Louisiana State University

NSF Awards · FY 2025 · 2025-02

In this project, funded by the Chemical Structure, Dynamics and Mechanism-B program of the NSF Chemistry Division, Professor Maria da Graça H. Vicente of the Department of Chemistry at Louisiana State University, in collaboration with Professor Petia Bobadova of the Department of Chemistry and Fermentation Sciences at Appalachian State University, will develop new fluorescent dyes for use in biology and materials science. The main goals are to design and synthesize new fluorinated boron dipyrromethene (BODIPY) dyes and their derivatives, followed by investigation of their photophysical and chemical properties, and their potential applications. This research is directed toward the development of near-infrared absorbing and emitting fluorophores that can find applications in sensing and/or be conjugated with biomolecules for bioimaging applications. Computational and experimental methods will be used to design, synthesize and evaluate new fluorescent dyes and their conjugates, seeking enhanced stability, biocompatibility and performance for practical applications. This project encompasses organic and inorganic chemistry, spectroscopy, chemical kinetics, computational modeling, cell and molecular biology, molecular recognition, and biomedical imaging, and is therefore well suited to the education of scientists at all levels. This group is also well-positioned to provide the highest level of education and training for students at all levels, including those underrepresented in science. Dr. Vicente leads programs at LSU that will enhance student learning and professional skills, enrich their educational experiences, and encourage them to pursue research careers. Outreach activities involving K-12 students will also be part of the funded project, both at LSU and at Appalachian State University. Boron dipyrromethene (BODIPY) dyes display a rich array of photophysical and optoelectronic properties, including intense absorption and emission profiles, high molar extinction coefficients, high fluorescence quantum yields and long fluorescence lifetimes. Chemical modifications of the BODIPY core can be used to tune the absorption and emission wavelengths, Stokes shifts, stability, quantum yields, water-solubility, and to introduce functionality suitable for conjugation to biomolecules. This project aims to provide new BODIPY-based systems with enhanced stability, solubility, and photophysical properties for practical applications in bioimaging and biosensing, including bis-boron derivatives designated as BOPHY, BOPYPY, BOPYPY and BOAPY. The main strategies are: 1) Regioselective functionalization to create push-pull BODIPY systems and dyads with enhanced molar extinction coefficients, fluorescence emission and Stokes shifts, increased oxidative stability and solubility. 2) Synthesis and investigation of unsymmetric BODIPY-based fluorophores bearing two electron-deficient boron centers and their investigation as solid-state emissive organic materials. 3) Use of poly-fluorination for enhancement of cellular permeability, stability, and biocompatibility, and to allow sites for conjugation with bio-thiols. Computational methods will be used to assist the target design by modeling BODIPY properties and mechanisms, thus facilitating the development of new methodologies for the synthesis and functionalization of BODIPY-based systems. This project encompasses a broad range of science and techniques, which will provide the highest level of education and training for students at all levels underrepresented in 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 · 2025-01

Subduction zones represent a boundary between Earth's tectonic plates where one slides under the other. Subduction zones host the largest earthquakes on Earth, and recent research has revealed more complex deformation events that take place over periods of months or years (referred to here as slow slip and tremor or SST). SST events do not have the sudden destructive power of earthquakes but they can influence the occurrence of large earthquakes. Despite the near ubiquity of SST in modern subduction zones, the mechanisms that drive SST remain poorly understood, in part because direct observations of processes occurring deep in the Earth's crust are not possible. Rocks that have been subducted to SST depths and brought back up to the surface provide a window into processes happening deep in subduction zones. This project will: (1) investigate the types of rocks that potentially hosted SST by analyzing the minerals that make up the rocks, including the ages, chemistry, and deformation and (2) compare the rock record to geophysical imaging of modern subduction zones by modeling the geophysical signature of these rocks based on the observations. Collectively, the results of this work will reveal the distribution of potential SST sources as preserved in the rock record and how they correlate to modern subduction zones. This project involves extensive international collaborations and training of graduate and undergraduate students as well as a postdoctoral scholar. Additionally, as part of this work yearly workshops will be developed in seismic properties, rheology/deformation and geochronology/geochemistry tailored in subduction zone science for the participants of the project. These workshops will result in scientific/methodologic resources and workflows for future research on rock record evidence of SST. These materials will become publicly available as resources for the subduction zone research community. At the base of the seismogenic zone stored elastic energy may be released gradually in slow slip events along with low frequency earthquakes and non-volcanic tremor that contribute significantly to the seismic cycle. This project addresses long-standing questions on whether slow slip events can be hosted in metasedimentary or meta-mafic rocks and the potential spatial distribution of SST sources. Geophysical studies find that SST often coincides with sheared and underthrusted metasedimentary rocks or with the uppermost oceanic crust of the downgoing slab. However, field observations integrated with experimental constraints suggest that metasedimentary rocks are not good candidates for SST sources. Three subduction complexes were selected that span deep SST depths and offer excellent exposures of underplated metasedimentary rocks intercalated with meta-mafic and meta-ultramafic lithologies. The project will investigate evidence of SST in these complexes (e.g., vein networks, geochemical signatures of alteration) using a field-based approach coupled with geochronology/geochemistry, structural geology, microstructural analysis, and rheology. By using the results from the rock record to calculate seismic properties coupled with the geophysical observations of SST in modern subduction zones, this work will test (a) whether SST may be preferentially accommodated by metasedimentary or meta-mafic lithologies, and (b) how structural heterogeneity along the subduction interface affects the spatial distribution of SST. The outcomes of this work will have implications for the geologic conditions and slip behavior at the base of the seismogenic zone and will therefore better inform both SST observations in active subduction zones and geodynamic models of the seismic cycle and associated earthquake hazards. 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 · 2025-01

This project will allow a mid-career scientist to learn cutting-edge geochemical methodologies related to Selenium (Se) isotope geochemistry. These methods will be used to investigate the geochemistry of Se in recent sediments and their potential as an archive to reconstruct paleoenvironmental conditions. While most paleo-redox work has focused on organic shales as an archive, carbonate rocks are more common in the geologic record, are often continuous over long time periods, and are widespread in open ocean basins. If successful, the Se technique can be applied to ancient carbonate deposits to better understand the history of oxygenation of the Earth. Broader impacts include strengthening collaborations with local (Louisiana) and international (Granada, Spain) scientists. The project will develop new research directions for the principal investigator and enable new research experiences for students. Selenium geochemistry has attracted significant research interest owing to its geochemical behavior being similar to sulfur (S) yet differing in important ways. Both elements are redox sensitive, having multiple oxidation states and multiple isotopes, yet different Se and S species are stable under different redox potentials and thus have different responses to oxidation under similar Eh and pH conditions. Therefore, selenium isotope signatures can serve as indicators of Se sources and biogeochemical transformations of selenium in the environment. To date, not much is known about Se isotopes in carbonate sediments. The scientific goals of this project are (1) to identify different pools of selenium and their impact on the Se isotopic composition of recent carbonate sediments from the Florida Keys and from laboratory co-precipitation experiments, (2) the speciation of Se in these sediments using a synchrotron facility, and (3) to determine whether carbonate sediments have the potential to capture a paleoredox signal. 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 · 2025-01

With support from the Chemical Structure and Dynamics (CSD) program in the Division of Chemistry and the Established Program to Stimulate Competitive Research (EPSCoR), Professor Daniel Kuroda of Louisiana State University is studying the structure and dynamics of complex lithium salt solutions in mixtures of organic solvents. The scientific challenge of this project lies in understanding the interactions between ions and solvent molecules and their implications to the solution macroscopic properties. Lithium salts in organic solvents exhibit unique molecular behavior due to weak dipolar interactions between solvent molecules, which contrast with the strong Coulombic interactions between the ions. Professor Kuroda and his students will combine linear and nonlinear ultrafast infrared spectroscopy with computer simulations to investigate the solvation structures and dynamics of these solutions and relate them to their charge transport, and electrochemical stability. Their findings could lead to the rational design of lithium salt solutions with direct implications for next-generation energy storage technologies, as well as new strategies for organic synthesis media. Beyond the scientific impact, the team will engage students through the hands-on battery outreach project, which introduces K-12 students to electrochemical principles by building simple batteries from everyday materials. This project focuses on an integrated experimental and computational approach to study ionic speciation and solvent molecular arrangement in complex lithium salt solutions, with a particular emphasis on understanding the role of the organic solvents and their mixtures. The work will combine an array of experimental techniques, such as ultrafast laser spectroscopy, with atomistic molecular dynamics simulations and electronic structure calculations. The project will systematically explore the role of key molecular factors of the anion and the solvent, such as chemical identity and structure, and their link to solution properties, like ion mobility and ion solvation. The results are expected to provide molecular insights into how these factors influence the solution structure with direct implications for electrochemical stability and charge transport. The research products of this project are expected to have an impact on different fields, including both science and engineering. In addition, this project will provide valuable training opportunities for undergraduate and graduate students, as well as postdoctoral researchers, and will engage K-12 students via outreach activities. 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 · 2025-01

The inevitable aging and degradation of infrastructure are critical concerns that require urgent attention. A viable solution to this challenge is meticulous structural health monitoring (SHM) to evaluate the service life of infrastructure and plan for rehabilitation. However, many existing sensors and SHM approaches have limitations, including high costs, limited durability, compatibility issues, and frequent maintenance needs. Self-sensing cementitious composites (SSCCs) offer a promising alternative for SHM due to their excellent sensing performance, compatibility with concrete structures, and relatively lower cost. Despite their potential, self-sensing concrete remains mostly confined to laboratory research after over two decades of development. This is due to the complexity of integration, the need for refined fabrication techniques, and a deeper understanding of its properties and mechanisms. Additive manufacturing offers a promising approach to address these issues due to its design flexibility. This opens the door for integrating innovative self-sensing materials into concrete structural design, specifically tailored to enhance structural health monitoring capabilities. To accelerate the practical application of this novel technology and advance the progress of science, this project will establish a synergistic multiscale and multiphysics characterization approach. The research will also be complemented by incorporating courses and outreach programs on additive manufacturing and advanced numerical methods for graduate, undergraduate, and K-12 students. Specifically, we will actively involve underrepresented students in the project, inspiring them to explore STEM fields, thereby enriching the educational landscape within the EPSCoR jurisdiction. This EPSCoR Research Fellows program provides a fellowship to an Assistant Professor and training for a graduate student at the Louisiana State University. Harnessing the expertise and resources available through a partnership with Oak Ridge National Laboratory (ORNL), the project aims to pioneer a comprehensive understanding of additively manufactured self-sensing concrete. The project will establish an innovative framework to investigate the multiscale and multiphysics characteristics, with a focus on identifying critical manufacturing parameters and material properties to unravel the process-structure-property relationships of 3D printed self-sensing concrete. By characterizing the microstructures under various environmental factors, the project aims to lay the scientific and technical groundwork necessary for the effective design and reliable implementation of these advanced materials in large-scale construction. Furthermore, the project is designed to serve as a springboard for the PI and his group, facilitating a significant advancement in research capacity and collaborative potential. Access to top-tier computational and characterization tools at ORNL will enable the research team to extend current scientific knowledge and practice, leading to transformative breakthroughs in the additive manufacturing of self-sensing concrete. This project has the potential to significantly impact the construction industry and contribute to the national aim of leading in the advanced manufacturing domain. 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 · 2025-01

Next generation (NextG) network systems are envisioned to be complex, ubiquitous, and smart, which are likely to consist of millions of heterogeneous mobile devices to connect everything digital, enable machine-to-machine communications, and support a variety of critical machine learning (ML) paradigms, including the most popular federated learning (FL) over mobile devices. However, stakeholders in many intelligent mobile applications/services are resource constrained in terms of spectrum, energy, computing, etc., which poses many challenges to FL inspired applications/services. This project targets to develop a novel NextG network with high degrees of resiliency to address those challenges, in particular, when there may be massive bursty workloads, insufficient spectrum availability, limited computational and storage capability on edge, and privacy concerns of the training data on mobile devices. The anticipated project outcomes will enrich the knowledge of wireless systems and machine learning technologies and provide multidisciplinary training especially for underrepresented students. Additionally, the findings and innovations will be shared across the 23-campus California State University (CSU) system, where 90% of campuses are minority-serving institutions. Outreach activities including high school internships and summer undergraduate training programs can provide early exposure to research in science and engineering, fostering interest and encouraging more female and minority students to pursue careers in these fields. This project aims to address the resilient issues of FL over mobile devices via a novel holistic NextG network design across network architecture, local mobile devices, and accessing networks. (1) From the networking system's perspective, to support FL over large-scale heterogeneous mobile devices, serverless computing is exploited at the edge to resiliently and efficiently provide ML computing as a service. (2) From the local mobile devices' perspective, to resiliently protect local training data privacy against inference attacks in FL, an energy-efficient piggyback differential privacy (DP) design is proposed by jointly considering DP amplification from gradient quantization and sparsification, and free Gaussian noises from wireless channels. (3) From the accessing networks' perspective, to improve the spectrum accessing resiliency, network scalability, and spectrum efficiency, a multi-bit over-the-air computation (M-AirComp) based spectrum accessing design is proposed, which can enable efficient transmission of FL model updates even with limited spectrum availability, reducing the total energy consumption for mobile devices. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-12

Louisiana State University (LSU), with support from the CC* Strategy-Campus award, is enhancing its cyberinfrastructure (CI) to address the gap between domain researchers’ computing needs and the limited data collection capabilities in rural coastal areas where network and computing resources are inadequate. By utilizing cost-effective, high-sensitivity devices such as smart cameras, drones, and health sensors, LSU aims to create a scalable Spatiotemporal Data Ecosystem. This ecosystem collects and stores large volumes of diverse sensor data from these communities, enabling LSU researchers to perform specialized data analytics. This project expands LSU researchers' ability to advance their work, particularly in biomedical, healthy built environments, and coastal research. Additionally, it bolsters existing communication and computing infrastructures, promotes interdisciplinary research, and paves the way for new research opportunities. The proposed ecosystem includes two core components: (1) an HPC cluster, offering domain experts a complete solution for data sharing, storage, and analytics, and (2) an edge server to facilitate remote sensing data collection. To ensure an optimized design that meets the needs of domain researchers, this project follows a structured plan aligned with four strategic objectives: (1) identifying scientific use cases and representative users; (2) documenting technical requirements of data analytics, networking, storage, user interface, and security by domain researchers; (3) creating a data ecosystem design that complies with the technical requirements of domain researchers; (4) devising an operational strategy to enhance and sustain the data ecosystem. These objectives form the foundation for advancing LSU's capacity to drive scientific discovery through a robust CI. This project is jointly funded by the Office of Advanced Cyberinfrastructure 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 2024 · 2024-11

Ocean basins form as continents break apart and seafloor spreading begins. The Atlantic Ocean began to form in the Early Jurassic (~190 million years ago) as the Pangaea supercontinent rifted. This project seeks to understand how the central Atlantic Ocean first formed. Geologic data of this time suggest an anomalous geology at the time of initial seafloor spreading, but for reasons that are poorly understood. New marine seismic data will address the first ~50 million years of seafloor spreading in the western Atlantic, next to Eastern North America. The new seismic data will reveal the evolution of oceanic crust and mantle as the Atlantic formed. Participation by students and early-career scientists will give them first-hand experience in marine geophysics. Formation of ocean basins is fundamental to plate tectonics, yet initial seafloor spreading processes remain enigmatic. The asthenosphere during incipient spreading is likely anomalous in its composition, temperature, and flow patterns compared to mature seafloor spreading. The timescales of thermal and chemical depletion of the mantle and establishment of normal seafloor accretion have not been resolved. This project will shed light on these processes by conducting a novel 2D/3D seismic experiment adjacent to the Eastern North American Margin, spanning the first ~50 Myr of seafloor spreading. The seismic survey will collect four profiles along which both multichannel seismic and ocean bottom seismometer (OBS) data will be acquired. Three shorter profiles will run parallel to paleo-spreading direction, and one long profile will be perpendicular to the shorter profiles. The new data will constrain the evolution of oceanic crustal thickness, composition, and basement roughness. In addition, OBS recordings of 3D active-source mantle refractions and ambient noise surface waves will be analyzed to infer the orientation and magnitude of asthenospheric anisotropy. Seismic observations will be synthesized with petrological models of mantle melting and oceanic crust crystallization to study the chemical composition and potential temperature of the mantle source regime and how it evolved over time during early seafloor spreading. This experiment will bridge a critical gap between existing seismic datasets across the rifted margin and on mature Atlantic oceanic lithosphere. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-10

Limb regeneration is a complex biological process not fully understood at the genetic level. Salamanders are the only vertebrates with limbs that can completely regrow a lost limb. However, some fish, like the African lungfish and the grey bichir (Polypterus), can fully regrow their fins, even if they are cut off at their base. This ability is not found in commonly studied fish such as zebrafish. The proposed research will use a multilayered, comparative approach, looking at salamanders, lungfish, and Polypterus to identify the key elements needed for limb and fin regeneration. The hypothesis being tested is that these species deploy a shared genetic program of regeneration. First, this proposal addresses whether a specific molecular signaling (the mTOR signaling pathway) is a common feature of both limb and fin regeneration. Next, a comprehensive dataset of gene expression information will be obtained from the animal models to search for a shared set of genetic and cellular tools for regrowing limbs and fins. Finally, DNA elements that control gene expression during limb and fin regeneration will be identified and the hypothesis that loss of the ability to regenerate is linked to changes in how tissues control gene activity will be tested. These studies using multiple species will help reveal general mechanisms that control the complex process of regeneration. This project will train researchers at multiple academic stages, from undergraduates to postdoctoral researchers. Outreach to middle school students will provide research opportunities to underrepresented populations and therefore contribute to broadening participation in STEM. Limb regeneration is a prime example of a complex biological trait for which the genetic and genomic underpinnings are poorly understood. Although salamanders are the only limbed vertebrate that can regenerate the entire limb, fishes such as the African lungfish (Protopterus annectens) and Polypterus fully regrow fins even when the amputation occurs at the very base of the fin, across the proximal endoskeleton. This ability to regrow entire fins is lacking in traditional fish models such as the zebrafish. This proposal uses a phylogenetically-informed, multi-scale approach, using the axolotl, the lungfish and the Polypterus, to identify the core components of a shared “toolkit” of limb and fin regeneration. The first aim of the project tests the hypothesis that a rapid activation of an mTOR-mediated translational program is a conserved feature of limb and fin regeneration and identifies transcripts differentially translated during the early event of wound closure that marks the onset of regeneration. The second aim is focused on the integration of bulk, single nucleus and spatial transcriptomics datasets to determine if our animal models activate an evolutionarily shared genetic and cellular “toolkit” for appendage regeneration. In the third aim, epigenetic profiling will be deployed to reveal shared gene regulatory networks of limb and fin regeneration and test the hypothesis that loss of regenerative capacity is associated with widespread divergence of tissue regeneration enhancers. The multi-species, systems-level studies proposed here will bring the field closer to uncovering the general mechanisms governing the complex trait of regeneration. This proposal is co-funded by the Division of Integrative Organismal Systems (via the EDGE program and the Developmental Systems Cluster), The Division of Emerging Frontiers, and the Division of Environmental Biology. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-10

This project will improve the ability to analyze attacks on formats for sharing machine learning models, as part of a larger research program to advance digital forensics techniques around artificial intelligence (AI). Digital forensics involves the scientific acquisition, authentication, and analysis of digital evidence as it applies to the law. It is used to gather evidence in court cases, aiding in the conviction of criminals, saving lives, and promoting equity and justice. The widespread use of AI in our daily lives and critical systems necessitates an understanding of how to investigate AI failures when they occur. However, little work currently exists in forensics for AI systems in general or shared machine learning models in particular. These shared model formats are important to developing AI-based systems that cross organizational boundaries, providing a practically important starting point for advancing science, practice, and education around digital forensics for AI-based systems. In particular, the work addresses a legal forensic necessity for the admissibility of digital evidence in courts of law that will have a substantial impact on the cybersecurity industry. The success of this initiative will have far-reaching implications for industries that rely heavily on ML models, such as government, finance, healthcare, and transportation, by improving their ability to detect and mitigate potential threats and vulnerabilities. To achieve this, the project focuses on the field of model forensics, particularly on HDF5 model file forensic parsing, data injection, and provenance attribution. The primary objective is to enhance the current state-of-the-art and develop novel tools and techniques to aid practitioners in analyzing and connecting model files to their respective training systems. The project has three specific aims: (1) Develop and deploy forensic tools for reconstructing HDF5 model files, and assess their precision in data recovery from extensive datasets using benchmark performance indicators; (2) Evaluate data injection and detection methods in HDF5 by implementing a large-scale experiment and assessing performance using established detection and retrieval metrics; and (3) Design and appraise a systematic approach for AI model ballistics (associating a model with its training hardware, software, and data) using standardized testing protocols, reference datasets, and performance criteria to ensure accurate, consistent, and replicable outcomes. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-10

This Faculty Early Career Development (CAREER) award will support research to develop fungal mycelial improvement and mud dauber-inspired 3D soil printing to strengthen, waterproof, compact, and additively print soil for sustainable, durable, and cost-effective earthen buildings. One-quarter of the global population resides in earthen buildings constructed using unsaturated soil. Earthen buildings are affordable, recyclable, fire-resistant, and have low carbon footprints due to the inexpensive, locally sourced, and environmentally sustainable nature of soil compared to fired brick, steel, and concrete. However, enhancing the resiliency and durability of earthen buildings often requires soil stabilization using energy-intensive binders (e.g., Portland cement) and labor-intensive compaction. These methods consume energy-intensive resources, and result in waste-intensive, high-cost, and prolonged construction processes. This CAREER project aims to address these challenges by developing innovative fungal mycelial soil improvement and mud dauber-inspired 3D soil printing techniques. These advancements have the potential to (1) address the affordable housing challenge posed by the significant increase in world population and (2) contribute to a sustainable building industry using eco-friendly materials and reducing greenhouse gas emissions. The integrated education and outreach program aims to increase interest and retention of students in engineering and raise public awareness about the benefits of earthen buildings and bio-mediated and bio-inspired geotechnics. The research team will engage local parish residents, undergraduate and graduate students, and middle and high school students in education and research through activities such as a citizen science project, a 3D soil printing lab module, a capstone design course, outreach demonstrations, and summer research opportunities. Fungal mycelium can improve the shear strength, erosion resistance, and durability of earthen building walls by binding soil grains, increasing capillary cohesion, and waterproofing soil surfaces. Unlike energy-intensive binders, fungal mycelium is self-grown rather than manufactured. Many fungal mycelia are non-pathogenic and self-healable in soil, ensuring their use in earthen walls is practical and durable. Mud dauber nest construction can inspire improvements in extrusion-based 3D soil printing by controlling soil type and moisture content, incorporating vibratory compaction, harnessing drying to improve soil shear strength, and printing tubular cellular elements to optimize soil use and improve structural stability. The research objectives include: (1) investigating the hydro-mechanical behavior of fungal-treated soil under wetting-drying processes, (2) developing mud dauber-inspired 3D-printing processes, (3) producing mud dauber-inspired 3D printed cellular wall elements enhanced by fungal mycelium, and (4) assessing the durability of earthen walls assembled with the cellular wall elements. These innovative techniques are anticipated to eliminate the need for energy-intensive binders and labor-intensive compaction, expedite earthen construction, minimize material waste, and ultimately lead to durable, and cost-effective earthen buildings. This project is jointly funded by the Engineering for Civil Infrastructure 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 2024 · 2024-10

The United States (U.S.) transportation sector remains a cornerstone of the economy, contributing over 8% to the country's Gross Domestic Product (GDP). Electrification efforts are transforming this sector, aiming to enhance mobility efficiency, reduce operating and maintenance costs, and cut greenhouse gas emissions. These efforts also seek to boost energy independence and security while significantly contributing to employment, particularly in technology and innovation fields. This shift has already placed more than 2.5 million Electric Vehicles (EVs) on U.S. roads, supported by over 70 thousand charging stations nationwide. To manage this advanced and complex cyberinfrastructure (CI), EV operators and vendors rely on cloud-based EV Management Stations (EVMS), crucial for provisioning services such as charging, billing, and authentication. However, the critical nature of EVMS has made them targets for malicious attacks, often state-sponsored, exploiting rarely investigated vulnerabilities. In response, this project establishes a collaborative ecosystem among academia, industry, and the public sector to bolster the resilience of the EV CI. It aims to develop proactive methodologies to identify and analyze Internet-connected EVMS and their software, thoroughly exploring and mitigating related vulnerabilities. This initiative connects several diverse Minority Serving Institutions (MSIs) within the established ecosystem, fostering joint research and providing enriching training opportunities. Through workshops, capstones, curricula material, virtual hands-on labs, professional development, and mentorship programs, the project enhances cross-disciplinary capacities at MSIs and beyond, driving forward the future of resilient, electrified transportation. In this context, this project serves NSF's mission in promoting the progress of science and securing national defense related to this ever-evolving CI. The project pioneers advanced fingerprinting techniques employing automated web scraping, recursive unsupervised learning algorithms, and pattern matching methodologies to identify and cluster Internet-scale EVMS. The primary objective is to detect deployed configurations and their interconnections, while retrieving critical artifacts, such as firmware binaries and compiled software, for comprehensive vulnerability analysis and disclosure. Leveraging robust industry connections, the project acquires auxiliary artifacts, including EVMS source code, through advanced supply chain reconnaissance and reverse engineering methods. This initiative also devises and implements an advanced digital forensic methodology rooted in ensemble techniques and machine learning classifiers. It integrates static analysis, file system forensics, memory forensics using volatility frameworks, data carving with custom heuristics, offensive security tactics, behavioral analysis through dynamic instrumentation, and virtualization methodologies such as hypervisor introspection to meticulously analyze the security posture of EVMS firmware and web endpoints. Furthermore, the project exploits state-of-the-art innovations in Large Language Models (LLMs) to automatically identify vulnerabilities in EVMS source code and suggest tailored and sound code fixes. This is accomplished by creating an unprecedented instruction-based training dataset using supervised fine-tuning, reinforcement learning, and transfer learning techniques. Additionally, the project establishes a large-scale data and threat repository to index discovered threat models, associated vulnerabilities, and retrieved EVMS artifacts. Accessible via RESTful APIs and web-based interfaces, this repository democratizes knowledge by making the harvested EVMS assets available at large, significantly empowering EVMS-centric threat situational awareness while fostering advanced research and development. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-10

With the support of the Future of Semiconductors (FuSe) Program, Professors Anthony Engler, Phillip Sprunger, Revati Kumar, and Christopher Marvel of Louisiana State University (LSU) will design, synthesize, and investigate new polymeric materials and processes for high-resolution patterning in semiconductor manufacturing. New research infrastructure will be installed at the LSU Center for Advanced Microstructures and Devices (CAMD) synchrotron that will enable industrial and academic researchers in the Southern U.S. to access high energy photons used in state-of-the-art manufacturing systems. Optical patterning, or photolithography, is the most critical technological bottleneck in the fabrication of computer chips. Recently the semiconductor industry started to utilize Extreme Ultraviolet photons (EUV, 13.5 nanometer wavelength) to generate smaller, critical features that make chips faster and more powerful. This project will develop new classes of EUV-sensitive polymers, investigate their fundamental interactions with photons and electrons, and co-design the materials with metrology and pattern transfer processes. The broader impacts of this award include educational components to college students, outreach to K-12 students and the local community, and workforce development to train future technicians, engineers, and researchers to support the U.S. semiconductor industry and its supply chain. This award will help launch a Semiconductor Fabrication Workshop that will teach the semiconductor manufacturing process and provide hands-on training in cleanrooms to students from colleges within Louisiana that lack access to such facilities and course opportunities. This project is focused on the design and synthesis of new polymers that can serve as dry-developed, chain scission resists for EUV lithography and the construction of an EUV exposure module at the LSU CAMD synchrotron. Chain scission resists (CSRs) have the potential to improve line edge roughness and resolution limit due to a lack of photoactive small molecules that can diffuse to reduce patterning fidelity. However, CSRs typically suffer from low EUV sensitivity. Therefore, this research will design new classes of polyaldehydes that are inherently sensitive toward EUV photons, or the secondary electrons produced during exposure, and harness their relatively low thermodynamic ceiling temperature to achieve chemical amplification via depolymerization during the post-exposure bake. The latent image will be developed by vaporization of the resulting small molecules without liquid solutions that can also contribute to pattern failure. These materials will be co-designed with critical, downstream processes of photoresists, including metrology and pattern transfer needed to reliably fabricate advanced devices. The research will utilize custom operando EUV experiments, state-of-the-art electron microscopy, and molecular dynamics simulations to provide fundamental insights into polymer interactions and mechanisms during EUV exposure and development. This polyaldehyde platform will enable next-generation semiconductor fabrication processes with their low energy transformation from solid to vapor. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-10

Federated Learning (FL) has emerged as a popular distributed machine learning paradigm in a wide range of sectors (e.g., healthcare, fintech, and autonomous driving) because of its potential of protecting people’s privacy - it does not require gathering all the data in one place for operation. Meanwhile, driven by the increasing ubiquity of mobile devices, FL applications are shifting from wall-plug powered artificial intelligence (AI) devices to battery-powered mobile AI systems (e.g., smartphones, tablets, wearables). Existing research largely ignore the role battery energy awareness plays in efficient FL training over mobile AI systems. This project addresses this challenge and innovates on developing an energy-efficient FL framework for mobile AI systems, making these systems more suitable for execution on everyday mobile devices without draining their batteries quickly. The project's broader significance and importance are its potential to advance mobile computing and AI technologies, ensuring both are energy-efficient and privacy-preserving. Furthermore, this project shares its research artifacts and results with the community and includes educational activities targeting under-represented groups in computing. This project investigates the efficiency, quality, and robustness of FL systems from an energy perspective, aiming to develop a comprehensive energy-efficient FL framework for mobile AI systems. The research is structured around three synergistic objectives. First, the project develops a universal energy estimation methodology applicable across a variety of devices engaged in FL training, incorporating Deep Neural Network (DNN) models with diverse architectures. Next, utilizing insights into energy consumption, the project explores strategies to enhance the energy efficiency of FL, particularly in high-speed communication scenarios such as autonomous driving and augmented/virtual reality. Additionally, the project integrates learning performance metrics, such as accuracy and latency, with energy parameters—including energy consumption and battery life—in the FL participant selection process. This integration aims to create a balanced and optimized learning environment. To support these goals, the project establishes a mobile AI testbed and energy measurement setup, equipped with real-world FL benchmarks and workloads. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-09

Mentors play an incredible role in supporting and cultivating the talents of the nation’s human resources, recognizing potential and helping to catalyze growth for advancing scientific innovation and discovery. The Presidential Awards for Excellence in Science, Mathematics, and Engineering Mentoring (PAESMEM) represents the best of the best STEM mentors in the U.S. and is widely acknowledged as one of the most prestigious mentor groups with demonstrated excellence in cultivating the talent of all. Though these leaders have worked effectively in their contexts, rarely have they had opportunities to convene in ways that allow this body to work together, learning from each other and supporting the learning and development of others who could benefit from their expertise. This represents a knowledge gap and a unique opportunity to leverage this brain trust to address critical needs in the development of STEM ecosystems that can effectively support the success of all. This activity will organize a convening of PAESMEM Awardees and Mentoring Scholars and Practitioners to collectively advance knowledge and evidence-based approaches to mentoring. The focal point of this conference of PAESMEM leaders is to reflect on local and national trends impacting STEM education and workforce development in the U.S. and ways to advance and scale STEM mentoring that effectively reduces and eliminates opportunity gaps by developing the talents of all in STEM-related disciplines. The conference will target 75 participants from the PAESMEM and broader STEM mentoring communities. A guiding coalition of PAESMEM Awardees and Finalists will direct the engagement plan, design the conference agenda, and oversee the development of a national report of findings. The report and recommended tools will promote mentoring practices that reduce opportunity gaps, particularly for the most vulnerable human resources in STEM. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-09

Quantum entanglement is a distinctive feature of quantum systems, presenting a novel resource for fundamental science and technology. In contemporary laboratories, entanglement is systematically generated and controlled across diverse systems. These advancements are progressively influencing technology, with entanglement being a cornerstone in the field of quantum technologies, set to revolutionize various aspects of daily life. Although most advances have been primarily restricted to non-relativistic systems, progress is starting to extend into the domain of relativistic quantum mechanics. This research project aims to deepen our understanding of entanglement in relativistic quantum field theories with special emphasis on the role of gravity in this structure. The goals include combining advancements in theory and technology to experimentally validate aspects of the intricate relationship between quantum entanglement and the geometry of spacetime. By fostering interdisciplinary collaboration with experimental groups, the project seeks to influence quantum technologies and train new researchers. Additionally, it includes an outreach program targeting the general public and local schools in Baton Rouge. The goal of this project is to deepen our understanding of entanglement in quantum field theories in curved spacetimes and quantum gravity. A primary objective is to understand the role of spacetime geometry in the entanglement content of typical quantum states of matter. The project is composed of a set of interconnected subprojects, encompassing different aspects of the interplay between quantum field theory in curved spacetimes, quantum information, and quantum gravity. These sub-projects include (i)Theoretical exploration of the entanglement structure in both flat and curved spacetimes, with a focus on finite sets of degrees of freedom. (ii) Investigating the potential to probe the entanglement of the quantum vacuum using relativistic particle detectors. (iii) Studying entanglement generation in rapidly rotating systems, including black holes, and collaborating with experimentalists to achieve experimental confirmation. (vi) Investigating laboratory systems capable of verifying the generation of entangled pairs by time-dependent geometries. Each subproject is self-contained, but collectively they work synergistically to push the boundaries of this important and timely research area. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-09

About 15-20% of the adult population identifies as neurodivergent. These individuals offer immense unrealized potential as employees; however, they are a vulnerable community subject to extreme social and systemic inequities in jobs and higher education. Neurodivergent adults experience chronic unemployment and underemployment. When employed, they are underrepresented in management and leadership roles within organizations. Solving this complex problem goes far beyond the reach of any single discipline, but requires theories, methodologies, and approaches that encompass policy, organizational, group, individual and technological insights as well as meaningful involvement of neurodivergent individuals. The objective of this planning proposal is to assemble a team of researchers, employers, educators, advocates, and neurodiverse individuals to study, develop and disseminate organizational and technological evidence-based practices to better support the advancement of neurodivergent individuals in meaningful, productive work and increase worker productivity, job satisfaction, and career advancement. This project brings together an interdisciplinary team of researchers with expertise in artificial intelligence, behavioral science, data science, game design, organizational psychology, physical therapy, rehabilitation science and special education, to collaborate with advocates, educators, employers, and neurodivergent individuals to transform the current state of employment for adults who identify as neurodivergent. Building on previous NSF funded research, the work described in this planning proposal will create a muti-university, multidisciplinary Center for Neurodiversity Development and Advancement that includes both researchers and key stakeholders collaboratively designing research questions and developing solutions. Integrating scientific knowledge from educational, organizational, technological, and psychological research, each participating university capitalizes on its unique strengths and builds a collaborative team with neurodivergent individuals and advocates included as partners. Products of the center will include research to solidify factors underlying lack of employment opportunities, development of supports to enhance access to higher education and job training in collaboration with the neurodivergent community, development of strategies to facilitate meaningful employment and career advancement, and education to organizations and other key stakeholders within the broader community to promote employment equity. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-09

Louisiana State University will organize and host a conference entitled “Multidisciplinary Science in the Multimessenger Era,” September 23-26, 2024. The conference will focus on the multidisciplinary aspect of multi-messenger astrophysics, being the study of the universe by combining information from light, gravity, neutrinos, cosmic rays, meteorites, and ocean sediments. This is one of the key focus areas of the Astro2020 Decadal, “Pathways to Discovery in Astronomy and Astrophysics for the 2020s.” A goal of the workshop is to bring together interested subject matter experts who operate within and between the fields of Astrophysics, Gravitational Physics, Nuclear Science, Plasma Physics, Fluid Dynamics, Computational Physics, Particle Physics, and Atomic, Molecular, and Optical Science in order to understand how to maximize the scientific return of major and minor facilities, with a focus on those supported by the United States. Even within astrophysics itself, greater integration of theory and data analysis is required for progress. This meeting will deliver a white paper as well as recommendations for joint sessions at the Spring 2025 meeting of the American Physics Society. The portion of the workshop costs funded by the NSF will be used to reduce financial barriers to participation for scientists who may not otherwise be able to attend. Bringing together an interdisciplinary group of scientists will help ensure the greatest scientific return of major NSF-supported facilities including the Vera Rubin Observatory, LIGO, IceCube, and other facilities across the electromagnetic spectrum including the proposed ngVLA. The conference is organized with several guiding sets of questions in mind: 1. What are the most important multidisciplinary questions of interest for time-domain and multi-messenger astrophysics? 2. What are the key measurements? How can we leverage current and forthcoming facilities? Do we need new ones? For astrophysical observations, are additional coordination recommendations needed? 3. What advances are relevant for other fields of physics and national strategic priorities? 4. How can multidisciplinary research be fostered? Research in this area is a priority in multiple scientific disciplines, and it is important to ensure that the scientific community draws on the broadest set of individuals to solve these exceptionally difficult problems. The scientific organizing committee is comprised of a diverse set of scientists by gender, cultural background, scientific background, and geographic region. The organizers will target funding to support participation by scientists from underrepresented backgrounds, minority serving institutions, and early career researchers. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-09

Explosive objects in the distant universe can now be studied by simultaneously combining information from multiple messengers - gravitational waves, particles, and light. The investigators will develop software to deliver new discoveries and physical constraints concerning the nature of explosive objects. The investigators will provide students opportunities for cross-institutional internships and collaborations with amateur astronomers and citizen scientists. The research, methods, and visualizations will be directly included in developing courses at multiple institutions. The work will provide training for students in critical areas for astrophysics and beyond, including robust application of machine learning. The team will partner with the LIGO Science Education Center and The Baton Rouge: Bringing Youth Technology, Education and Success programs to utilize multimessenger astronomy to inspire K-12 students in the state of Louisiana. A 4-year research program led by investigators at the Louisiana State University, Harvard University, University of Minnesota-Twin Cities, and University of Maryland, College Park will improve our understanding of explosive transients. The exotic zoo of explosive transients is still being explored, and the overlap of signals seen at different wavelengths is key to their taxonomy. Explosive transients occur at the extremes of physics, beyond the reach of terrestrial laboratories. Multiwavelength and multimessenger observations of these transients enable advances in areas including gravity, fundamental physics, dense matter, cosmology, and the origin of the elements. The proposed work will enable new discoveries through the power of the Vera Rubin Telescope with concurrent observations provided by high energy and gravitational-wave observatories. The research team will combine observations of compact objects with the Vera C. Rubin Observatory’s Legacy Survey of Space and Time with space-based gamma-ray burst monitors and ground-based gravitational-wave interferometers. Focusing on gamma-ray bursts and supernovae, the team will construct new optical transient classifiers, develop the formalism to associate distinct signals across wavelengths and messengers from the same event, characterize these events through dedicated follow-up, and enable global discovery via public alerts. The result will be an end-to-end multiwavelength and multimessenger discovery machine. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-09

The Poisson 2024 Summer School and Conference will take place July 1-12, 2024 in Naples, Italy at the headquarters of the Accademia Pontaniana at the University of Naples. Poisson geometry is a highly interdisciplinary field which has found numerous applications both to mathematics and to many areas of physics. The goal of Poisson 2024 is to make the latest developments in the field accessible to participants at all career stages, initiating an active exchange of ideas and giving them an opportunity to form new collaborations. The first week of this program will consist of a summer school of five minicourses delivered by senior experts, whose goal is to introduce junior researchers to a broad range of topics in some of the most active areas of Poisson geometry. The second week will feature a research conference with twenty talks by leading researchers and early-career emerging experts that will highlight recent breakthroughs. The purpose of this award is to support the participation of US-based researchers, especially those at an early career stage, in this international event. The program of the meeting will survey the latest progress in a broad range of areas with links to Poisson geometry. The summer school will have a strong training component that features both introductory courses in Dirac geometry and shifted symplectic geometry as well as more advanced surveys of deformation quantization, mathematical physics, and quantum groups. The conference will include talks on a variety of rapidly developing research topics, including symplectic and Dirac geometry, generalized complex structures, Lie algebroids and Lie groupoids, geometric mechanics, Poisson algebraic geometry, integrable systems, higher structures, non-commutative geometry, quantum groups, and rapidly developing areas of Poisson-Lie groups and cluster theory. During the opening of the conference, the winners of the Andre Lichnerowicz prize for notable early-career contributions to Poisson geometry will also be announced. More details can be found at https://sites.google.com/view/poisson2024/poisson-2024. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-09

This project aims to provide a novel perspective on machine learning and explore its impact on biomedical research. Online machine learning has the benefit that it allows for sequential data processing which can ultimately support decision making - key topics in an era of shared economy, cybersecurity, and big data. The investigator plans to develop new approaches and the underlying theory for online machine learning in the context of game theory and partial differential equations that have a range of applications, including finance, medicine, and spam detection. Furthermore, in collaboration with biomedical researchers, the investigator will analyze the performance of algorithms for predicting unknown medical data from images of the human body, which is used in obesity, metabolism, and nutrition research. Undergraduate and graduate students will be trained on some of the latest machine learning algorithms as part of this project, preparing them for careers both in research and in industry jobs. A fundamental challenge in online machine learning is the increasing complexity of existing algorithms' implementations. Furthermore, most algorithms are not tailored to specific use cases and thus provide suboptimal solutions. This work will provide alternatives to the currently existing online machine learning approaches by introducing ideas and techniques from partial differential equations and optimal control theory. By targeting specific models and using scaling limits, these continuous methods are expected to provide a new framework to find and analyze novel, proved-to-be-optimal algorithms for big data. This research involves a novel approach for using continuous tools to solve discrete problems, providing a fresh view on classically discrete frameworks. A novel game-theoretic interpretation of the optimal strategies can also inform properties of the corresponding partial differential equation solution. This work also entails the analysis of biomedical data using various regression, supervised learning, and semi-supervised learning 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 2024 · 2024-09

The project will provide state-of-the-art computational tools for the development of novel 2D materials and their potential application to ultra-fast electronic, opto-electronic, and magnetic devices; unconventional optical and photonic devices; communication devices; and quantum computing applications. The project will address interconnected challenges in emerging areas of quantum science, computational mathematics and computer science by effectively merging highly domain-specific techniques with general machine learning techniques, thus informing and motivating analogous research on model order reduction across the sciences and engineering. 2D materials research is an ideal platform to motivate new mathematics training and curricula in the analysis, modeling, and computation of electronic structure, mechanical and topological properties of materials, and analysis of experimental data. The project’s outreach to female and underrepresented student populations will broaden the diversity of the mathematical research community, and the project provides research training opportunities for graduate students. Many quantum phenomena of scientific and technological interest emerge naturally at the moiré length scales of layered 2D materials which makes those materials an exciting platform to explore quantum materials properties and to prototype quantum devices. For example, correlated electronic phases such as superconductivity have been recently observed in twisted bilayer graphene (tBLG). Such pioneering results have opened up a new era in the investigation and exploitation of quantum phenomena. Despite the continuing increase in computational resources, high-fidelity modeling and simulation of many quantum materials systems remains out of reach. The limitation is particularly serious in 2D heterostructures due to the large scales at which the quantum phenomena of interest emerge. The objective of this NSF-NSERC Alliance project is to develop an advanced computational modeling workflow, merging state-of-the-art quantum modeling and machine-learning methods to enable rapid, automated, high-fidelity exploration of mechanical and electronic properties of 2D quantum materials. This award is jointly supported by the Division of Mathematical Sciences, the Division of Materials Research and the Office of Advanced Cyberinfrastructure. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-09

With the support of the Chemical Synthesis Program in the Division of Chemistry, Professor Rendy Kartika of Louisiana State University is studying the development of new organic reactions using epoxides. Epoxides are abundant chemical feedstocks that are reactive due to their inherent ring strain. Because of their unique reactivity, epoxides can be readily transformed into value-added chemical derivatives. This award is allowing Professor Rendy Kartika and his team to investigate new chemistries for the synthesis of chlorine-containing organic compounds from epoxides having widespread relevance including in the chemical, pharmaceutical, and materials industries. In addition to their applications, the Kartika group is studying the mechanistic pathways by which the reaction products are formed. In addition to the research, this project is providing valuable training and mentoring opportunities for the next generation of scientists who, in the future, will make valuable contributions to industry, academia, and national laboratories. In addition to training the next generation of researchers, Professor Rendy Kartika is committed to fostering excellence in undergraduate chemistry education at LSU. His activities include efforts to develop new degree initiatives to increase the number of chemistry majors, the modernization of chemistry teaching laboratory instrumental infrastructures and pedagogy, and the creation of a three-semester sequence of General Chemistry for students who require reinforcement of mathematics and chemistry foundation to improve success in this gateway course to STEM degrees. Professor Rendy Kartika and his team at Louisiana State University are examining the reactions of epoxides and related structures in the presence of triphosgene and pyridine. Epoxides are a unique functional group. Due to ring strain, the two adjoining carbon atoms in epoxides are electrophilic. However, the same ring strain also renders epoxides nucleophilic at the oxygen atom. This project is exploiting this dichotomy of reactivity to develop new organic synthetic transformations enabled by triphosgene and pyridine. More specifically, the combination of these reagents activates epoxides to epoxonium ions. These emerging reactive species then undergo nucleophilic ring opening by the chloride ions liberated from triphosgene degradation at the sterically more accessible direction, thereby forging carbon-chlorine bonds. Facilitated by this chemistry, Professor Rendy Kartika is investigating new stereoselective reactions for the synthesis of value-added organochlorine heterocyclic compounds. A particular heterocyclic motif that is a subject of this project are tetrahydropyrans, a six-membered oxygen-containing cyclic structure commonly found in biologically active natural products and pharmaceutical compounds. In addition to focusing on applications and the products from the methodology, this project also features investigations into the reaction mechanisms by which epoxonium ions are formed using in situ NMR spectroscopy 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 2024 · 2024-09

This project is jointly funded by the Established Program to Stimulate Competitive Research (EPSCoR), and funds allocated to Clean Energy Technology Initiative investments. This Research Advanced by Interdisciplinary Science and Engineering (RAISE) award is made in response to Dear Colleague Letter 23-109, as part of the NSF-wide Clean Energy Technology initiative. Developing critical floating offshore wind (FOW) technology is imperative to achieve the net-zero carbon goal by 2050. This award is dedicated to advancing the fundamental understanding of the complex individual- and system-level performance of FOW turbines (FOWTs) under operational and extreme conditions, and the comprehensive impacts of large-scale FOW on local and regional climate and ocean environments. Supercomputing capabilities will be integrated with multiscale multidisciplinary modeling to offer new knowledge and computational capabilities to achieve extreme-condition resilient, cost-competitive, and environmentally sustainable offshore wind energy. This research will advance the state of knowledge of the complex performance of individual FOWT and FOW farms exposed to wind-wave-current-wake flows under operational and extreme conditions. On the other hand, the short- and long-term impacts of FOW on local and regional climatic and oceanic environments will be studied systematically and comprehensively. Research findings from this project will enable optimized planning and design of the next-generation FOW facing climate change and extreme marine conditions. Research outcomes will help offshore wind industries reduce the costs associated with design, installation, operation, maintenance, and decommissioning to minimize the life cycle cost and environmental impacts. Cohesive outreach and educational programs will be developed and integrated with research activities. Specific outreach activities include developing new curricula for relevant STEM courses, engaging graduate and undergraduate students, especially those from underrepresented groups, in research, offering seminars/webinars to stakeholders, coastal community managers, and governmental officials. The overall goal of this research is to reveal the highly complex multiscale interaction mechanisms among wind-wave-current-wake flows and FOW (individual FOWTs and FOW farms), and the comprehensive FOW impacts on the local and regional climatic and oceanic environments. Novel multi-fidelity hydrodynamics computational modules will be developed and integrated with aerodynamic and aeroelastic modules to simulate the complex multiscale performance of FOW. To meet the huge computational demand, supercomputing capabilities will be leveraged to implement multiscale multi-fidelity multidisciplinary modeling to achieve the research goal. Specific research objectives of this project include: 1) development of a novel large eddy simulation based multi-fidelity model to simulate the complex dynamics of FOWTs; 2) understanding the individual- and system-level performance of FOW under operational and extreme conditions; 3) parameterization of FOW farms (FOWFs) via multiscale modeling; 4) modeling local and regional climatic and oceanic impacts of FOWFs; 5) exascale computing acceleration of high-fidelity models. The research project will answer the following fundamental questions: (i) how do extreme conditions affect the dynamic stability and structural integrity of an individual FOWT, and the system-level performance of FOWFs? (ii) what is the optimized FOWF layout under given wind-wave conditions? (iii) how do FOWFs impact the local and regional climatic and oceanic environments? The research data and developed computational programs will be made open source and shared with the offshore wind and natural hazard research community to educate the next generation of scientists, engineers, leaders, educators, and managers to be prepared for large-scale deployment of offshore 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 2024 · 2024-08

The research aims to understand and exploit randomness as it appears in a diverse collection of settings. For example, defects and impurities in materials or microscopic turbulence in fluids are fundamentally random, but they can be described deterministically using models based on a complicated averaging, which provide an accurate description of the original system and are used to develop efficient numerical simulations. In other settings one is interested in modeling the likelihood of rare events, such as mechanical failures in engines or extreme concentrations of heat. The probability of these events can be understood using equations with a random noise, where the structure of the noise is determined by the system's small-scale dynamics. As a final example, in machine learning randomized algorithms are used to explore enormous data sets whose size makes computation impractical if not outright impossible. Neural networks are trained by deliberately introducing randomness into the algorithm, where at each step the system is optimized over a small but random sample of the data. Graduate and undergraduate students will be mentored as part of this project. In addition, seminars and workshops in probability will be held at LSU, and a K12-outreach program with local students will be developed. The research will focus on the areas of stochastic homogenization, stochastic partial differential equations (SPDEs) and interacting particle systems, and randomized algorithms in machine learning. The first topic, homogenization theory, analyzes the properties of systems with complicated microstructures. The current project will make the first connection between the homogenization of incompressible flows and SPDEs with Brownian transport noise and will enhance our understanding of the longtime and equilibrium behavior of diffusion processes in random environments. For the second topic, this project will make a mathematically rigorous connection between macroscopic fluctuation theory and fluctuating hydrodynamics in the context of certain interacting particle processes and interacting diffusive systems. The PI will develop a robust well-posedness theory for the related SPDEs and characterize their stochastic dynamics. The third area of research will put forth a local analysis of randomized algorithms in machine learning that avoids unrealistic conditions like global convexity or contractivity. The research will establish a quantitative criterion to identify basins of attraction in the loss landscape and sharp estimates for the convergence of stochastic gradient descent in the basin of attraction. 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.