Texas A&M Engineering Experiment Station

NSF Awards · FY 2026 · 2026-10

Semiconductor chips power nearly every aspect of modern life, from smartphones and medical devices to national defense systems and critical infrastructure. As these chips grow more complex, verifying that they function correctly and securely has become one of the most costly and time-consuming stages of development. At the same time, the United States faces a critical shortage of engineers trained in chip verification, a gap that threatens both economic competitiveness and national security. This project establishes a Research Experiences for Undergraduates (REU) Site at Texas A&M University to train the next generation of verification engineers and semiconductor researchers. Each summer, ten undergraduate students will participate in a ten-week immersive research program focused on chip verification and the application of artificial intelligence (AI) to semiconductor design evaluation. The project specifically targets students from community colleges, regional universities, and institutions with limited research infrastructure, with emphasis on first-generation college students, veterans, and students with no prior research experience. Participants will receive layered mentorship from faculty, graduate students, and industry professionals, along with professional development training in scientific communication, ethics, and career readiness. Industry partners will contribute guest lectures, mentorship, and site visits, connecting students directly to career pathways in the semiconductor workforce. By combining cutting-edge research training with inclusive recruitment and sustained post-program engagement, this project addresses a pressing national workforce need while broadening participation in a strategically vital field. This project engages undergraduate researchers in four interconnected themes spanning hardware security, performance analysis, design automation, and functional verification. The first theme develops scalable security verification frameworks that adapt fuzzing, formal analysis, and symbolic execution to detect vulnerabilities in hardware designs described at the register-transfer level and prototyped on field-programmable gate arrays. The second theme applies machine learning (ML) to processor performance debugging, training models to automatically detect and localize performance anomalies using hardware counter data, estimate fine-grained performance breakdowns from limited counter sets, and accelerate design simulation through early-run inference. The third theme investigates the use of large language models to automate chip design tasks, including hardware description language code generation, physical synthesis using open-source tool flows, and timing optimization. The fourth theme targets ML-driven functional verification, developing techniques for automated testbench generation, structure-aware coverage acceleration, multimodal failure triage that combines text and waveform data, and closed-loop testbench correction guided by dynamic bug mutation analysis. Students will work with commercial electronic design automation tools and open-source processor platforms, producing weekly progress reports, final presentations, and potential conference submissions. The program integrates structured mentorship, professional skills training, and industry exposure through site visits and seminars with engineers from leading semiconductor companies. An external evaluator will conduct formative and summative assessments, and participants will be tracked for five years to measure long-term impact on graduate school enrollment, research productivity, and career outcomes. 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 · 2026-07

This Faculty Early Career Development Program (CAREER) grant will advance the national prosperity and economic welfare by enhancing the analytical capabilities of organizations in sectors such as healthcare, finance, construction, and national defense that leverage stochastic computer simulation models to make critical decisions in the face of uncertainty. This award supports a fundamental reinvention of how such models are paired with optimization methods to inform decision makers of risks and tradeoffs in stochastic system performance. This research will make simulation optimization approaches more systematic, productive, and aligned with user needs and facilitate more holistic decision making than conventional approaches. Close collaboration with industry partners will ensure the methods created are intuitive, informative, and practicable. The educational component of the project will create high school outreach activities and teaching modules that explore analysis techniques for simulation data and improve programming proficiency and statistical literacy. This project will also produce software, including open-source implementations of the methods, a prototype of an interactive dashboard, add-ins for commercial simulation software, and versions that are compatible with an open-source simulation optimization testbed used by researchers and educators. The research is motivated by shortcomings of existing simulation optimization (SO) approaches, which generally require decision makers to specify summary performance measures to serve as objectives or constraints in an optimization problem. By beginning with a narrow problem formulation, SO practitioners often fail to think about their simulation model in the broadest stochastic sense. This research shifts the initial focus of SO from summary performance measures to distributions of performance measures, exposing the user to inherent risks and tradeoffs. The research invents a transformative framework that couples exploratory simulation analysis with powerful optimization technologies to facilitate more holistic decision making. This project will create new search methods for discovering solutions with differing output distributions, incorporate user input in pursuit of optimization goals, exploit parallel computing resources to accelerate the search and optimization processes, and extend the framework to handle simulation trace data. The research will require the invention of new methods for dynamically clustering multivariate distributions and stochastic processes and metamodeling simulation outputs and output distributions that will be rigorously analyzed from both a theoretical and computational perspective. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2026 · 2026-05

This grant provides funding to partially support approximately 20 early-career researchers (including graduate students, postdoctoral researchers, and early-career faculty) to participate in the Workshop on Advanced Manufacturing of Ceramic and Composite Materials for Extreme Environments, to be held in Fall 2026 in College Station, Texas. The workshop will convene a cross-disciplinary community spanning advanced manufacturing, ceramic and composite processing, densification science, modeling and sensing, and extreme-environment performance and qualification. It will produce a publicly available workshop report that will identify high-priority scientific challenges and outline coordinated opportunities to accelerate reliable manufacturing of ceramic and composite materials for extreme-environment applications. Support from the National Science Foundation will enable participation of early-career researchers who might otherwise be unable to take part in the workshop. These early-career researchers will gain access to mentorship, networking, and visibility, strengthening the workforce pipeline in advanced manufacturing of ceramic and composite materials for extreme environments. The workshop report will be disseminated openly to the broader community, helping align research directions, accelerate translation of reliable ceramic and composite manufacturing technologies, and strengthen United States competitiveness in aerospace, energy, defense, and other sectors that rely on materials for harsh conditions. Recruitment and selection for travel support include outreach to universities, national laboratories, and companies. Engagement of these early-career researchers will continue through post-workshop working groups that foster continued collaboration. Advanced manufacturing (including additive manufacturing) of ceramics and composite materials can enable complex, lightweight components for extreme environments, but widespread adoption is limited by coupled process–structure–property challenges. Across multiple additive manufacturing methods, reliability is often controlled by defect formation and evolution, such as green-body damage, cracking and distortion induced during binder removal and sintering, retained porosity or incomplete densification, and interfacial flaws. These defects can lead to large scatter in mechanical performance and durability under thermal gradients, oxidation, corrosion, wear, and cyclic loading. Addressing these challenges requires integrated approaches that connect feedstock and process design to densification kinetics, residual stress development, and defect-sensitive failure mechanisms. The workshop will advance knowledge in this area by convening experts across processing science, modeling and sensing, and extreme-environment performance to synthesize knowledge gaps, define prioritized research questions, and discuss validation needs that can accelerate development of shared datasets, model verification, and reproducible manufacturing strategies. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2026 · 2026-05

Carbon dioxide (CO₂) can be used to make useful products like fuels, plastics, and bioplastics. One way to obtain CO₂ is direct air capture (DAC), which removes carbon dioxide from air. The most common method, called solid sorbent direct air capture (S-DAC), uses special materials that trap CO₂ from air. Despite its potential, the technology is not widely used yet because its performance depends on weather conditions like temperature and humidity. This joint project between NSF (US) and EPSRC (UK) will improve this technology by creating digital models that help scientists and engineers design and operate S-DAC systems more efficiently while saving energy and lowering costs. It will also study how much land, energy, and water these systems need and how they might affect nearby ecosystems, which will help policymakers in the UK and the US make informed decisions about using this technology. The findings of this project will establish a new standard for the design and analysis of solid-sorbent direct air capture (S-DAC) systems worldwide. By integrating process engineering principles with techno-economic analysis, life-cycle assessment, experimental validation, and system-level integration, the project will identify cost-effective and environmentally efficient deployment pathways for S-DAC technologies. The program will commission an experimental testing unit to validate promising sorbent materials and determine optimal operating conditions under varying ambient environments. It will develop optimized and robust S-DAC system designs through modelling and optimization, enabling comparison of sorbent performance and identification of configurations suitable for different regional climates. It will evaluate system layouts that incorporate integrated energy and water management strategies, including quantification of heat recovery potential and associated costs. It will derive eco-efficient solutions for regional-scale deployment using optimization frameworks that capture the interconnected energy–water–land resource nexus and geographic constraints. The project will advance multiple research fields by contributing new knowledge to separation process engineering via novel S-DAC designs, open-access process models, and experimental datasets. The project will also support materials science through process-informed screening tools for DAC sorbents and expand the capabilities of Process Systems Engineering with data-driven methodologies that accelerate the evaluation and optimization of separation process configurations beyond DAC applications. 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

This NSF CAREER project aims to advance the safe, stable, and economically optimal operation of integrated transmission-distribution power systems through the real-time coordination of ultra-large-scale power electronic converter-based distributed energy resources (C-DERs). The project will bring transformative changes to the operation and control paradigm of modern power systems characterized by the widespread deployment of heterogeneous C-DERs, including battery energy storage systems, electric vehicles, photovoltaic generation, and other flexible loads. This will be achieved by developing a systematic hierarchical coordination framework that enables real-time autonomous control of massive C-DERs and leverages their collective flexibility to support integrated transmission-distribution system operation. The intellectual merit of the project includes: (i) distributed optimal control of utility-owned C-DERs; (ii) scalable human-in-the-loop control of user-owned C-DERs; and (iii) AI-enabled dynamic aggregation and transmission-level coordination. The broader impacts of the project include: an immersive learning platform for hands-on experiential education; an AI-based intelligent teaching system to enhance individualized instruction; industry-oriented short courses that incorporate research outcomes into workforce education; a vertically integrated Pre-K-12 STEM pipeline to broaden early engagement; and strengthened collaboration with industry partners to maximize real-world impact and enable technology transfer. Coordinating ultra-large-scale heterogeneous C-DERs in real time offers substantial potential to support integrated transmission-distribution operation and enhance overall grid reliability. However, realizing this potential is hindered by four fundamental challenges: (i) scalability in coordinating extremely large C-DER populations; (ii) inherent nonlinearity of physical system dynamics; (iii) significant uncertainty of human user behaviors; and (iv) safety and stability requirements in grid operations. This NSF CAREER project will develop scalable AI-enabled dynamic aggregation and learning-based control algorithms and tools with provable performance guarantees to address these challenges. The core approach is to integrate model-based methods that leverage physical system structures with advanced data-driven techniques to exploit their complementary strengths and harness the benefits of both. Ultimately, the project will advance fundamental theory and methodology for the control and optimization of large-scale complex human-cyber-physical infrastructure systems with rigorous high-performance guarantees. 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

This Faculty Early Career Development Program (CAREER) award enables contribution of new knowledge in advancing the state-of-the-art light-based additive manufacturing (AM) for scalable fabrication of high-performance, nanostructured polymeric materials. With the rising need of heterogeneous integration and III-V-on-Si photonic integrated circuits (PICs) in microelectronics industry, 3D nanostructured polymeric materials are playing increasingly important role beyond sacrificial 2D photoresists in pattern transfer. Traditional lithographic approaches fall short in fabricating continuous 3D nano-pathways due to difficulties in repeated molding and etching processes. In contrast, high-resolution additive manufacturing approaches, such as digital-light-projection (DLP) processes, readily create micrometer resolution 3D structures, serving as promising platform candidates. This award supports the establishment of fundamental knowledge that pushes the boundary of low-energy, continuous wavelength DLP that addresses critical challenges in existing patterning resolution-scalability tradeoff, provides direct understanding on in-situ photochemical reactions at nano-resolutions, and develops advanced photosensitive resins with high-performing thermo-mechanical properties. This research program will be integrated with educational and outreach activities, including incorporation of AI-driven model development in material characterization coursework, tutorial YouTube videos for characterization of soft and hybrid materials, and organization of industry career panel involving regional industrial leaders. The research goal of this project is to establish a research program that produces the scientific knowledge for enabling scalable fabrication of nanostructured high-performance polymer materials. To break the nanometer resolution-scalability trade off in low-energy, continuous wavelength digital-light-projection (DLP) platforms, this project will establish a dual-wavelength DLP platform that combines frequency-controlled structured illumination with wavelength-specific photoiniferter chemistry to enable sub-diffraction, area-unlimited printing. To elucidate in-situ reaction kinetics at the nano-resolution that is unachievable via existing infrared microscopy, a mid-infrared pump-probe spectroscopy will be built within the DLP platform. The in-situ IR spectroscopy characterization will also inform the physical model that combines optical and reaction-diffusion kinetics simulations to predict the crosslinking kinetics and inter-wavelength material property evolution. To enable high-performing polymer material patterning that is compatible with nano-resolution DLP platform, a dual-cure, photo- and thermally sensitive resin material will be developed, through utilization of dynamic covalent chemistry that enables the tunability of mechanical and thermal properties. Taken together, the this research aims to establish a new paradigm for fabricating sub-diffraction limited, high-performance polymeric materials, with critical process-relevant reaction kinetics obtained from in-situ microscopy characterization. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2026 · 2026-03

NON-TECHNICAL SUMMARY This CAREER project, with support from the Solid State and Materials Chemistry Program and the Ceramics Program, both in the Mathematical and Physical Sciences Directorate, aims to create a new class of two-dimensional (2D) materials derived from earth-abundant transition metals such as titanium, vanadium, chromium, and molybdenum. These materials, known as carbide and nitride MXenes, possess chemical and electronic characteristics similar to noble metals but are much less expensive. The building blocks of these layered materials are hundreds of times thinner than a human hair and more electronically conductive than most metals. This has the potential for enabling transformative advances across biomedicine, energy, food, transportation, and health. The carbide materials are experimentally accessible but degrade rapidly in air and water, restricting their practical use. By contrast, the nitride MXenes (MNenes) are predicted to be substantially more stable, yet only two phases have been prepared so far, leaving many other variations unrealized. This CAREER project employs a new synthesis strategy to create different MNenes for applications ranging from clean energy and high-capacity energy storage to quantum computing and electrochemical manufacturing. By integrating real-time characterization with advanced synthesis and computational tools, the project aims to uncover how the atomic structure of MNenes controls their properties, stability, and performance, guiding the design of next generation 2D materials while accelerating discovery across diverse fields. The project engages K-12 students in hands-on research through the On-the-Fly Lab program, involves undergraduates from Historically Black Colleges and Universities in a summer research program, and provides U.S. graduate students with mentoring, career development, and international collaboration opportunities. It also extends mentoring and outreach to students across geographic and institutional boundaries through the Connect with Dr. Djire (CwD) initiative. Overall, this CAREER project will strengthen U.S. leadership and national security in critical materials while cultivating a highly skilled scientific workforce through broadened participation in STEM and the advancement of transformative materials technologies. TECHNICAL SUMMARY MXenes, 2D nanomaterials derived from the selective etching of the A element from ceramic MAX phases (where M is a transition metal, A is a group 13-16 element, and X is carbon or nitrogen), combine high electronic conductivity, large surface area, and tunable chemistry, making them a versatile platform for addressing existing and emerging energy and catalysis challenges. However, currently available carbide MXenes are constrained by poor chemical and electrochemical stability, which limits their practical applications. In contrast, nitride MXenes (MNenes) are expected to be far more stable but remain largely inaccessible, as traditional synthesis methods such as hydrofluoric acid etching have been unsuccessful due to the strong M-A bonds in the parent MAX phases. This CAREER project, with support from the Solid State and Materials Chemistry Program and the Ceramics Program, aims to develop stable MNenes using innovative molten-salt fluoride chemistries, testing the hypothesis that controlled oxidation of the A layer weakens the M-A bond in the parent MAX phase, resulting in selective A-layer removal. The research addresses three key gaps: limited understanding of the MAX-to-MNene etching mechanism, the scarcity of nitride MAX precursors, and the lack of structure-property-stability relationships urgently needed to elucidate their fundamental behavior and unlock their full potential. Combining advanced molten-salt etching with operando synchrotron X-ray diffraction, quasielastic neutron scattering, and first-principles calculations, this integrated experimental-computational approach will generate predictive design rules for stable and highly functional MNenes, advancing next-generation 2D materials discovery. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2026 · 2026-01

The Texas Power and Energy Conference (TPEC) is a student-run IEEE technical conference that started in 2017. TPEC 2026 will be held on the campus of Texas A&M University in College Station, Texas, in February 2026. This two-day event will bring together participants from industry and academia to present and discuss the latest technological developments and challenges in the area of electric power and energy engineering. The conference will include paper sessions, keynote speakers, a student poster competition, and a mini-job fair. Both graduate and undergraduate students will participate in the conference. Tours of the Texas A&M campus and the Center for Infrastructure Renewal (CIR) at Texas A&M University will be held. This grant will be used to support student travel to and attendance at TPEC 2026. Students will benefit from being exposed to new research developments, hearing keynote speakers, networking with representatives of industry and academia, and having the opportunity to present their work in a paper or poster. The research community in electric power and energy engineering will benefit as well, from the value and perspectives the students will bring to the event. 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 project will create a new class of computational tools for physiologically accurate human motion simulation by bridging the critical gap between computer graphics and biomechanics. Simulation methods in computer graphics have historically prioritized computational efficiency and visually compelling results for animations and virtual experiences, often at the expense of physical accuracy. Conversely, biomechanical simulations emphasize realism and experimental validation but tend to be slower, more specialized, and less adaptable to interactive applications. By combining the strengths of both fields, the project will result in simulation methods that are fast, general-purpose, and physiologically grounded. This work will open the door to new cross-disciplinary collaborations, providing movement scientists in fields such as sports, health, and rehabilitation with tools to simulate complex, real-world movements that were previously infeasible. The resulting validated models can enhance training simulators and, when combined with existing open-source physics engines, will create new avenues for high-fidelity simulation and modeling in applications ranging from robotics to gaming. This project will deliver a next-generation, open-source physics simulator that accurately models musculotendon dynamics for graphics and other fields. To achieve this, the project explores four research thrusts. The first thrust establishes a unified, constraint-based simulation framework that treats muscles, tendons, skeletal structures, and environmental contacts as a coupled, fully implicit system. This formulation enables stable and accurate simulation of complex, high-contact human motion while maintaining physiological realism. The second thrust addresses muscle-based control by leveraging reinforcement learning to train neuromuscular controllers that produce realistic activation patterns, improving upon traditional joint-actuation systems/models that often generate unnatural and/or biomechanically implausible motions. The third thrust focuses on validation, using in-vivo biomechanical and physiological data, as well as benchmarking against existing simulation tools, to evaluate both the accuracy and computational performance of the system. The fourth thrust demonstrates broad applicability by enabling physiologically informed animation, injury-aware motion planning, and optimization of complex, contact-rich tasks. The resulting simulation platform is expected to support research and development across disciplines, contributing to improved understanding of human movement, better tools for clinical and biomechanical analysis, and enhanced realism in interactive systems. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2026 · 2026-01

This BRITE Pivot award supports research that contributes new knowledge and novel robotic solutions related to the repair of aged electric vehicle (EV) batteries, thereby promoting the progress of science, and advancing prosperity and welfare. EV battery repair currently relies heavily on skilled manual labor, requiring extensive expertise and effort just to dismantle a battery before the repair process can even begin. This challenge arises from the complex design and the uncertain conditions of aged batteries, as well as the significant safety risks associated with handling high-voltage components. This project seeks to solve this challenge by leveraging emerging artificial intelligence (AI) technologies, especially large language models (LLMs), to significantly enhance robotic capabilities. By providing an efficient robotic repair solution that is both economically viable and safe for human workers, this project has potential to extend the automotive lifespan of EV batteries, manage the growing volume of aged EV batteries, address skilled labor shortages, maximize the use of critical materials before recycling, and promote a sustainable, long-term, domestic, and circular battery supply chain. A series of workshops and webinars will be organized to provide training opportunities for next generation workforce in robotic EV battery repair and remanufacturing. Existing automation solutions for EV battery repair are highly customized, expensive, and often require extensive reconfiguration and human intervention to address the associated complexities and uncertainties. This research aims to create novel battery repair solutions with greater flexibility and adaptability in robotic task planning, motion planning, and manipulation by leveraging emerging AI technologies. The project looks to develop a fine-tuned, multimodal LLM tailored for robotic EV battery repair. This model seeks to enable more flexible, human-like task planning beyond rigid, pre-programmed sequences that struggle to handle complex battery repair processes. Furthermore, the research seeks to create a new motion planning framework to incorporate real-time human guidance for coordinated planning among heterogeneous robots. The research also includes activities that look to design a specialized robotic gripper, integrated with a modified residual reinforcement learning algorithm, to handle interlocking structures commonly found in EV batteries. Collectively, these foundational advancements seek to enable adaptable EV battery repair encompassing disassembly, replacement, and reassembly, and will lay the groundwork for a new paradigm in the EV battery remanufacturing 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 · 2025-12

This three-year REU Site: Interdisciplinary Research Experiences in Metrology & Inspection is hosted by Texas A&M University. The overarching goal is to enhance the knowledge and skill-levels of a cohort of undergraduate students through empowering, hands-on and interdisciplinary research experiences in both traditional/advanced metrology and destructive/non-destructive inspection technologies. Metrology is the science of measurement and inspection and transcends scales, materials, and disciplines. Ten students each year will participate in research projects featuring inline process monitoring of pharmaceutical additive manufacturing, forensic metrology of fractures in additive manufactured parts, lithium-ion battery performance, and additive manufacturing of copper alloys for heat exchangers. Engaging each student cohort in intentional metrology/inspection activities through inter-disciplinary research projects will help create empowered future researchers and a workforce that is well-rooted in metrology/inspection technologies. REU students will also explore career opportunities available to them through advanced STEM degrees and other associated careers. REU students will be immersed in hands-on research experiences comprised of a state-of-the-art research project, technical sessions, seminars, lab practice, field tours, and professional-development workshops. The REU site will advance scientific knowledge through inter-disciplinary research projects and especially help further the energy and manufacturing sectors. During the summer program, five vertically integrated teams (two REU students, one senior undergraduate, one graduate student and a faculty mentor) will work on selected research projects. Students will disseminate research results and create a follow-up plan tailored to each student's career interests. The overarching outcome of this project is to prepare students who, when faced with a technical problem, will learn to ask the right research questions, formulate an educated plan, use the resources available, analyze results, leverage the group collaboration, and grow as a researcher. 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

Examining the role of fellowships funding on engineering students and their professional development seeks to redefine graduate fellowships as transformative tools that promote all American engineering students’ success. By investigating how fellowships function beyond funding, we aim to highlight their potential as way to pursuing graduate education, mechanisms of financial support, and tools for fostering degree completion and workforce development. This approach will enhance their ability to navigate academic and professional challenges. Our work will guide engineering programs and faculty by equipping them with insights into fellowship design and strategies to provide support. This comprehensive approach will improve retention, accelerate time-to-degree completion, and better prepare students for fulfilling engineering careers. In the long term, the outcomes of this research will transform fellowship infrastructure at federal agencies, private organizations, and universities, aligning with national efforts on the engineering field and workforce. Our findings will influence strategies and policies at the national level, promoting organizational and conceptual changes in fellowship initiatives to recruit, support, and retain more americans graduate students. The proposed project will strengthen Engineering and Innovation in the US by advancing understanding of how fellowship variables impact engineering graduate students, addressing the issues and unintended drawbacks often overlooked in fellowship programs. While fellowships are widely seen as funding mechanisms, this research will uncover their implications, including their effects on student recruitment, academic experiences, graduation, and early career outcomes. The study will examine how fellowships shape engineering students’ professional development. Through a national-level assessment, the research will provide insights for institutions, programs, and faculty to design fellowships that better support all American students. The anticipated outcomes include conceptual models of fellowships as ways to expanding all American students participation, improving retention, and advancing professional identity. Students will benefit from an enhanced understanding of fellowships and their career implications, while institutions will gain tools to create fellowships that promote academic and professional success. Additionally, the project will produce a transferable set of best practices for funding agencies, academic programs, and faculty to ensure adequate support for fellowship holders, enabling their successful transition to the workforce. These findings will strengthen fellowship programs and advance the engineering 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.

NSF Awards · FY 2025 · 2025-11

Improving the performance of condensation heat transfer reduces size, weight, and cost in refrigeration, air conditioning, heat exchangers, and thermal management systems. To achieve a large heat transfer coefficient, condensates on a surface must be rapidly removed to provide liquid-free areas for vapor-liquid phase change to occur. The ideal surface is one that provides a large heat transfer area and rapidly removes condensates for sustainable condensation. Present challenges include the removal of ultralow surface tension refrigerants and the lack of long-term durability of engineered surfaces. The goal of this CAREER project is to address those challenges by developing a vapor-liquid separation process to advance condensation heat transfer of ultralow surface tension fluids, and integrate the new knowledge into education to train the next generation of heat transfer leaders. The objective of this project is to achieve: (1) dropwise condensation of ultralow surface tension fluids (e.g., R134a), and (2) sustainable vapor-liquid separation that provides large liquid-free areas for rapid condensation. The proposed approach will use the newly developed durable quasi-liquid surface to achieve dropwise condensation and rapid removal of ultralow surface tension condensates. The super slippery quasi-liquid surface will prevent the dropwise to filmwise transition. To achieve a high heat transfer performance, the vapor and liquid will be separated on a slippery rough surface with quasi-liquid lubrication which will provide a large surface area for condensation. X-ray nano-imaging will be used to investigate the nucleation and liquid removal inside microstructures of the slippery rough surfaces. The surface durability and sustainable condensation performance of quasi-liquid lubricated microstructures will be studied under various subcooling and shear stress conditions. The preliminary results in heat transfer and materials fabrication provide a solid foundation to execute those activities. The proposed project is in line with the PI’s long-term career goal to address heat transfer challenges by incorporating learning from multidisciplinary areas. 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

Non-technical Description: Phase change materials (PCMs) absorb, store or release energy when they change from liquid to solid and thus can regulate temperature in personal care products (such as cooling pillows, pads and patches in sporting and biomedical products), as well as for regulation of temperature in buildings. One promising type of phase change materials, inorganic salt hydrates, is inflammable and thermally efficient but suffers from fluidity/leakage during thermal cycling. The challenge is to controllably shape stabilize these materials without sacrificing their thermal performance. This project explores the use of star-shaped block copolymers to control thermomechanical properties of the resulting PCM-polymer composites, i.e. salogels. The team addresses the challenges of polymer solubility and gelation (i.e. formation of a polymer network) in a complex ionic environment of inorganic salt hydrates via the use of material discovery and optimization using modeling, theory and AI/machine learning (ML) in a coordinated US-India team effort. This project provides a knowledge base that will correlate molecular parameters of star polymers and type of inorganic salt hydrates with thermomechanical properties of salogels, enabling accelerated development of salogels for diverse applications. The project will create a fertile training ground for the graduate, undergraduate students and high school students. Examples include hands-on demo on the salogel cooling pads and high-performance research computing outreach events for K-12 students. Technical Description: The ability to make re-processable salogels, i.e., polymer gels in inorganic salt hydrates (ISHs) can transform the future of thermal energy storage (TES) materials and afford novel products for thermal regulation of buildings and advanced biomedical applications. To date, the use of bulk ISHs for TES is not practical due to supercooling and fluidity/leakage during thermal cycling. This proposal aims to change that by developing a versatile family of star block copolymer gelators for tailoring salogel thermomechanical properties. This will be achieved via establishing fundamental understanding of polymer-salt-hydrate interactions and ISH solubility and gelation conditions for a wide range of synthetic polymers. Star-shaped block copolymer gelators will be designed and refined in a synergistic theory-simulation-ML-experimental approach, overcoming the laborious guess-and-check approach currently used. This work will: (i) establish fundamental polymer solubility, phase separation, and gelation mechanisms; (ii) achieve a tailorable temperature gap between ISH melting temperature and salogel gelation temperature for robust TES performance; and (iii) achieve tunable salogel rheology making them suitable for diverse TES applications and 3D printing. The project will synergistically use the expertise of the US team in polymer synthesis and characterization and assembly, simulations and theory of polymer-solvent interactions, network formation and AI/ML-enabled materials design, with the expertise of the Indian team in mechanistic modeling and 3D printing. The work will showcase the efficiency of the coherent and iterative approach which embraces material discovery and optimization using modeling, theory and ML and will establish the fundamental science that will enrich the areas of data- and computationally driven polymer and materials science. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-10

This project addresses the critical challenge of optimizing the use of radio frequency spectrum, a limited and finite resource essential for modern communications. Currently, lower frequency bands (below 10 GHz) are highly congested due to the legacy line-of-sight systems, limiting their availability for vital applications such as rural broadband, emergency services, and infrastructure applications. Simultaneously, higher frequency bands (12-40 GHz), such as Ku-, K-, and Ka-bands, remain largely underutilized due to uncertainties about their reliability. The goal of this project is to develop methodology that will enable satellite communication links operating in these higher, less congested bands to achieve the same level of reliability and performance as systems in the lower bands. Demonstrating this capability will free up valuable low-frequency spectrum for broader societal benefit, promoting national health, prosperity, and welfare by enabling wider access to critical communication services. The project will also develop tools and train future engineers, fostering evidence-based decision-making for spectrum management and ensuring flexible access to this crucial resource. The project's primary goal is to establish the feasibility of transitioning line-of-sight communication systems, specifically satellite-to-ground links, from congested sub-10 GHz frequencies to higher-frequency bands (12-40 GHz). This will be accomplished through developing a comprehensive framework that integrates spectrum policy considerations with advanced propagation-aware service assessments. The methodology involves training deep learning models on real-world weather and radio-frequency telemetry data to predict atmospheric disruptions at high frequencies. Furthermore, the project will develop adaptive control mechanisms, utilizing reinforcement learning agents, to maintain service continuity under challenging environmental conditions like rain attenuation and cloud cover. A modular digital twin simulation environment will be employed to validate these predictive and control models across diverse operational scenarios. The anticipated contribution is the establishment of a robust technical foundation for resilient, high-frequency satellite operations, alongside a replicable methodology for future spectrum reallocation initiatives, providing actionable insights for regulatory bodies. 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

Millimeter-wave (mm-wave) radios are essential for many applications such as radio backhaul links, small-cell 5G base stations, wireless signal relaying, vehicular radars, wireless links for virtual reality headsets, electronic warfare, and navigation systems. This project aims to demonstrate a mm-wave full-duplex radio based on silicon photonics and integrated electronics that can advance communications and sensing capabilities for both civilian and defense applications. The project will develop new silicon photonics mm-wave interference cancellers to significantly improve the performance of mm-wave full-duplex radios. The advanced knowledge of modeling and designing silicon photonics and complementary-metal-oxide semiconductor (CMOS) circuits created by this project will be leveraged to investigate other integrated photonics systems such as the electro-optical Lidars and high-speed data converters. The outcome of this project will have significant potential impacts in both wireless and semiconductor industries. In addition, the research results generated through this project will be used in an advanced graduate course developed by the principal investigators. The project also promotes outreach activities to increase broader student participation in science and engineering, including annual summer camps for high school teachers and students. The research and educational results of this work will be widely disseminated to academic, industrial and government sectors. The goal of this project is to develop a novel chip-scale silicon photonics mm-wave full-duplex transceiver architecture with ultra-wideband self-interference cancellation capability based on wireless channel response estimation using hybrid silicon photonics and nanometer CMOS chips. While it is extremely challenging through conventional electronic approaches to achieve an ultra-wideband self-interference cancellation with real-time wireless channel response estimation at mm-wave frequencies, silicon photonics technology has the potential to accomplish mm-wave interference cancellation with simultaneous ultra-wide bandwidth and small footprint. The goal will be accomplished by several research tasks: (1) Design a hybrid mm-wave full-duplex transceiver architecture based on wideband antennas with orthogonal polarization, a photonically-enabled feedforward canceller, and an analog canceller unit along with performance analysis. (2) Develop wideband antennas with orthogonal polarizations for wideband self-interference cancellation in antenna domain. (3) Design and build a novel silicon photonics unit and its components as a feedforward canceller in RF circuit domain including modulators, interferometers, delay lines, combiners, as well as algorithms and hardware for their automatic calibration and tuning to address process variations and estimate wireless channel response. (4) Implement a novel nanoscale CMOS chip including power amplifier, low-noise amplifier, up- and down-converters, analog canceller circuitry, frequency synthesizer, and automatic tuning hardware. (5) Integrate silicon photonics and CMOS chips along with wideband antennas and perform system-level tests. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-09

Growth of renewable energy leads to new challenges for electric power grid planning and operation. Many renewable energy resources, such as solar and wind, heavily depend on the weather conditions that are inherently uncertain. Such uncertainty is usually revealed progressively over time. Consequently, the grid planning and operation decisions need be adjusted accordingly across multiple stages to achieve optimal efficiency. The multistage decision structure calls for study on multistage grid optimization algorithms that can accommodate the discrete decisions, such as battery charging versus discharging decisions, and scale well with the number of renewable resources, which can go up to tens of thousands. Moreover, several major tripping and disturbance incidences in the past decade have underscored the heightened stability concerns of a power grid with high renewable penetration. In contrast to conventional thermal generators that have large rotating masses to stabilize themselves, renewable resources are typically power electronics-interfaced resources, which lead to lower system inertia, faster grid dynamics, more frequent disturbances, and greater control difficulty. Hence, it is increasingly essential to integrate stability considerations into grid optimization algorithms to enhance reliable power system operation. This research will include open-source implementations of the algorithms developed, which can provide a computational infrastructure and benchmark for assessing long-term energy integration plans, or for evaluating the daily operational efficiency and reliability of power grids. To address these critical challenges of uncertainty and stability, this project aims to develop novel dynamic grid optimization algorithms and modeling tools to effectively accommodate high penetration of renewable energy and ensure reliable grid operation. The first part of this project is focused on a class of algorithms, called stochastic dual dynamic programming, for multistage stochastic optimization models. The investigators will fundamentally advance these algorithms to handle both continuous and discrete grid decisions effectively, and to enable better statistical guarantees by exploiting the structure of grid optimization with renewable uncertainty. The second part of this project plans to directly integrate stability considerations into the objective function and constraints of grid optimization, establishing a framework of stability-augmented grid optimization. Such framework enhances conventional grid planning and operation decisions to be stability-informed and optimizes both the economic efficiency and dynamic stability performance. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-09

Technologically advanced and sustainable deconstruction is crucial for meeting the nation’s growing demand for demolition services and a skilled workforce to mitigate the impact of extreme weather events, including wildfires, floods, and hurricanes, which can pose safety and time barriers that prevent deconstruction professionals from engaging in efficient and sustainable demolition practices. This need is also combined with the urgent imperative to renew the aging urban infrastructure, ensuring resilient, resource-efficient rebuilding that minimizes environmental impact and maximizes community safety. To address this need, this project aims to serve the national interest in several ways: (1) advance the knowledge and innovation in transforming demolition practices toward more technologically advanced and sustainable ones to mitigate the impact of natural hazards, reduce safety risks, minimize material disposal costs, alleviate supply chain disruptions, and minimize demolition environmental impact; (2) collaborate with professionals in the sustainable demolition construction practice to understand their role-specific competencies and how they perceive being part of an innovative profession focusing on environmental impact and efficiency and adding value to the general engineering profession, and (3) support the development of skilled deconstruction engineers fluent in digital literacy and able to implement technologies for construction and demolition waste management, and conducting safe, efficient, sustainable, and economic demolition operations. This aligns with research on the professional formation of engineers, which seeks to better understand how engineering education programs develop future engineers. This proposed study aims to understand the competencies required of the future workforce to engage in technology and data-driven sustainable demolition practices and how this could shape the professional identities of professionals in demolition-related fields. A mixed-method research study will be conducted to answer research questions that address (1) competencies required for the future workforce to engage in technology and data-driven sustainable demolition practices in construction and (2) the impact of acquiring these competencies on shaping the professional identities among sustainable demolition professionals and what specific identities are most likely to emerge. A nationwide survey of demolition contractors who actively implement or are transitioning to sustainable practices will be conducted to identify the skills required for technologically advanced and sustainable demolition practices, and to determine the value and demand for these skills in the construction industry. In the second phase of the project, the influence of acquiring the identified competencies on the formation and evolution of professional identities among practitioners in construction-related fields will be examined. Industry practitioners with expertise in sustainable demolition and construction practices that utilize technology will be interviewed to understand the types of identities they have developed through acquiring these competencies, how they developed their professional identities, and how those identities have evolved. Results will be cross-validated through focus group discussions with both practitioners and researchers who specialize in studying professional identity development and its formation in technical fields such as the construction industry. Currently, there is limited research exploring the specific competencies required for implementing advanced technologies and data-driven analytics in sustainable demolition practices. Additionally, no existing study has explored how acquiring these competencies can alter the perception of being part of an innovative profession that focuses on enhancing safety, productivity, and environmental performance in the demolition industry. We aim to leverage our research to bridge the knowledge gap, where developed competency models and pedagogical guidelines will be adaptable across construction-related engineering programs, thereby broadening participation in STEM, accommodating diverse learning needs, and leaving a lasting impact on both education and practice. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-09

This I-Corps project focuses on the development of advanced materials and devices that can actively respond to changes in temperature, mechanical stress, and environmental conditions. By using a specialized 3D printing process, this technology enables precise control over the local properties of metallic components. The approach creates materials with tailored responses within a single, seamless component, eliminating the need for complex assemblies or extensive finishing processes. The adaptable materials and devices have the potential to benefit industries such as healthcare, aerospace, automotive, and energy, by providing reliable, compact, and customizable solutions. For instance, this technology can improve medical implants for spinal surgeries, reducing surgical invasiveness and enhancing patient outcomes, and can create robust pipe couplers that replace less reliable connections currently used in aerospace, automation, and oil and gas industries. This I-Corps project utilizes experiential learning coupled with a first-hand investigation of the industry ecosystem to assess the translation potential of the technology. This solution is based on the development of functionally-graded shape memory alloys featuring precise local chemical compositions, microstructure, and mechanical behavior. The solution leverages a controlled evaporation phenomenon during the laser powder bed fusion 3-D printing process, allowing exact control of certain elemental concentrations within shape memory alloys. This precision enables the deliberate adjustment of solid-state phase transformations and mechanical properties, achieving tailored superelasticity, thermal actuation temperatures, and tunable stiffness within a single component. Unlike existing manufacturing approaches, this method avoids extensive post-processing and complex component assembly, offering scalable and reproducible material behavior across a wide temperature range. The technology provides enhanced reliability, reduced component complexity, and better multifunctionality. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-09

Semiconductors are important building blocks in today's electronics. They drive technological innovations in fields such as transportation, telecommunication, health care, and defense. Semiconductor manufacturing plays a critical role in fueling innovations and sustaining economic growth. This IRES project creates a mechanism to engage U.S. undergraduate STEM students in semiconductor manufacturing research in Taiwan. By leveraging the expertise and resources of the project partners in Taiwan, the summer research internships include an intensive semiconductor short course and a 4-week research immersion at National Chung Hsing University in Taichung, Taiwan. The project engages students in international research collaboration, develops their research skills, and enhances their global fluency, adaptability, and intercultural knowledge. Innovation and leadership in the semiconductor industry require a highly skilled workforce. This IRES project provides students with experiential learning opportunities to build international research networks and helps cultivate globally engaged future leaders to ensure the competitiveness and long-term success of the U.S. semiconductor industry. The IRES semiconductor short course comprises topics from fundamental principles to advanced fabrication of semiconductors, such as semiconductor physics, semiconductor materials, thin film technology, nanolithography, and packaging and testing. The research immersion projects focus on advancing scientific knowledge by addressing critical areas that impact performance, efficiency and scalability in semiconductor manufacturing. These projects investigate important topics such as (1) quantum transport and phase transitions in heterogeneous nanomaterials, (2) electronic states of transistor devices in 2D materials, (3) resonator qubit frequencies and transmission line impedance of flip-chip architecture, (4) effects of fabrication processes on surface morphologies of semiconductors, and (5) multielement doping approach to enhancing catalytic efficiency for sustainable manufacturing. Under the guidance of a research mentor, each participating student is integrated into the mentor's research team to conduct independent research. Professional development activities are organized throughout the summer internship and post-travel follow-up program. Through the IRES project activities, students develop leadership, communication, and lifelong learning skills that are critical for successful global engagement in semiconductor manufacturing. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-09

With the support of the Macromolecular, Supramolecular, and Nanochemistry Program in the Division of Chemistry, Dr. Emily Pentzer and Dr. Jodie Lutkenhaus of Texas A&M University will design, synthesize, and characterize polymers for electrochemical energy storage. The polymers that will be developed could be created from domestic feedstocks and used in advanced battery technologies, such as flexible batteries. This work will answer the fundamental scientific questions needed to create new polymers for energy storage: how does polymer composition and structure impact the movement of electrons in and out of the polymer and how can this be improved. The answers to these questions will expand our understanding of polymers for energy storage, leading to the rapid development of new materials. Through this work, students will be trained in cross-disciplinary research such that they are prepared to be leaders in the next generation of the American STEM workforce. New educational modules on polymers for energy storage will be development for the public and shared at the Texas A&M Chemistry Open House. Non-conjugated redox active polymers will be synthesized in which redox active groups and highly polar dopant groups will be incorporated onto the same polymer scaffold. Different organization of the two types of groups will be used: random distribution, spatially defined organization, and block copolymers. Polymers will be synthesized by controlled polymerization strategies and the redox and dopant groups will be attached through high yielding click reactions. The redox active group used will be 2,2,6,6-tetramethyl-1-piperidinyloxy along with a polysiloxane backbone (for example, polydimethylsiloxane (PDMS)). The modular polymer design enables the use of azide-alkyne click chemistry to modify the PDMS-type backbones with cationic or anionic dopant units (imidazolium and trifluoromethanesulfonylimide, respectively) and/or neutral units (tetraethylene glycol). The electrochemical properties of the different polymers will be characterized in solution and in the solid state, and the heterogeneous and homogeneous rate constants and apparent diffusivity quantified and related to the polymer’s chemical structure and bulk physical properties. This research will advance our fundamental understanding of the effect of spatial arrangement and confinement on the electron transfer kinetics and overall properties of self-doping redox active polymers. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-09

This project will develop a sub-millimeter scale robot with sensing and movement capabilities inspired by the amoeba. Like the amoeba, this robot will be able to follow biological and chemical traces in the surrounding environment by extending and contracting its body. The robot will be distinguished by its construction from uniformly soft composite materials, and by a seamlessly integrated information-processing system for converting sensed chemical signals into motion commands. The developed robot will have applications for minimally invasive medical diagnostics as well as structural inspection in confined spaces. This project will develop a micro hydrogel crawling robot with novel capabilities in selective electrochemical sensing, neuromorphic control, and thermal actuation, specifically enabled by (1) a hydrogel-MXene skin capable of detecting electrochemical changes in its surroundings; (2) 3D micro thermally activated hydrogel-nitinol actuators to power swimming and crawling gaits; (3) a functional hydrogel skin to facilitate thermal transport and interfacial friction reduction; and (4) neuromorphic circuitry incorporating MXene-hydrogel memristor elements to learn, compute, and control the sensorimotor connection. The design approach is inspired by the amoeba, whose behavior is governed by biochemical pathways linking chemical sensing and actuation mechanisms. Like the amoeba, the robot developed under this project will be capable of multimodal sensing and feedback-controlled motion in complex environments with unstructured sensing signals. The performance of the robot will be demonstrated for minimally invasive biomedical diagnostics and confined-space inspection applications. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-09

Immunoglobulin G (IgG) is a type of antibody. It is primarily active in controlling infection in body tissues. It actively binds viruses, bacteria, and fungi. Nanobodies (Nbs) are fragments of IgG. Some nanobodies exhibit anti-cancer activity. Bacteria can be modified to produce Nbs and one of these, E. coli Nissle 1917 (EcN), has been shown to selectively colonize tumor tissue. This project will develop two gene circuits in EcN. The first will sense signals in the tumor microenvironment and respond by synthesizing antitumor NBs. The second circuit will sense the presence of the artificial sweetener erythritol and will break open the bacterial cell, releasing the Nbs and killing the bacteria and the tumor cells. If successful, this could be adapted to a range of solid tumors. which are very difficult to treat with conventional therapies. The project includes extensive educational and outreach components, offering hands-on research opportunities for students and trainees at multiple levels. The technical thrust is to program a probiotic strain with multi-input genetic circuits. These circuits couple therapeutic nanobody expression with regulated release. The system targets two immune checkpoint receptors, PD-1 and LAG-3. It is designed to operate by applying exogenous erythritol to induce bacterial lysis and release of the therapeutic Nbs into the tumor microenvironment. Erythritol, artificial sweetener not naturally present in tumors, offers a reliable external trigger to control timing and dose. The approach will be validated in mouse models using in vivo bioluminescence tracking of bacterial populations. The research also aims to establish a robust biocontainment system utilizing integrases or toxin-antitoxin systems to enhance the stability and reliability of the genetic circuits for biocontainment purposes. The project will achieve scientific advancements through the design of genetic circuits, the development of microbial chassis, and the application of microbial immunotherapy. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-08

This project investigates how artificial intelligence can overcome current challenges in designing biosensors based on two-dimensional materials, moving beyond laborious trial-and-error methods, while simultaneously enabling human experts to acquire new knowledge in this domain. Rational design for these crucial devices faces four primary challenges: insufficient data due to expensive experimentation and inherent measurement uncertainties; the absence of accurate theoretical models to capture complex biochemical interactions; the constant demand for rapid responses to emerging needs (e.g., new pathogens); and the necessity for transparent, data-driven design approaches in high-stakes biomedical applications. To address these issues, we propose a “white-box” data-driven design and knowledge discovery framework that integrates interpretable machine learning, data fusion, and statistical inference. This methodology seeks to accelerate on-demand biosensor development through a “design-by-learning” paradigm and enhance expert capabilities to address new demands by providing interpretable insights derived from the design process (“learning-from-design”). This project looks to advance biosensor design, promote national healthcare readiness, and support STEM education through novel research and practical applications. The developed design methodologies are expected to generalize beyond biosensor development to broader areas, including advanced materials design. This research looks to spearhead studies of AI trustworthiness in data-driven design for high-stakes applications, shedding light on how model transparency impacts design cognition and performance. The core objective of this project is to establish a “white-box” data-driven design framework for 2D material-based biosensors, emphasizing model transparency and quantifying its influence on designers’ knowledge acquisition, perception, and overall performance. To achieve this, the research looks to develop and implement several key innovations: (i) the development of novel methods for discovering analytical relationships between biosensor properties and performance, even when confronted with highly uncertain experimental data; (ii) the creation of interpretable multi-fidelity modeling approaches designed to distinguish between generalizable and non-generalizable influences of design variables, thereby guiding reliable extrapolation to new design scenarios; and (iii) design studies to quantify how model transparency impacts designers’ cognitive processes, knowledge acquisition, and performance, thereby advancing our understanding of the practical importance of transparency in data-driven design. This research framework will undergo rigorous evaluation through its practical application in designing biosensors for various biomarkers. The developed design interfaces will be showcased in dedicated workshops aimed at disseminating findings, training new users, and gathering valuable feedback to quantify the impact of this research and further refine the framework. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-08

This Faculty Early Career Development (CAREER) award supports research into how the movement of the uterus, called peristalsis, affects the cells in its inner lining. Many cells in the inner lining grow and shed in sync with this movement. The mechanics of the uterus may be key to this process because all cells in the body sense and respond to mechanical forces. Abnormal uterine movements may change growth rates and the immune environment, leading to problems in the uterus. Using a special device to apply uterine mechanics to different cells in the inner lining to investigate how these cells sense uterine movements and convert them into responses like growth, migration, and immune activation. The insights gained are likely to help better understand women's health issues like endometrial cancer, endometriosis, and adenomyosis. The project also includes educational programs. This combined research and education effort will help close gaps that exist in women's health research, engineering, and technology. This award will create new fundamental knowledge on how different cells in the uterine endometrium transduce the mechanics associated with uterine peristalsis using the peristalsis bioreactor, a device capable of applying mechanical patterns associated with uterine peristalsis to several types of cells in the uterine endometrium (endometrial cells, macrophages, etc.). The research team will isolate and experimentally interrogate if the mechanics of uterine peristalsis (or dysregulation thereof) results in endometrial cell motility or invasiveness, and the amplification of macrophage inflammation. The research approach integrates bioreactor methodology, gene expression and protein localization assays to investigate mechanobiological signal transduction pathways that contribute to aberrant cellular behavior in the uterine endometrium. The mechanobiological insights gained from these studies will advance the understanding of many conditions that impact women’s health outside of pregnancy like endometriosis, adenomyosis and endometrial cancer. In parallel, the research and integrated educational objectives plan to create programs across learning spheres in K-12, higher education, and community learning spaces to improve awareness of women’s health engineering. 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.