AOS) channels in ferroelectric-gated field-effect-transistors (FeFETs) to achieve controllable volatile hysteresis in drain current–voltage characteristics. Volatile memory arises from reversible ion or vacancy exchange at the drain/channel interface, modulated by gate voltage and gradual gate polarization switching, enabling co-located, co-dependent memory akin to the human brain. This device is expected to deliver enhanced energy efficiency and functionality for brain-inspired computing. In particular, it will advance two neuromorphic architectures: (i) Spiking Neural Networks (SNNs) with Spike Frequency Adaptation (SFA) and (ii) CMOS-compatible Physical Reservoir Computing (PRC). SFA, which self-regulates neuron spiking through internal negative feedback, improves SNN performance and energy efficiency but typically requires complex circuitry. DFeFET will be used to enable three bio-inspired SFA mechanisms with improved energy efficiency and reduced area. Additionally, the research aims to develop a novel PRC architecture with task-specific timescale adaptability and coupled higher-order nonlinear dynamics. It will leverage CMOS-compatible DFeFETs to build both reservoir and readout layers using a single device for efficient chip integration. For both neuromorphic architectures, a corresponding device-algorithm co-optimization framework will also be developed to optimize the accuracy, latency, area and energy efficiency of the proposed analog implementations. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-10

This Level 1 Engaged Student Learning project aims to serve the national interest by preparing construction professionals to safely and efficiently work with rapidly advancing assistive technologies, such as exoskeletons and virtual or augmented reality tools. These technologies hold great promise for improving safety and productivity, but they also pose risks when their capabilities do not align with the abilities of human users. This mismatch, called Misalignment of Augmented Capability, can lead to overreliance, reduced skill development, or safety concerns. By helping students recognize and manage these risks, this project fosters critical thinking, responsible decision-making, and technological fluency. Hands-on and scenario-based learning activities will ensure that all students are equipped to thrive in a technology-driven workforce. The resulting educational modules, (a) Learn Physical and Cognitive Misalignment and (b) Learn Spatial Reasoning Misalignment, are expected to promote experiential learning as well as advance the theoretical foundation for human-technology interaction in STEM education. This project also includes outreach to K-12 students and partnerships with industry to expand its broader importance. By creating scalable educational tools, this initiative promotes innovation and educational excellence while addressing workforce needs in a changing technological landscape. Through design-based implementation research informed by situated learning theory, this project will develop and evaluate experiential and scenario-based Misalignment of Augmented Capability learning modules. These modules will be implemented in undergraduate construction education curricula and involve hands-on activities with assistive technologies to simulate real-world decision-making. Driven by the overarching goal to enhance educational efficacy and safety in construction management technology, specific objectives are to (1) investigate and analyze the risks of misalignments of augmented capability from using assistive technologies (i.e., exoskeleton and immersive tools) in construction, (2) enhance the awareness of capability misalignment associated with assistive technologies in construction education, and (3) develop student proficiency in evaluating and addressing capability misalignments associated with assistive technologies to ensure their safe and effective implementation in construction management. This project advances the understanding of capability gains and losses associated with assistive technologies, including exoskeletons for kinetic capabilities, and virtual and augmented realities for spatial reasoning capabilities. By generating empirical data on students' learning outcomes, this project will advance curriculum design and instructional strategies in STEM education. It will also produce scalable and evidence-based educational resources for nationwide use, directly contributing to the preparation of a safety-conscious, and technologically adept construction workforce. The project outcomes have the potential to broaden participation in STEM and lay the groundwork for long-term transformation in how future construction professionals are educated to work with rapidly evolving technologies. The NSF IUSE: EDU Program supports research and development projects to improve the effectiveness of STEM education for all students. Through the Engaged Student Learning track, the program supports the creation, exploration, and implementation of promising practices and tools. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-10

This project aims to serve the national interest by developing new organizational approaches for scaling and sustaining convergence education practices that prepare undergraduates to address complex socio-technical challenges through interdisciplinary collaboration—moving toward enhancing higher education's value and relevance. Convergence is a paradigm that first emerged in universities to strengthen research and innovation by removing institutional barriers to cross-disciplinary collaboration. While convergence has resulted in integrated research facilities, funding, and partnerships, translating this paradigm to undergraduate education presents a promising opportunity to improve teaching and learning in STEM education and higher education more broadly. Convergence education provides students and instructors with experiences that promote shared practices across disciplines and the development of innovative solutions to real-world challenges. Unlike traditional problem-based or integrated STEM methods, convergence education takes an organizational approach to transdisciplinary teaching by (1) placing compelling, student-relevant problems at the center of the learning experience and (2) intentionally bringing together instructors and students from various academic units to blend their knowledge, theories, expertise, and methods. Rather than focusing solely on disciplinary content coverage, convergence emphasizes the design of authentic solutions to meaningful problems—creating an additional educational value that can transcend a student's major alone. By exploring how convergence can be implemented through institutional transformation efforts, this Level 2 Institutional and Community Transformation project aims to bridge disciplinary silos, expand participation in STEM, foster innovation by blending disciplinary perspectives, and equip more students with the skills and experiences considered valuable for workforce readiness and societal impact. The project's goals are to advance understanding of how convergence education can be sustained and scaled within—and beyond—research university environments and to generate actionable models for broader institutional adoption. Building on an exemplar innovation-focused convergence education program that features collaborative teaching across the engineering technology, liberal arts, and business disciplines, the project will conduct research to develop organizational guidance for convergence education implementation. This work will be parallel to the frameworks developed for convergence research. Using a Communities of Transformation theory of change and design-based research methodology, the project will investigate the institutional, cultural, and pedagogical conditions that enable or hinder convergence practices in undergraduate education. Key research activities include (1) in-depth case studies of convergence-related programs to identify institutional strategies in different settings, (2) ethnographic observations and stakeholder interviews to study the transformation of institutional culture through the implementation of the exemplar convergence program, (3) evaluations of undergraduate outcomes in integrative learning and innovation competencies, and (4) design-based research retreats for refining convergence education materials based on the collected data. The project will also develop and pilot a doctoral fellowship and convergence teaching program to prepare future scholars capable of teaching across disciplinary boundaries—helping to ensure the long-term sustainability of collaborative teaching efforts involving multiple academic disciplines. Finally, a national Convergence Education Conference will be convened to disseminate findings and build a collaborative community. Evaluation efforts will include both formative and summative components to support continuous improvement and transferability of the project's results. The NSF IUSE: EDU Program supports research and development projects to improve the effectiveness of STEM education for all students. Through the Institutional and Community Transformation track, the program supports efforts to transform and improve STEM education across institutions of higher education and disciplinary 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 · 2025-10

Catalyst materials that speed up chemical reactions play a critical role in the production of energy and chemicals. The catalyst can change during this process, as metal atoms rearrange on the nanoscale, forming new structures with distinct properties and performance. Manipulating such changes could lead to improved materials for industrial reactions, but research progress has been limited by a lack of general principles to understand and control catalyst dynamics. To address this challenge, researchers will integrate advanced computer modeling, accelerated by artificial intelligence and machine learning, with experimental tools to study how catalyst structures evolve during reactions. This workflow enables efficient screening of a wide range of materials to accelerate the discovery and design of more effective catalysts by controlling their dynamics. Specifically, the project will study ammonia fertilizer production, which supports global food supply but is highly energy-intensive (~2% of annual global energy consumption goes to this process), to guide the design of new energy-efficient catalysts. The project will also study how ammonia can be used as an energy carrier through cracking to hydrogen over earth-abundant catalysts. Interdisciplinary training of graduate students in state-of-the-art computer modeling and experimental methods, combined with educational outreach efforts to K-12 students, will prepare students to become leaders in catalytic materials design. This project will construct a unified, predictive model of the dynamic restructuring of metal nanoparticles on metal-oxide supports by elucidating the effects of materials properties and reaction environments on dynamic catalyst performance. In turn, these principles will enable the design of more active, stable, and ‘self-healing’ materials for industrially relevant ammonia synthesis and cracking reactions by tuning material properties to stabilize the most active nanostructures under reaction conditions, and enabling regeneration treatments that reverse the deleterious effects of catalyst sintering. The research team will develop a closed-loop workflow to integrate ab initio molecular modeling and artificial intelligence/machine learning (AI-ML) tools to efficiently screen materials composition space, combined with experimental synthesis of shape-controlled metal nanoparticles on metal-oxide supports, in situ characterization of dynamic behavior using high-resolution microscopy and spectroscopy, and high-throughput reactivity evaluation using steady-state and transient methods. Insights from this project will be used to develop more energy-efficient and stable non-precious metal catalysts for catalytic ammonia synthesis and ammonia cracking to hydrogen. The general principles developed here will have broad relevance to industrially important catalytic reactions involving catalyst restructuring. Databases and AI/ML workflows will be made publicly available to enable use of research products by the catalyst materials 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 · 2025-10

This project aims to serve the national interest by enhancing the preparation of the engineering workforce through the development of "world readiness"—the knowledge, skills, and behaviors necessary to collaborate effectively in interdisciplinary, multi-stakeholder environments and to create long-term technological solutions. Traditional engineering coursework often emphasizes technical learning alone. However, to address real-world challenges effectively, engineers must be equipped to analyze problems from multiple stakeholder perspectives, identify root causes, and assess the long-term societal and environmental impacts of potential technological solutions. This Improving Undergraduate STEM Education Level-1 Engaged Student Learning project seeks to cultivate world readiness among Engineering Technology students by integrating elements of Cultural Intelligence (CQ) and Humanity-Centered Design (HumD) into the curriculum through short, self-paced online modules- Portable Intercultural Modules (PIMs). The project will develop theoretically grounded educational materials on CQ and HumD that will be openly available to be used by STEM educators to train themselves and implement at their institutions. The project activities will help students reflect on societal challenges associated with engineering problems and understand their roles as design professionals in addressing them. The project will advance the understanding of potential correlations and interlinkages between an increase in CQ and an increase in understanding of and commitment to HumD, and their impact on world readiness. The goal of this project is to support undergraduate students in engineering technology courses to develop world readiness skills through CQ and HumD frameworks. The project will use the design-based research framework for implementation. PIMs will be developed involving case studies, reflection, and other learning activities designed to improve and foster motivation for improving CQ, teach HumD principles, and help students understand the link between CQ and HumD. To cultivate the HumD lens among students, specialized workshops on real-world design considerations and implementation challenges will be conducted by experienced design professionals. Furthermore, as a component of their coursework, students will be required to showcase the application of the HumD approach to their capstone project solutions. To evaluate the efficacy of these educational interventions, a sequential explanatory mixed-method research design will be used. The findings from the project will be disseminated in different forms including publications, educational material, and implementation guidelines. The NSF IUSE: EDU Program supports research and development projects to improve the effectiveness of STEM education for all students. Through the Engaged Student Learning track, the program supports the creation, exploration, and implementation of promising practices and tools. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.