North Carolina Agricultural & Technical State University

NSF Awards · FY 2026 · 2026-06

Nuclei are the basic building blocks of matter and understanding their fundamental properties is the main goal of this award. This project will investigate the vibrational degrees of freedom superimposed on the deformed ground states and the expected rotational excited states built upon them in rare earth nuclei. The investigators aim to determine the characteristics of excited states by the measurement of conversion electrons in coincidence with γ rays using fIREBAll, the only array of its kind in the US. Undergraduate and graduate students are trained in each step of the process, taking advantage of the unique training opportunities at an NSF-funded laboratory. The North Carolina Agricultural and Technical State University (NCA&T) group will train students in hands-on research including accelerator operations to begin filling the gap that is forming in our nuclear workforce and training a globally competitive STEM workforce. This project will also inform the public through the podcast, My Nuclear Life which explores the intersection of nuclear science and society. Nuclei show some emergent collective behavior across isotopic and isobaric chains and are known to be deformed in shape in regions of the chart of nuclides away from closed shells. The existence of vibrational degrees of freedom superimposed on rotational states addresses a degree of freedom in nuclei and is one of the open questions in nuclear structure physics today. This project will use the newly commissioned fIREBAll spectrometer located at the University of Notre Dame's (ND) Nuclear Science Laboratory and multiple (α,2n) reactions to measure conversion electrons in rare-earth nuclei. From these experiments, extracted electron conversion coefficients will be used to calculate E0 components of the relevant transition probabilities depopulating relevant levels. Analyses will be performed at NCA&T during the academic year and at the Institute for Structure & Nuclear Astrophysics at ND during the summer months while the group is in residence. Results will be widely distributed by all members of the group via conference presentations and peer-reviewed journal publications. 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 project advances the scholarship of future developmental scientists by fostering connections between scholars in teaching-intensive and mentors appointed at research-intensive institutions. One strategy is to catalyze opportunities and resources to enhance research productivity of scholars appointed at teaching-intensive institutions. This includes access to library resources; access to statistical/survey software; and access to mentoring pertaining to research development, proposal writing, and grant management. Such access improves the STEM workforce and results in compelling research questions that help shape the future trajectory of scientific discovery. This project pairs early-career faculty or postdoctoral fellows with tenured faculty who share similar areas of research. This pairing catalyzes cutting-edge developmental science research using projects focused on families at pivotal developmental transitions while considering the familial, financial, and other factors impacting those developmental stages. The project brings scholars together to enrich learning and awareness and build strategies for success. The project is designed to provide a supportive scholarly community and mentoring experience to encourage early-career faculty or postdoctoral fellows at teaching-intensive universities to acquire the skills needed to advance the discovery and innovation of future 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

Federated learning is a method that enables multiple devices or organizations, referred to as clients, to collaboratively train a shared machine learning model without sharing their private data. Although it has shown promise in real-world applications, it still faces several challenges. One major issue is the lack of fairness, where clients who contribute higher-quality data do not often see their efforts properly reflected in the final model, especially in applications where each client's data is large and valuable. Another challenge is that federated learning algorithms are often not designed to cope in real-time environments, such as traffic and autonomous systems, where data is received continuously. Furthermore, in real-world environments, clients are susceptible to poor network conditions or limited communication capacity, which can inhibit collaboration. Moreover, federated learning is liable to privacy leakage from a powerful adversary. Although local differential privacy can prevent privacy leakage, it reduces model performance, making it hard to balance privacy and accuracy. This project aims to address these pressing issues by designing strategies to fairly incentivize clients in challenging, dynamic environments while maintaining strong privacy protections. It overcomes the limitations of the existing game theory approach, which requires knowledge of a utility function difficult to compute in federated learning. Moreover, unlike the game theory approach, it does not utilize money to incentivize clients to contribute high-quality data because budget constraints will result in inadequate compensation, which will discourage clients with high-quality data from further collaboration. Other existing methods based on reinforcement learning are computationally demanding and impractical under resource-limited conditions. Therefore, this project will incentivize clients based on the quality of their data contributions in real-world harsh environments for various applications while ensuring data privacy. Moreover, it will foster collaborations among industry partners necessary for economic growth. This project aims to develop effective methods for incentivizing clients to contribute high-quality data in cross-silo federated learning, while ensuring privacy in dynamic environments. Unlike existing game-theoretic or deep reinforcement learning approaches, it introduces novel strategies across three thrusts. Thrust 1 focuses on incentive mechanisms when there are no communication bottlenecks, using optimistic mirror descent and Hedge techniques to assess and reward data quality. Thrust 2 addresses communication constraints, based on bandit optimization and difference compression. Thrust 3 enhances privacy through local differential privacy with tree-based aggregation and the optimistic follow-the-regularized-leader technique. The project will provide rigorous mathematical foundations and will ensure reproducibility by sharing open-source 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

Human communication has traditionally been dependent on sensory systems such as seeing, hearing, and/or touch, but words and symbols that are available to senders and are understood by receivers still limit most current communicative methods, even when they include non-verbal content. Brain-to-brain interface (B2BI) is an emerging technology that combines sensing the brain (brain-computer interface, or BCI) and stimulating the brain (computer-brain interface, or CBI) to enable communication between two brains directly through their neural activities. A BCI (for example electroencephalography- or EEG-based motor-imagery or MI BCI) reads a sender’s brain activity and dispatches information to a CBI (for example, via transcranial magnetic stimulation), which activates a receiving brain, thereby facilitating direct brain-to-brain communication. Since its proof of concept in 2013, B2BI has been demonstrated in both animal models and human subjects where the same or different brain regions are recorded and stimulated in a variety of interesting contexts. Despite exciting advances in B2BI, there are still major gaps and barriers that need to be addressed, including but not limited to the lack of B2BI that work directly with neural information instead of indirectly through computer interpretation. The intellectual merit of the project lies in its innovative and integrative approach to developing an emergent neural interface technology that enables an individual’s brain to communicate with another’s by bypassing sensory exchange and language entirely. Project outcomes will have broad impact in medical applications such as enhanced communication with behaviorally non-responsive or less-responsive patients, neuro-rehabilitation for stroke victims, and ultimately advanced communication for healthy users. The research will help facilitate the adoption of high-accuracy, image-guided, ultrasound technology in B2BI applications, and will produce the first truly bi-directional B2BI thereby laying the groundwork for the next step in human communication. The overarching goal of this exploratory project is to create a direct bidirectional B2BI using non-invasive technology that combines a contemporary EEG-based MI BCI as the neuro-imaging technology and transcranial focused ultrasound (tFUS) as the neuro-stimulation technology. To this end, two objectives will be pursued: First, we will determine the optimal parameters (including, duty cycle, inter-stimulus interval, and acoustic intensity) of the proposed excitatory tFUS. These parameters have been used in other studies, but not in a comparative analysis to determine the ideal combination. Furthermore, tFUS has been sparingly used with humans in a B2BI. Experiments will be conducted to develop and test parameters for tFUS modulation of the human brain, especially determining what values of key parameters produce the greatest excitatory response in a participant’s brain. Then, a direct bidirectional B2BI built upon a MI BCI and the tFUS system resulting from the first objective will be assessed, with a focus on healthy human subjects in a more realistic task setting than those used in previous studies. The approach aims to replace the peripheral nervous system device with another BCI and CBI component, allowing brain information to be transmitted in both directions around the loop. Two research questions to be addressed include: Can the MI and tFUS based bidirectional B2BI system allow subjects to perform better than chance in a bidirectional collaborative task? and, Do the more detailed measures of performance (AUROC, bit rate, mutual information, and classifier accuracy) vary with task condition? 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 implementing and evaluating an innovative instructional model to improve student success and persistence in foundational mathematics courses, which are critical gateways to STEM degree completion. Specifically, this Level 1 Engaged Student Learning project will adopt and adapt the evidence-based hybrid-flipped learning (HFL) approach for teaching College Algebra and Calculus, embedding interdisciplinary data science applications to promote mathematics relevance and data literacy among STEM students. By emphasizing interactive learning, the model promotes deeper student engagement, enhanced conceptual understanding, and improved achievement. By connecting mathematical concepts to meaningful data science applications, the model fosters positive attitudes toward mathematics and supports persistence in STEM pathways. Moreover, by exposing students early to data-driven thinking, the project will attract and prepare a large pool of students for further study and careers in data-intensive fields, responding to the growing national demand for a data-literate workforce. The project seeks to achieve three main goals: enhance student engagement and learning outcomes in College Algebra II and Calculus I courses; inspire early interest and participation in data science education; and build a faculty community dedicated to fostering analytical and problem-solving skills in foundational mathematics. Using Python-based computational notebooks, the project will develop online curricular materials for the HFL model and launch the weekly virtual math applications lab (V-MAL) in College Algebra II and Calculus I courses to support active learning, including collaborative problem-solving and project-based learning of mathematics in data-driven contexts. Thus, helping students develop mastery of math concepts, computational thinking, and analytical and communication skills. To implement and institutionalize this instructional model, the project will engage the introductory mathematics faculty in a suite of professional development activities on adopting the HFL model with a data-centric pedagogy in their teaching. The project will use a mixed-methods research design to study the effectiveness of the instructional model and curriculum in improving student engagement, conceptual understanding, and achievement in introductory mathematics. Thus, providing much-needed evidence to guide the implementation of innovative pedagogies in introductory mathematics education. Evaluation efforts will also examine impacts on students' interest in pursuing data science education. Through targeted dissemination efforts, including conference presentations, blog posts, and journal articles, the project will promote the instructional model and curriculum to encourage adoption by other institutions seeking to make their STEM curricula more data-driven. 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 Level 2 Engaged Student Learning IUSE (Improving Undergraduate STEM Education) project aims to serve the national interest by improving STEM students' engagement in science through the linkage of academic experiences with real-world problems that students will investigate in their mathematics, social science, and environmental classes. Participatory science, also known as citizen science, engages the public in the scientific process by having individuals assist with data collection, data interpretation, and research question formation. This type of science has demonstrated its ability to advance scientific knowledge while also connecting the public to various biological and community issues and concerns. The significance of this when considering the university campus communities of this collaborative project is that participatory science activities can also increase undergraduate student engagement in STEM classes. Recent research also suggests that these efforts may have broader impacts on students' overall educational experiences in college, which is an additional goal. This project will scaffold activities that move students from simply learning basic scientific concepts to applying best scientific practices, and then to evaluating and using qualitative and quantitative results to inform student recommendations for resolving a local problem they are investigating. As these community-based projects are incorporated into course curricula, the impact will be seen in the improvement of student performance in STEM classes and an increase in the number of students interested in STEM careers. This is often the result of helping students see the link between academic experiences and real world problems and how the scientific process can be used to study these issues. This project's overall goal is to enhance student learning outcomes in mathematics and data analytics through participatory science experiences. The Undergraduate Participatory Science Initiative (UPSI) in Data Analytics and Interdisciplinary StudY (DAISY) will consist of a scaffolded sequence of learning modules and research experiences grounded in participatory science, that will be available to students in general education mathematics courses and select courses in the social and environmental sciences. By integrating participatory science in the classroom, the project's scope of work will: 1) create an alternative research curriculum across disciplines; 2) train faculty across disciplines in utilizing participatory science in classroom; 3) provide students with engaging, community-based research training and leadership skills; and 4) improve environmental and student outcomes on campus and aid local communities through scientific research. The project will also involve a rigorous assessment of these experiences regarding enhancement of course-specific learning outcomes, as well as the extent to which they positively impact students' interest, self-efficacy, and connection to mathematics, science, and students' disciplinary major. 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 serves the national interest by transforming engineering education to enhance student retention and promote the success of learners with varied educational needs through the use of adaptive learning (AL) methodologies. This Level 3 Engaged Student Learning project utilizes innovative technologies to deliver personalized educational support and enhance faculty capacity across three institutions: North Carolina State University, the University of North Carolina at Charlotte, and North Carolina Agricultural and Technical State University. By addressing key challenges such as learning gaps, imbalances in instructional supports, and faculty adoption of new technologies, the project seeks to create a cohesive curricular spine of interconnected AL course modules in Statics, Dynamics I, and Dynamics II. The AL platform offers tailored content, assessments, and feedback, promoting deeper student engagement and enabling faculty to support the varied needs of learners better. These efforts aim to achieve measurable gains in student retention and academic outcomes through personalized support strategies that are automatically tailored to each learner's level of understanding. The project also develops readiness models and best practices to foster widespread institutional adoption and scalability, ensuring the sustainability of AL methodologies beyond the project period. The project has two primary goals: (1) enhancing student learning by implementing a curricular spine that interconnects key engineering courses and supports personalized, just-in-time learning interventions; and (2) empowering faculty and institutions to adopt and sustain AL practices through targeted training and resource development. The research evaluates the effectiveness of AL interventions in enhancing student retention and engagement, while providing insights into the faculty and institutional needs for successful implementation. A comprehensive evaluation plan tracks progress, assess learning outcomes, and refines interventions to ensure effective learning. Findings are disseminated widely to inform best practices and encourage the adoption of AL methodologies in engineering education and other STEM disciplines. The NSF IUSE: EDU Program supports research and development projects to improve the effectiveness of STEM education for all students. Through the Engaged Student Learning track, the program supports the creation, exploration, and implementation of promising practices and tools. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-09

The Division of Nuclear Physics annual meeting brings together the country’s nuclear physicists to share the newest scientific discoveries. Among them are hundreds of undergraduate and graduate students, who are considering a career in nuclear science. These students will create the future workforce and contribute to a variety of fields from basic science to nuclear medicine and national security. This award will aid in the retention of both groups of students by supporting a near-peer mentoring network. Graduate students and postdoctoral researchers (near-peers) will be recruited to mentor the undergraduate students in attendance. The mentors will participate in an 8-hour mentoring workshop the day before the conference begins, and then practice these skills during the conference with their assigned undergraduate mentees. Research has shown this type of model increases a participant’s scientific identity and sense of belonging, allowing them to make well informed decisions on their future career choices. Effective mentorship plays a critical role in developing a young scientist’s identity including the ability to see themselves reflected in others in the field. The use of near-peer mentors allows the field to recruit young researchers from a variety of backgrounds. To prepare mentors to understand mentees, an evidence-based mentoring workshop will be provided which was recently highlighted in the “Science of Effective Mentoring in STEMM’’ report by the National Academies. The goal of this mentoring program is to encourage students from all areas of society to see themselves as scientists and continue into nuclear science careers as emphasized in the 2023 Long Range Plan for Nuclear 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 · 2025-09

In this project, funded by the MPS-LEAPS (Launching Early-Career Academic Pathways) Program and managed by the Division of Chemistry (CHE), Professor Bangle and her students at North Carolina Agricultural & Technical State University will perform studies focused on the improvement of light harvesting for photocatalysis via upconversion. Photocatalysis uses light energy to drive desirable chemical reactions, but many such reactions require high energy blue and green light and waste low energy red and infrared light. Upconversion is a promising strategy to improve photocatalysis efficiency by combining two low-energy photons into one high-energy photon capable of activating photocatalysts. Professor Bangle and her students will improve the efficiency of upconversion by integrating upconverting supramolecular assemblies into nano-scale plasmonic cavities and engineering the light-matter interactions in the cavities to promote intermolecular energy transfer. Their studies could produce upconverting surfaces which use low energy light to drive reactions when integrated into a wide variety of existing photocatalytic systems. This research will introduce early-career undergraduate students to advanced training in chemical and physical experiments and encourage further scientific education. Professor Bangle and her students will use a combination of surface chemistry, chemical synthesis, nanofabrication, and ultrafast optical measurements to create bespoke nanocavities and supramolecular assemblies designed for controlled molecular orientations. Supramolecular assemblies will be formed from donor and acceptor molecules known to undergo upconversion via sensitized triplet-triplet annihilation. Professor Bangle and her students will build combined molecule-nanocavity systems which systematically control the orientation of molecular transition state dipoles and energy transfer vectors relative to electric fields in the nanocavity. They will further tune the resonance of the nanocavity relative to the absorption, emission, and energy transfer energies of the molecules and quantify the influence of field orientation and resonance energy on yields and kinetics of each photochemical process. This research will develop design rules for controlling photochemical outcomes in plasmonic nanocavities, produce ultrabright upconverted emission, and use this upconverted emission to drive green photocatalytic reactions with low energy and broadband light. 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

In the United States, individual states set their own carbon pricing policies based on their individual objectives. The variation in carbon prices set on the commonly shared electric power network, which is federally regulated through the Regional Transmission Operators (RTOs), can distort the operations and planning of the power grid, leading to environmental leakages, loss of carbon tax revenues, and other undesirable outcomes. The goal of this research project is to provide practical evaluation tools to support policy makers on carbon pricing integration, particularly modeling and setting the carbon border adjustment and carbon tax revenue recycling effectively into the operations and planning of the US electric power network such that electricity market distortion is reduced, environmental leakages are mitigated, impacts on ratepayers are minimized, individual interests of states and RTOs are balanced, and the impacts of carbon prices are quantified. Furthermore, this project presents an opportunity to train and educate K12/undergraduate/graduate students on the technical challenges of electricity market design, power systems economics, optimization, game theory, and operations research This research project views the setting of regulatory policies, particularly carbon border adjustments and carbon tax revenues, as complementary mathematical optimization problems. Specifically, the project will incorporate and tune constraints representing policy intervention, which are imposed on the existing operations and planning optimization model of electric power network, to adjust the operations and planning’s optimal solutions, both primal and dual, to balance the interests of states and RTOs and optimize certain economic, and environmental metrics representing carbon pricing impacts. For computational tractability, the mechanism design problem will be conducted on an aggregated power network model, while the evaluation of regulatory policies can be regarded as the sensitivity analysis of the power system operations and planning model. The latter can be conducted on a realistic large-scale network using recent advances in grid modeling tools. The project looks to leverage machine learning approaches to adaptively model used in mechanism design, so that the policy evaluation conducted on the large-scale realistic grid model is improved. The project seeks to produce formal mathematical models and planning and analysis tools that help policymakers with sustainable carbon pricing integration. A collaboration with the Pacific Northwest National Laboratory will provide access to data and power systems expertise. 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

Cancer is the second leading cause of death in the United States. To fight cancer, we need early detection and new treatments to lower death rates and improve survival. Recently, Chromatin-Sensitive Partial Wave Spectroscopic (csPWS) microscopy has become a key tool for early cancer detection and treatment monitoring. This technique measures changes in chromatin structure within cell nuclei at the nanoscale level. To make it easier to analyze chromatin packing and nuclear shapes, software that automates cell selection and nuclei segmentation is needed. Current manual methods are slow, complicated, and vary from user to user. Manual segmentation is also challenging due to the unique features of label-free csPWS images. This project aims to create an Artificial Intelligence(AI)-based segmentation technique that quickly and accurately selects nuclei from various cancer cell lines and imaging conditions. This will help streamline chromatin analysis for early detection and treatment of cancers using csPWS data. The project will also develop an AI algorithm that predicts early cancer and tracks treatment responses using spectral information from raw csPWS images. These AI tools will expand the use of csPWS microscopy, making it accessible for cancer screening, diagnosis, and treatment. This is a significant step toward better health outcomes. The project will provide undergraduate and graduate students in electrical, computer, and biomedical engineering with valuable research training and experiences. This research project aims to develop novel deep learning frameworks for nuclei segmentation and evaluate the performance of these algorithms when applied to diverse csPWS microscopy image datasets from different cell lines and imaging conditions. Additionally, the project develops an AI based unified nuclei segmentation and predictive analysis platform using csPWS microscopy-specific spectral data for cancer screening and treatment monitoring. The novel attention mechanism based convolutional neural network (CNN) and transformer models will be developed. Furthermore, the project will create large-scale, high-quality, labeled (annotated-ground truth) csPWS-specific training datasets tailored to various cell lines for training deep learning models, ensuring robustness and trustworthy data across diverse biological conditions. The resulting AI frameworks will be integrated into the csPWS analysis software platform, facilitating the broader adoption of csPWS microscopy by the wider research community for early cancer detection and diagnosis. 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

Intersections are some of the most dangerous and congested parts of roadways, despite making up only a small portion of the entire transportation network. As a result of recent breakthroughs in sensors, vehicle communication, and small but powerful computers, society is moving toward a future in which traffic can be managed by using connected and automated vehicles (CAVs). The central goal of this project is to replace traditional traffic controls, such as stoplights and signs, with computer systems that help vehicles move more safely and smoothly through intersections — reducing accidents, shortening delays, and lowering fuel use and emissions. At the center of this paradigm is a system called the Autonomous Intersection Manager (AIM). AIM acts like the brain of a smart intersection, constantly analyzing incoming vehicle data and deciding how each vehicle should move to avoid collisions and to keep traffic flowing. To do this effectively, AIM needs fast and reliable decision-making tools that can work with complex and changing traffic patterns. This project develops new computer algorithms that allow AIM to make these decisions quickly, even when facing limited time and computing resources. The work combines ideas from computing, scheduling theory, and traffic control to create transportation technologies that are safe, efficient, and practical for real-world use. In parallel with its technical contributions, the project fosters educational and societal impact by engaging students in hands-on research at the intersection of computing, cyber-physical systems, and intelligent transportation. Furthermore, it includes outreach to K-12 educators and students, providing mentorship and experiential learning opportunities in robotics and computational thinking. These activities aim to broaden participation in STEM disciplines and cultivate a technically proficient workforce equipped to address future challenges in autonomous mobility and intelligent infrastructure. The overarching technical objective of this research is to establish a computationally efficient algorithmic framework for autonomous intersection management with formal safety guarantees. The research pursues three primary objectives: (i) Develop a deeper understanding of the concept of computational control for complex autonomous systems, defined as the generation of online control policies using iterative heuristic algorithms. These policies are designed to compute effective solutions for high-dimensional problems, avoiding reliance on data-driven or handcrafted, closed-form analytical methods. (ii) Bridge the gap between constrained decision-making and real-time computing by quantifying the algorithmic complexity of generating CAV trajectories as a function of physical intersection parameters (e.g., number of lanes). The goal is to design algorithms that produce optimal or near-optimal trajectories within firm processing deadlines. (iii) Integrate scheduling theory, such as Gantt charts and time-graph formulations, into the control framework, enabling systematic generation of multi-parameter CAV trajectories. This project advances the field of autonomous intersection management by pioneering a computational architecture that integrates real-time optimization, heuristic search, and scheduling theory. It addresses nonconvex safety and motion constraints using general-purpose constraint-handling techniques, reinforced by bio-inspired metaheuristics to enable scalable trajectory planning. The framework supports adaptive decision-making through dynamic refinement of search horizons and service windows, ensuring the system meets hard real-time constraints. By reducing computational complexity and enabling guarantees of feasibility, the integration of scheduling and optimization techniques enhances both system performance and scalability. This interdisciplinary effort unites concepts from dynamic programming, real-time systems, optimization, and cyber-physical systems, yielding theoretical and practical contributions that support the deployment of intelligent traffic infrastructure on resource-constrained embedded platforms. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-08

The Historically Black Colleges and Universities Undergraduate Program (HBCU-UP) through Targeted Infusion Projects supports the development, implementation, and study of evidence-based innovative models and approaches for improving the preparation and success of undergraduate students enrolled at HBCUs so that they may pursue science, technology, engineering, or mathematics (STEM) graduate programs and/or careers. The goal of this project is to enhance undergraduate students’ data science education at North Carolina Agricultural & Technical State University (NCA&T) by modernizing existing academic programs in science and engineering by integrating Generative Artificial Intelligence into core courses for degrees in data science. The project aims to accomplish this goal by 1) promoting AI literacy and ethical awareness among undergraduate STEM students, 2) equipping students with practical skills to integrate AI tools and enhance productivity in data science careers, and 3) training faculty and graduate teaching assistants in effective practices for leveraging AI in STEM education. This initiative will strengthen data science education at the institution, enrich ethical generative AI techniques, and enhance retention and graduate rates of students pursuing data science degrees. In addition, the research study is designed to embed generative artificial intelligence (AI) education within undergraduate statistics and data science curricula at NCA&T. This project will lead to the better preparation of students for careers in the competitive fields of data science and address the national need for skilled professionals in these fields. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-07

4D printing, a form of additive manufacturing that fabricates “smart” structures capable of changing shape in response to external stimuli, offers a promising solution for self-disassembly of multiple components. However, broader adoption of 4D printing for environmentally favorable applications is hindered by challenges such as the difficulty of printing high-strength structures with precise shape-morphing properties and high printing fidelity, as well as the lack of methodologies to optimize structures for functionality, mechanical strength, and sustainability. This award funds research that seeks to advance process science by developing a versatile 4D printing platform for creating mechanically reinforced, smart structures that enable self-actuation and self-(dis)assembly, ultimately fostering circularity in manufacturing. By advancing computational and design tools, research funded by this award seeks to transform modern additive manufacturing platforms with improved material compatibility that are better suited for environmentally sustainable applications. The findings are also expected to support a cutting-edge experimental and computational framework to expand access to high-quality engineering education and training for students. Research to be enabled by this award is expected to develop a versatile direct ink writing-based 4D printing platform for continuous fiber-reinforced stimuli-responsive composites and advance its capability through three tasks: (i) Developing a cross-material multi-property prediction framework to accurately map matrix material and fiber compositions to their stimuli-response behaviors and mechanical properties, accommodating a broad range of composite configurations; (ii) Advancing direct printing techniques with reinforcement learning-based adaptive control with transfer learning modules for improved algorithmic generalization to enhance printing fidelity and adaptability across different materials and printing platforms; and (iii) Establishing a concurrent design method for continuous fiber-reinforced smart composites, ensuring high-performance structures that balance mechanical strength and shape-morphing capabilities for sustainability-driven applications. The research looks to generate new insights into material representation and multi-property prediction for smart composites and develop adaptive processing strategies to establish a generalizable and accessible smart manufacturing infrastructure. The outcomes may also inspire the design and manufacture of functionally engineered, advanced materials tailored for dynamic environments and environmentally conscious 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 2024 · 2024-12

DNA sensing techniques have been widely applied in daily life such as medical diagnosis, biowarfare defense, forensic science, and environmental monitoring, and were significantly promoted during the past pandemic, e.g., reverse transcription polymerase chain reaction (RT-PCR) test for COVID-19. Rapid DNA detection with high sensitivity, specificity, and accuracy is in high demand, however limited by signal readout. This project is aimed at developing an innovative dual signal amplification method by integrating two different signal amplification methods, i.e., materials science- and optical-based. The research goals are to strengthen signal readouts and build field-friendly DNA sensors that are amenable to point-of-need applications with ultrasensitivity. The discovery of fundamental science and transformative technology will potentially enable a reliable multiplexed high-throughput DNA analysis platform that may greatly benefit health care in society and facilitate research and applications in biomedical and life science. The scientific learning of this interdisciplinary research performed at the HBCU (NC A&T) and MSI (UNC Greensboro) will advance sensing mechanism understanding, instruct and train students especially underrepresented students, in research and education, and engage K-12 STEM educators and students in science. Genetic information with or without variation coded within nucleic acids, indicating an illness or health outcome, is termed a nucleic acid biomarker, thus plays a crucial role in precision medicine. Sensitive and selective detection of nucleic acid biomarkers with rapid signal amplification is the key for early screening and diagnosis of human diseases. This project is aimed at developing an innovative dual signal amplification method and understanding the signal transduction mechanism for enhanced DNA sensing. The work is built on the seamless integration between amplification-by-polymerization (AbP) in DNA sensing for optical clarity change on surface based on effective mass growth upon DNA recognition and in-planar metallic film nanoarrays for plasmon-exciton coupling (PEC) optical enhancement. The research will be conducted in three stages to (1) fully explore the potential of the AbP-PEC dual signal amplification platform, (2) investigate the fundamental mechanism of the amplified signal transduction pertaining to the AbP-produced film thickness and plasmonic nanoslit structure, and (3) optimize the AbP-PEC platform for a portable DNA sensor in point-of-care diagnostics. The outcome may be transformative towards a multiplexed, rapid, highly sensitive, visible (by naked eyes) analysis of DNA in biofluids. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-10

The National Science Foundation Historically Black Colleges and Universities Undergraduate Program (HBCU-UP) supports projects that enhance undergraduate science, technology, engineering, and mathematics (STEM) education and research at HBCUs, as means to broaden participation in the nation's STEM workforce. Brain vessels play a crucial role in regulating the passage of substances between the bloodstream and the brain, preventing harmful substances from entering the brain. This vasculature directly communicates with the neuronal system in the brain. Understanding the crosstalk between the neuronal system and vasculature is vital for maintaining the health and function of the nervous system. The goal of this project is to establish a platform in organoid-vasculature intelligence, providing new insights into the direct communication between organoids and vasculature integrated with a machine learning model. This will be achieved through the systematic integration of a microfluidic organoid-vascular tissue construct, microelectrode array recording/stimulation, and machine learning models. The project has two specific objectives 1) Develop a microfluidic cortical organoid-vasculature platform using iPSC-derived multiple cell types such as endothelial cells and vascular smooth muscle cells, systematically integrated with a microelectrode array and 2) Apply an artificial neural network model of organoid-vasculature intelligence. The outcomes of this research will yield: 1) an understanding of the role of each cell type in neurovascular function including vascular smooth muscle cell, 2) new insights into neuron-vasculature crosstalk with organoid-vascular smooth muscle cell-endothelial cell interactions, 3) blood flow regulation mechanism, 4) a machine learning model to control vascular function, and 5) innovative concepts showcasing the interface between organoid intelligence and machine learning for the first time. The broader impacts include developing an interdisciplinary training program to enhance education, diversity, and outreach at the interface between bioengineering, artificial intelligence, and biological sciences. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-10

Non-technical Abstract: Hybrid quantum system that combines magnetic materials with engineering-tailored microwave signals has been one of the most promising contenders in producing highly efficient platforms for quantum information transduction, processing and computing. This project aims at developing such frontier quantum research activities and expanding the quantum education and talent training capacity at North Carolina triad and triangle regions through close collaboration between North Carolina Agricultural and Technical State University (NCAT) and the University of North Carolina at Chapel Hill (UNC-CH). The project investigates optimized high-frequency signals produced collectively from microwave and magnetic samples, and efficient ways to process and interconvert them for electronic applications. The project includes a research and education plan to involve graduate and undergraduate researchers at both institutions, especially students from minority groups. The research activities are also complemented by rich outreach activities to engage with students from local high schools and community colleges, and dissemination plans to share the research findings with the public research community. Technical Abstract: Spin wave(magnon)-based hybrid quantum systems manifest several advantages including low energy loss, novel quantum states, small wavelength, and compatibility with memory architectures. In the past decade, different hybrid magnon-photon coupling systems have been proposed by interacting microwave cavities and transmission line resonators with ferrimagnetic materials to achieve quantum logics, sensing and signal transduction. In this project, an applied physics and engineering approach is taken to develop novel microwave photon-magnon coupling systems based on engineered subwavelength photon resonators and mode-tailored magnon cavities. The project addresses several intellectual challenges: 1) developing novel sub-wavelength microwave photon resonators and periodic waveguiding structures through photon mode engineering for hybrid magnonics systems and their characterization at ambient temperatures; 2) down-scaling of magnon wavelength through research of novel magnonic resonant cavity architectures and excitation of short wavelength magnon modes; 3) developing on-chip, non-reciprocal photon-magnon coupling systems for unidirectional signal transduction through both selectively polarized photon engineering and non-reciprocal properties of magnon-magnon coupling. The project strides a multi-disciplinary research path for quantum system development, and the insights gained from the research have the potential to catalyze advancements in various applications, including spin-based logic and memory devices, magnon-based signal processing, and quantum sensing and information processing. The project involves training, outreach, and educational activities that broadly engage students at the levels of graduates, undergraduates, and high schools. This project is co-funded by the Historically Black Colleges and Universities Undergraduate Program (HBCU-UP), which provides awards to strengthen STEM undergraduate education and research at HBCUs, and by the Directorate for Mathematical and Physical Sciences, Office of Strategic Initiatives. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-10

This project aims to serve the national interest by investigating the structural, systemic, and social barriers significantly impacting STEM students' participation in Innovation Competitions and Programs (ICPs). Student ICPs are central to college-level innovation and entrepreneurship ecosystems–fostering students' meaningful STEM-based collegiate experiences and enhancing their content-rich skill development, career readiness, and social connections. Despite these benefits, a noticeable discrepancy exists in ICP participation between STEM undergraduates who have been historically underrepresented and those from student groups dominating STEM fields. The project intends to advance equity in innovation ecosystems by uncovering the structural, systemic, and social barriers and their impact on students in an area that has been understudied. Additionally, the project proposes to provide outreach and training activities to help organizers, mentors, and advisors redesign ICPs to be more inclusive for all students. This transformation aims to enhance underrepresented STEM students' career readiness and participation in innovation and entrepreneurship ecosystems, promoting inclusivity and robust engagement. The goals of this collaborative research project between the Pennsylvania State University (PSU) and North Carolina Agricultural and Technical State University (NCAT) are threefold: (i) to advance the understanding of the structural, systemic, and social barriers that limit ICP participation of underrepresented student groups; (ii) to validate a theoretical framework based on the Situated Expectancy-Value Theory to explain the complex relations among these barriers and student perceptions and choices toward ICPs; and (iii) to test the efficacy of interventions designed to increase participation in ICPs by lowering these barriers through innovative scenario-based field experiments. The project employs mixed research methods to identify barriers to ICP participation of students from underrepresented groups, develop a theoretical model to explain how these barriers affect underrepresented STEM students, and test interventions to mitigate their negative impact. Data and resulting trends will be interpreted through a participatory meaning-making process that engages stakeholders with diverse perspectives and voices. 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 2024 · 2024-10

This project is an ExpandAI Capacity building pilot (CAP), which focuses on establishment of a robust Artificial Intelligence (AI) infrastructure at North Carolina Agricultural and Technical State University (NCAT) thereby enhancing the research capacity of the institution and facilitating AI-focused educational curriculum development and training. Towards this goal, the project will address the challenges in AI in the development of robust, explainable, fair, and privacy-preserving models for sensitive COVID-19 data. Five research programs are pursued to develop new AI models and tools. The research activities aim to broaden the participation of faculty members at NCAT and especially graduate and undergraduate students from underrepresented groups to enroll and explore degrees in different departments at NCAT, completing AI related thesis or dissertation through ExpandAI team collaboration. The project will also build community and new centers of excellence in AI where such activities were not previously well developed. This includes faculty participation in training and workshops will increase the number of faculty members using AI in research, development of more AI student researchers, and hosting of AI workshops featuring hands-on experiments to teach AI models based on experiments derived from this project’s research. This project expands the AI capacity at NCAT through interdisciplinary AI research, education, and workforce development. The interdisciplinary collaboration and cross-disciplinary AI research spans five use-inspired research thrusts centered on pandemic response patterned after the lessons learned in COVID-19 detection. Specifically, the project will (1) develop robust AI models based on federated knowledge distillation for COVID-19 detection with great generalization ability on new emergent dataset; (2) apply explainable AI (XAI) techniques based on SHAP and LIME to identify and visualize the important features of COVID-19 images that play a significant role in AI models for COVID-19 detection; (3) use the identified features of COVID-19 images from XAI techniques as an input to the proposed generative multi-modal language model to generate COVID-19 images to (4) address biased and fairness issues of AI models based on fairness regularization techniques for COVID-19 detection; and (5) apply differential privacy in federated learning frameworks to build secure AI models to protect the private information of individuals and local clients’ data. The proposed AI models are targeted for application in a broader range of biomedical image analysis research. The project’s educational capacity building focuses on new curricular materials and course modules, including undergraduate/graduate AI senior design classes to increase students’ understanding and use of AI, private AI, fairness of AI, explainable AI, and general interest in this important, emerging field. Faculty members will conduct and participate in training and workshops will increase the number of faculty members using AI in research, leading to more AI student researchers and increasing integration of AI research and education capacity at NCAT. The ExpandAI Program supports AI-powered education and workforce development, infrastructure and research at Minority Serving Institutions to strengthen and diversify U.S. research and education pathways and provide historically marginalized communities with new opportunities in STEM careers. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-10

Artificial Intelligence of Things (AIoT) and its applications are of paramount importance in the ongoing fourth industrial revolution, which is marked by the seamless fusion of physical and digital systems. This project aims to revolutionize Artificial Intelligence (AI) education by establishing a remotely accessible AIoT infrastructure, making state-of-the-art labs available to a broader student audience for immersive learning of AI with hands-on experiences. This project seeks to address several fundamental issues in AI education and workforce development. First, it will address the imbalance in AI education, which focuses on primarily building software skills, by integrating exposure to essential hardware components for a comprehensive understanding of the field. Second, it will address the resource constraints faced by many educational institutions, which limit their ability to offer state-of-the-art, hands-on AI learning experiences. Third, it will promote access for underserved minority groups, fostering diversity and innovation in the field. Through collaboration and resource sharing among participating universities, University of Florida, North Carolina A&T, and Prairie View A&M University, this project will directly impact over 30 instructors and 1,500 undergraduate students across institutions. Additionally, the project will leverage partnerships with industry leaders to align educational content with industry needs and standards, enhancing the relevance and applicability of AIoT education. From a broader perspective, the project will advance the AI field by developing foundational content through an integrated approach that highlights the interplay among AIoT components. It will also introduce immersive learning strategies to enhance student engagement and understanding, making AI education more inclusive and accessible. This inclusivity fosters diversity, creating a talent pool with varied perspectives that can drive innovation in the industry. Through the deployment of an innovative hardware-in-the-loop system, the project focuses on developing an immersive AIoT learning platform, which will be remotely accessible for students across institutes. This platform will include well-integrated modules on AIoT fundamentals, including security, connectivity, sensor design, and machine learning. The research will be guided by key questions aimed at enhancing AIoT technology, expanding its accessibility to a diverse student population, and investigating the educational impact of immersive technology in AIoT learning. To evaluate the impact of the immersive learning environment on student outcomes, relevant data will be collected and analyzed. Usability and feasibility studies will initially test the modules with qualitative analysis assessing their impact on learning and engagement. During classroom integration, student activity data will be analyzed using learning analytics and deep learning techniques to identify common challenges. Finally, the impact of the modules will be evaluated by comparing baseline data from unmodified courses with data from those incorporating AIoT modules. Paired t-tests will examine pre- and post-learning differences, while qualitative analysis of interview transcripts will offer supplementary insights. The project will help address workforce shortages, promote technological advancements, and help maintain global competitiveness in the evolving AI landscape by preparing a new generation of AI professionals. This project is funded by the Research on Innovative Technologies for Enhanced Learning (RITEL) program that supports early-stage exploratory research in emerging technologies for teaching and learning. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-09

This Ideas Lab project represents a pivotal stride towards inclusivity and advancement in semiconductor technology, leveraging a consortium of seven Historically Black Colleges and Universities (HBCUs). This partnership, led by Central State University, with Alabama A&M University, Coppin State University, Fayetteville State University, Hampton University, Meharry Medical College, and North Carolina A&T State University, is strategically designed to tackle persistent national challenges that limit access to advanced technological resources. By enhancing the research infrastructure and educational capabilities across these institutions, the project aligns with the U.S. National Science Foundation's mission to promote the progress of science and contribute to national prosperity and welfare for all. The project will support interdisciplinary research collaborations, and develop a technical workforce ready to address future technological challenges of national concern as addressed by the CHIPS and Science Act in 2022, thereby ensuring broad societal benefits and extending opportunities within the high-tech industry. Technically, the project is structured around four groundbreaking research thrusts: i) From Artificial Intelligence in Quantum Materials to Automation in Semiconductor Manufacturing, ii) Biomimetic Waste Remediation in Semiconductor Manufacturing, iii) Wide-bandgap Inorganic Semiconductors, and iv) Organic/Inorganic Semiconductor Integration and Packaging. These areas represent the forefront of innovation in semiconductor technology and provide a framework for a transformative educational model. By leveraging the combined strengths and resources of the seven participating HBCUs, the project aims to build a comprehensive network that enhances the collective research capacity and educational prowess of these institutions. Furthermore, the initiative will establish a sustainable online platform and repository to share resources and knowledge widely, ensuring the longevity and impact of the project extend well beyond the immediate academic community. This effort will further elevate the participating HBCUs and contribute significantly to the national and global advancement in semiconductor technologies. This project is co-funded by the NSF CISE Research Expansion Programs (CISE MSI), which provides research funding for institutional capacity building efforts for minority-serving institutions across the nation. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-09

Flooding is one of the most catastrophic and frequently occurring natural disasters, causing extensive damage to life, infrastructure, and the environment. The severity and frequency of floods have increased in recent years due to extreme weather events such as hurricanes and the expansion of urbanization. Accurate monitoring and mapping of flood extent and damage assessment in both spatial and temporal measurements are critical to assessing flood risk and developing comprehensive relief efforts immediately after flooding occurs. Remote sensing data, including both optical and radar data, have increasingly been used to develop flood mapping and modeling in a cost-effective and efficient manner, as establishing and maintaining rain and stream gauging stations can be costly. Remote sensing data are effective for determining the spatial extent of coastal and river flooding, providing essential information for delineating flood-affected areas, assessing damage to infrastructure such as roads and bridges, and feeding models that predict vulnerability to flooding in both inland and coastal areas. The recent proliferation of remote sensing platforms, such as satellites, aircraft, and UAVs, equipped with advanced sensor technologies like optical, SAR, and LiDAR, has enabled the systematic production of massive amounts of high spatial, spectral, and temporal data. This research develops a novel framework for automatically extracting spatio-temporal features using integrated data-driven analysis and generative models to create a comprehensive and detailed knowledge base of environmental dynamics for rapidly changing events like floods. Additionally, this project develops a language-guided self-supervision fusion method for heterogeneous remote sensing data for efficient damage assessment. The self-supervised multisource visual question answering framework allows real-time communication between human and robot agents for rapid response and recovery after natural disasters. This enables effective monitoring and notification of flood states, identification of affected or at-risk areas and people, which are essential for rescue and disaster operations. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-09

The Partnership for Research and Education in Materials (PREM) between the North Carolina Agricultural and Technical (NCAT) State University, an HBCU (Historically Black Colleges and Universities), and the University of California at San Diego (UCSD) Materials Research Science and Engineering Center (MRSEC) will use advanced materials research to rally students around the concept that new materials and research discoveries have the power to transform their communities as well as society at large. Advanced materials research and education will serve as a gateway to attract students to STEM fields at the graduate, undergraduate, and K-12 levels. At the core of this PREM effort is a synergistic partnership to establish an alliance of scientists, engineers, social scientists, and education experts. Consistent intellectual interaction and reciprocal exchange of NCAT and UCSD MRSEC faculty members, students, and postdoctoral fellows will reinforce this partnership. The NCAT-UCSD PREM will use advanced materials research and a seamlessly integrated education program to develop a network of relationships that reinforce and populate the PREM pathway and increase recruitment, retention, and degree attainment of students at all levels. The PREM pathway will provide both financial support and mentoring for over 50 undergraduate and 30 graduate students and trainees during its tenure at NCAT. This project is partially supported with co-funding from the HBCU-UP program from the Division of Equity for Excellence in STEM (EES) in the Directorate for STEM Education (EDU) and Sustainable Chemistry from the Office of Strategic Initiatives (OSI) in the Directorate for Mathematical and Physical Sciences (MPS). The NCAT-UCSD PREM is centered on three research thrusts focusing on Two-Dimensional (2D) Materials (Thrust 1), Plasmonic Materials (Thrust 2), and Biomaterials (Thrust 3). Thrust 1 will innovate novel 2D materials with wafer-scale integration using equilibrium and non-equilibrium synthesis techniques. This effort will develop new methods to control rationally and model defects in 2D materials, enabling the development of robust, flexible electronic devices. Thrust 2 will expand the horizon of conventional optics by investigating new types of plasmonic materials that enable the concentration and enhancement of light beyond the diffraction limit. Creating new knowledge regarding the plasmonic and photonic behavior of transition metal nitrides, including thin films and nanostructures and their transformation into oxynitrides, will be the focus of Thrust 2. Thrust 3 will elucidate the chemical reactions that give rise to biodegradable and bio-conjugated metals. The novelty of Thrust 3 is two-fold: (i) an increased understanding of how metal micro- and nano-particles biodegrade through systematic experiments and theoretical validation, and (ii) the synthesis of biocompatible polymer/nanoparticle composite materials to enable controlled metal degradation in complex biological environments. The research partnership across NCAT and UCSD will lead to new methods for theory-assisted materials design, functional and precisely tunable materials, and detailed insights into fundamental materials behavior that will open new avenues in technology, environmental sustainability, human health, and civil infrastructure. Together, the research and educational accomplishments that will be realized during the NCAT-UCSD PREM will result in establishing NCAT as an HBCU that is a globally recognized hub for materials research and education. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-09

Environmental sustainability is vital to human society. Climate change, water scarcity, and declining biodiversity are closely intertwined threats. At the same time, technological solutions require manufacturing capability and steady supplies of materials. The twin threats of declining planetary resilience and U.S. supply chain risks must be addressed by individuals and organizations. The actions and practices of business enterprises play an outsize role in solving these problems, and therefore, the goal of this project is to develop a graduate program to fulfill an area of national need: integrating environmental sustainability into business. This National Science Foundation Research Traineeship (NRT) award to Purdue University and North Carolina Agricultural and Technological State University (NC A&T) will enable a commerce system that practices innovation for environmental sustainability. The project anticipates training 22 NRT-funded trainees and approximately 70 additional graduate students at both master's and doctoral degree levels. Fields of study include environmental and ecological engineering, materials engineering, civil engineering, computer science, mechanical engineering, industrial engineering, and architectural engineering. The NRT training and research outcomes will contribute significantly to an advanced STEM workforce in an area of critical national need. The education and training activities are guided by the Engineering for One Planet (EOP) framework, and include four components: 1) an outcomes-based curriculum; 2) doctoral dissertation; 3) at least one non-academic research experience at a for-profit organization aligned with the student’s dissertation topic; 4) formal, explicit training in communication, ethics, and teamwork in a project-based course. The technical rigor of a doctoral degree in engineering uniquely accelerates innovation over the entire business cycle of products and services, enabling optimal trade-offs among performance, environmental impact, and cost. The project team consists of ~20 researchers at Purdue University and NC A&T, who will conduct convergent research organized into three pillars: 1) Greening the Digital Economy, 2) Decarbonizing Steel and Electricity, and 3) Transportation. Key outcomes from each pillar include reducing the environmental impacts of computing, including data centers, in the context of the digital economy; utilizing lifecycle assessment methods for considering the consequential impacts of macro energy system transitions in producing large quantities of low-carbon fuels and materials; and developing frameworks and modeling tools that integrate techno-economic analysis and supply chain risk evaluation into environmental assessments of emerging technologies such as electrification, shared mobility, and e-commerce. These methods will impact multiple disciplines and help change the business paradigm around environmental sustainability. The NSF Research Traineeship (NRT) Program is designed to encourage the development and implementation of bold, new potentially transformative models for STEM graduate education training. The program is dedicated to effective training of STEM graduate students in high priority interdisciplinary or convergent research areas through comprehensive traineeship models that are innovative, evidence-based, and aligned with changing workforce and research needs. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-09

Social and environmental sustainability is key to human and environmental well-being, both now and in the future. Engineers have an ethical responsibility to consider the social and environmental impact of their designs. Engineering education plays a vital role in shaping future engineers equipped to tackle sustainability challenges. Traditionally, sustainability has been taught merely as a skill or topic, but this approach is no longer sufficient. A systems approach is needed to integrate sustainability into engineering courses. This project will examine how integrating sustainability, particularly environmental and social sustainability, into Industrial and Systems Engineering (ISE) can enhance student learning and prepare them to become professional engineers capable of addressing sustainability challenges in complex system. It aligns well with the NSF-Lemelson Initiative on Environmental and Social Sustainability in Engineering Education. The specific objectives of this project include: (1) integrating sustainability into various ISE courses at North Carolina A&T State University (NCA&T) using the Engineering for One Planet (EOP) framework, (2) preparing course materials for sustainability following the design-based engineering learning (DBEL) model, (3) conducting research on student learning using data collected from the courses, including pre- and post-tests, log files, formative and summative assessments, and self-reports using questionnaires, and (4) developing and implementing a course material transferability plan to broaden the impact of this project beyond ISE and NCA&T. This project will integrate sustainability into ten critical ISE courses spanning from freshman to senior year. Students will progressively learn sustainability concepts, theories, and tools throughout their academic journey. Each course will be revised, and student learning outcomes from the EOP framework will be implemented. This project will adopt DBEL to teach students sustainability concepts and tools. Two application areas—manufacturing and hunger relief—will be used to develop design problems that address social and environmental sustainability. This project will also identify and address critical challenges in integrating sustainability into the ISE curriculum. The findings of this project will significantly contribute to the knowledge base regarding innovative pedagogies for integrating sustainability. It will address questions such as how EOP can be applied to ISE courses and how effective these applications are. The research outcomes will provide engineering educators with a roadmap to integrate sustainability into their curriculum. This research will provide substantial benefits to the environment and society by preparing the next generation of engineers to tackle environmental and social sustainability issues, thereby improving human and environmental well-being. By adopting a systems approach to integrating sustainability into the curriculum, graduates will become ethically responsible engineers, equipped with the necessary knowledge and skills to design for a better future planet. Given that African Americans are underrepresented in engineering and disproportionately affected by environmental and social impacts, this research will help cultivate a large number of African American engineers passionate about generating effective and innovative designs to address sustainability challenges and improve social and environmental justice. The results of this project will be disseminated through a web portal, where course materials, design problems, and project ideas will be shared with the engineering education 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.