University of Alabama Tuscaloosa

NSF Awards · FY 2025 · 2025-10

Offshore wind energy is an emerging, safety-critical sector facing persistent workforce challenges in building and sustaining a skilled workforce. These include limited early exposure to real-world work environments, costly and risky site access, unclear career pathways, and rapidly evolving digital and automation technologies. These challenges reduce interest, slow skill development, and make it harder to retain workers. By coupling cohort-based mentoring, cross-sector partnerships, participatory co-designed modules, micro-credentials tied to industry skills, and shared digital assets, the project will broaden participation and build a prepared, resilient, and technologically agile workforce. The resources and training model developed through this work can also be applied to other maritime and emerging technology fields with analogous workforce challenges, helping to grow the U.S. workforce and increase access to science, technology, engineering, and mathematics careers. This project will pilot a multi-phase offshore wind experiential learning framework led by an interdisciplinary team with complementary expertise from Northeastern University, the University of Alabama, and a range of industry, educational, and public sector partners. It will support community college and engineering students through low-cost, immersive at-home training that includes personalized coaching powered by artificial intelligence, followed by a supplemental certificate program and co-op or internship opportunities for students who demonstrate strong interest. The program features a structured, modular curriculum aligned with industry needs; a cohort-based model that fosters peer support, reflection, and iterative improvement; and continuous mentorship from the project team, industry professionals, and previous cohort participants to support both learning and career exploration. The program will track participants’ readiness, learning outcomes, and retention to support an iterative co-design process that refines training modules and credentials. This project offers scalable early exposure to offshore wind careers, technical skills, and worksite challenges, preparing students for success in the field. The work aligns with ExLENT’s mission by creating scalable and data-driven experiential learning pathways for emerging technologies and by openly sharing its tools and outcomes for broader impact. The ExLENT Program, supported by the NSF TIP and EDU Directorates, seeks to support experiential learning opportunities for individuals to increase their interest in and access to career pathways in emerging technology 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 · 2025-09

New advances in data science have paved the way for the development of transformational technologies that touch upon many aspects of daily lives, including health, transportation, and security, among others. Consequently, there is a critical need for today's students to gain an increased understanding of data science, building the workforce needed to continue data science innovations in service of society. The Sensing for Data Science (SensDS) project responds to this need through the design and development of an educational platform and instructional materials focused on the use of Cyber-Physical Human Systems for hands-on data science education. This interdisciplinary project takes place in urban and rural high schools in Alabama and uses gesture-driven radio frequency sensors to introduce 260 students and their teachers to data science concepts and processes. Specifically, the project will develop a web-based, interactive, machine learning platform, tailored to hybrid learning for grades 9-12, which will engage students with sensing + machine learning + control technology as they interact with virtual games and robots. The project involves activities that make visible how data moves across platforms in a system and then uses tools to make those patterns meaningful for students. SensDS follows a design-learn-play methodology for translating abstract data science concepts into a more easily understandable, tangible domain, enabling students to intuitively relate their movements with patterns observed in radio frequency sensor data. Using this platform and associated curricular materials, students will flexibly experience the entire data lifecycle and observe the impact of their design choices on the real-world behavior of actual systems. Connections among data, system design, and performance will be established via an easy-to-use visual user interface, allowing students to experiment, play and learn through interactive activities. This approach aims to spark student interest in science, technology, engineering, and mathematics, showing students how data science relates to everyday life and opening doors to future careers in artificial intelligence, data science, and related technologies. The primary goal of this project is to develop a web-based, interactive machine learning platform tailored and integrated into hybrid (on-site and online) curriculum for Grade 9-12 data science education, accessible to all students regardless of academic background, learning abilities, or prior knowledge. The project's specific objectives are to 1) develop integrated sensing, data science, and control functionalities within a visual user interface to make the entire data science pipeline accessible to Grade 9-12 students; 2) develop a data science instructional sequence that exploits student experimentation with Cyber-Physical Human Systems via the SensDS platform and addresses Grades 9-12 learning standards; and 3) implement and evaluate the sensor-based data science learning instructional sequence within in-person and hybrid learning environments. This project employs a learning-in-community methodology that engages high school teachers and students in participatory design research to develop technological and curricular resources to aid teachers in designing and integrating data science education into Grade 9-12 curricula, ensuring the materials are relevant, accessible, and effective. A key outcome of this project will be the development and dissemination of the open-source SensDS platform and relating curricula, which will contribute to the research and understanding of how new Cyber-Physical Human Systems technologies can enhance experiential data science education. This project is funded by the Innovative Technology Experiences for Students and Teachers (ITEST) program, which supports projects that build understandings of practices, program elements, contexts and processes contributing to increasing students' knowledge and interest in science, technology, engineering, and mathematics (STEM) and information and communication technology (ICT) 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 2025 · 2025-09

Advancing Professional Development of Transformative Elementary Mathematics Specialists (TEMS) addresses the critical need for improved mathematics education of elementary teachers and their students. The project will achieve this goal by preparing and supporting 60 Elementary Mathematics Specialists (EMSs) who are highly effective mathematics teachers and teacher leaders. The program provides these EMSs with professional development grounded in research-informed practices and focuses on refinement of an existing program. The project aims to develop ambitious, responsive mathematics instruction and to provide high-quality coaching to teacher candidates and novice teachers. With a renewed call for mathematics specialists to be in every elementary school, they need specialized research-based preparation to be transformative and impactful in their settings. The program aims to make needed contributions to the knowledge base on elementary mathematics specialist preparation through evaluating the effectiveness of the professional development program and iteratively leveraging informed advice and evidence-based feedback to improve the model. Dissemination of the refined program resources is a critical component and will include sharing materials for other elementary mathematics specialist program designers. The research and evaluation will include both formative and summative components. The formative research will help support networked improvement communities to identify measurable improvement aims and key drivers of intended change. The research team will test and refine change ideas, focusing on supporting EMSs in translating their learning into classroom practices, coaching teacher candidates or novice teachers, and integrating and scaling elementary mathematics specialists' work within individual schools. The summative evaluation will use a school-randomized experiment to evaluate the causal effect on teacher and student outcomes. Researchers will randomly assign 30 schools to either the TEMS group or a waitlist control condition. On average, two teachers of grades 3-5 in each school, and their 50-60 students, will participate. The evaluation will focus on refining the program logic model, assessing Fidelity of Implementation (FOI), measuring elementary mathematics specialists' effects on student and teacher outcomes, and supporting sustainability and scale-up in the post-experiment period. The research team will address the following questions: (1) What are the key components of TEMS and the corresponding FOI thresholds?; (2) Do the program development team and participants meet the FOI thresholds during the experiment? How does the program affect teachers' Mathematical Content Knowledge, Instructional Practices, Mathematics Pedagogical Beliefs, Coaching Practices and Beliefs, and Teacher Leadership?; (3) Do teacher, class, and student characteristics moderate impacts on teacher and student outcomes?; (4) Do impacts on intermediate teacher outcomes mediate subsequent impacts on student achievement?; and (5) Does impact on student achievement persist with a new student cohort 1 year after delivering the mathematics courses? Among treatment schools after the experiment, to what extent does the program scale in terms of sustainability, spread, depth, and ownership? 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

While artificial intelligence (AI) is becoming increasingly central to modern life, more educational opportunities on this topic are needed in pre-college settings, especially at the elementary level and in rural communities. Many students perceive AI as "magic," highlighting the need for developmentally appropriate instruction that demystifies how AI tools and processes work. Addressing this need, the three-year Researcher-Practitioner Partnership focuses on providing AI education for elementary students in rural contexts, a demographic facing significant opportunity gaps due to limited resources, professional development, and support networks for adopting new technologies. Motivated by a national push for enhanced AI literacy and a need for accessible curricula in rural regions, the project expands prior NSF-funded efforts by developing AI-themed children's literature aligned with the Five Big Ideas of AI from the NSF-funded AI4K12 Initiative. This literature serves as both instructional material and narrative entry points, making complex AI and machine learning (ML) concepts engaging and accessible to students in grades 4-6. Complementing the children's literature, the project adapts the Compose and Code (CoCo) computer science education platform into a universally designed digital learning environment called CoCo+AI to teach AI and ML concepts through storytelling, writing, and hands-on computational activities. A key innovation of this initiative is its tailored approach to rural contexts, providing developmentally appropriate children's literature and professional learning for elementary teachers. By demystifying AI through age-appropriate storytelling and interactive digital tools, the project aims to expand access to AI and ML learning experiences. Through direct classroom implementation, this mixed methods research will reach approximately 750 students and provide professional development for approximately 20 rural elementary teachers, equipping them with the tools and strategies needed to teach AI concepts. Key research questions guiding this work include: (1) How do students and teachers engage with AI-themed children's literature, and how does it influence their AI understanding?; (2) To what extent and in what ways does CoCo+AI professional development increase rural elementary teachers' self-efficacy for teaching AI concepts?; (3) In what ways and to what extent does the CoCo+AI platform and curriculum integrating storytelling, coding, and machine learning promote understanding of and interest in AI concepts among students in rural settings?; and (4) In what ways and to what extent does the CoCo+AI platform promote rural teachers' understanding of and interest in AI concepts? Contributions of this project include a comprehensive, integrated AI/ML and literacy curriculum; high-quality, technically accurate AI-themed children's literature; a refined, universally designed digital learning platform (CoCo+AI); and evidence-based professional development strategies. These deliverables will be freely disseminated through a project website, academic publications, and professional conferences, serving as a model for other under-resourced communities nationally and into the emerging knowledge base of integrated AI 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 2025 · 2025-09

Groundwater contamination poses a serious threat to public health, ecosystems, and water security. Cleaning up contaminated aquifers often requires injecting chemical solutions underground to degrade pollutants. However, the efficiency of these treatments is limited by difficulties in mixing the treatment chemicals with the contaminants in complex groundwater aquifer systems. This research will investigate a novel way for improving mixing by leveraging natural fluid movement induced by density variations. The goal is to develop more efficient, cost-effective, and ecologically friendly methods for remediating polluted groundwater. In addition to advancing scientific understanding, the project will provide graduate and postdoctoral training at two institutions, encourage collaboration between modeling and experimental research teams, and create new open-source groundwater modeling tools that can be used by academic researchers and environmental professionals. Outreach initiatives include incorporating research findings into curriculum, organizing summer student exchanges across universities, and collaborating with an industry partner to transfer innovative remediation technologies into practice. The technical goal of this project is to create and verify new ways for delivering dense, reactive treatment fluids into contaminated aquifers in a way that facilitates spontaneous mixing via hydrodynamic instabilities. The dense fluids will be fed through surface infiltration galleries and injection wells to promote convective fingering and increase interaction between treatment chemicals and contaminants. The research will use laboratory visualization experiments, mathematical modeling, and high-performance numerical simulations to study the behavior of multi-species reactive transport in both homogeneous and heterogeneous systems. A new open-source modeling tool will be developed by incorporating density-driven reactive transport features into a popular MODFLOW family software tool. The study team will also conduct uncertainty studies to assess the reliability and limitations of these methods in real-world scenarios. 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

Most deaths from breast cancer occur because the cancer becomes metastatic and spreads to one or more organs. Brain metastatic breast cancer (BMBC) is very aggressive. The prognosis for patients with brain metastases from breast cancer remains poor, often because of resistance to treatment. The environment of BMBC includes tumor cells, immune cells and extracellular matrix (ECM), which is the network of molecules secreted by cells that surround and support them within tissues. Hyaluronic acid (HA) is a primary component of the brain ECM. The immune cells in the BMBC environment are mainly microglia (brain resident immune cells) and macrophages (from blood-derived monocytes). It is not known how these immune cells influence BMBC response to treatment. The goal of this project is to engineer an experimental model incorporating BMBC cells, human microglia, and macrophages as microtumors in HA hydrogels. The model will be used to study how microglia and macrophages influence BMBC cell characteristics and response to treatment. The model may also serve as a platform for the development of new treatment strategies for BMBC or be used for fundamental studies of neurological disorders where disease progression is influenced by immune cells. The project will contribute to training the next generation of the science and engineering workforce by engaging high school students and teachers, as well as undergraduate and graduate students in cancer bioengineering research and enhancing undergraduate and graduate education with new course modules focused on cancer-immune cell interactions. This project aims to develop a biomimetic three-dimensional engineered model incorporating innate immune cells (i.e., macrophages, microglia) to study drug resistance in BMBC. The model will be used to study how microglia/macrophages and their polarization state influence BMBC cell phenotype and therapy response. The research plan is organized under 3 specific aims. Aim 1 will develop and characterize the in vitro bioengineered model by incorporating BMBC cells, human microglia, and human macrophages as spheroids in co-/tri-culture using clinically relevant ratios in a biomimetic HA hydrogel. The engineered innate immune environments will be validated by assessing the expression of BMBC niche markers at the transcriptional and translational level. Aim 2 will elucidate the impact of microglia and macrophages (individually and in combination) as well as their polarization state on BMBC cell phenotype. Aim 3 will determine if the engineered tumor environments support resistance of BMBC cells to therapy, and if therapy resistance can be reversed by targeting the innate immune niche. The educational and outreach plans are integrated with research and include a “Scientist for a Day” program for high school students, a mentored cancer bioengineering interdisciplinary research experience for high school students and teachers (INTEREST) program, providing research opportunities to undergraduate students, and enhancing undergraduate and graduate education by incorporating topics such as cancer-immune cell interactions, and cancer drug resistance into the cancer bioengineering course developed by the principal investigator. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-09

With the support of the Chemical Synthesis (SYN) program in the Division of Chemistry Professor Kevin Shaughnessy of The University of Alabama is studying the synthesis of novel boron cluster compounds. Dodecahedral (12-vertex) and decahedral (10-vertex) boron clusters are interesting materials with potential applications as advanced materials and pharmaceuticals. The 10-vertex boron cluster provides unique properties, including high stability, low toxicity, and efficient electron conduction. A lack of effective synthetic approaches to these materials currently limits their applications, however. The proposed research will develop new methods for the chemical functionalization of the boron cluster to provide materials with applications in materials science, catalysis, and medicine. The work will provide undergraduate and graduate students with specialized training in boron cluster synthesis and characterization. The materials produced through this project will be studied in collaboration with other researchers to develop their uses as advanced materials. Outreach efforts associated with this project will expose middle and high school students to the science of catalysis to help develop the next generation of scientists. This research project will systematically study the metal-catalyzed functionalization of halo-closo-borates. The knowledge gained through these studies will allow rational design of catalyst systems for the synthesis of functional anionic boron cluster materials having carbon substituents attached to boron vertices. These methods will be applied to the synthesis of specific targets of interest for materials and pharmaceutical applications. Novel approaches to strongly electron-donating B-boranylphosphines will also be developed. These materials will provide ligands and organocatalysts with unique properties that are applicable to the development of new organic synthetic methodologies. 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

As artificial intelligence (AI) continues to advance and transform society, it is essential that researchers work in direct partnership with teachers to prepare students to understand the world in which they are growing up. Advancing this goal across K-12 education requires a clear understanding of how to introduce AI concepts to elementary school students and how to effectively support teachers in doing so. The PrimaryAI scale-up project advances foundational knowledge in K-12 AI education that leverages immersive problem-based learning pedagogies for upper elementary learners in grades 3 to 5. The project will reach over 5,000 upper elementary students and more than 60 teachers while expanding the research and implementations across multiple states. The project team will partner with teachers from rural communities in Alabama, Indiana, and North Carolina to engage their students in authentic AI-infused problem solving. This approach aims to foster students' interest in science, technology, engineering, and mathematics (STEM) and equip them with fundamental AI knowledge they will need to thrive in the future. The project will investigate key factors that influence successful scaling of an AI education curriculum across multiple state contexts. It will examine the interplay among teacher professional development, localized classroom adaptation, collaborative design methods, and student learning and interest. These elements are central to understanding the conditions for implementation and mechanisms that sustain and expand the use of AI curricula on a large scale in rural upper elementary classrooms. The project will address three primary research questions: (1) What AI concepts serve as entry points for rural teachers to integrate AI into instruction, considering local contexts and individual pathways? (2) What are the impacts on student outcomes for learning, engagement, and STEM interest across rural contexts? and (3) How do local factors in each state's rural context influence the reception, implementation, and outcomes of PrimaryAI? Research questions will be addressed using multiple data sources as part of Design-Based Implementation Research (DBIR) (Fishman & Penuel, 2018). Pre-and post-tests will be used to assess impacts on student learning and interest. The research team has developed assessments for AI concepts, AI planning, computer vision, and machine learning (Chakraburty et al.,2023). To address the first question, the team will collaborate with teachers from rural communities in Alabama, Indiana, and North Carolina. The team will document ongoing collaborative discussions, professional learning processes, teacher designs, and plans for implementation. For the second question, the project will conduct comprehensive analyses of student outcomes using pre-post assessments of AI knowledge and skills, student engagement, STEM interests, observations of student interactions, and student interviews. Additionally, a cross-case analyses to explore commonalities and differences across various rural contexts and implementations will be conducted. To address the third question, a detailed case studies within each rural community to understand local factors such as pedagogical goals, student interests, community priorities, and educational policies is planned. Outcomes will include locally-contextualized versions of the PrimaryAI curriculum, comprehensive teacher professional development guides, case studies that detail successful strategies and challenges, and recommendations for scalability. Ultimately, the project will advance understanding of effective practices and approaches for integrating AI education into rural elementary classrooms. This project is funded by the Innovative Technology Experiences for Students and Teachers (ITEST) program, which supports projects that build understandings of practices, program elements, contexts, and processes contributing to increasing students' knowledge and interest in science, technology, engineering, and mathematics (STEM) and information and communication technology (ICT) 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 2025 · 2025-09

The conduct of nearly all modern scientific research depends on software, yet the systems through which research software is developed, shared, and deployed—its supply chain—remain vulnerable to cyber threats. These Research Software Supply Chains (RSSCs) are complex networks of tools, libraries, collaborators, and institutional processes, and they form a critical foundation for the U.S. research enterprise. However, there is no shared understanding of what these supply chains look like or how to protect them. This project will initiate a coordinated planning effort, called CROSS (Community around Securing the Research Software Supply Chain), to bring together researchers, research software engineers, and government stakeholders to identify and mitigate risks to RSSC security. Through community workshops, empirical studies, and a comprehensive review of existing knowledge, this effort will produce a roadmap for securing the RSSC—helping to safeguard the integrity of scientific knowledge, promote national security, and support the development of a resilient research ecosystem. The project will also engage undergraduate students at Purdue and Loyola, supporting workforce development in cybersecurity and research software engineering. This planning project will develop foundational knowledge to guide future efforts in securing the research software supply chain. The research team will (1) conduct a systematic literature review to synthesize current knowledge into a conceptual model of the RSSC and its security threats; (2) empirically measure the security posture of real-world research software projects and their dependencies, using datasets provided by national laboratory collaborators and applying a range of software and security metrics; and (3) convene workshops with research software engineers and scientific collaborators to capture practitioner insights and build community consensus. The findings will be integrated into a unified system model and threat model, guided by the STAMP (System-Theoretic Accident Model and Process) and TOE (Technology–Organization–Environment) frameworks, and will culminate in a strategic report for the NSF’s Research on Research Security (RoRS) program. This work will support the development of new security interventions and lay the groundwork for future collaborative research to protect the software that underpins scientific innovation. 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 Major Research Instrumentation (MRI) award supports the acquisition of an advanced scanning probe microscope (SPM) that enables co-localized nano-spectroscopic imaging capabilities integrating Atomic Force Microscopy (AFM)-Raman, Tip-Enhanced Raman (TERS), and Tip-Enhanced Photoluminescence (TEPL). Such capabilities were not previously available in the Southeastern region. Integrating this advanced SPM into the Core Analytical Facility of the Alabama Materials Institute (AMI) at the University of Alabama will establish the region's first major shared-user facility with high-resolution SPM/Raman/PL analytical capabilities. The system is a major enhancement and complement to the materials facilities, enabling new multi-investigator, multidisciplinary, and multi-campus collaborative research across institutions in the Southeast, including Historically Black Colleges and Universities, and more remote institutions. The new instrument also enhanced the teaching and training of undergraduate and graduate students in advanced materials characterization through a specially developed course. Additionally, data produced by the instrument is utilized in a Machine Learning course. The instrument’s capabilities will be highlighted in annual SPM/TERS/TEPL User Workshops and will support outreach efforts to K-12 students at local high schools. This project is jointly funded by the Division of Materials Research (DMR) and the Established Program to Stimulate Competitive Research (EPSCoR). The LabRAM Odyssey nano-spectroscopic imaging system combines the nanoscale spatial resolution of scanning probe microscope (SPM) and the high chemical specificity of Raman and photoluminescence (PL) spectroscopy. By delivering unique co-localized microscopic and spectroscopic capabilities, the system enables detailed correlated nanoscale analysis of various materials, including electronic, photonic, proteins, polymers, biological, and extreme environment materials. The instrument provides unambiguous insights into the nanoscale structure and heterogeneity of two-dimensional (2D) heterointerfaces. It enables the quantification of local spectroscopy features in semiconductor photocatalysts. It delivers experimental proof of the core-shell structure of colloidal quantum dots based on Cd3(As/P)2, a potential alternative II-V near-IR semiconductor for single-photon emitters. It offers insights into the relationship between local chemical stoichiometry and photocarrier distribution in polycrystalline, low-dimensional non-cubic chalcogenide light absorbers. It enables previously unattainable insights into the mechanisms of lipid-induced amyloid aggregation, a pathological hallmark of a broad class of diseases and neurodegenerative disorders such as Alzheimer’s. It grants access to the nanoscale spatial and optical spectral features associated with the intermolecular interactions between individual isolated chains and aggregates of conjugated polymers. It facilitates the discovery and characterization of unique chemical and phase transformations in geologic materials exposed to the transient, high temperatures of lightning strikes. It yields better understanding of the complex interplay between process, structure, and properties in ultrahigh temperature carbide fibers grown by chemical vapor deposition. 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

Electrostatic free energy (EFE) calculations are indispensable for the quantitative analysis of biological processes, as they characterize the polar interactions between charged biomolecules such as proteins, DNA and RNA, and their surrounding ionic solvent environments. As one of the most widely used implicit solvent models, the Poisson-Boltzmann (PB) model computes EFE as the difference between PB energies of biomolecules in two reference states, typically vacuum and solvent. The success of classical PB theory relies on two restrictive assumptions: (i) biomolecules remain rigid and do not change shape when moving between states, and (ii) identical computational procedures are used for both states. However, under physiological conditions, proteins are inherently flexible and undergo conformational changes during solvation and binding. This project aims to overcome the rigidity limitation by introducing a generalized PB theory that accommodates non-rigid biomolecular structures. This enables PB models to handle shape changes in key biological processes such as solvation and binding. The proposed algorithms will be implemented in DelPhi, an open-source PB package, and they will be applied to other popular PB solvers in the form of post-processing patches. The new computational tools will be distributed free of charge to academic users, making them accessible to the broader biological research community. In addition, this project will provide interdisciplinary research and training opportunities for undergraduate and graduate students in biophysical modeling, computation and mathematical analysis. Outreach and dissemination activities will be developed to engage broader audiences and foster public understanding of how computational science contributes to human health and biomedical innovation. The limitations of the classic PB theory essentially stem from the fact that EFE computed by the PB model involves self-energy terms, that is, the singular charge at an atom center will interact with the potential induced by itself, which yields infinite energy values at each atom center. The rigidity assumption and identical numerical discretizations in two reference states enables the cancellation of these infinitely large self-energies terms between states. This project introduces a novel partition of the PB energy functional to separate the singular self-energies from the regular parts, so that the self-energy difference due to conformational changes can be analytically formulated and is free of singularities in subsequent numerical computation. This approach applies to both sharp-interface and diffuse-interface PB models, and it employs distinct strategies for regularization and non-regularization approaches in the numerical treatment of the PB equation. The proposed PB theory represents the first computational method capable of accurate EFE prediction for non-rigid biomolecules. This innovation provides a more precise physical modeling of solvation and binding processes and yields more accurate polar solvation and binding energy predictions. The proposed research will have a broader impact to the field of molecular biosciences, by providing improved binding energy estimations to several PB applications in drug design and mutation predictions. This project is jointly funded by the Mathematical Biology Program in the Division of Mathematical Sciences and the Chemical Theory, Models and Computational Methods Program in the Division of Chemistry. 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 award will enable the development of advanced cyberinfrastructure to digitize and integrate over one million dragonfly and damselfly (Odonata) specimens from major natural history collections across the United States. The project, called Di-ODE (Digital Integration of Odonata), will create a unified, publicly accessible digital platform through Odonata Central, linking high-resolution specimen images with critical data such as collection localities and species identifications. This initiative will expand access to these important biological resources for scientists, educators, students, and the public. Di-ODE includes robust training programs to build skills in biodiversity data science and collections digitization. The project will enhance STEM education, promote data literacy, and engage community scientists, contributing to environmental awareness and scientific literacy. Through outreach and digital accessibility, Di-ODE will strengthen efforts to monitor environmental change and inform freshwater conservation across the globe. The project will transform how Odonata biodiversity data are accessed and analyzed by the research community. Dragonflies and damselflies are ecologically sensitive indicators of freshwater health and have been the focus of major studies in evolutionary biology, systematics, and biogeography. However, much of the valuable specimen data remains locked in poorly accessible physical collections. Di-ODE addresses this gap by creating efficient, scalable digitization workflows, using customized optical character recognition (OCR), advanced georeferencing, and data management tools. The resulting infrastructure will enable novel research in global change biology, comparative ecology, and phylogenetics. By improving data quality and access, Di-ODE will foster cross-disciplinary collaboration and provide a model for digitizing and mobilizing data from other invertebrate groups. 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

Developing a science and engineering workforce trained in research software development is essential for advancing research in scientific and engineering domains. It is important that the researchers who create the software that will drive tomorrow’s critical scientific discoveries receive training in appropriate software engineering disciplines, to not only make them more productive, but also to make the software they create more reliable, sustainable, and secure. This project (INTERSECT 2.0) addresses the knowledge gap by providing targeted training on research software development and engineering best practices to research software developers who already possess an intermediate knowledge of software development. INTERSECT 2.0 training activities bring together Research Software Engineer (RSE) instructors from multiple U.S. institutions to leverage the growing RSE community’s capabilities, knowledge, and expertise. The project continually refines, updates, and extends the training material to ensure that it stays current with best practices. An open-source platform is used to disseminate the training material, to allow for continued engagement with and reuse of that material across the RSE-trainer community. The focus on community fosters and connects RSE practitioner-instructors from across the country, to not only take advantage of the knowledge of multiple institutions but also to build and strengthen the community of this group of software professionals, providing these professionals increased access to peer support, resource and knowledge sharing, networking opportunities, and continued growth of an RSE pipeline – all crucial elements to address the cyberinfrastructure research workforce development needs of the future. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-08

This project aims to serve the national interest by organizing coordinated multi-site replication studies on open computing education research questions. Research relies on the ability to replicate studies to understand the limits and applicability of research findings and to build confidence in results. Replication studies are especially important in computing education research where results are drawn from human subjects studying computing. The goal of this IUSE:EDU level two Institutional and Community Transformation project is to create cohorts of researchers to design replication packages for rigorous research that will support cross-study meta-analysis, build fundamental knowledge on key topics, and provide the foundation for computing education research theory building. The project will involve approximately ten core replication designers and forty replication participants impacting thousands of students across the United States. A replication design team will be convened to create replication packages that address research goals and questions of interest to the computing education research community. A project team will then be formed to recruit a series of replication participants who will run the study in their context using the replication packages produced by the replication design team. Members of the computing education research community will be invited to run the replications in their classrooms, contributing to the overall investigation into the topic. Results of these studies will address common research questions and lay the foundation for computing-specific educational theory. The results of the coordinated, multi-site replication studies will provide deeper insights into important computing education research topics and will provide a better understanding of the impacts of various contextual factors on improving student learning and success in computing. The project evaluation will consist of formative and summative assessments of the replication process and objective measures such as publications and citations. The NSF IUSE: EDU Program supports research and development projects to improve the effectiveness of STEM education for all students. Through the Institutional and Community Transformation track, the program supports efforts to transform and improve STEM education across institutions of higher education and disciplinary communities.  This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-08

This project provides international experiences for undergraduate electrical and computer engineering students from the University of Alabama to research advanced electronics in the Czech Republic (Czechia). The goal is to help prepare a new generation of engineers who are ready to meet the growing demands of the U.S. semiconductor and microelectronics industry. Through hands-on experience in designing and building circuits, students will gain valuable technical skills while also learning to work as a member of an international team. By living and working in Czechia for 12-weeks, students will gain a deeper understanding of different cultures and global collaboration in engineering. All research conducted through this IRES project has a shared focus on fractional-order circuits and systems - a cutting-edge area in electronics. Students investigate challenges such as modeling and fabricating fractional-order components, designing new types of circuits for signal processing using fractional-order components, and using fractional-order electrical models to analyze crops and liquids for food safety. Thes research experiences help increase student interest to pursue careers in circuits and systems, increase science and interpersonal communication skills, and increase intercultural maturity through living abroad. Students are trained through intensive research projects at the Brno University of Technology (BUT) in Czechia, a global leader in fractional-order circuits and systems research. These projects require both theoretical understanding and practical skills in electronics design, simulation, fabrication, and validation. With support from expert mentors at BUT, students contribute to advancing fractional-order systems while gaining skills directly aligned with future U.S. workforce needs in microelectronics. 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

Complex physical systems, such as those found in material science, fluid dynamics, and life science, often involve intricate interactions between energy-conserving mechanisms and entropy-generating processes. These systems are usually modeled by reversible-irreversible thermodynamically consistent partial differential equations (RITC-PDEs). However, solving these RITC-PDEs accurately and efficiently over a long time period remains computationally challenging due to their non-equilibrium nature and the need to preserve thermodynamic properties at the discrete level. This project will develop a general computational framework to solve RITC-PDEs while maintaining their energy conservation and nonnegative entropy production for long-time dynamic simulations and predictions. The proposed research will lead to a unified computational framework for studying multiscale non-equilibrium phenomena across various scientific disciplines, along with open-source tools for the broader research community. The project will support STEM education through outreach to K-12 students and educators. Reversible-irreversible thermodynamically consistent (RITC) PDEs arise from the principles of thermodynamics. They are essential for modeling coupled processes involving energy-conserving(reversible, dispersive) and entropy-producing (irreversible, dissipative) dynamics. This project will develop high-order, accurate, efficient, easy-to-implement, and structure-preserving numerical schemes that maintain thermodynamic properties for RITC-PDEs. Specifically, the project will (a) design innovative structure-preserving discretization methods, including decoupled and high-order schemes, to accurately simulate complex RITC systems while maintaining their inherent thermodynamic consistency; (b) develop advanced time-stepping algorithms that ensure efficiency and accuracy for multiscale dynamics, by leveraging system properties such as energy budgets, entropy production, and topological changes to guide adaptive time step sizes; (c) construct a structure-preserving model order reduction framework that can generate reliable surrogate models for large-scale RITC systems; and (d) implement an open-source software package for simulating RITC-PDEs with GPU acceleration, adaptive meshing, and user-defined model inputs. Ultimately, the proposed research will lead to a unified computational framework to study multiscale non-equilibrium phenomena and contribute to the fields of numerical analysis, scientific computing, material science, fluid mechanics, and interdisciplinary modeling. 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

One of the major challenges in water treatment and desalination is scaling—the buildup of insoluble mineral deposits that reduce the efficiency and lifespan of treatment systems, much like limescale in household appliances. Current methods to mitigate scale formation often rely on chemical additives such as softeners and antiscalants, which generate chemical waste and consume large volumes of water. This collaborative project will develop new classes of vanadium oxide layered materials for selective removal of magnesium ions, the dominant scale-forming species in the water, using electrochemical methods for energy-efficient water pre-treatment. The technology is expected to surpass the performance and sustainability challenges of current water pretreatment technologies. The project will offer cross-disciplinary training opportunities for students in chemical engineering, materials science, and computational chemistry. The investigators will actively recruit and mentor students to foster a research environment that encourages collaboration and innovation in addressing global water challenges. This project will explore electrochemical pretreatment strategies to remove divalent scale-forming cations (SFCs), such as magnesium, using intercalative electrode materials, including vanadium oxides with a controlled interlayer distance for ion transport and a tailored electrode local structure to encourage Mg²⁺ ion intercalation against competing Na⁺ ions. The team will integrate material synthesis, electrochemical testing, advanced atomistic simulations, and in situ synchrotron X-ray techniques to test the following three hypotheses: (i) narrow interlayer spacing of α-V2O5 (<5 Å) will selectively uptake small-size Mg²⁺ over large-size Na+, (ii) introducing electronegative dopant to α-V2O5 will weaken the lattice oxygen charge to alleviate the electrostatic interaction between electrode and Mg²⁺ ions to favor Mg²⁺ transport; and (iii) disordered local structures of the host materials (e.g., turbostratic disorder) will decrease Mg²⁺ transport barrier. The research outcomes hold promise for the electrochemical removal of Mg²⁺ from water using high-capacity intercalative battery electrode materials, which could address longstanding challenges in selectivity and structural stability under aqueous conditions, leading to a sustainable and energy-efficient electrochemical water pretreatment system. 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

U.S. households spent on average 5.6% of their income on energy, with 40% of energy losses through the envelope. Homes with deteriorated insulation and aging structure face disproportionately high energy costs. Retrofitting and weatherization are key strategies for enhancing building performance and resilience against environmental stress. However, traditional energy audits and retrofitting methods are labor-intensive, costly, and often inaccessible to the populations who need them most. This project addresses these challenges by developing an advanced, autonomous drone system equipped with thermal imaging and Artificial Intelligence (AI) that can quickly and accurately detect energy loss in building envelopes. The drone system will work in coordination with Digital Twin (DT) – an interactive, informative 3D model that helps visualize and plan retrofitting strategies at multiple scales. By partnering with local community organizations, the project ensures that these technologies are not only technically effective but also socially acceptable, accessible, and impactful. Through interdisciplinary collaboration in building science, robotics, AI, DT, and community-based research, the project aims to transform weatherization practices and promote energy retrofitting through high-tech innovation. This project proposes integrating embodied AI and DT into drone systems to formulate an autonomous, intelligent robotic system that streamlines weatherization assessments and energy retrofitting planning in residential buildings for multi-stakeholders. The project will assess technical, regulatory, and community requirements to prepare for the development and implementation of this system, focusing on three key components: 1) Embodied AI-Driven Autonomous Sensing Drone: Study embodied AI, edge/cloud computing, and human-robot interaction methods to enable real-time, autonomous, and intelligent navigation of drones within complex building environments. 2) DT-Enabled Interactive Retrofitting Analysis: Explore bi-directionally interaction between drones and DT to support precise on-site surveying for retrofitting analysis, design, and planning. 3) Socio-Technical Feasibility and Scalability: Identify and address legal, ethical, and economic considerations to ensure that the deployment of the DT and AI-infused drone systems is feasible, privacy-conscious, and scalable across various community settings. The project involves co-design sessions, community focus groups, and pilot demonstrations to ensure that the technologies are aligned with the needs of stakeholders such as weatherization officials, community agencies, and homeowners. The research team will evaluate usability, scalability, and interpretability to guide future full-scale implementation. This interdisciplinary initiative spans civil engineering, robotics, computer science, and community engagement, aiming to produce transformative, deployable solutions that advance residential building’s sustainability and resilience. 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

Rare earth elements are important in many engineered applications, including in catalysis. This project will enable low-cost production of sustainable aviation fuel from renewable feedstocks using catalysts. This requires upgrading renewable feedstocks (e.g., ethanol produced from corn stover) over rare earth element-based catalysts. Improving the selectivity of these catalysts will allow for low-cost production of these fuels. This will be accomplished in this project through the design of multifunctional rare earth catalysts. Educational activities will increase achievement and retention in graduate chemical engineering programs, integrate experimental catalysis research with undergraduate and graduate chemical engineering education, and broaden awareness of chemical engineering among K-12 students. Rare earth elements in zeolites are an emerging class of materials with broad applications. Despite reports of their use in various chemistries, little is known regarding the local structure of these elements when they are present in the framework of zeolites. This hinders efforts to probe their functions in catalysis at the molecular level and to understand performance of bifunctional materials containing these elements. This project will advance the state of the art by developing the first experimental evidence, through novel spectroscopic studies combined with computational studies, of the structure of yttrium atoms in zeolites. Kinetic and mechanistic studies of a model Meerwein-Pondorff-Verley reduction reaction (an essential step in the Lebedev reaction that converts ethanol to butadiene) will correlate the local structure of these atoms to their function as catalysts. The role of volumetric active site concentration and zeolite crystal size on product selectivity will be determined. The studies included in this project will advance knowledge on industrially relevant routes from ethanol to sustainable aviation fuel. Research outputs include the development of in situ infrared methods for quantification of adsorbed intermediates at rare earth elements, novel solid state nuclear magnetic resonance methods, kinetic and mechanistic insight into alcohol upgrading chemistries, and catalyst design strategies towards production of higher olefins in ethanol to olefins catalysis. The project will benefit undergraduate and graduate STEM programs through integration of experimental catalysis research with undergraduate and graduate chemical engineering education. The project will train graduate student and undergraduate student researchers in synthesis, characterization, and testing of catalysts. Student researchers will also participate in interdisciplinary design projects using new experimental apparatuses featuring in situ IR methods developed during this project. These research efforts will be integrated with educational activities across the state of Alabama. A new math and coding bootcamp will be developed to boost achievement and retention of chemical and biological engineering graduate students, as well as students from nearby colleges. The development of new scientific demonstrations and lesson plans for educators throughout Alabama will be disseminated via the Scientific Research and Education Network (SCiREN). Demonstrations and presentations regarding academic programs and research opportunities in chemical engineering will increase scientific literacy among local K-12 students. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-08

This project lies in the field of Commutative Algebra, which provides the local language of Algebraic Geometry, offering a powerful framework for studying local solutions to systems of polynomial equations, as well as more general systems that can be approximated by such equations. This project includes opportunities for students, and outreach to local and academic communities. The field of Commutative Algebra is foundational not only to pure mathematics but also to a wide range of applied disciplines, including computer science, physics, and engineering. It is closely intertwined with other areas of mathematics, such as Number Theory, Complex Analysis, Topology, Geometry, and Representation Theory. The Principal Investigator specializes in singularity theory, a branch of Commutative Algebra concerned with the behavior of algebraic objects that fail to be smooth. Unlike smooth structures, where the tools of differential calculus apply directly, singularities require alternative methods. One such method involves Rees valuations, which generalize the concept of tangent spaces and provide an algebraic mechanism for understanding local behavior near singular points. These valuations correspond to ideal blowups, a geometric construction central to singularity theory. This project will focus on the role of Rees valuations in understanding singularities, especially in settings of positive characteristic, an arithmetic context where classical tools of differentials often break down. The Principal Investigator will investigate how concepts like multiplicity, valuation theory, and S2-ification (a type of algebraic approximation) interact in this setting. The work aims to address fundamental conjectures in the field, including the Weak Implies Strong Conjecture and the LC Conjecture, which are central to understanding the behavior of singularities in positive characteristic. A key component of the project is the study of symbolic powers of ideals, particularly in singular rings. The Principal Investigator will develop and apply new techniques to advance the Uniform Symbolic Topology Property, which connects the algebraic properties of ideals with the underlying geometry of the space they define. Ultimately, the goal is to extend known theorems from smooth settings to singular ones, thereby deepening our understanding of singularities and their applications across mathematics and the 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 2025 · 2025-07

The National Science Foundation (NSF) EPSCoR Graduate Fellowship Program (EGFP) supports EGFP-designated institutions and programs in EPSCoR jurisdictions by providing funding for graduate fellowships for new or continuing EGFP-eligible applicants. EGFP awards support a total of three years of stipend and associated cost-of-education (COE) allowance for each NSF EPSCoR Graduate Fellow. This award at the University of Alabama Tuscaloosa provides support for 10 EPSCoR Graduate Fellows whose research will align with the unique goals and programs supported by the Directorate for Engineering (ENG) and Directorate for Mathematical and Physical Sciences (MPS). The central theme of the project revolves around engineering and materials science addressing both experimental as well as computational research. Through this fellowship program, student participants will have multiple opportunities for research and educational interactions that will strengthen each Fellow's scientific impact. Apart from an intensive research experience, Fellows will have the opportunity to network and interact with partners from industry and national laboratories. In this project, Fellows will participate in graduate studies in engineering and science fields related to plastics degradation, laser chemical vapor deposition processes, high voltage electrochemical synthesis, nanoparticle catalysis, machine learning and computational design of ceramic materials, bioprinting, biosensing, and porous storage materials. Fellows will be recruited nationally and mentored by a team of well-qualified and dedicated faculty affiliated with the Alabama Materials Institute (AMI) at the University of Alabama. This project has the potential to increase the number of skilled scientists and engineers in areas of relevance to energy, defense, transportation, and human health. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-07

With the support of the Chemical Catalysis Program in the Division of Chemistry, Professor Elizabeth Papish of the University of Alabama is studying the development of nickel catalysts for the conversion of carbon dioxide to fuel precursors and organic building blocks for pharmaceutical products. Carbon dioxide is readily available from fossil fuel combustion, but it is challenging to use in chemical reactions. New nickel complexes have been discovered with record-setting, long-lived excited states, which serve to capture solar energy and enable new catalytic transformations with carbon dioxide. Current work ongoing in the Papish group includes 1) systematically modifying new nickel and cobalt catalysts to improve their activity for reactions with carbon dioxide, 2) studying the reactivity of these molecules using spectroscopy, crystallography and other methods to understand and visualize how the molecules interact to lead to a lower energy pathway, and 3) testing new types of reactivity to insert carbon dioxide into organic molecules and thereby form valuable products which can lead to fuels, pharmaceutical products, and other high value chemicals. This project is being used to train graduate and undergraduate students at the University of Alabama. There is an urgent need to develop better catalysts to use abundant carbon dioxide sources for commodity chemical synthesis. Specifically, Prof. Papish and her research team are determining how ligand structure-function relationships of nickel metal-organic complexes influence the lifetime of their excited state to improve their reactivity. Mechanistic studies are further being used to elucidate how the lifetime of the nickel catalyst excited state influences reactivity between carbon dioxide and organic substrates. These results of these activities are then being used to guide the design of new organometallic photochemical catalysts for carbon dioxide reduction and organophotoredox chemistry. The results of this work are being publicized by presentations at conferences and are reported in scientific journal articles. Prof. Papish and her group also use catalysis research to provide lessons in science ethics to undergraduate and graduate students. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-07

The project focuses on answering the question "How to divide a collection of objects into two groups such that the objects within each group can interact while the objects from different groups rarely interact?" As is well known, the past two decades have witnessed an exponential increase in the use of social or other networks, which consist of objects and connections between them, and carry a lot of information. These networks can be used to detect different groups of objects, each with similar characteristics or preferences. In materials science, advanced engineering alloys, such as steels and high-entropy alloys, may be thought of as 3-dimensional graphs with atoms residing at the nodes of a graph and its edges as bonds. These materials are often polycrystalline, and sudden, unexpected failures occur frequently in the form of fractures along crystal boundaries. Such failures are extremely costly, resulting in, for example, oil and gas spills and even bridge collapses. All the above problems can be cast as balanced graph cut problems, which are very challenging. In the literature, many existing approaches resort to approximate solutions. However, these solutions may differ significantly from the optimal ones. This proposal mainly focuses on the development of efficient and reliable methods for finding optimal graph cuts, which could significantly promote their applications to practical problems in the fields of materials science and social sciences. Partitioning a large data set into a prescribed number of subsets is a fundamental problem in machine learning, and it assumes wide applications in fields such as social networks, computer science, chemical engineering, and materials science. To conduct the partition, different balanced graph cuts have been proposed, including the Cheeger cut, the ratio cut, and the normalized cut. As one of the most important balanced graph cuts, the Cheeger cut is a challenging NP-hard problem. Existing approaches only provide approximate solutions. Recently, a novel nonlinear spectral graph theory was developed, and finding the Cheeger cut amounts to solving a constrained optimization problem with a non-smooth objective function over a non-convex set that consists of many different-dimensional simplex cells. The number of these cells is an exponential function of the number of vertices of a graph. Therefore, this raises another tough optimization problem. The proposed research in this project consists of three parts: 1) developing novel efficient and reliable numerical algorithms to solve the above-mentioned problem by using optimization techniques; 2) extending the existing nonlinear spectral graph theory to weighted graphs to enrich the theory and significantly expand its applications to real problems; 3) applying the proposed methods to tackle practical problems in metallurgical engineering and materials science. The projects discussed in this proposal also offer training opportunities for graduate and undergraduate students. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-06

This project aims to serve the national interest by improving curricula in undergraduate computing education to prepare students for the challenges of understanding and managing Technical Debt (TD) in software systems. Technical debt arises when software developers make technical compromises that may bring short-term benefits but result in lower software quality in the long term, often leading to challenges in maintaining and evolving software. By integrating technical debt concepts into computing curricula at multiple levels, the project intends to contribute to building a strong foundation for students to develop high quality software, and prepare them to become part of a more effective and competitive STEM workforce. The project plans to develop an innovative inquiry-based learning tool, called TD-Tutor (Technical Debt Tutor), to help students recognize, evaluate, and manage technical debt. TD-Tutor will enhance outcomes for student populations from different backgrounds and types of institutions, aligning with NSF’s mission to advance STEM education and workforce development. TD-Tutor will be implemented, used, and evaluated at three curriculum levels: introductory programming, mid- level software engineering, and senior level decision-making courses. The tool will feature annotated examples, interactive exercises, and conceptual feedback to guide student learning, and will incorporate guided inquiry and spiral learning approaches. Pre- and post-evaluations will assess the tool’s impact on student learning, skill development, and readiness to manage software quality. 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 · 2025-06

This research examines how healthcare practices and economic conditions affect patient access to assisted reproductive technologies and how family building options are impacted by changing social and regulatory environments. Specifically, this doctoral dissertation examines and compares how fertility treatment governance shapes healthcare delivery and patient experiences through an investigation of the sociocultural and economic factors influencing donor assisted reproductive technologies in two contexts: one where access to fertility treatment has been in flux, and another where health services are being curtailed by market-driven healthcare reforms. The research asks what cultural and socioeconomic variables influence access to these technologies, how prospective parents are navigating changing landscapes of access, and how this specific bioeconomy is structured and functions in these contexts. The broader impacts include training a graduate student in scientific anthropological methodology, and disseminating the data and results through academic publications, policy briefs, and public engagement initiatives to improve the public’s understanding of the scientific analysis of the bioeconomy. The investigators employ ethnographic methods including clinical and participant observation and semi-structured interviews with 80 participants across both sites, including healthcare providers, intended parents, and egg donors. This systematic investigation analyzes how varying approaches influence clinical practices and patient outcomes. The study contributes to medical anthropological theory by examining how political economy and embodiment shape reproductive healthcare. Through rigorous scientific methods, this research advances understanding of healthcare system adaptation while providing practical insights for developing sustainable frameworks for reproductive care access. 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.