University Of North Texas

NSF Awards · FY 2025 · 2025-04

The National Science Foundation (NSF) supports the research of Professor Xin Cui at Mississippi State University. This project is jointly funded by Chemical Catalysis Program of NSF's Division of Chemistry and the Established Program to Stimulate Competitive Research (EPSCoR). Professor Cui is developing new routes to organic compounds that could be used in drugs and polymers. The specific goal is the invention of ways to make organic compounds that are behave differently from their mirror images. This type of structure requires methods that rely on particularly subtle chemical structures. Professor Cui proposes to solve these challenges using catalysts containing the element called ruthenium. Ruthenium forms compounds with appropriately subtle structures, which facilitate the formation of desired organic products . New insights will be obtained by systematic changes in the ruthenium catalysts. The award also supports the training of undergraduate and high school students through “student-to-student” science education. Hands-on laboratory experience is provided to participating undergraduates. The chemistry club organized by Professor Cui provide STEM education to K-12 students. Overall, this research project helps to improve the sustainability and innovation of the chemical industry while increasing the technical workforce of the United States. The National Science Foundation (NSF) supports the research of Professor Xin Cui at Mississippi State University. This project is jointly funded by Chemical Catalysis Program of NSF's Division of Chemistry and the Established Program to Stimulate Competitive Research (EPSCoR). Stereoselective C–H functionalization has attracted much attention because the opportunities are significant both scientifically and economically, but still faces major challenges. This challenge is being met by Professor Cui whose research shows that ruthenium(II)-based catalysts are highly effective for functionalization of aromatic C–H bonds. Of still greater interest, his research reveals new stereoselective, particularly enantioselective, functionalization of various organic substrates. These developments open access to new pharmaceuticals and related bioactive compounds. Products targeted include alkaloids, chiral polymers, and future chiral ligands. In one approach, chiral directing groups are being developed for conferring stereocontrol. In another approach, a new class of chiral umbrella-shaped ligands enable long-range stereocontrol of C‒H functionalization reactions. Overall, the two themes are providing new fundamental insights that will enhance synthetic methods as well as to demonstrate the untapped potential of ruthenium(II)-catalysis. In parallel with the laboratory research, Professor Cui leads an undergraduate team that aims to familiarize young generations with modern organic chemistry. His team also aims to encourage the participation of high school and undergraduate students. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-02

Methanotrophic bacteria (methanotrophs) use methane as a source of carbon and energy; thus, play a critical role in Earth’s biogeochemical cycling of methane by either preventing its release to, or direct sequestration from, the atmosphere. These microbes have also garnered interest for biotechnology applications targeting conversion of methane to valuable products; and several studies support the feasibility of using methanotrophs to mitigate anthropogenic gas emissions and utilize natural gas and biogas methane for biomanufacturing. However, a more complete understanding of the metabolism and physiology operating in these bacteria is required to understand their role in the environment and to develop biotechnologies for efficient methane capture and conversion. This project will advance the understanding of methanotroph metabolism and physiology to enable the development of carbon and energy-efficient, methanotroph-based biotechnologies. This project will also foster scientific workforce development through advanced training of undergraduate and graduate students in basic and applied microbial sciences at the University of North Texas. Evidence shows that the methanotroph Methylococcus capsulatus can uniquely co-utilize CH4 and CO2 as carbon sources, but the underlying metabolism and its regulation is incompletely understood. This project will leverage recently developed methanotroph genetic tools and high throughput CRISPR interference (CRISPRi) functional genetic techniques to further define the role of CO2 and the ribulose-1,5- bisphosphate carboxylase/oxygenase (RubisCO) in M. capsulatus metabolism and physiology. These insights will guide the engineering of M. capsulatus biocatalysts for improved CH4 and CO2 conversion efficiency. These investigations will also provide a detailed systems understanding of an uncharacterized dual organic and inorganic carbon metabolism and its regulation that could be widespread in nature. 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 · 2025-02

This project aims to serve the national interest by developing undergraduate chemistry students’ competency to engage in the practices of scientists, as well as facilitating students’ beliefs in their abilities to engage in these science practices, which is known as science practice self-efficacy (SPSE). Facility with scientific practices and confidence in carrying them out are critical for students’ ability to apply their scientific understandings both in school and in post-school life. The project team plans to implement a novel approach to engaging chemistry learners in science practices. They also plan to develop new survey instruments to generate evidence that can be used to better understand the relationships among students’ science practice competency, self-efficacy, science identity, and chemistry course performance. The findings have the potential to improve introductory chemistry education and support the development of scientifically literate STEM graduates. This project is a collaboration between researchers at University of Nebraska-Lincoln and California State University-Fullerton. Project activities will include (1) developing and testing high-quality instruments for measuring science practice self-efficacy and science practice competency, (2) conducting a quasi-experimental study in general chemistry and organic chemistry courses to identify how engaging chemistry students in science practices supports their science practice competency and SPSE, and (3) modeling the relationship between SPSE and relevant outcome variables using a structural equation model. The project will leverage the complementary expertise of the research team and tailored expertise of the advisory board members to ensure the quality of all products generated through research activity. The measures of science practice self-efficacy and science practice competency to be developed will have the potential be useful for researchers across STEM education fields. These products will be disseminated through publications and resource databases accessible to the wider chemistry education community such as the Chemistry Instrument Review and Assessment Library (CHIRAL) and Organic Chemistry Educational Resources (OrganicERs). The NSF IUSE: EHR Program supports research and development projects to improve the effectiveness of STEM education for all students. Through the Engaged Student Learning track, the program supports the creation, exploration, and implementation of promising practices and tools. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-01

This award will fund travel and lodging for junior participants and others without access to sufficient funds to attend the 37th annual Workshop on Automorphic Forms and Related Topics (AFW). The AFW is an internationally recognized, well-respected conference on automorphic forms, which play a key role in many recent breakthroughs and constitute a major area of study in number theory and related fields. The workshop will meet at the University of North Texas in Denton, Texas, April 30 -- May 4, 2025, this will mark the return of the Automorphic forms workshop to Denton after exactly 20 years. The conference will provide a supportive setting for researchers to disseminate new results, learn from other researchers, and begin new collaborations. Automorphic forms have played a key role in many breakthroughs in mathematics, including the proofs of Fermat’s Last Theorem (by Wiles and Taylor--Wiles, employing work of Frey and Ribet), Serre’s Conjecture (by Khare, Kisin, and Wintenberger), the Sato-Tate Conjecture (by Barnet-Lamb, Geraghty, Harris, and Taylor), the Monstrous Moonshine Conjecture (for which Borcherds was awarded the Fields Medal), and the Fundamental Lemma (for which Châu was awarded the Fields Medal). Additional information can be found on the conference website: http://automorphicformsworkshop.org/ 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 S-STEM project at the University of North Texas (UNT) will contribute to the national need for well-educated computing professionals by supporting the retention and graduation of high-achieving, low-income students with demonstrated financial need. Over a six-year duration, 30 scholars in three cohorts, pursuing degrees in the Department of Computer Science and Engineering at the UNT, will receive support for up to three years. The goal of this project is to increase the retention and graduation rates by 20%. To achieve this goal, the project team will develop, adapt, and integrate a suite of programs to recruit, support, and prepare students to enter the workforce by leveraging strong support from the institution and industrial partners. Existing programming at the UNT will provide students with multi-faceted mentoring, cohort, and research experiences. The project will identify (1) the influential factors to students' decision to pursue a computing degree and continuing to graduation; (2) the effect of AI-focused programs and multi-faceted mentoring on the academic success and persistence of students; and (3) effective strategies to prepare students ready for a successful career in a STEM field. This project is funded by NSF's Scholarships in Science, Technology, Engineering, and Mathematics program, which seeks to increase the number of academically talented low-income students with demonstrated financial need who earn degrees in STEM fields. It also aims to improve the education of future STEM workers, and to generate knowledge about academic success, retention, transfer, graduation, and academic/career pathways of low-income students. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-01

Buyouts are government-funded property acquisitions that typically occur in the aftermath of disasters, removing homes from hazardous areas. Decisions about what to do with land acquired through buyouts are typically deferred and made without community input, resulting in land uses (such as vacant lots) with limited social or ecological benefits. This project is examining how land management practices after buyouts can integrate ecosystem services and community preferences to improve ecological and societal outcomes. By documenting and categorizing current land uses, estimating a diversity of ecosystem functions, and gathering perspectives on land use preferences from local residents and government leaders, the study is providing information to help maximize the benefits of buyout programs across the country. Lessons learned may be extended to other public lands, such as existing urban vacant land and properties acquired through managed retreat programs. Products include a series of community engagement workshops and a guide for increasing community engagement in the design and management of open space. Use of buyouts is increasing as communities face impacts from chronic, repetitive hazards. Studies are needed to analyze post-buyout land through a socioenvironmental systems (SES) lens and to evaluate the quantity and quality of ecosystem services, such as biodiversity or carbon sequestration, at these sites. This project is advancing understanding of how management of open space acquired for disaster mitigation affects long-term community resilience. Ongoing management of acquired land presents a societal challenge yet also an opportunity to improve ecological services and address environmental justice concerns. This project is bringing together an interdisciplinary team of physical and social scientists to examine post-buyout land management practices and increase understanding of related ecosystem services and stakeholder perspectives. Data collected using a mixed-methods approach will provide comprehensive assessment of ecological services present on existing buyout sites, perspectives about current and potential land uses from residents in surrounding communities, and perspectives regarding land-use decisions and decision-making processes from government leaders. The project is contributing to a growing body of literature on nature-based solutions to hazard mitigation and climate adaptation, aligning with NSF’s Build a Resilient Planet initiative. 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

This grant supports fundamental research in titanium alloys manufacturing and promotes the progress of science and engineering. Titanium alloys are promising structural materials due to their lightweight, high strength and toughness, high temperature and corrosion resistance, and biocompatibility and have many critical applications in transportation, such as airplane engine components, and healthcare, such as human implants. However, the manufacturing of titanium alloys requires the addition of expensive alloying elements and high processing temperatures, which leads to their high costs and significantly restricted commercial use. This project investigates the scientific mechanisms involved in deformation twinning and develops a prototype system for low-cost manufacturing of advanced lightweight titanium alloys. A combination of experimental, computation, and machine learning efforts is performed to search for new compositions of titanium alloys with low-cost alloying elements and activate novel deformation mechanisms in order to achieve their room-temperature manufacturing. The new knowledge generated by this project advances the titanium industry and promotes technologies to reduce carbon dioxide emissions and improve human health, thus promoting national prosperity and welfare. This research provides a platform to train the next generation of titanium experts and skilled workforce, especially those from underrepresented groups, in the manufacturing of advanced materials as well as high-performance computing. This project is jointly funded by Advanced Manufacturing (AM) program and the Established Program to Stimulate Competitive Research (EPSCoR). This project aims to advance cost-effective room-temperature manufacturing of titanium alloys by a novel alloy design and processing strategy. In this strategy, a large portion (greater than 50 volume percent) of the body-centered cubic beta phase is stabilized at room temperature using low-cost elements after casting and homogenization processes. Furthermore, room-temperature ductility and workability of these alloys in the subsequent cold deformation process are improved by activating sufficient highly-indexed deformation twinning modes in the beta phase utilizing coupled twinning-induced plasticity (TWIP) and transformation-induced plasticity (TRIP) mechanisms. Two specific approaches, involving integration of experiment, simulation and machine learning, are followed. The first approach is to identify and tune the coupling mechanisms between phase transformations and highly-indexed twinning in representative titanium alloys through advanced characterization, crystallography models and atomistic simulations. The second approach is to manipulate and investigate alloying effects on twinning and room-temperature workability of these alloys by iterative feedback between the machine learning models, informed by first-principles calculations, and high-throughput fabrication and mechanical testing experiments. These results guide the discovery of beta phase stabilized titanium alloys containing low-cost alloying elements and attain high room-temperature workability. Finally, large-scale samples of titanium alloys with optimized compositions are cold deformation processed by rolling and drawing into specific shapes and tested for mechanical behavior to verify their room-temperature workability. 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 award is funded in whole or in part under the American Rescue Plan Act of 2021 (Public Law 117-2). Multi-robot systems consist of autonomous robots interacting in a shared environment to achieve common goals. They are widely used in real-world application domains such as transportation, disaster management, as well as warehousing and manufacturing. This project develops an efficient, robust, and secure multi-robot system, called EdgeRobot. EdgeRobot establishes an edge computing based architecture and algorithmic framework to facilitate multi-robot collaboration and coordination in dynamic environments. This work provides new model, architecture, and theory for coordinated multi-robot systems. In addition, this project builds research capacity, sustainable for training underrepresented students via the partnership of six geographically diverse minority-serving institutions in the United States: the University of Houston-Clear Lake (South), the University of Michigan Flint (North), CUNY-New York City College of Technology (Northeast), Morgan State University (East), San Francisco State University (West), and California State University Dominguez Hills (West). The cross-institutional collaboration not only boosts research capacity in all six participating institutions but also provides integrative research and education experience to their underrepresented minority students. Ultimately, this project establishes and exemplifies an effective collaboration model for training and educating underrepresented students from geographically diverse minority-serving institutions. This project consists of the following three research thrusts. First, the novel edge computing infrastructure provides optimal and location-aware computing services for collaborative robots to achieve their common goals. Besides, reinforcement learning-based algorithms solve the multi-robot scheduling and routing problems, modeled as variants of the prize-collecting traveling salesman problem. Second, in tasks requiring collaborative actions, such as cooperative target tracking, multi-agent reinforcement learning enables teams of robots to operate, learn, and adapt in dynamic and human-populated environments robustly and safely. Third, integrating modern cryptographic and security primitives secures the collaboration among edge nodes in multi-robot systems. Consequently, the interface between EdgeRobot and its human team members builds a shared autonomy model. 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.

NIH Research Projects · FY 2024 · 2024-09

PROJECT SUMMARY Tyrosine kinase inhibitors (TKIs) are specified small-molecule inhibitors of the activity of tyrosine kinases and have shown therapeutic impact on treating cancers. Despite being a prevalent class of drugs in the pharmaceutical industry, their development and clinical application are frequently hindered by a wide range of cardiovascular complications: QT prolongation/arrhythmia, left ventricular dysfunction, congestive heart failure, ischemia, myocardial infarction, and hypertension. Cardiotoxicity's repercussions impede the advancement of novel TKIs for cancer therapy and furthermore cause failures in preclinical drug discovery and clinical development. Therefore, there is an urgent need to assess cardiotoxicity induced by TKIs used in cancer patients' treatment. However, there is still no targeted and effective preclinical trial due to a) only focusing on anti-tumor effects without systematic examination of coexisting cardiovascular effects, b) limited efficiency and accuracy of cardiotoxicity prediction, and c) inter-species differences between animals and humans on cardiotoxic responses. To address this unmet need, a new in vitro human model for comprehensively assessing TKIs-indued cardiotoxicity is proposed here. Recently, human pluripotent stem cell (hPSC)-derived vascularized cardiac organoids (VCOs) developed in PI’s and other labs have shown great promise in emulating the human heart in both cardiovascular structure and function, which makes it an ideal in vitro drug toxicity evaluation system targeting cardiovascular cells by TKIs. However, three significant challenges remain in applying hPSC-derived VCOs to cardiotoxicity evaluation: 1) insufficient and uncoupled assessment of organoid structural and functional properties; 2) continuous generation of large datasets from multifaceted characterizations that requires more integrated analysis without human bias; 3) low efficiency with more individual hPSC lines. It is hypothesized that artificial intelligence (AI)-driven biomedical data featuring and predicting address the challenges for accurately assessing TKI-induced cardiotoxicity by phenotyping the cardiovascular structure and function of VCOs. The central goal is to establish an organoid-AI system to assess TKI-induced cardiotoxicity efficiently and accurately with three specific aims: Aim 1. Predict TKI-induced toxicity on cardiovascular structure in VCOs by Generative AI algorithm; Aim 2. Phenotype TKI-induced cardiovascular dysfunction in VCOs; Aim 3. Establish a TKI-induced cardiotoxicity assessment system with an organoid-AI system. Upon completion of the proposed project, a comprehensive human organoid-AI system for assessing TKI-induced cardiotoxicity will be established to increase the efficiency and accuracy of preclinical drug safety evaluation of TKIs on human cardiotoxicity on both cardiovascular structure and function.

NSF Awards · FY 2024 · 2024-09

Society has grown to rely on smart, embedded, and interconnected systems. This has created a great need for well-qualified and motivated engineers, scientists, and technicians who can design, develop, and deploy innovative microelectronics and Artificial Intelligence (AI) technologies, which drive these systems. This project will address the need for a more robust computer science and engineering workforce, a matter of national security, by broadening access to microelectronics and AI education leveraging the cutting-edge technologies of Tiny Machine Learning and low-cost microcontroller systems in diverse Florida, Kansas, and Texas high schools. This project will leverage the partnership with the Scientist for Every Florida School network and nurture new relationships with industry partners. The goal of this project is to engage about 500 high-school students and approximately 25 teachers from under-resourced communities in the design and creative application of AI-enabled smart, embedded technologies, while supporting their engineering identity development and preparing them for the STEM jobs of tomorrow. This project will benefit society with its timely and accessible high-school curriculum that integrates Computer Science and Engineering using the rich context of microelectronics and AI. The curriculum will be accessible because it has no prerequisites for programming or hardware knowledge. Every module is centered around a real-world application of microelectronics and AI with direct implications for improving the quality of life in local communities, making learning relevant and place-based. All course materials and resources will be disseminated as open source via the platforms popular among K-12 stakeholders, broadening access and inspiring the next generation of AI practitioners. The focus of this design-based implementation research program is to conduct a systematic inquiry into the effective conditions for designing and integrating curricula and technologies that foster engineering identity development and conceptual understanding of AI in embedded systems as an important trend in engineering. To this end, the research is informed by both qualitative research questions (How are the altruism informed activities perceived and used by students?) and quantitative questions (What are the quantifiable impacts of this approach on students’ motivation and conceptions of edge AI and microelectronics?) The research plan will employ a concurrent triangulation mixed-method research design, incorporating phenomenology, comparative case studies, and mixed-effects modeling. Specifically, the researchers will conduct classroom observations, interviews with students, teachers, and parents or caregivers, surveys, and learning tests to examine the uses and effects of the proposed approach in high school classrooms. This research will contribute new data for building theories on a) altruism as a motivation framework for supporting engineering education, and b) negotiation of engineering identities when engaging students in community-relevant AI and microelectronics projects. This Design and Development project is funded by the Discovery Research preK-12 (DRK-12) program, which seeks to significantly enhance the learning and teaching of science, technology, engineering and mathematics (STEM) by preK-12 students and teachers, through research and development of innovative resources, models and tools. Projects in the DRK-12 program build on fundamental research in STEM education and prior research and development efforts that provide theoretical and empirical justification for proposed projects. 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

Recent years have seen a remarkable surge in interest and use of unmanned aerial systems (UAS), also known as drones, leading to rapid advancements in UAS technologies. Despite this progress, much remains to be explored to fully harness the potential of UAS. This underscores the critical demand for skilled researchers specializing in UAS. However, shaping the future UAS research workforce encounters multiple challenges, including system complexity, lack of open platforms, and shortage of training materials. To tackle these challenges, this project develops a new training program that will equip students with the essential skills needed for conducting and potentially transforming foundational UAS research. This project involves a team of educators with complementary expertise in various aspects of UAS. The training program includes four modules, each focusing on a fundamental aspect of UAS: control, communication and networking, computing, and artificial intelligence (AI) applications. This modular design ensures scalability and facilitates integration into existing curricula. Additionally, by leveraging an open UAS Cyber Infrastructure (UAS-CI) developed by the project team, each training module includes numerous hands-on projects to equip trainees with practical skills in operating and advancing UAS-CI. Implemented through a month-long summer program and tutorials at relevant international conferences, this project develops at least 50 skilled UAS professionals annually, who are expected to become the future UAS-CI research workforce and drive transformative advancements in foundational UAS research. 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 study of the arithmetic and geometry of solutions to systems of polynomial equations goes back many centuries to the Greeks. There is a deep interplay between these two disciplines, and the guiding principle is that the geometry determines the arithmetic. This principle rings true when the system of polynomial equations defines a curve (i.e., a one-dimensional object), and in particular the number of rational solutions of a curve is governed by the geometry of the set of solutions when considered with complex entries. When the system defines a higher-dimensional object, the geometry becomes much more complicated and as a result so do the arithmetic properties. Instead of trying to understand the algebraic nature of their geometry, mathematicians have sought to understand their complex analytic geometry, and this approach has led to useful analytic characterizations of these solution sets. The main goal of this project is to study the algebraic aspects of a system of polynomial equations using an analytic approach founded in non-Archimedean geometry. This is a topic at the intersection of various areas of research such as complex geometry, non-Archimedean geometry, algebraic geometry, and number theory. In addition to these research activities, the investigator will organize undergraduate learning seminars on topics in number theory and arithmetic geometry which will aid in recruitment and retention in the sciences of our students, many of whom are underrepresented. Furthermore, the investigator will organize several weekend mini-conferences for students which will feature mathematicians from underrepresented groups with the goals of building community and also fostering future mentorship opportunities. The research of this project aims to answer several questions regarding the geometry and arithmetic of varieties of general type. The major goal of this project is to prove a long-standing conjecture of Demailly and Lang regarding the complexity of curves inside of varieties of general type. The investigator has already used non-Archimedean methods to make significant progress towards this conjecture, and this project will continue this methodology through the development of new techniques which utilize recent advances in non-Archimedean pluripotential theory. The secondary goal is to further develop non-Archimedean characterizations of varieties of (log)-general type, primarily focusing on the cases of surfaces and closed subvarieties of commutative algebraic groups. The third goal is to study integral points on quasi-projective varieties with infinite tame fundamental group using methods from algebraic geometry and Diophantine approximation. 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

Human activities have significantly increased atmospheric CO2 concentration. Marine carbon dioxide removal (mCDR) techniques have emerged as a promising solution to mitigate climate change caused by increased CO2 levels, aiming to enhance natural biological and chemical processes in the ocean to absorb and store more carbon from the atmosphere. Monitoring pH is fundamentally important for mCDR, playing a crucial role in assessing environmental impact, optimizing the processes, and ensuring the overall success and sustainability of mCDR efforts. However, current pH sensors face limitations such as low sensitivity and accuracy, drifting with pressure and temperature changes, and discontinuity due to limited power. In this proposal, researchers from three universities (UNT, UMich, and UCSD) are collaborating to develop a novel marine energy-powered multimodal MEMS (Micro-Electro-Mechanical System) sensor array that can simultaneously detect pH, pressure, and temperature. This innovative device aims to provide uninterrupted, highly sensitive, and accurate pH measurements across vast ocean areas with varying depths. The proposed self-powered sensing system can also be adapted for other applications such as wave and tide gauging, tsunami detection, ocean surveys, seabed subsidence monitoring, inverted echo sounders, towed arrays, and calibration of underwater mapping systems. Additionally, the researchers will engage industrial offshore instrument developers and governmental labs to accelerate the deployment of the proposed instrument, particularly in mCDR, sustainable ocean monitoring, and ocean renewable energy fields. Furthermore, this project will also significantly benefit the three participating universities by supporting curriculum development, professional non-technical skills training, and research mentoring for graduate, undergraduate, and K-12 students, with an emphasis on diversity, equity, and inclusion. In this project we propose a MEMS resonant pH sensor that can significantly enhance the sensitivity, accuracy, and energy-sustainability of current pH sensors, addressing the challenges confronting the mCDR research community. This research focuses on three key areas: (1) the design, fabrication, and testing of a multimodal pH, pressure, and temperature sensor based on a piezoelectric single crystal wafer for highly sensitive and accurate pH measurements while remaining unaffected by variations in pressure and temperature; (2) the design, fabrication, and testing of a novel ocean wave energy converter that can break the fundamental challenge of mismatch of vibration frequency of small buoys and the low ocean wave excitation frequency, thus enabling efficient energy harvesting to provide sustainable power for uninterrupted long-term operation of the sensor system; and (3) system integration and test in the wave tank at UMich, and field demonstration at the marine lab facility at the Scripps Institution of Oceanography at UCSD. Upon successful completion, this novel tool will be suitable for long-term deployment in marine environments for mCDR monitoring. 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 use of environmental DNA (eDNA, or genetic material shed by organisms) to measure biodiversity is a revolutionary approach that transforms the ability of biologists to observe biodiversity on Earth. In freshwater environments, eDNA in just a liter of water can indicate what fish, insects, and bacteria are present. Despite the rapid advances and adoption of this approach, very little is known about how long eDNA lasts and how fast it disintegrates in nature. Understanding the fate of eDNA in streams and rivers presents a major challenge for interpreting an eDNA “hit”. This NSF award, known as the "DISTANCE" project, will address this knowledge gap by studying the environmental factors that promote or inhibit eDNA movement and degradation in U.S. streams, especially those that are part of the National Ecological Observatory Network (NEON). Infrastructure of the NSF-funded Emerge training program, which broadens undergraduate and graduate student participation in freshwater science, will be expanded as part of the DISTANCE project. Opportunities for student and postdoctoral training will be integrated into the research studies. The term “eDNA spiraling” has been used to describe the fate of eDNA as it flows downstream, where it can be degraded by microbes, deposited in streambed sediments, resuspended from the streambed, and transported further downstream. Hypotheses will be tested that relate water chemistry, microbial communities, and hydrogeomorphology to the three major processes driving eDNA fate: degradation, deposition, and transport. NEON infrastructure will be leveraged by conducting eDNA spiraling experiments at NEON stream sites. Replicated eDNA spiraling experiments will be conducted in two NEON streams and one Critical Zone Observatory site to determine how the type of eDNA (i.e., originating species) and eDNA particle size distribution (determined through sequential filtering) influence eDNA spiraling metrics. Fish and macroinvertebrate biodiversity assessments will be paired at NEON sites with eDNA metabarcoding to investigate whether eDNA spiraling metrics can predict the congruence of community data generated by eDNA metabarcoding compared to traditional methods. DISTANCE has three broader impacts. First, infrastructure of the NSF-funded Emerge program, which broadens participation in freshwater science, will be expanded. Emerge trains undergraduate, graduate, and early career scientists from underrepresented groups in data analysis and visualization (using R software) and in collaborative science. Training in data analysis and visualization for Emerge alumni will be expanded by offering in-person workshops on “Introduction to bioinformatics of eDNA and DNA metabarcoding data.” Workshops will follow The Carpentries pedagogy and be made open access for other Data Carpentries instructors to teach. Second, we will extend NEON infrastructure by generating new, open-access eDNA datasets for NEON sites. Third, this work will provide training experiences for undergraduate students, graduate students, and one postdoc funded by the project, giving them opportunities to practice teaching and mentorship themselves, as implemented in a hierarchical mentoring plan. 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

Nontechnical Description Understanding how light, matter, and heat interact at ultrafast speeds is crucial for a wide array of applications, such as solar cells and heat management. However, ultrafast optical techniques provide only partial insight into these interactions. This project seeks to delve deeper into the dynamics of heat transport and temperature changes by focusing on thermal radiation. All materials above absolute zero emit electromagnetic waves, a process also known as black body radiation. The PI will analyze transient changes in thermal-radiation spectra induced by femtosecond laser pulses on both semiconductors and metals, thereby yielding new insights into ultrafast thermodynamics. Moreover, this research extends to exploring light emission immediately following laser excitation from hot carriers that have not yet reached thermal equilibrium. If this excess heat can be harnessed, there are prospects for more efficient devices such as hot solar cells. Research findings from this project will be integrated into physics courses at the University of North Texas, a minority-serving institution. This will provide invaluable learning and training opportunities for both undergraduate and graduate students, including those from historically underrepresented groups in STEM. The team will also create engaging planetarium shows to communicate their discoveries to a broader audience. Technical Description The primary research goal of this project is to investigate ultrafast light-matter-heat interactions through the lens of thermal radiation. To achieve this goal, the team intends to develop an innovative system capable of measuring thermal-radiation spectra with femtosecond time resolution, facilitating direct observation of these interactions at unprecedented speeds. Expanding upon this technological advancement, this research delves into a diverse range of materials, including semiconductors such as silicon and gallium arsenide, as well as metals like gold and aluminum. Through systematic experimentation, the team seeks to unravel the complex thermodynamics occurring within these materials after laser excitation. Additionally, the investigation extends to exploring light emission from hot carriers that remain nonthermalized shortly after excitation, shedding light on a phenomenon of significant scientific interest. This project integrates both experimental and theoretical approaches within the principal investigator's laboratory, leveraging a multidisciplinary framework to advance our understanding of ultrafast light-matter-heat interactions. Anticipated outcomes include the achievement of femtosecond-scale measurements of thermal-radiation spectra, the development of robust methodologies for extracting refractive index and temperature profiles from thermal-radiation spectra, and the generation of novel insights into the radiation emitted by nonthermal hot carriers. This research project is poised to represent a substantial leap forward in our fundamental understanding of ultrafast light-matter-heat interactions. By elucidating the energy balance within thermodynamic systems and uncovering the intricacies of radiation from nonthermal hot carriers, this project promises to yield transformative contributions to the field of ultrafast optics, materials science, and thermal physics. 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

Computer modeling results and empirical data analyses have indicated that anthropogenic global warming has caused hurricanes to become stronger, wetter, and slower-moving. As a result, coastal communities are facing increasing risks from compound flooding events caused by a combination of stronger hurricane winds, higher storm surges, heavier precipitation, and more severe flooding from rivers. For coastal risk managers, it is critical to have an improved understanding of the relative contribution between marine and terrestrial sources in these compound flooding events. This study will use an innovative approach to discriminate between marine and terrestrial deposits in sediment cores collected from multiple wetland sites impacted by recent hurricanes. Identifying sediment provenance will also elucidate the relative contributions of marine and terrestrial inputs to wetlands aggradation and provide valuable insights on wetland sustainability for coastal management agencies. This study will employ a multi-proxy approach to the discrimination of saltwater and freshwater sediment beds within sediment cores collected from a variety of coastal wetland environments. Spearheading the analysis will be the use of X-Ray Fluorescence (XRF) to discriminate between marine and terrestrial sediments based on their chemical elemental composition. Pilot studies of the XRF technique, aimed at establishing elemental signatures of storm surge and fluvial sediment beds resulting from Hurricane Harvey (2017), show promise, but additional work to refine the technique and extend it to other environmental settings is warranted. Additional analytical techniques to be employed in the study include textural, loss-on-ignition, palynological, and foraminiferal analyses, and radio-isotopic dating for chronological control. 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

Real-world applications, such as software modeling, digital circuit design, manufacturing control, and status modeling of smart devices and smart systems, often require efficient techniques to model their behaviors and changes over time. Based on their specific requirements, different algorithms (including machine learning) are needed, such as reachability computation, pathfinding, and state prediction. For example, the graph neural network (GNN) algorithm can help to learn the compact vector representations of the states and transitions to capture the complex patterns and dependencies. However, existing computation architectures for such techniques are not very efficient for two major reasons: (i) the algorithms are not computationally efficient, and (ii) the data size is very large. This research pioneers the development of an accelerated computation architecture for system modeling techniques and applying them to critical smart environment applications. This project will address the growing national need for professionals in accelerated computation architecture, algorithms, and machine learning. The research will produce an accelerated computation architecture that serves as a foundational tool for fellow science and engineering practitioners in academia and industry. Educational initiatives integrate the research findings into graduate and undergraduate curriculum development. Additionally, outreach and educational activities are conducted to promote participation from K-12 and undergraduate students from populations underrepresented in computing. The overarching goal of this project is to design an accelerated computation architecture for state modeling techniques and to apply them to important smart environment applications. Towards that, this project includes three synergistic research thrusts. Specifically, Thrust 1 designs efficient computation techniques to accelerate the reachability computation in a state transition representation, which can be used to detect if any undesired (e.g., unsafe) state is reachable. Thrust 2 accelerates the computation of graph machine learning algorithms by adaptively reducing the overhead of instant updates and maintaining high-quality communities. Thrust 3 applies the techniques in Thrusts 1 and 2 to an important application domain of smart environments. 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

Many minority-serving institutions (MSIs) encounter challenges to establishing and maintaining a robust research enterprise, including a lack of training in proposal writing, difficulties in starting research partnerships, and limited involvement of MSI students in research endeavors. To address these obstacles, this project will organize four workshops to aimed at training aspiring principal investigators (PIs) from MSIs in the southern and southeastern US states, preparing participants for submitting proposals to the CISE MSI program in the 2025 and 2026 competitions. The events will provide a platform for participants to meet at large, share research insights, identify challenges and opportunities, and initiate potential research collaborations. Broader-impact aspects of the project comprise the broadening participation of over 60 MSI scholars as well as the fostering of MSI collaborations across nine US states. The workshops will pursue four major objectives: 1) Community building - the strengthening of relationships among data science (DS), artificial intelligence (AI), and extended reality (XR) to boost broader data-intensive research communities at MSIs; 2) Cross-learning: the facilitating of knowledge sharing, the exchanging of best practices, and the training of aspiring PIs from MSIs to leverage success and enhance proposal development skills; 3) Mentoring - the providing of each research team with experienced coaching; 4) Collaboration development - the fostering of new collaborations, the exploring of future opportunities to advance research initiatives, and the preparing of collaborative proposals towards CISE MSI-focused programs. 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.

NIH Research Projects · FY 2025 · 2024-07

PROJECT SUMMARY Title: Synthetic glycosaminoglycan mimetics as regulators of megakaryopoiesis and thrombopoiesis. Key Words: Platelets, Glycosaminoglycans, thrombopoiesis, G6b-B, NSGMs The Candidate is an NIH K12 postdoctoral scholar on an academic career path. His focus is on the roles of glycosaminoglycans (GAGs) in thrombopoiesis. He has significant research experience studying GAG–protein interactions, and a strong background in organic synthesis notably, the preparation of aromatic-scaffold-based GAG mimetics known as non-saccharide GAG mimetics (NSGMs), which are functional mimics of GAGs. Career Development Plan: This proposal is well structured and involves 2 years of mentored research training, which will ensure that the candidate develops advanced research skills critical for an independent academic career. He has assembled an advisory committee of experienced and well-funded PIs, with proven track records of mentoring young academic researchers. He also has a well-resourced environment for the proposed research. Research Plan: The number of circulating platelets is tightly balanced through continuous production and removal of platelets to prevent potentially detrimental thrombosis. Platelets are produced through sequential processes, wherein hematopoietic stem cells commit to the formation of megakaryocytes (megakaryopoiesis), which release cytoplasmic extensions into the blood stream to produce platelets (thrombopoiesis). While some mechanisms and molecular regulators of these process have been identified, much remains to be elucidated. Of these, the roles of extracellular matrix and GAGs are poorly characterized. Although GAGs are regulators of various proteins, their heterogeneous nature and the challenges associated with obtaining homogeneous forms of these complex biomacromolecules remain bottlenecks for elucidating their biological roles. Our lab has developed a diverse chemical library of NSGMs which possess an aromatic scaffold carrying multiple sulfate groups mimicking the sulfated sugar scaffold of GAGs. NSGMs bind and selectively modulate several GAG- binding proteins involved in diseases, and thus serve as excellent chemical biology probes of GAG function. We have identified G4.1, a flavonoid-based NSGM as having potent thrombopoietic potential in vitro and in vivo. Our preliminary studies show that G4.1 binds with high affinity to G6b-B, an inhibitory receptor found on megakaryocytes and platelets, involved in the regulation of platelet production. Our studies also show that G4.1 promotes G6b-B dimerization, which is required for downstream signaling. Based on this data, we hypothesize that, G4.1 promotes thrombopoiesis, in part, by its highly selective interaction with G6b-B. We will determine the nature of the interaction of G4.1 with G6b-B, probe the selectivity of G4.1 for G6b-B, and elucidate the structure- activity-relationship (SAR) of this class of compounds. This research proposal benefits from; 1) the candidate’s personal track-record, 2) robust preliminary data, 3) a highly experienced advisory committee with relevant expertise to the proposed research, and 4) a supportive and well-resourced research environment. The three aims of the proposal are : I) Determine the nature of interaction of G4.1 with G6b-B, II) Evaluate the selectivity of G4.1 recognition of G6b-B, and III) Synthesize a library of G4.1 analogs and elucidate SAR.

NSF Awards · FY 2024 · 2024-07

The Creating Inclusive Scientific Societies through Policies and Practices (CRISSPP) project brings three universities, University of Michigan, University of Connecticut, and University of North Texas into a partnership to develop, implement, and assess a set of evidence-based guidelines and practices for scientific organizations (beginning with Psychology) to promote inclusion and minimize systemic exclusion. The research literature indicates that academic exclusion includes social, informational, and epistemic exclusion, and professional societies can play a central role in members’ academic careers, facilitating the dissemination of their scholarship and providing opportunities to establish prominence within the field. The guidelines and practices will help professional societies create and sustain positive disciplinary environments that lead to success for all faculty. The project will empower organizations to shape individual members’ experiences of inclusion/exclusion and the organization’s climate in four critical areas: governance, awards, conferences, and publications. The CRISSPP guidelines and practices will (1) conduct climate surveys and audits, (2) construct interventions (to include transparency audits, toolkits, including guidelines and rubrics as appropriate, commitment to optimal processes, pathway development (for governance), educational workshops and, (in the case of conferences), a brief daily online climate assessment tool), and (3) assess the overall impact of these interventions on the organizations and on members’ sense of belonging. The guidelines, practices, and lessons learned will initially be shared within Division 9 of the American Psychological Association and up to nine additional partner organizations in psychology, reaching over 20,000 members. This partnership will be evaluated internally and externally, formatively and summatively, to improve the guidelines and practices for other organizations and identify implementation issues that may need to be addressed. The NSF ADVANCE program is designed to foster gender equity through a focus on the identification and elimination of organizational barriers that impede the full participation and advancement of diverse faculty in academic institutions.  Organizational barriers that inhibit equity may exist in policies, processes, practices, and the organizational culture and climate.  ADVANCE "Partnership" awards provide support for the adaptation and adoption of evidence-based strategies to academic, non-profit institutions of higher education and non-academic, non-profit organizations. 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 · 2024-07

The University of North Texas ADVANCE Adaptation project, UNT CRECE, will induce positive, sustainable, systemic change for STEM faculty by implementing evidence-based practices that will foster a climate of belonging in UNT's STEM departments. The project will develop a sustainable, data-driven, systemic change model that removes structural barriers and builds equitable workspaces, policies, and support systems. Guided by over a decade of institutional data, CRECE seeks to build inclusive departmental climates, develop institutional strategies for faculty success, and implement data-based decision-making processes. Toward the first aim of building inclusive departmental climates, this project will implement a chair institute for inclusive excellence and healthy workplace initiative. Strategies for faculty success will be accomplished through holistic and transparent assessment policies and a community of practice built on mentoring and mentor training. Data-based decision-making processes will be achieved through intersectional data-collection founded on equitable practices and department chair data-based decision-making initiative. The project can serve as a model for other research-intensive Hispanic-Serving Institutions. The NSF ADVANCE program is designed to foster gender equity through a focus on the identification and elimination of organizational barriers that impede the full participation and advancement of diverse faculty in academic institutions. Organizational barriers that inhibit equity may exist in policies, processes, practices, and the organizational culture and climate. ADVANCE “Adaptation” awards provide support for the adaptation and adoption of evidence-based strategies to academic, non-profit institutions of higher education as well as non-academic, non-profit organizations. 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 · 2024-06

Hurricanes and other severe weather events have catastrophic impacts on day-to-day life activities and cause disruptions to the operations of emergency response teams. Effective and rapid actions are of extreme importance during rescue operations. Locating and providing people with necessary primary services and facilitating coordination among first responders and decision-makers are critical in saving lives. There is always the need to incorporate technologies into various operations that can improve the rate of rescue success, making the process more effective and efficient. However, there is a need for coherent mechanisms to facilitate integration between advanced technologies and multiple organizations and individuals involved in disaster relief operations. This research aims to deepen the understanding of existing emergency communications, the use of public safety analytics, and available decision support systems, and of the roles of humans in collecting, processing, and synthesizing vast amounts of information, particularly in high-pressure, rapid-response scenarios. The team will partner with different stakeholders such as the Office of Emergency Management, Denton City’s Fire Department, Emergency Planning Advisory Council, FEMA, and the National Institute of Standards and Technology (NIST). The goal of the research is to develop a comprehensive framework to streamline diverse processes and integrate advanced technological solutions into these processes, thereby enhancing the overall effectiveness and speed of disaster relief operations, and educate students, engineering professionals, and city planners in the potential of these technologies and value of this approach to resilient systems engineering. This research investigates multi-disciplinary (social and engineering) aspects surrounding the design and deployment of emergency communications systems and situational awareness platforms during disaster relief operations. It explores the complex interactions that take place among the individuals (first responders and decision-makers) and organizations (government, utilities, transportation, and volunteers) during disaster relief operations. This research addresses the knowledge gaps in human and inter-organizational communications to inform technology solutions and consists of the following tasks: (1) exploring the dynamics of human and inter-organizational Interactions; (2) exploring innovations in emergency communications; and (3) evaluation and testing of proposed ecosystem during emergency drills. Overall, this research maps the role of human communication channels, situational awareness, information sharing, and decision-making processes during relief operations. The expected outcome is a living laboratory with state-of-art technologies and tools to continually develop, refine, and deploy innovative solutions that have a significant impact on disaster relief operations. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-06

This award is to assist US-based graduate students to attend the 2024 IEEE Computer Society International Symposium on Very Large Scale Integration (IEEE ISVLSI) in Knoxville, Tenn. Participation in IEEE ISVLSI and similar conferences are valuable and important activities of the graduate school experience. It provides students with the opportunity to interact with more senior researchers, and exposes students to leading work and practical industry practices in this important area of research. The long-term merits of this program's impact on very large scale integration within computing research are well established. The conference will be held from July 1-3, 2024, will span three days and will consist of technical paper presentations, panels, posters, and a student research forum. It will also feature keynote speeches from leading researchers and practitioners in the field. Student travel to conferences is an important activity. Funds will be dispersed with preference given to students who would not otherwise be able to attend the conference and students who are not already scheduled to present a paper, paying particular attention to diversity and relevance of the student's research interest. 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.

NIH Research Projects · FY 2025 · 2023-09

Summary Statement An evolving extracellular mechanical landscape accompanies the progression of multiple diseases including cancer, pulmonary fibrosis, and hypertension. While the influence of stiffening tissue on gene expression, cell migration, and phenotype is well established, how these changes affect delivery of nanoparticle therapeutics is less well understood, especially for materials that experience dynamic force (e.g., stretch). Conventional in vitro nanoparticle discovery models use plastics that do not have mechanical properties reflecting tissue. These models limit the effectiveness of conventional screening processes. Therefore, my research program will examine how dynamic forces impact nanoparticle uptake and fate and apply this information to design more efficient nanoparticles for cellular entry. Specifically, we will focus on lung epithelial tissue and vascular endothelium, two tissues with important delivery routes for nanotherapeutics. In support of this goal, research theme 1 will examine how substrate mechanics modulate the nanoparticle uptake pathway of cells. Nearly all nanoparticles enter through endocytosis. However, the productivity of different endocytosis routes can vary, especially when stiffness and dynamic forces are included. Our goal is to identify and understand how mechanically-linked regulatory processes direct nanoparticles to different uptake pathways. To achieve this goal, we will utilize tissue models that include 2D and 3D stretches that are observed in physiological/pathophysiological tissue environments. Theme 2 focuses on understanding how cell surface structures, particularly the glycocalyx, change when cells experience different forces. Identifying key changes in glycocalyx structures will present potential routes for targeting specific cells based on the underlying dysfunctional physical environment. These models will be combined with liposomal nanoparticle designs that facilitate delivery to target cells within complex cell environments. Taken together, this research program will allow us to reimagine cellular targeting by factoring in the mechanical characteristics of cells and multicellular interactions to redesign NP formulations with enhanced efficacy, safety, and control.

NIH Research Projects · FY 2025 · 2023-08

Abstract Triple-negative breast cancer (TNBC) is a type of breast cancer that does not express the estrogen receptors (ER), progesterone receptors (PR), and human epidermal growth factor receptor 2 (HER2) protein which accounts for 15–20 % of all breast cancer cases. TNBC tumors are generally larger, are of higher grade, and are more aggressive than other breast cancer types. TNBC is unlikely to respond to hormonal therapy medicines, including tamoxifen and aromatase inhibitors. TNBC also is unlikely to respond to medicines that target the HER2 protein, such as Herceptin (trastuzumab). Treatment of TNBC patients has been challenging due to the lack of molecular targets. Therefore, there is a critical unmet need to develop more effective therapies for TNBC and drug-resistant breast cancers. The development of new treatments would require radically different approaches that rely on enzymatic reactions specific to TNBC cells rather than the cell receptors. This proposal hypothesizes that properly designed peptide substrates of Eyes Absent enzyme (EYA) can selectively inhibit TNBC cell growth by a self-assembling process. The central hypothesis is that tyrosine phosphatase activity of EYA in TNBC could specifically convert the peptide therapeutics into nanofibers and induce controlled apoptosis of TNBC cells. The preliminary data suggested that a modified sequence of peptides with only one added amino acid could be de-phosphorylated by EYA and self-assemble into nanostructures to inhibit the growth of TNBC cells (MDA-MB-231). In this project, different peptide substrates of EYA will be synthesized by changing variable factors and the correlation of structure to enzyme activity and cell apoptosis will be determined by focusing on the following aims: Aim 1: Design and synthesize self-assembling peptide substrates for EYA enzymes; Aim 2: Determining the efficacy of peptide substrates for inhibiting TNBC in spheroid 3D cell cultures. The correlation between the enzyme kinetic and the activity of nanostructures for targeting EYA will be evaluated. Aim 3: The apoptosis response of the TNBC cells will be determined. This study will lead to finding the potent peptide substrate and the effective dose for inhibiting TNBC cells with apoptosis cell death.