Auburn University

NSF Awards · FY 2026 · 2026-10

This award establishes a renewed Research Experience for Teachers (RET) Site at Auburn University. The site will provide unique and holistic research experiences for 24 middle school math and science teachers in the 7th-8th grades from rural areas of Alabama. The research focus is on smart humanoid and mobile robots enabled by cutting-edge technologies of artificial intelligence (AI) and machine learning (ML). The goals of the site are to equip teachers with knowledge and skills in AI and ML and robotics and promote their interests in these areas and facilitate teachers’ development and implementation of engaging project-based curricular modules for their classrooms. The site will provide research experiences to eight (8) middle school math and science teachers in the 7th-8th grades each year via a six-week summer program and nine-month academic year follow-up, with the research focused on smart mobile robots based on AI and ML. The site has five primary objectives to reach its goals of providing a rigorous and engaging RET experience: 1) provide education and training activities on the fundamentals of AI/ML and robotics, and novel platforms of ML-based smart humanoid and mobile robots for research and education; 2) engage teachers in hands-on research projects on ML-based smart robots that match well with faculty mentors’ research projects; 3) allow teachers to collaborate with engineering and STEM education faculty to develop the project-based curricular modules; 4) foster teachers’ leadership and pedagogical skills via teacher leader mentoring and practice of teaching the RET curricular modules; 5) assist teachers to implement the RET curricular modules via academic year follow-up. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2026 · 2026-10

This project investigates the evolutionary origin of collective building behaviors in termites over approximately 150 million years of evolution. Termites exhibit a remarkable diversity in nest construction: some species are simple wood-dwellers, while others build large mounds connected by extensive underground foraging tunnel networks. This addresses a fundamental question in biology, how do complex behavioral traits evolve from simpler ones. Focusing on underground tunneling behaviors, the project quantitatively compares behavioral diversity across 21 species at local, national, and global sampling scales. The data obtained will be integrated with computer simulations and phylogenetic comparative analysis to identify the key behavioral changes responsible for the evolution of complex nest building. The project also utilizes the unique termite diversity in Alabama to increase public awareness of termite biology and evolution. Broader impacts include outreach activities targeting K-12 students and homeowners, development of science-based educational resources for local termite species, and establishment of a termite identification service in Alabama. This supports evidence-based pest management and public education for wood-damaging insects. This project uses an integrative approach to identify the behavioral mechanisms underlying the evolution of complex nest construction in termites. Aim 1 quantifies behavioral diversity in tunnel excavation across 21 termite species representing a wide range of nest complexity. By comparing both the individual behavior and outcome structures within the phylogenetic comparative framework, the project tests whether interspecific variation in tunneling arises primarily through modification of shared behavioral rules or through shifts between fundamentally different behavioral modes. Aim 2 develops an individual-based model of termite tunneling dynamics to test how variation in social interaction parameters can generate diverse group-level structures from the same set of behavioral rules. Evolutionary simulations evaluating the search efficiency of tunneling patterns will provide complementary support for empirical findings from Aim 1. Aim 3 expands the analysis from tunneling to nest construction more broadly, investigating how the evolution of excavation mechanisms may constrain or facilitate the emergence of external nest structures. Overall, this project provides a comprehensive framework for understanding the evolution of collective behavior and self-organized construction in social animals. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2026 · 2026-07

Augmented reality (AR) overlays digital information onto the real world, and its use in workplaces, healthcare, and daily life is growing rapidly. Despite this, few guidelines exist for how AR should be designed or how people learn to use it safely and effectively. Without clear design standards, AR systems risk confusing or endangering users, particularly in high-stakes environments like hospitals or industrial settings. This project addresses that gap by partnering with public libraries to offer free, hands-on AR experiences for community members, particularly those with little prior exposure to technology. The project will broaden public access to emerging technology and train the next generation of researchers to engage meaningfully with diverse communities. This project develops a validated design framework for AR displays by synthesizing knowledge from industry, academia, and policy. Data collected from a broad and diverse user population will ensure the framework is generalizable across contexts. The project also establishes the theoretical foundation needed for AR systems to adapt to individual user needs in real time, with the goal of improving safety and usability in complex environments. This framework will serve as a resource to guide future human-centered AR research and development. This project advances NSF’s mission by building the scientific foundation needed to make emerging technologies safer and more accessible for all Americans. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2026 · 2026-05

This Research Infrastructure Improvement (RII) EPSCoR Research Fellows project provides a fellowship to an Assistant Professor and training for a graduate student at Auburn University. This work is conducted in collaboration with researchers at Los Alamos National Laboratory (LANL). Through the fellowship, the principal investigator (PI) will develop high-fidelity, massively parallel computational tools to enable robust simulations of fluid-structure interactions and turbulent combustion. The flow data generated from the proposed simulations will be utilized to construct a machine learning (ML) algorithm to control flow behavior. The project integrates applied mathematics, fluid dynamics, and artificial intelligence to develop numerical tools that will reduce the cost and improve the accuracy of computational methodologies for aircraft and spacecraft design. The developed tools will help alleviate the dependency on expensive wind tunnel and flight tests in transonic and supersonic regimes. The project will also help train a graduate student on applying ML to fluid dynamics. This project will implement high-order numerical methods for execution on heterogeneous supercomputing platforms to conduct fluid-structure interaction (FSI) simulations in practical domains at transonic/supersonic flow conditions. The complex geometries and moving boundaries of interest in engineering applications make it challenging to ensure high-order solution accuracy without prohibitive computational cost. This study will leverage the computing expertise and resources at LANL to integrate and implement adaptive mesh refinement with cut-cell methodology, developed by the PI, helping enable scale-resolving FSI simulations in flow regimes that have been computationally intractable so far. Furthermore, a flow control strategy based on deep reinforcement learning will be tested to mitigate the adverse FSI effects. The fellowship will help the PI establish a strong multiphysics computational research program to train graduate and undergraduate researchers in Alabama for fundamental flow physics investigations in aerodynamics and propulsion. This long-term capability will allow meaningful collaborations with NASA Marshall and the Propulsion Research Center at the University of Alabama Huntsville. This project is supported by the EPSCoR Research Infrastructure Improvement Program: EPSCoR Research Fellows, which supports early- and mid-career investigators in eligible jurisdictions to develop collaborations at the nation’s private, government or academic research institutions. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2026 · 2026-04

Non-technical Summary In this REU program entitled “Research Experience in Multiscale Modeling of Matter Embracing Disciplines in Engineering and Sciences”, ten undergraduate students will work with faculty mentors across campus at Auburn University on computational studies of materials and molecules. Many modern technologies, from energy systems to additive manufacturing and medicine, rely on understanding how matter behaves at different size scales. This project will introduce students to methods that explore these behaviors, from the motion of individual atoms to the performance of complex materials at macroscopic scales. Through collaborative research, group discussions, seminars, and professional development activities, the program will prepare participants for future careers in science, technology, engineering, and mathematics (STEM). The project will place special emphasis on recruiting students from institutions with limited access to research opportunities or high-performance computing resources. This REU will support the national interest by providing interdisciplinary training in collaborative research and developing a skilled STEM workforce. Technical Summary This REU project entitled “Research Experience in Multiscale Modeling of Matter Embracing Disciplines in Engineering and Sciences” will engage ten undergraduate participants in computational research focused on multiscale modeling of materials and molecules. The scientific goal of the program is to explore systems in which physical behavior emerges across distinct length and time scales, requiring the integration of methods such as electronic-structure theory, atomistic and molecular simulations, continuum modeling, and data-driven approaches, including machine learning. Student teams will address common scientific questions while working at different scales, allowing them to compare computational strategies, assess the accuracy of different modeling techniques, and understand the advantages and limitations of each method. Research problems may involve structural, mechanical, electronic, or transport properties of materials, as well as molecular interactions relevant to chemical and biological systems. For ten weeks, participants will develop skills in scientific programming, workflows for high-performance computing, and best practices for analyzing and validating simulation results. They will also receive training through seminars, group meetings, and workshops designed to strengthen communication, teamwork, and professional preparation. By combining independent research with coordinated multiscale investigations, the project will provide students with a broad perspective on modern computational materials sciences and equip them with knowledge that is essential for emerging research areas driven by advanced modeling and data science techniques. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2026 · 2026-04

Vortex rings are swirling fluid structures that appear in many natural and engineering flows. Applications include the way the heart moves blood to how jets cool hot surfaces and control airflow over vehicles. How these vortices interact with a surface determines how heat is transferred, how fluids mix, and how forces develop. Most of our current understanding of this process comes from studies of vortex rings interacting with flat or smoothly curved surfaces. In many real applications, vortex-wall interactions often occur over surfaces with significant geometric corrugations. The effects of these local surface features on vortex behavior remain largely unexplored. This project investigates how specially designed wavy surfaces influence vortex behavior. The research investigates whether surface shape can be used to guide and control flow outcomes. The results will provide new knowledge for designing surfaces. Applications include improved cooling and reduce energy losses in advanced manufacturing energy and transportation systems. The project will also train students in experimental and computational methods and engage K–12 audiences through hands-on demonstrations of fluid motion. The research will examine how local variations in the shape of a surface and the timing of incoming vortices work together to influence flow behavior. Carefully controlled experiments and computer simulations will be used to study these interactions. Vortex deformation, stretching, and interactions will be tracked as they encounter wavy surfaces. The study will measure how surface features affect pressure, flow separation, and the formation of new swirling structures near the wall. Single and repeated vortex interactions will be compared. The project will identify patterns that determine when vortices remain stable, reconnect, or break down. The research will generate high-quality datasets. These datasets can support the development of data-driven models and artificial intelligence tools. The results will support design principles for using surface shape and timed flow forcing to improve heat transfer in several applications. The results could impact microelectronics, turbine cooling, enhance mixing in additive manufacturing and chemical processing. The results could impact control of airflow over aircraft wings to reduce drag and noise. The results could provide direct guidance for engineering surface design in manufacturing, energy, and transportation systems. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2026 · 2026-01

Curvature serves as a fundamental concept in geometry. Intuitively, it measures the amount by which a space deviates from being flat. A central topic in geometry is to investigate how various positivity conditions on the curvature tensor restrict the shape of the underlying space. This project delves into a variety of curvature conditions under different geometric settings, aiming to deepen our understanding of the topological implications. Additionally, the project extends its impact beyond research by fostering engagement with K12 students through Math Circle and AMC (American Math Competition) 8 activities in Wichita region in Kansas, alongside mentorship programs for undergraduate and graduate students. This project unfolds through three research objectives. The first one is an in-depth investigation of the curvature operator of the second kind, which gained attention following the recent resolution of Nishikawa’s 1986 conjecture by Cao-Gursky-Tran and the PI. The goal is to classify Riemannian and Kahler manifolds with k-positive curvature operator of the second kind. The second explores Ricci flows with positive isotropic curvature, aiming toward a classification of compact manifolds with positive isotropic curvature. The third objective entails an investigation of gradient shrinking Ricci solitons and Einstein four-manifolds with positive sectional curvature, aiming to make progress toward two folklore conjectures. Primary strategies include finding new Ricci flow invariant cones, studying the evolution of various curvature under Ricci flow, analyzing partial differential equations satisfied by various geometric quantities, and understanding the relationship between various notions of curvature via tensor algebra and Lie algebra. This project is jointly funded by Topology and Geometric Analysis Program and the Established Program to Stimulate Competitive Research (EPSCoR). This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-10

This Designing Materials to Revolutionize and Engineer our Future (DMREF) joint NSF-Department of Science and Technology of India (NSF-DST) project aims to establish a transformative framework for the development of structural alloys that simultaneously achieve high strength at high temperatures and enhanced ductility at room temperature. The research focuses on a relatively new class of metallic materials known as refractory multi-principal element alloys (RMPEAs), which are recognized for their high-temperature strength but typically suffer from limited plasticity under ambient conditions. The team will develop the new alloy design paradigm through a concept called “metastability engineering,” which activates novel nano-scale deformation mechanisms by controlling dislocation dynamics and phase stability. The research integrates combinatorial synthesis, advanced in-situ experiments, atomistic and mesoscale simulations, and machine learning (ML)-guided discovery. The resulting framework will enable accelerated design of high-performance RMPEAs across broad temperature ranges. In parallel, the project will contribute to training a new generation of materials scientists in experimental, computational, and data-driven methods, while supporting outreach and international collaboration through partnerships with five US universities and Indian Institute of Technology Bombay. This project aims to establish a transformative framework for metastability engineering in refractory-type multi-principal element alloys (RMPEAs) that combines high-temperature strength with improved room-temperature ductility and strain hardenability. This project will address two key technical thrusts: (1) understanding dislocation dynamics for solid-solution strengthening at both room and high temperatures, and (2) enabling nano-scale transformation-induced plasticity (nano-TRIP) and twin-induced plasticity (nano-TWIP) mechanisms for enhancing ductility at room temperature. To navigate the vast composition and processing space, the team will integrate combinatorial synthesis, high-throughput and autonomous mechanical testing, and advanced machine learning techniques to accelerate the discovery of high performance RMPEAs. In the first thrust, the project will quantify the contributions of dislocations to high-temperature strength through autonomous nanoindentation creep testing, in situ neutron diffraction, and atomistic simulations. Advanced microscopy techniques will be used to reveal how local chemical ordering and lattice distortion affect dislocation motion. In the second thrust, the team will identify composition-processing pathways that promote metastable deformation modes using thermodynamic modeling, combinatorial deposition, and transformer-based machine learning models. These models will predict TWIP/TRIP propensity and guide multi-objective optimization across large alloy design spaces. Down-selected alloy systems will be validated through multiscale mechanical testing and simulations that span atomic to bulk scales. Collectively, the project will deliver a mechanistic foundation and data-driven design tools for metastability engineering in RMPEAs, aligning with the DMREF project’s mission to accelerate materials innovation through the integration of theory, experimentation, and data science with closed-loop design cycles. 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 REU Site award to Auburn University, located in Auburn, AL, will support the training of 10 students for 10 weeks during the summers of 2026- 2028. The program will focus on the Southeastern US, including the North American Coastal Plain Biodiversity Hotspot, a unique region in need of ecologically trained scientists. Research projects may be conducted on campus or at nearby off-campus research and natural areas. The project goal is to provide educational, research, and professional development activities to undergraduate students through an engaging hands-on experience working in applied terrestrial ecology and conservation. Through this experience, students will develop independence, skills, and knowledge, increasing their opportunities to pursue a career in science, especially in the Southeastern US. Students will learn how research is conducted, and many will present the results of their work at scientific conferences. Assessment of this program will be done through an online tool. Students should apply to the REU site using NSF ETAP (Education and Training Application: https://etap.nsf.gov). The focus of the REU is in applied terrestrial ecology and conservation within a U.S. biodiversity hotspot. This interdisciplinary program will include mentors from the College of Forestry, Wildlife and Environment and the Department of Biology. Together, students will be trained in a unified course experience, including a systems approach to ecology, hypothesis development, experimental design, data analysis and interpretation, research ethics, and responsible conduct of research. Students will receive professional development opportunities, site visits to research stations, and exposure to various ecosystems while engaging in research projects studying, for example, herpetological diversity, community ecology in natural and human-disturbed areas, genomic insights into population connectivity and extinction risk, irregular architecture in honey bee nests, characterizing ecosystem structure using remotely sensed data, nocturnal call monitoring of frogs, mechanisms of forest flammability, local facilitation of invasive ant species, and disease ecology of wildlife. The results of this REU site will increase our understanding of the North American Coastal Plain Biodiversity Hotspot while promoting workforce development in the southeastern U.S. 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

Additive Manufacturing (AM) is a multibillion-dollar industry that uses 3D printers to manufacture parts on the basis of computer aided design files. Aircraft parts, space mission parts and biomedical devices are manufactured by AM. Manufacturing-as-a-Service (MaaS) business model has grown significantly in this area because general purpose 3D printers can be used to print a variety of shapes and materials without the need for expensive and time-consuming retooling. Customers on demand outsource part production to providers that offer the best cost, delivery time, and quality. However, the appeal of MaaS is inhibited by various security concerns, both on the customer and the manufacturer side. The customer is concerned with design file misuse, such as its theft, illegal distribution, or infringement of parts. The manufacturer is concerned with a possible bait-and-switch of design files between the quote request and contract. The project’s novelties are to develop security techniques and their interplay that enable a robust watermarking scheme for commonly used design files to address the concerns of both part designers and part manufacturers. The project's broader significance and importance are that the widespread use of MaaS manufacturers for production of industrial components will become more secure for both designers and manufacturers. The novel security measures will help the manufacturing industry grow further significantly. This project devises a solution that addresses concerns of both designers and manufacturers. The conceptual idea relies on the inherent causation between the digital design that defines a part and the physical qualities of the manufactured part. In the envisioned solution, during the quote request phase, a lower quality (LQ) design is shared with multiple manufacturers. The parts produced with such LQ design can exhibit properties such as distorted form, looser fit in the assembly, and degraded functional characteristics like mechanical strength. In addition, such parts can also contain digital and physical watermarks pointing to the manufacturer from whom a quote was requested. Embedded and entangled in each such LQ design are (i) the quality restoration instructions that become available to the contracted manufacturer only and (ii) the robust digital watermark, whose removal or manipulation would prevent quality restoration. Such digital watermarks can be used to establish the provenance of the design and to identify the manufacturer who leaked the design files. The novel security methods that are tested on various complex geometries will help the manufacturing industry to increase the trust in the dynamic supply chain. 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

Transport is an important concept in many STEM fields and serves as the focus of this National Science Foundation Research Experiences for Undergraduates (REU) site. Transport phenomena can be described as the flux (or rate at which something is happening) being equal to the driving force (stimulus) divided by the resistance (obstacles). This can be used to describe blood flow through tissues, water permeation through soil, or the transport of electrons through a circuit. In a more abstract sense, the concept can be applied to the trajectory of a research career, wherein the outcomes or accomplishments are a function of intrinsic and extrinsic motivations divided by the obstacles researchers face. This REU site seeks to address obstacles related to the availability of research programs at participants’ home institutions (for instance, community colleges or four-year colleges with limited research infrastructure). The REU provides a driving force through 10-week intensive research experiences to enable growth of these students in research career pathways. REU participants will learn how fundamental engineering principles related to transport phenomena can be applied to significant real-life problems – combining theory and practice to promote curiosity and inspire life-long learning. In addition, training given to REU participant mentors will enhance the undergraduate and graduate research experiences for generations of students to come. The goal of this REU site is to promote careers in engineering research. The objectives of the project are to develop and present projects related to transport phenomena; provide meaningful research experiences for 10 participants annually; engage in comprehensive professional development activities; provide a cohort experience between participants and faculty mentors that stimulates and fosters future pursuits; and provide training on best practices in undergraduate research for faculty mentors. The program will recruit 10 students annually to participate in a 10-week summer program. Activities will include an orientation on multidisciplinary research in engineering; a professional development seminar series (topics include best lab practices, hypotheses development, experimental design, and technical presentations); daily meetings with mentors; and engagement with experts from industry and academia that conduct research and development activities relevant to this program. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-09

Nontechnical Description: This project will advance the understanding of nano-polaritons—waves that combine light and matter and travel within nanoscale materials. These waves can be used to control energy and information at extremely small scales, offering potential for faster optical computing, more efficient energy use, and advanced sensing technologies. The research team will explore how these waves behave when moving across materials of different shapes and sizes. These new setups for nano-polaritons could lead to more efficient ways to send and control information and energy at the nanoscale. The research will also support hands-on education and outreach for the general public on state-of-the-art optics and materials research. In addition, the project will integrate the research products into university coursework and involve K-12 participants in summer activities. Undergraduate students will receive comprehensive training in nanomaterial fabrication and optical simulations, helping prepare the next generation of researchers in optics, materials science, and engineering. Technical Description: This project will investigate cross-interface polariton nano-light in heterostructures formed by mixed-dimensional and reduced-symmetry materials. By breaking the traditional symmetric behaviors of polaritons, the research will enable the discovery of unconventional light-matter interactions and propagating nano-optical modes over quantum-confined nanostructures. The principal investigator will explore different dimension configurations to vary the polariton propagation characteristics. The project will also utilize materials with low crystal symmetry and extreme optical anisotropy to enable non-reciprocal, directional, and non-symmetric polariton nano-light. Scattering-type scanning near-field optical microscopy will be used to provide direct visualization of polariton nano-light behaviors with nanometer resolution. At the same time, finite element method simulations will be conducted to offer theoretical insights into field distributions and dispersion relations of the polaritons. Together, these methods will elucidate the mechanisms of polariton energy transfer and photonic density of states enhancement at the nanoscale interfaces. These polaritonic heterostructures are expected to yield novel optical phenomena that cannot be achieved in conventional systems or material combinations. The research outcomes will contribute to the fundamental understanding of nanoscale optical energy transport and hold promises for practical applications such as fast photonic circuits, efficient energy transport, precise biomedical treatment, effective thermal management, and quantum information and communication. This research may also expand current knowledge in physics and optics by revealing novel mechanisms of nano-light propagation and energy transfer at intriguing nano-interfaces. 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

Accurate computations of compressible (high-speed) flows in practical domains are crucial in numerous scientific and engineering applications, e.g., bridge/building vibration in hurricanes, wing flutter in aircraft, human speech/phonation, etc. These computations are challenging because handling complicated boundaries often leads to numerical errors that compromise the accuracy of the predictions. Moreover, setting up these computations (which involves meshing or grid generation) for complex domains can be time-consuming. This project will develop computational approaches (and a theory to evaluate and improve the numerical properties of those approaches) to eliminate the need for complex grid generation, while ensuring high-fidelity flow predictions. It will enable accurate simulations of fluid-structure interactions in flow situations that have been intractable so far, helping uncover the physical mechanisms that drive the interactions and devise strategies to control them. The research tasks will be paired with instructional plans to train undergraduate and graduate students in leveraging computational tools for scientific investigations. Additionally, summer research opportunities focused on flow visualization tools and algorithms will be provided to encourage undergraduate research participation. Embedded boundary (EB) methods simplify the handling of practical geometries; however, they have been restricted to low (first/second) order of accuracy when non-dissipative interior schemes are used because of EB instabilities and small-cell issues. This project will address several key challenges associated with the application of EB methods to compressible Navier-Stokes equations. First, a stability theory will be developed for the construction of high (fourth and higher) order energy stable EB schemes for multi-dimensional compressible flow equations. Second, the theory will be extended to derive shock-capturing EB schemes that are provably stable. Finally, the derived schemes will be implemented to simulate the flow-induced vibrations of an airfoil/wing in supersonic flows. The theoretical (energy or time) stability will be proven by utilizing the dimensionally-split structure of the proposed schemes, which allows the multi-dimensional scheme to be written and analyzed as combinations of one-dimensional schemes in individual directions. No-slip wall boundary conditions will be enforced via penalty terms at the EB to satisfy the energy stability constraints. The small-cell issue, commonly encountered in existing EB methods, will be addressed by constructing a dual grid, containing separate solution and flux points, where the flux-point (or cell) spacings will be constrained to remain finite when the solution point spacings vanish near the EB. 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 Program of the Division of Chemistry, Professor Rashad Karimov of the Department of Chemistry and Biochemistry, Auburn University is studying new catalytic methods to build complex, non-aromatic heterocycles — structural motifs commonly found in medicines and natural products. The goal of this research is to improve how these molecules are made, with a focus on introducing chemical groups at a later stage in the synthesis process. This approach can speed drug discovery by allowing scientists to explore how small chemical changes affect biological activity more efficiently. It can also help researchers more efficiently build bioactive molecules found in nature. As part of the broader impacts, the project will involve mentoring undergraduate and graduate students and engage the public through community-based science outreach programs. These efforts aim to strengthen the scientific workforce and increase public understanding of the role of chemistry in improving human health. This research will develop enantioselective tandem functionalization strategies for the late-stage functionalization of partially saturated heterocycles containing nitrogen, oxygen, or sulfur atoms. Two complementary approaches will be pursued: (1) asymmetric alkene isomerization followed by functionalization to access stereochemically rich five- to eight-membered heterocycles, and (2) asymmetric diene isomerization strategies followed by functionalization enabling regio- and stereoselective cycloaddition and diene functionalization reactions. These catalytic methods leverage the reactivity of in situ-generated polarized alkenes and dienes to install stereocenters adjacent to heteroatoms, addressing long-standing synthetic challenges. The anticipated outcome is a suite of broadly applicable reactions that expand the synthetic toolkit for medicinal chemistry and natural product synthesis, with a primary emphasis on nitrogen heterocycles. 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 combinatorial mathematics, a graph is a set of points, some of which may be joined by lines. Graphs are useful models for electrical grids, chemical structures, the internet, transportation maps, and many other objects -- anything that can be viewed as a network is, abstractly, a graph. Real world problems involving such networks benefit from the theorems, algorithms, and insights of graph theory. The PI is most interested in graph problems involving so-called colourings and flows, and this project focuses on three sub-problems within these topics. Graduate students will be mentored as part of this project. The three sub-projects prioritized in this proposal are titled: flows with boundary conditions in graphs; clique subgraphs and strong colouring, and; total colouring and overfullness in graphs with large maximum degree. The first of these is about developing concepts for use as inductive tools towards Tutte's Flow Conjectures. The second is an attack on the Strong Colouring Conjecture, using a clique reduction technique. The third and final sub-project seeks to prove a special case of the Overfull Conjecture (involving large graphs with large maximum degree) and to use this work towards the Total Colouring Conjecture. 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

Artificial intelligence (AI) and data science are revolutionizing the way complex systems are modeled, data is analyzed, and decisions are made across science, technology, and industry. There is a growing need to train researchers with a rigorous mathematical foundation to ensure that AI and data-driven methods are reliable, efficient, and adaptable to real-world challenges. This Research Training Group (RTG) project will train undergraduate students, graduate students, and postdoctoral researchers to conduct advanced research at the intersection of mathematics, AI, and data science. Through a structured program of interdisciplinary research, AI and Data Science summer school, seminars, and industry-partnered projects, participants will acquire the mathematical, computational, and analytical tools necessary to contribute to the future of AI and data science, both in theory and in practice. The project centers on three integrated research modules: (1) diffusion modeling for generative AI, (2) topological data analysis (TDA) for complex datasets, and (3) partial differential equation-based machine learning for anomaly detection. These modules pair fundamental mathematics with application areas including wireless communications, medical imaging, and cybersecurity. By rotating through all three modules, trainees will develop a comprehensive skill set on stochastic modeling, algebraic topology, inverse problems, and algorithmic implementation. The program emphasizes both conceptual understanding and hands-on experience through research, capstone projects, and collaboration with industry. Trainees of this project will be prepared to lead in the development and application of mathematically grounded methods in AI and data 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

This award supports operation of the Magnetized Plasma Research Laboratory (MPRL) facility at Auburn University to study structure and pattern formation in plasmas, and specifically dusty plasmas, at high magnetic fields and to broaden external user access to the facility. Dusty plasma is a gas-like state of matter composed of electrons, ions, neutral atoms, and small charged solid particles. The study of pattern formation in dusty plasmas is connected to many areas of fundamental and applied research, from planet formation within protoplanetary disks, to fabrication of computer chips by the semiconductor industry, to plasma dust contamination in magnetic confinement fusion research. The supported research effort seeks to understand how the extremely fast interactions that occur between electrons, ions, and solid particles on very small scales can lead to long-lived and large scale patterns in a magnetized dusty plasma. The MPRL facility has the unique high magnetic field capability for conducting these studies. The award will also enable broad external user access to the MPRL, support local graduate students and postdoctoral researchers at Auburn University, develop a network of institutions to broaden the introduction of plasma science and engineering at the undergraduate level, and support a week-long plasma physics summer/winter school for undergraduates. The technical mission of the Magnetized Plasma Research Laboratory is to understand and control how micro-scale spatial and temporal behavior enables the emergence of long-lived, macro-scale structures in strongly magnetized plasmas and dusty plasmas. This mission is centered on three main questions: (1) What makes ion magnetization act as the apparent threshold condition for the emergence of self-organized phenomena in magnetized plasmas and dusty plasmas at high magnetic fields? (2) How do the thermodynamic properties of a strongly coupled dusty or complex plasma evolve as it transitions from a self-organized to an imposed organized state with increasing magnetic field, and can this be used as an analogue for two-dimensional magnetic materials? (3) How are the transport, diffusive, and turbulent properties of plasmas and dusty plasmas modified in the presence of a strong magnetic field? Targeted experiments will include investigating the temporal and morphological stability of confined filamentary structures, validating the structural transitions of a plasma crystal at high magnetic fields, using a combination of experiments, data mining, and machine learning tools to characterize the transport, diffusion, and turbulent properties of plasmas and dusty plasmas, extending the use of dust particles as probes of flows and potential structures within plasmas and plasma filamentary structures, and continuing the development of new, non-invasive diagnostic techniques for studying plasmas and dusty plasmas in high magnetic 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

In this project, funded by the Chemical Mechanism, Function, and Properties Program of the Chemistry Division, Professors Ethan Hill and Paul Ohno of the Department of Chemistry and Biochemistry at Auburn University are investigating the use of externally-applied electric fields (E-fields) in transition metal catalysis with the ultimate goal of developing new “switchable” catalysts for advanced chemical synthesis. The tight collaboration between a primarily synthetic inorganic research group and primarily experimental physical chemistry group will enable students to receive broad training across the chemical discipline and prepare them for success in the modern research landscape where societal grand challenges require large collaborative efforts across traditional scientific disciplines. Finally, a tiered approach has been developed with appropriate activities for educating and engaging with high school students, college students, and the general public through this work and related scientific topics. Metalloporphyrin catalysts have been immobilized onto gold electrodes at a specific orientation using a self-assembled monolayer (SAM). Using an applied E-field, properties and ligand exchange dynamics around the transition metal center will be altered to influence chemistry. A combination of electrochemical techniques and vibrational sum frequency generation (SFG) spectroscopy will be used to monitor in situ changes at the metal center and connect these changes to observed reactivity. Using vibrational handles present in the SAM linkers and on certain ligands, shifts in peak position and intensity will be used to determine the strength of the local E-field and investigate the local electrostatic environment at the metal center in order to carry out the following objectives: 1) Quantify local E-field effects on the reactivity of an immobilized Ru-porphyrin 2) Determine the influence of transition metal identity on E-field driven reactivity 3) Elucidate inductive vs. through-space effects in E-field driven reactivity. Altogether, completion of these objectives will advance our understanding of the quantitative connection between applied E-field and chemical reactivity in transition metal complexes and how this connection is influenced by chemical properties and chemical structure. 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 support from the Chemical Structure and Dynamics (CSD) program in the Division of Chemistry, Professors Evangelos Miliordos, Marcelo Kuroda, and Konstantin Klyukin of Auburn University are employing computational methods to a novel class of electronic materials known as solvated electron precursor electrides (SEPEs). Electrides are materials comprised of atomic or molecular layers with diffuse electrons occupying the interstitial space between the layers. These diffuse electrons could give rise to new electronic and magnetic properties that could be harnessed for a range of technologies, including advanced quantum computing, but their behavior is difficult to describe, making it difficult to connect it with the underlying molecular framework. Professor Miliordos, Kuroda, and Klyukin and their students will develop state-of-the-art computational simulations based on molecular and periodic approaches to model these diffuse electrons. Their work could clarify the structure-property-function relationships in these systems and advance the rational design and experimental realization of SEPE-based materials. The project will also provide research opportunities for graduate and undergraduate students and thus contribute to the development of STEM workforce. Solvated electron precursors are made of a positively charged metal centers fully coordinated by ligands which can sustain at least one peripheral diffuse electron, not bound to a specific atom or molecule. The aggregation of multiple SEPs leads to the formation of nanoparticles and materials known as SEPEs. The work of Professors Miliordos, Kuroda, and Klyukin will analyze the properties of structures where SEPs are connected via molecular bridges (linked-SEPEs) or are deposited on surfaces (surface immobilized SEPEs). The tunable composition of the electride-based material platforms offers enhanced control of the topological and electronic features owing to the variety of metals, ligands, materials, and linkers that can be used. Employing high-level multi-reference and density functional studies, this study will elucidate the underlying mechanisms defining their properties. This work will also provide design guidelines for a range of applications, including the development of chemical catalysts with low activation energy barriers, the reduction of overpotentials in electrochemical processes, the facilitation of multi-electron redox reactions, and the creation of qubits with extended coherence times and controlled entanglement. 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 objective of this project is to create a research experience that integrates undergraduate students in team-based research projects focused on converting biological wastes into products of value. The proposed REU site will provide a summer research experience for nine students per year, training 27 students over a 3-year period. Besides gaining research experience, we aim to impact student growth and development in two major areas: 1) communication of scientific results and 2) effective teamwork practices. In the course of growing as researchers, we also expect to observe positive changes in participant attitudes about research. Preparing future researchers aligns with the NSF mission of promoting the progress of science as well as advancing the field of bioprocess engineering. The objective of this project is to continue an existing REU site that integrates undergraduate students in team-based research projects focused on converting biological wastes into products of value. Solving this problem is an interdisciplinary challenge and we will engage teams of REU fellows and collaborating faculty mentors and graduate students. This team-based REU Site will i) result in a greater number of REU authors on peer-reviewed manuscripts compared to peer sites, ii) improve confidence and knowledge regarding effective team practices among fellows, and iii) increase fellows’ research self-efficacy and sense of identity as a researcher. Interdisciplinary projects will focus on 1) conversion of waste lignin into adhesives and carbon fibers, 2) aquaponics and bioponics for sustainable vegetable production, and 3) conversion of poultry processing wastes into valuable products. We will also support the REU fellows’ professional development through a range of rich learning activities that include hands-on workshops focused on 1) teamwork best practices, 2) research practice, 3) scientific writing and presentation, 4) literature evaluation, and 5) field trips to project-relevant sites. 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 proposal centers on two core themes: the optimal decomposition of a graph's edges into matchings, and the Hamiltonicity problem in tough graphs with some “expansion” properties. The decomposition of the edges of a graph into non-conflicting groups, such as matchings, is deeply rooted in combinatorial theory and holds practical significance across various fields, including computer science, operations research, and telecommunications. Additionally, the Hamilton cycle problem, a fundamental NP-complete problem, has been extensively studied, with recent advancements highlighting the importance of concepts like expansion and quasi-randomness. The project includes several research problems well-suited for graduate student involvement. In addition, the PI has planned outreach activities centered around graph theory concepts to stimulate interest in STEM fields, particularly among middle school students. The PI's proposal delves into these two pivotal problems and extends her ongoing investigation into four longstanding conjectures: the Overfull Conjecture (Chetwynd and Hilton, 1986), the Multigraph Overfull Conjecture (Stiebitz, Scheide, Toft, and Favrholdt, 2012), the Total Coloring Conjecture (Behzad and, independently, Vizing, 1960s), and the Toughness Conjecture (Chvatal, 1973). Along with collaborators, the PI has made substantial progress on each of these conjectures and aims to develop new techniques to further advance the field. This work builds on a combination of classical edge decomposition and coloring methods, alongside probabilistic techniques, which the PI will continue to refine and extend. 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

Matroids are objects that axiomatize the notions of independence and dependence. Because of their generality, matroids have found applications in a wide variety of areas including optimization, network theory, and cryptography. This project seeks to prove new upper bounds on how many elements a given matroid can have as a function of its rank, and show that in many important special cases the upper bounds are attained by matroids that arise from networks with labeled edges. Graduate students will be trained and mentored as part of this project. More specifically, this project will focus on minor-closed classes of matroids for which the densest matroids have a quadratic number of elements as a function of rank, such as the class of binary matroids without a fixed projective geometry minor and the class of real-representable matroids without a fixed rank-2 uniform minor. The first part of this project seeks to refine the celebrated matroid Growth Rate Theorem by showing that the maximum density of a matroid in a class of this type is always determined by a finite group associated with the class. This will require new constructions for matroids from graphs with edges labeled by a finite group, and the second part of this project will explore applications of these new constructions in optimization, structural rigidity theory, and network flow theory. 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 solar wind carries particles and magnetic fields throughout the heliosphere and towards the Earth. It is important to understand structures and turbulence in the solar corona, which produces the solar wind, in order to predict and understand space weather. This project addresses the Solar, Heliospheric, and Interplanetary Environment (SHINE) goal of understanding the solar wind, through research that combines remote-sensing observations, in-situ satellite observations, and numerical simulations. The project supports an early-career researcher and broadens the participation of underrepresented groups in STEM and includes public outreach to K-12 students. Understanding the inner heliosphere, especially the Sun-Earth interaction system, requires analysis of the sources of the solar wind in the solar corona, emergent large-scale solar wind structures like corotating interaction regions (CIRs) and the heliospheric current sheet (HCS), and the small-scale solar wind fluctuations or turbulence including the structure called magnetic switchbacks (SBs), treated as an integrated system. The science focus of this project is to study the interaction between large and meso-scale structures and turbulence in the solar wind and understand the solar coronal origins of the structures and turbulence. To this aim, the research will combine numerical simulations with long-term multi-satellite observations and remote-sensing observations to address the following two questions: 1. How do the properties of the corona such as the distribution of coronal holes, streamers, and pseudostreamers lead to different properties of the solar wind observed in-situ? 2. How do solar wind structures like HCS, CIRs, and SBs modify the turbulence properties, such as the power spectrum, Alfvenicity, energy transfer rate etc., during their radial propagation? The 3D magnetohydrodynamic simulations combined with analysis of conjugated in-situ satellite data collected at different locations in the heliosphere will shed light on the physical mechanisms that determine how the turbulence properties are modified by the solar wind structures. The work utilizes a large volume of remote-sensing data to understand how the magnetic and plasma structures on the Sun determines the properties of the structures and turbulence in the solar wind. 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

Biological invasions threaten stable ecosystems, sustainable agriculture, and food security. Aphids are among the most invasive animal species and the most important agricultural pests. This research project aims to increase our ability to predict and prevent disruptive invasions by analyzing phylogenetic and ecological patterns in the diversity of aphids, their environments, and the drivers of invasiveness in the group. By providing opportunities for student training and international collaboration, it also aims to develop the scientific literacy and human capital needed to address the challenges of biological invasions. The specific goals of this project are to (i) estimate phylogenetic relationships among aphid lineages, (ii) use comparative phylogenetic analyses to shed light on the drivers of biological invasion, and (iii) extend the reach of practical biodiversity science. For the first goal, researchers will obtain and analyze target-enriched genomic data sampled from approximately 700 aphid species. These data will be used to advance the phylogenetic systematics of aphids. For the second goal, researchers will use phylogenetic path model analyses to test integrative hypotheses about the causal relationships between variation in aphid invasiveness, niche identity, niche breadth, reproductive output, dispersiveness, and the ecological similarity between source and recipient areas. For the third goal, researchers across the US, Canada and France will collaborate to train undergraduate and graduate students, and outreach in Alabama about regional insect ecology and invasion biology. Beyond these goals, this project will develop biodiversity data infrastructure in ways that will facilitate other lines of basic and applied comparative research, for example, research addressing general questions about the evolution of geographic ranges, and the responses of natural populations to climate change. 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

Microburst precipitation is a key loss mechanism for electrons in the Earth’s inner magnetosphere and a significant energy source for the ionosphere. Chorus waves play a crucial role in driving electron microbursts. This study employs a simulation model to quantify microburst generation by chorus waves. The project will have a broader impact on human communication, as electron microbursts enhance ionospheric electron density, altering conductivity and consequently affecting communication signals. The project will support a scientist who obtained a PhD in 2021 and a graduate student. This project aims to quantify electron microbursts driven by chorus waves and to investigate their temporal and spatial evolution using a self-consistent simulation model. The study has three scientific objectives: 1) identify the dominant mechanism for electron microburst formation, 2) characterize the temporal evolution of electron microbursts, and 3) determine the spatial characteristics and dependencies of electron microbursts. To achieve these goals, General Curvilinear Particle-In-Cell (GCPIC) simulations in a dipole field will be performed. The motion of resonant electrons will be analyzed, with a focus on nonlinear physical processes. Electron microbursts will be quantified in both the meridian plane and an L-shell-fixed surface, and their spatial scale dependence will be examined. Simulation results will be validated through microburst observations from the Electron Loss and Fields Investigation (ELFIN) satellite. 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.