University Of Houston

NSF Awards · FY 2025 · 2025-07

NON-TECHNICAL SECTION: Polymer nanocomposites, which are comprised of mixtures of inorganic nanomaterials (e.g. silica, clay etc.) dispersed in a polymer matrix. are used extensively innumerous applications, from automotives to advanced aerospace materials. Designing polymer nanocomposite materials with tailored properties is a major bottleneck, since inorganics and polymeric materials do not naturally mix, rather they tend to separate. While researchers have tried to address this issue by adjusting how polymers interact with inorganic nanomaterials, we chemically attach (graft) polymers to the individual nanoparticles to create molecular polymer grafted nanoparticles which guarantees their dispersion in a polymer matrix. This attachment is done to create new types of molecularly dispersed nanomaterials that offer unique combinations of properties that cannot be achieved with polymers or nanoparticles alone, or by simply blending the two. Tethering polymers to nanoparticles allows scientists to precisely control how these hybrid building blocks organize and behave, but it also changes the way the materials mix, often in unpredictable ways. This research explores how polymer–nanoparticle attachment alters the balance of molecular forces that determine their mixing. Through systematic study of the size, structure and composition of these systems, the project seeks to explore the mixing rules (phase-behavior) in complex molecular nanocomposites. The work engages undergraduate and high school students in hands-on scientific research of these materials, providing both valuable early experience in nanoscale science and long-term American workforce development. TECHNICAL SECTION: This study aims to understand the thermodynamics and kinetics of phase separation in binary blends of polymer-grafted nanoparticles (PGNPs), specifically poly(methyl methacrylate)-grafted and poly(styrene-acrylonitrile)-grafted silica nanoparticles. The project seeks to understand how tethering polymer chains to nanoparticle cores alters entropic contributions to mixing, potentially offsetting the enthalpic interactions that typically dominate in polymer blends. Three specific aims are pursued: (1) identifying entropy-dominated phase boundaries by tuning core size, grafting density, and chain lengths; (2) linking phase behavior to mechanical properties in thin films under confinement; and (3) studying entropic partitioning effects in nanopatterned films through imprinting and relaxation. The research specifically investigates entropy-enthalpy compensation effects, where a gain in entropy is offset by a loss in enthalpy (or vice versa), during pattern relaxation. It utilizes low volatility ionic liquids to enhance PGNP mobility and temporal pattern fidelity. Overall, this work aims to develop predictive models for phase behavior and mechanical properties in PGNP/PGNP blends, providing a foundation for molecular functional nanocomposite design. The project’s broader aims support NSF’s mission to promote scientific progress in the United States by integrating the research with hands-on, high-impact research opportunities for undergraduate and high school students. By cultivating scientific and technical skills in emerging young researchers, the project strengthens the United States’ science and engineering workforce and contributes to maintaining America’s leadership in materials science and nanotechnology. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-07

Modern transportation systems have undergone a significant transformation, marked by increased design complexity, advanced networking capabilities, and an overwhelming surge in data. As a result, today's automotive system is a collection of interconnected embedded systems, some of which (such as the infotainment system) are also connected to the Internet. As the number of connected vehicles grows, traffic systems become networked and autonomous fleets emerge in the consumer space, the potential for cyberattacks on U.S. transportation infrastructure increases significantly. Given the criticality of the transportation cyberinfrastructure (CI), this project builds expertise in the automotive cyber domain through development of testbed and training curriculum material and summer training workshops for educators, students, and researchers. The project addresses critical issues in cyber workforce development in the transportation and automotive sectors through three initiatives. The first leverages faculty from different disciplines to develop a coherent open-source CI that provides a unified research platform for automotive and autonomous systems. Leveraging this CI, the second initiative delivers modular training materials on secure transportation system design. Finally, the third initiative arranges a series of workshops to directly train 80 participants, including faculty members, graduate students, and cyberinfrastructure professionals. The project also produces and disseminates royalty-free resources to support workforce development and education in transportation system security. 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 · 2025-07

Project Summary/Abstract Pregnant Mexican-American women (the largest subgroup of Hispanic women), hereafter referred to as Latinas, are at increasing risk for psychological distress which leads to adverse birth outcomes such as preterm birth (PTB, gestational age < 37 weeks) and low birthweight (LBW, <2500 grams). Our prior research, using a psychoneuroimmunology (PNI) framework, has identified psychological risk factors (depressive symptoms, anxiety, stress, coping) and neuroendocrine risk factors (high Corticotropin Releasing Hormone [CRH], lower progesterone, higher estriol) at 22-24 weeks gestation as strong predictors of PTB in Latina women. New interventions targeting these risk factors need to be identified and rigorously tested. To address the gaps related to interventions for Latinas, we have developed and successfully pilot tested the Mastery Lifestyle Intervention (MLI): a culturally-relevant, manualized psychosocial group intervention that integrates two evidence-based behavioral therapies – Acceptance and Commitment Therapy (ACT) and Problem-Solving Therapy (PST). The MLI is a 6-week program designed to be integrated into regular prenatal care to facilitate more comprehensive care delivered by a nurse practitioner (NP) or certified nurse midwife (CNM). We propose the following aims for a randomized controlled trial: Primary Aim 1: Determine the efficacy of the MLI in pregnant Latina women to decrease depressive symptoms, anxiety, perceived and acculturative stress, and to improve coping, versus usual care (UC), from baseline (14-20 weeks gestation) to end-of-treatment (20-26 weeks gestation) and at a 6 week follow-up (26-32 weeks gestation), with acculturation and psychological flexibility as mediators. Exploratory Aim 2: Explore the effect of the MLI on neuroendocrine risk factors of PTB (CRH, progesterone, and estriol) versus UC from baseline to end-of treatment. Exploratory Aim 3: Explore the effect of the MLI on infant birth outcomes (gestational age, birthweight, NICU admission). Analyses for each hypothesis will rely on generalized linear mixed modeling (GLMM) with random effects for participant and time as necessary to account for correlated observations. Longitudinal analyses will model each outcome as a function of treatment group, time, and the interaction between treatment group and time. We will also use SEM to analyze for mediators. We expect the MLI will provide a greatly needed, novel, feasible, and effective nonpharmacological program added to the toolbox of treatments assisting providers to improve health during pregnancy. Embedded into prenatal care, it targets psychological distress among pregnant Latina women, an underserved population. It may substantially reduce the risks for poor birth outcomes, thus reducing devastating and long-term effects for both mother and infant.

NIH Research Projects · FY 2025 · 2025-07

SUMMARY Autism Spectrum Disorder (ASD) involves challenges in social communication and intensive, restricted, or repetitive behaviors and/or interests. Sensory atypicalities are prevalent in approximately 74% of individuals with ASD, can range from pleasurable experiences to distressing outbursts caused by hypersensitivity affecting self-control and decision-making. Severe outbursts may lead to self-harm. However, proactive strategies to prevent these outbursts are under-researched and underutilized compared to reactive approaches. This study aims to develop algorithms predicting outbursts using wearable biosensor data and advanced machine learning (ML) models. It is aimed at enhancing caregiver support for individuals with ASD using modern ML frameworks. We accomplish this goal across three aims. Aim one’s objective is the collection of a novel dataset of the peripheral physiological and movement precursors of outbursts. We accomplish this aim by observing such events naturally in children with ASD who are prone to such behaviors using wearable wristband-based biosensors along with recording devices to capture potential triggers. Aim two characterizes the peripheral physiological indicators as individuals progress into and through an outburst, exploring internal stages of dysregulation that comprise these behaviors. In this aim, we use statistical analysis to identify differences in the stress-reactivity profile, which inform later ML models. Aim three seeks to develop predictive ML algorithms capable of predicting outbursts that occur in individuals with ASD. Altogether, our study will lay technology capable of detecting the precursors of outbursts in real-life situations using state-of-the-art wearable devices and artificial intelligence (AI) algorithms. This technology could deliver immediate alerts for parents or caregivers, providing individualized recommendations to prevent or mitigate the severity of an imminent outburst, developed using evidence-based behavioral interventions. Moreover, as proactive strategies for preventing outbursts are under-researched compared to reactive strategies, and the fact that it is little known about the underlying mechanisms associated with an outburst, our results the foundations of our long-term aim: the development of will also contribute to a better understanding of the nature of outbursts and a more detailed and operationalized description of their physiological correlates.

NSF Awards · FY 2025 · 2025-07

Super resolution is a technology that enhances the quality of digital images by increasing their resolution, making them appear sharper and more detailed. This capability is important in a wide range of applications, such as medical imaging, satellite analysis, security monitoring, and immersive technologies like virtual reality (VR). However, current super resolution methods—many of which are based on deep learning—can be slow and require large amounts of computing power. This project aims to develop a new generation of learning-based super resolution that delivers high image quality, fast performance, and low power consumption. This project will have a transformative impact across a wide range of fields like medical imaging, entertainment, VR, surveillance, and autonomous vehicles. Moreover, by enabling sharper images with fast, energy-efficient processing, this research allows edge devices, such as drones and mobile phones, to perform real-time image enhancement without cloud reliance, saving bandwidth and power. This will drive more efficient processing in constrained environments, reduce costs for high-quality imaging, and unlock new possibilities for Artificial Intelligence (AI)-driven applications in smart cities and augmented reality. The project will expose more students to computing research through outreach, curriculum development activities, and disseminating research infrastructure for education and training. This project develops an integrated framework to enable high-performance, energy-efficient learning-based super resolution (SR). The research focuses on three key innovations: (1) extracting computer graphics (CG) information from GPU architecture during low-resolution rendering and integrating it into a convolutional neural network (CNN)-based SR as an intermediate feature, along with CG-guided architecture search and dynamic weight pruning, and architectural co-designs for high-fidelity, real-time SR processing; (2) improving Swin transformer-based SR on preserving fine details by utilizing patch entropy for dynamic window partitioning and replacing patch merging with splitting, supported by architecture-level optimizations; and (3) developing a multi-model framework that integrates proposed CG-guided CNN and details-enhanced transformer-based SR networks for excellent VR user experience on SR image quality and latency, with final evaluation on field-programmable gate arrays (FPGAs). 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 · 2025-07

PROJECT SUMMARY Glaucoma is the leading cause of irreversible blindness worldwide, and a majority of cases are undiagnosed. Loss of vision due to glaucoma cannot be reversed, only slowed or halted. Therefore, it is imperative that damage is detected and glaucoma is diagnosed early, so it can be managed and vision can be preserved. The most common form of glaucoma presents with an elevated increase in intraocular pressure (IOP) with no known cause. This elevated pressure causes gross anatomical changes to the posterior eye over time, such as optic disc cupping. However, once the optic disc, or other structures in the posterior eye, has changed its structure, damage has already occurred with the elevated IOP driving the increase in stress and structural alteration. Current techniques for diagnosing glaucoma are woefully lacking in their sensitivity to minute structural changes in the posterior eye. Optical coherence tomography (OCT) was developed to perform depth-resolved imaging of the layers of the retina and optic nerve head with micrometer-scale resolution. However, the changes in the gross anatomical structure and thickness of retinal layers imaged by OCT may also only be detectable after damage has already occurred. It has been postulated that the difference between intracranial pressure (ICP) and IOP may play a significant role in determining whether damage will occur in cases of elevated IOP. According to this hypothesis, there should be a change in the stress distribution across the lamina cribrosa, signaling that stress is too high and damage may be imminent. In cases where the translaminar pressure difference, and hence imposed stress, is large, there may be a great risk of developing glaucoma. One method to detect small changes in stress relies on the photoelastic effect, where the optical properties of a material, e.g., index of refraction and birefringence, can change due to applied stress. The tissues in the posterior eye have notable birefringence, i.e., different optical properties corresponding to different orientations of light polarization and direction of propagation, and OCT is capable of imaging the birefringence properties of tissues with its polarization-sensitive functional extension. Therefore, this project proposes the development and application of a polarization-sensitive swept source OCT (PS-SSOCT) system for 3D mapping of the birefringent properties of the posterior eye in a non-human primate model. The PS-SSOCT system will be capable of micrometer-scale structural imaging and mapping any minute changes to the tissue stress, driving changes in its birefringent properties induced by changes in the IOP. Here, we propose exploratory in vivo non-human primate studies where the IOP is carefully controlled and gradually stepped up and down, during which PS-SSOCT imaging will be performed. The local distribution of the birefringence will be quantified in the retina, optic nervehead, and lamina cribrosa. The birefringence will be correlated with the IOP and well-established metrics of the geometry of the posterior eye based on the OCT structural image. The outcome of this research will lead to further in-depth research, including human studies and additional animal studies correlating tissue damage to the IOP-ICP pressure difference, which has significant implications for glaucoma diagnosis, therapy development, and disease management.

NIH Research Projects · FY 2025 · 2025-06

ABSTRACT Cryptosporidium species, including C. hominis and C. parvum, are protozoan parasites that present a significant health threat, particularly to young children and immunocompromised adults. These organisms have the potential to be deliberately introduced into the water supply, making them a CDC class B bioterrorism agent. Current treatment options for Cryptosporidium infections are very limited to one FDA-approved drug that is not effective in young children and immunocompromised adults. In addition, vaccines are unavailable. Calcium dependent protein kinase 1 (CDPK1) has emerged as a promising target for treating cryptosporidiosis, since silencing this protein significantly reduces parasite survival. Consequently, selective CDPK1 inhibitors that can efficiently access the gastrointestinal (GI) system, where parasites reside, will provide an effective strategy for the treatment of cryptosporidiosis. We have identified a structurally distinct class of CDPK1 inhibitors that demonstrate promising enzymatic potency, selectivity over representative human kinases, encouraging anti-parasitic activity in cell culture and a mouse cryptosporidiosis model. The overall goal of this study is to refine this new class of CDPK1 inhibitors and to define the optimal tissue targeting for efficient treatment of cryptosporidiosis. This will be achieved by pursuing three specific aims. The first aim will conduct structure-activity relationship analysis and optimization using structure-guided design to achieve highly potent and selective CDPK1 inhibitors with minimal activity on human kinases. We will also develop an innovative design allowing for glucuronidation-mediated enterohepatic recycling to maximize GI tissue targeting for direct comparison to systemically available derivatives. The second aim will evaluate in vivo pharmacokinetic, biodistribution, impact on the host microbiota and acute toxicity properties of the CDPK1 inhibitors. The third aim will evaluate and compare optimized GI-tissue targeting with systemically bioavailable CDPK1 inhibitors in a mouse model of cryptosporidiosis and dose escalating toxicity. Achieving the goal of this study will provide valuable tool compounds for the biomedical research community, candidates for further clinical development and guidance for optimal tissue distribution that can be applied in other anti-Cryptosporidium drug discovery programs.

NSF Awards · FY 2025 · 2025-06

This award supports the NSF-CBMS (Conference Board of Mathematical Sciences) Regional Research Conference in the Mathematical Sciences "Research at the Interface of Applied Mathematics and Machine Learning" to be held December 8-12, 2025, at the University of Houston in Houston, TX. The conference will expose early career researchers to cutting-edge research at the interface of applied mathematics and machine learning. It will also help identify new research directions and will foster the building of new collaborations between research groups in the Texas-Louisiana area and other regions. The conference will include graduate students, postdoctoral fellows, and established researchers from academia and industry, and provide a platform for early career researchers to learn and discuss recent advances in mathematical methods for machine learning and data science. In more detail, the conference will feature ten lectures delivered by Dr. Lars Ruthotto from Emory University. The lectures will be divided into three modules. Module 1 consists of three introduction lectures on machine learning (e.g. deep neural networks, learning problems). The second module, also of three lectures, will introduce important components of applied mathematics in machine learning (e.g. optimization, regularization). The last module will focus on the use of machine learning in critical problems in computational and applied mathematics (e.g. inverse problems, high dimensional partial differential equations). These lectures will be supplemented by a dozen contributed talks from participants, a poster session, a mentoring academic panel and a second panel that will feature researchers from industry (e.g. oil and gas, medical center). For more information, please visit the conference webpage: http://www.math.uh.edu/cbms-amml. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-06

Next generation electric vehicles will require batteries that have a higher energy storage capacity than current lithium-ion batteries for increased vehicle driving range and performance. One promising technology is based on a solid-state battery that uses lithium metal as the high energy storage anode. This battery design has potentially a very high energy storage density based upon the battery volume. However, a battery of this design may suffer from corrosion, which could cause short circuits, battery failure, and safety issues such as excessive heating. This project examines a new composite material, a metal-carbon mixed ionic-electronic conductor (c-MIEC), as a solution for overcoming corrosion issues. The project will educate students in cutting-edge in situ diagnostic technologies, machine-learning algorithms, and advancements in solid-state electrochemical battery energy systems. It will focus on serving K-12 students from a broad range of Houston area schools through STEM outreach programs, provide research opportunities for undergraduates at the University of Houston, and offer interdisciplinary training for graduate students. These efforts will help inspire a future workforce in STEM fields. This project will bridge the knowledge gap in the formation and optimization of pore-free c-MIEC interlayers, enhancing their reliability and cost-effectiveness to promote the progress of solid-state lithium metal battery science. The research will systematically address the interfacial challenges in solid-state lithium metal batteries by focusing on the formation of pore-free c-MIEC interlayers. The project will combine automatic in situ diagnosis technologies, machine-learning methodologies, and multiphysics modeling. This integrated approach will facilitate automated data acquisition, quantitative analysis, and process prediction of lithium metal plating and stripping behaviors under various interlayer lithiation pore-filling status. By quantifying the lithiation pore-filling process and elucidating its intricate correlations with the interlayer's physicochemical properties, the research will establish critical microstructure-property-function relationships necessary for optimizing efficient pore-free interlayers. Specific objectives include: (1) measure, analyze, and understand microstructural and chemical evolutions of interlayer during lithiation, (2) measure and understand electrochemical and mechanical evolutions of interlayer during lithiation, (3) integrate experimental findings into predictive modeling. By addressing key interfacial instabilities and advancing diagnostic methodologies, this project will enhance the performance and scalability of solid-state lithium metal batteries. Furthermore, the insights gained will extend to post-lithium systems, providing a robust foundation for transformative energy storage technologies that align with sustainability goals. 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 · 2025-05

This award will provide partial support for the Summer School on Artificial Intelligence (AI) in Healthcare, Biology, and Medicine, which will be held from May 27-June 2, 2025, in Rhodes, Greece. The program aims to explore impacts of AI on healthcare, focusing on both the exciting opportunities and the practical challenges that have emerged over the past decade. The summer school will introduce undergraduate and graduate students in engineering, computer science, and biology to the emerging field of artificial intelligence in healthcare, medicine, and biology. The program will bring together students and faculty to discuss AI innovations in these fields and explore how AI can be applied to develop cutting-edge healthcare solutions. Additionally, the summer school will highlight the need for a shift in engineering and science education to keep pace with technological advancements and their impact on healthcare and economic growth. This unique opportunity will allow participants to engage with AI-focused healthcare research and contribute to shaping the future of biomedical engineering education and practice. Artificial intelligence (AI) technologies have transformed healthcare by analyzing diverse health data, including patient records, multi-omics data, clinical data, and behavioral factors, and by automating many tasks that previously required human intervention. Recent advancements in computer hardware, software, and the digitization of health-related data are accelerating AI adoption in medicine. However, while this progress presents vast opportunities, it also brings challenges, raising critical questions about AI's future impact on healthcare and related disciplines. The summer school will address both the advances and challenges of AI in healthcare. Major areas of focus will include: (1) AI in biotechnology, (2) AI in cardiology, brain discovery, and oncology, (3) AI in tissue engineering, and (4) AI, digital twins, and the metaverse in healthcare. Each of these areas will be discussed in depth, with an emphasis on challenges, practical implications, and real-world applications. The summer school will also examine future directions and innovation opportunities as AI continues to shape the future of healthcare and biomedical research. The summer school will stimulate interdisciplinary research and collaborations among engineers, mathematicians, computer scientists, and medical researchers, and help identify new directions for artificial intelligence, engineering innovations, and technologies in medicine, biology, and engineering. 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 2026 · 2025-05

PROJECT SUMMARY Many (>70%) individuals that suffer a severe, high-level spinal cord injury (SCI) develop bladder detrusor sphincter dyssynergia (DSD). A substantial number of these individuals will develop serious urological complications such as urinary tract infection, renal reflux effect, or renal failure. Current treatments are limited and the complications rates can be significant using stent implantations or other surgical procedures. Thus, development of new tools/therapies that help stimulate more reliable/efficient bladder emptying could significantly improve quality of life and the overall health of SCI patients. The overall objective of the project is to develop a wireless, battery-free implant capable of supporting artificial intelligence (AI) algorithm for autonomous closed-loop neuromodulation (CL-NM) management of DSD after SCI. We will fabricate and validate a wireless, battery-free bioelectronic implant that allows continuous, long-term monitoring and autonomous electrical stimulation through a closed-loop AI system. We will also demonstrate closed-loop neuromodulation (CL-NM) treatment of detrusor sphincter dyssynergia (DSD) in rat SCI model. The soft stretchable electrodes with high charge injection capability will be able to stimulate the acute and sub-acute bladder wall to induce detrusor contraction, while simultaneously fatiguing the EUS and PFMs to reduce outflow resistance. Such a combination effect will result in a near normal voiding process. The proposed bioelectronic system will have broad clinical utility in monitoring and modulating bladder function, and provide a new treatment method together with closed- loop technologies for patients with severe DSD that do not respond to traditional therapies. This technology platform can also be easily adapted to address a range of application possibilities beyond those associated with the bladder. It will have significant impact in field of Rehabilitation and regeneration after SCI.

NSF Awards · FY 2025 · 2025-05

NONTECHNICAL SUMMARY This award supports theoretical research and education to advance understanding of nonequilibrium systems using and further developing stochastic thermodynamics. Stochastic thermodynamics is an emerging theoretical framework that generalizes standard thermodynamics to small nonequilibrium systems. Examples of such systems in experiments include colloids, quantum dots and molecular biochemical systems. The research group will investigate four relevant topics in stochastic thermodynamics. The first topic is focused on the connection between stochastic thermodynamics and Chimera states, which are two prominent lines of research in statistical physics that remain disconnected. A Chimera state is a phase of interacting oscillators where only part of the oscillators synchronize. The PI will use a model to establish a connection between stochastic thermodynamics and Chimera states, which will enable the role of energy dissipation in Chimera states to be analyzed. The second topic is an open problem in stochastic thermodynamics. The thermodynamic uncertainty is a prominent relation that establishes the minimal universal cost of precision in stochastic thermodynamics. This relation has been found to be violated for the so-called underdamped Langevin dynamics. The PI aims to find bounds on fluctuations for underdamped dynamics similar to the thermodynamic uncertainty relation. The remaining two topics are on active systems, systems with hidden dissipative degrees of freedom such as living systems. The research group will investigate the emergence of net motion in a stochastic molecular system in an active medium and heat engines in an active medium. Students will be trained, and the PI will engage in international collaboration. TECHNICAL SUMMARY This award supports theoretical research and education to advance understanding of nonequilibrium systems using and developing stochastic thermodynamics. Stochastic thermodynamics is an emerging theoretical framework in nonequilibrium statistical physics. It has contributed to the understanding of small nonequilibrium systems through the discovery of relatively novel universal relations such as the fluctuation theorem and the thermodynamic uncertainty relations. Examples of such systems include colloids, quantum dots and molecular biochemical systems. The research group will investigate four relevant topics in stochastic thermodynamics: 1.) A Chimera state is a phase of interacting oscillators for which part of the oscillators synchronize and the other part does not. Considerable work has been done on Chimera states; however, the connection between Chimera states and stochastic thermodynamics remains unexplored. The research group will connect Chimera states with stochastic thermodynamics using a model the PI introduced. 2.) The thermodynamic uncertainty relation has been found to be violated for underdamped Langevin dynamics. The team aims to find bounds on current fluctuations for underdamped dynamics based on a discretization of underdamped dynamics. The last topic is an important continuation of the recent work of the research team on active heat engines. The research team will develop a linear response theory for active heat engines. 3.) Stochastic pumps are systems that generate unidirectional motion in the form of a net current with a time-periodic protocol. Their theoretical research is inspired by experiments with artificial molecular machines, which are often operated with a time-periodic protocol. A goal in this area of research is to build artificial molecules capable of performing a function inside a living organism, that is, in an active medium. The PI will investigate active stochastic pumps, an area that has been largely unexplored. In particular, the team will determine the necessary conditions for the emergence of a flux in active stochastic pumps. 4.) The PI will continue working on active heat engines. The research team will develop a linear response theory for active heat engines. Students will be trained, and the PI will engage in international collaboration. 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 2026 · 2025-05

PROJECT SUMMARY The cornea is continuously subject to insults from the external environment that can lead to stromal scarring and neovascularization, and, consequently, vision loss. Although corneal opacity is a leading cause of sight impairment worldwide, there are limited treatments available for preventing corneal scarring and angiogenesis after corneal injuries. Ultimately, loss of transparency is treated by corneal transplantation; however, corneal angiogenesis in the host cornea is a major risk factor for corneal rejection. Therefore, there is an urgent medical need for alternative treatment options. Decorin is a major ECM component in the cornea that has a central role in regulating collagen fibrillogenesis, as well as, cell behavior via the direct interaction with growth factors and cell surface receptors. Studies have shown that decorin antagonizes TGF-β1 signaling, a central regulatory cytokine in corneal wound healing that promotes ECM deposition and scar tissue formation. Following corneal injury, increased TGF-β1 levels induce keratocyte transdifferentiation into myofibroblasts, and, subsequently, corneal fibrosis (scarring). Moreover, decorin also regulates the physiological balance between pro-and anti-angiogenic factors in the normal and injured cornea via binding to VEGFR2. Decorin binding to VEGFR2 inhibits VEGFA, the natural ligand of VEGFR2, thereby suppressing VEGF signaling, and consequently inhibiting angiogenesis. Decorin deficiency aggravates corneal vascularization, while decorin upregulation, via gene therapy using an adeno- associated virus, successfully inhibits corneal neovascularization in vivo. Therefore, decorin holds great pharmaceutical potential for treating corneal injuries to prevent corneal scarring and neovascularization. This proposal aims to characterize binding interface of decorin:TGFβ1 and decorin:VEGFR2 to engineer novel decorin based drugs that inhibit TGFβ1 (Aim 1) and VEGFR2 (Aim 2) with higher affinity than decorin. Our central hypothesis is that by obtaining in depth mechanistic understanding of the molecular interactions between decorin and its targets we can design decorin-based pharmaceuticals with increased ability to prevent corneal scarring and angiogenesis compared to native decorin and without off-target effects. For such, decorin:TGFβ and decorin:VEGFR2 complexes will be modeled using deep learning computational methods, and decorin based drugs produced by mutagenesis to increase their affinity to TGFβ and VEGFR2. The efficacy of the decorin based drugs for preventing corneal scar formation and pathological neovascularization will be verified using in vitro (Aim 1 and 2) and in vivo assays (Aim 3).

NSF Awards · FY 2025 · 2025-05

This project will study the flow behavior of suspensions consisting of polymers and micron-sized particles during extrusion through a nozzle. When there is no flow, polymer molecules tend to stick to the particles, form bridges between particles, and create a network of interconnected particles. The formation of the network, in turn, affects how the suspension flows through nozzles. This project will investigate the possibility of tuning the extent to which polymers stick to the particles, which will affect the number and shape of the polymer bridges and the flow properties of the suspension. This process represents a simple model of a variety of applications including fabrication of membranes for water purification, batteries for energy storage, and drug formulations to improve delivery. As a result, the project has the potential for significant scientific impact by advancing the science needed to process a class of feedstocks into useful materials. This project will also support the training of graduate students in advanced technical skills needed in a future science and engineering workforce. To communicate the practical importance of this project for society, demonstrations will be developed for the annual Houston Energy Festival for the public and for the STEM Zone Saturday event for K – 12 students and their parents. The objective of this award is to elucidate the effects of polymer bridges on the extensional flow properties of colloid-polymer mixtures. Using a model colloid-polymer mixture with attractive bridging interactions that are tunable through solution pH as well as polymer concentration and molecular weight, the overarching hypothesis that flow-induced conformational changes in the polymers will also affect the extensional flow of colloid-polymer bridging mixtures will be examined. This will be done by (1) linking extensional response of bridging mixtures to polymer macromolecular properties; and (2) determining the effect of normal stresses on extensional flow of bridging mixtures. Optical microscopy experiments will be used to characterize the structure of quiescent mixtures. Data from shear and extensional flow experiments will be used to test models and elucidate the contributions of polymer elongation and particle-induced stresses to the rheological properties. This award will examine the extent to which properties such as the extensional relaxation time and filament lifetime are controlled by flow-driven changes in polymer conformation, which is expected to vary as polymers adsorb to the colloids. The award will also address a longstanding open question about the relationship between normal stresses and extensional thickening, both of which arise from flow-induced polymer deformation. This knowledge is expected to lead to rational design principles to generate complex fluids feedstocks for materials processing routes that involve extensional flow. 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 · 2025-04

UNRAVELING THE CONNECTION: HOW METABOLISM SHAPES MAMMALIAN PERSISTER SURVIVAL Summary Our overall goal in this research project is to evaluate the role of metabolism in persister cell survival in mammalian cell populations. Persister cells are defined as a small fraction of quiescent cells in a tumor bulk population that exhibit temporary tolerance to drugs. These cells are an important health concern because they are thought to underlie the proclivity of recurrent tumors to relapse, and they serve as a reservoir from which drug-resistant mutants can emerge. Due to their transient state, persisters possess the capacity to revert to a state of growth and produce drug-sensitive daughter cells; however, their physiological properties underlying entry into and exit from this quiescent state remain unclear. The central hypothesis of this proposal is that persisters have a transient metabolic state characterized by increased oxidative phosphorylation and decreased anabolic activities. Our previous studies indicate that transient metabolic rewiring in persisters is largely induced by chemotherapeutic agents, which may result from the inhibition of cell growth. In our 1st Aim, we will verify our hypothesis through the use of persister assays, proteomics, metabolomics, redox sensors, phenotype microarrays, and a range of mammalian cell types and chemotherapeutic agents. By utilizing fluorescence microscopy and fluorescent reporters, we will uncover the metabolic dynamics of persister cells through the examination of individual cell trajectories during and after drug treatment. In our 2nd Aim, we plan to uncover the active metabolic pathways and their significance in persisters through a combination of experimental data and mathematical models. Our mathematical approach will involve utilizing optimization programming with various objective functions to assign weight values for each metabolic pathway. The models will take into account the topological properties of the metabolic network, as well as constraints such as reaction rates, thermodynamics, and other regulatory mechanisms. By comparing the results of multiple models, we will identify the common or unique crucial features. Our hypothesis is that a metabolic pathway that is consistently identified as significant through different models will play a crucial role in the survival of persister cells. Our hypothesis will be tested by disrupting the targeted pathways through genetic and chemical means. The proposed research program is expected to have a substantial impact for the following reasons: (i) There is an ongoing debate about whether dormant, quiescent, tolerant, and persister cells in tumors are the same population or related to tumor stem cells. Despite this uncertainty, the existence of a small fraction of phenotypes that survive initial treatments is undisputed, and this should raise significant concerns within the medical community. (ii) Our approach uniquely combines experimental results with computational models to identify unknown biochemical pathways driving persister cell metabolism. (iii) The proposed research will have a broader impact as the methods developed here will be applicable to persister cells in diverse mammalian and microbial organisms.

NSF Awards · FY 2025 · 2025-03

Thermal radiation refers to energy exchange by electromagnetic waves between heated surfaces. It is common in a wide variety of thermal, energy, and optical systems. Most research has focused on the scenario where the thermal radiation between surfaces is equal in both directions. However, there are important cases where the heat transfer is unequal between surfaces, which is called nonreciprocal radiation exchange. Nonreciprocal radiation exchange is essential in optimizing operations such as solar energy conversion and building insulation. This project will help overcome significant challenges for designing systems with nonreciprocal radiation exchange. For example, the project will conduct experiments to better understand how wide variations in wave frequency of thermal radiation affect energy exchange. The project will construct a numerical modeling method suited for calculating nonreciprocal radiation exchange. Results will help establish a new direction for heat transfer research and generate a direct impact on advanced thermal and energy technologies with significant economic and environmental impacts. The project will also support educational activities including Everything Thermal - a YouTube channel for students and the public to learn about concepts and research in thermal photonics, and P2 - a paper-cutting and painting art demonstration with light and heat to help K-6 students visualize the beauty of symmetry, heat, and optics. The goal of this project is to understand and control the unique directional heat transfer phenomena associated with nonreciprocal surfaces. The research involves achieving broadband nonreciprocal radiative properties, measuring radiative heat exchange between nonreciprocal surfaces, and exploring the tunability of this radiation exchange. Multilayer magnetized materials will be used to broaden nonreciprocity in the infrared, which will provide a general design rationale to achieve broadband nonreciprocity. A modeling method will be established to directly simulate nonreciprocal radiation exchange between surfaces of various geometries and properties. The directional radiative heat transfer will be experimentally measured in an environment with controlled conduction and convection, unraveling the hidden potential of nonreciprocity for advanced photon flow control. The project will serve as a strong foundation to advance radiative thermal and energy technologies with performance approaching the thermodynamic limit. 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 2026 · 2025-02

Abstract Rhabdomyosarcoma (RMS) is a type of malignant (cancerous) sarcoma that typically arises in or near muscle beds and shows features of myogenic differentiation, such as expression of myogenic regulatory factors and muscle structural proteins. However, RMS cells continue to proliferate and do not undergo terminal differentiation. Transforming growth factor β-activated kinase 1 (TAK1) is a major signaling protein involved in the activation of various intracellular pathways. However, the role of TAK1 in RMS or any other sarcoma has not been investigated. Our preliminary results demonstrate that TAK1 is highly activated in both embryonal RMS and alveolar RMS cells and human RMS samples. Inhibition of TAK1 using genetic or pharmacological approaches represses epithelial-mesenchymal transition (EMT) and cancer stem cell phenotypes in cell culture and in vivo models of RMS. Inhibition of TAK1 also improves differentiation of various RMS subtypes cell lines into myogenic lineage and inhibits tumor formation by promoting myogenic differentiation in xenograft. In addition, TAK1 forms a complex with intracellular myostatin protein in RMS cells. However, the cellular and molecular mechanisms by which TAK1 promotes tumorigenesis and impairs differentiation of RMS remain entirely unknown. Furthermore, therapeutic potential of inhibition of TAK1 in RMS has not yet been examined using preclinical animal models. Based on our preliminary studies, we hypothesize that aberrant activation of TAK1 causes tumorigenesis and growth of RMS and inhibition of TAK1 can be a potential therapeutic approach for RMS. In this project, we will investigate the molecular mechanisms through which TAK1 induces tumorigenesis and inhibits myogenic differentiation in RMS (Aim I); investigate the signaling mechanisms through which TAK1 causes CSC enrichment and inhibits differentiation of RMS (Aim II); and examine the therapeutic potential of inhibition of TAK1 on xenografts and patient-derived xenograft (PDX) models of RMS (Aim III). Our study aims to identify a novel therapeutic target that plays a critical role in the induction of tumorigenesis and suppression of myogenic differentiation in RMS. Our unique strategy to target tumor progenitor cells and differentiation of RMS through modulating TAK1 will open new avenues to treat RMS.

NSF Awards · FY 2025 · 2025-02

The study of how macromolecules in solutions group together is essential to advance research in pharmaceuticals and biotechnology. The fundamental mechanisms governing this process is poorly understood. This knowledge gap limits our ability to precisely control these processes for therapeutic and industrial innovation. This project uses advanced computer modeling to explore the fundamental steps, aiming to unlock new possibilities for designing drug delivery systems, improving food preservation, and developing self-healing materials. This research will also provide insights into how certain cellular structures linked to aging and diseases behave. The project integrates education and outreach to enhance STEM participation and awareness. Activities include developing a summer program for high school students, fostering undergraduate research experiences, and creating publicly available resources. These efforts aim to inspire students to pursue careers in molecular and computational sciences, aligning with NSF’s mission to advance science and promote societal welfare. This CAREER project investigates the nucleation and growth of liquid-like macromolecular condensed phases using multiscale computational techniques. The research targets critical gaps in understanding nonclassical nucleation pathways, mesoscale clustering, and long-term condensate behavior. Free energy methods will map the stability landscapes of mesoscale clusters and identify metastable states, while advanced Monte Carlo simulations will quantify nucleation barriers under varying molecular compositions. Additionally, continuum modeling will bridge molecular dynamics and macroscopic behavior, characterizing growth, coalescence, and dissolution of condensates over extended timescales. Specific research aims include: (1) identifying the role of mesoscale clusters in nonclassical nucleation; (2) quantifying the effects of molecular composition on nucleation free energy; and (3) elucidating the determinants of long-timescale growth and stability using dynamical density functional theory. This work will provide mechanistic insights critical for applications in pharmaceuticals, material science, and biotechnology. Educational initiatives complement this research by broadening access to computational science through hands-on workshops, mentorship programs, and interdisciplinary courses, fostering a new generation of STEM innovators. 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 U.S.-French joint research project addresses the growing interest in modeling, analyzing, and generating 3D shapes and movements of human bodies and faces. Advances in scanning technology, 3D mesh-extraction algorithms, computer vision, and hardware-accelerated computer graphics have enabled access to large-scale datasets of human body representations. However, while artificial intelligence and machine learning have achieved remarkable success in processing image data, working with 3D shapes in the form of meshes presents unique challenges that often degrade performance in common computer vision and graphics tasks. To overcome these challenges, this project aims to integrate rigorous shape analysis concepts into the design of geometric deep learning models. These models will directly process raw 3D surface scans, independent of acquisition methods, to develop robust algorithmic pipelines for key problems in human body and face analysis. Applications of this work include single-object data representation and reconstruction, body motion generation, facial expression retrieval, and automatic animation. Beyond its implications for augmented and virtual reality, the project will train graduate students and strengthen collaboration between investigators from four institutions in the U.S. and France. The research focuses on developing efficient deep learning architectures that incorporate fundamental shape invariances into machine-learning pipelines. It consists of three key thrusts: (1) Invariant 3D-to-3D Registration and Reconstruction: This thrust will develop a framework adapted to human body shapes and face scans, combining mathematical shape analysis concepts with advancements in latent space and auto-encoder models in computer graphics; (2) Extension to Time-Dynamic (4D) Data: Building on static 3D data, this thrust will extend methods to dynamic 4D data (3D plus time) for motion analysis and generation. The approach will involve constructing a non-linear structure in the human shape latent space, using a blend of data-driven techniques and physically motivated elastic deformation energies. This will allow accurate modeling of the complex nature of real-life human body motions and deformations; and (3) Prompt-to-Shape Learning: This thrust will focus on mapping prompts, such as text or voice recordings, to shape spaces. Through these efforts, the project seeks to advance the state of geometric deep learning and its applications while fostering international collaboration and academic training. 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

Bioprinting is an additive manufacturing process that aims to create three-dimensional biological constructs to repair or replace damaged tissues or organs. Despite significant progress on material and process development in the last two decades, there is an unmet need for bioprinting large functional tissues with life-sustaining capillaries. This Faculty Early Career Development (CAREER) project investigates a hybrid bioprinting technology that can contribute to the development of capillary-incorporated bioprinting for optimal tissue regeneration. The technique includes electrospinning of porous microtubes between layers of hydrogels in order to provide channels for nutrients to reach cells which are to be grown inside the scaffold. The outcomes of this research could provide a path toward novel manufacturing solutions for complex diseases and bioinspired therapies that can advance regenerative medicine. The project aims to cultivate and engage a diverse student body to achieve future excellence in manufacturing for medicine by implementing a skill-based immersive teaching program for K-12 teachers and students, and establishing university-community partnerships in rural areas with global connections. These educational outreach activities should help to build an ecosystem of biofabrication in west Texas, with scalable models for enhancing research and education networks and partnerships in rural areas. In addition, this award will broaden the participation of underrepresented groups in research outside the customary manufacturing portfolio, with a desire to contribute to sustaining America's global leadership in advanced manufacturing and biomedicine. This CAREER award supports fundamental science and engineering research in capillary-incorporated bioprinting for fabricating centimeter-sized scaffolds integrated with biomimetic porous microtubes that function as capillary vessels. The research tasks include (1) modeling the microtube morphology and permeability, (2) characterizing the effects of material and process parameters on the printing quality of the system, and (3) building the process-function relationship of biomimetic constructs. By successfully completing these three research tasks, this CAREER project will generate new knowledge about the mechanisms of porous microtube formation, the effect of wall thickness on the microtube permeability, and the interrelationships between material composition, process parameters, and the time-dependent properties of capillary-incorporated scaffolds. 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 2025 · 2025-01

Calcium is linked to the carbon cycle through the precipitation of marine carbonates [e.g., limestone (CaCO3)] in seawater. Carbonate minerals typically sink to the bottom of the oceans and can be transported back into Earth’s mantle at subduction zones. Thus, subducted carbonate minerals are an important link between the carbon cycles at the Earth's surface and in the deep Earth. It is currently not well understood what happens to carbonate minerals after they are subducted. Researchers use calcium isotope signatures to trace carbonate minerals in the Earth. Ca isotope data have been used to suggest that mantle-derived igneous rocks called ocean island basalts (OIB) represent the return of subducted marine carbonates and/or oceanic crust back to Earth's surface after long-term storage in the mantle. This project will investigate Ca isotope variations in OIB to refine their potential use as tracers of the deep Earth carbon cycle. Results will help clarify details about how carbonates from Earth's surface are processed during the deep Earth carbon cycle. This project will also provide equity-based research opportunities for students, to help train the next generation of Earth scientists. Additionally, researchers will develop a publicly available educational video series related to obtaining and interpreting Ca isotope data. Global OIB compilations suggest that stable Ca isotope ratios (δ44Ca) are inversely correlated with radiogenic isotope tracers sensitive to crustal recycling, implying that mantle source variations likely play a role in generating δ44Ca variability. On the other hand, these relationships often break down when looking at single localities, and similarly strong global correlations exist between δ44Ca and partial melting proxies (e.g., Th, Nb, total alkalinity), suggesting that magmatic processes may instead control the δ44Ca variability. To address these conflicting observations, this research will analyze stable and radiogenic Ca isotope variations in large suites of previously-collected and well-characterized OIB lavas, cumulates, and phenocrysts from 5 different locations (Samoa, Canary, Reunion, Azores, Mangaia). The δ44Ca data and available major/trace-element and radiogenic isotope data will be used to tune location-specific phase equilibrium models that trace the elemental and δ44Ca evolution of OIB magmas during partial-melting and crystallization. These models will yield important constraints on (i) magmatic processes, (ii) the source compositions of three important mantle types (PUM, HIMU, and EM-2), and (iii) whether marine carbonates and/or oceanic crust are required to explain the δ44Ca values of OIB sources. Radiogenic Ca isotope data (εCa), which have only rarely been investigated in OIBs, will allow us to (i) better constrain the 40Ca/44Ca of bulk-silicate Earth and (ii) possibly identify ancient pelagic/terrigenous (high K/Ca) sediments in the mantle. In addition to providing public-facing educational outreach and equitable training opportunities for the next generation of Earth scientists, the results of this project will greatly advance our understanding of how surface-derived carbon behaves in Earth’s mantle – an essential part of the deep Earth carbon cycle. 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

Digital twins mimic actions and processes of physical assets in real-life executions. This research project concerns with development of a framework for learning digital twins of physical systems capable of incorporating real-world data into first-principles based mathematical representations. Learning digital twin from real data is a novel capability which can help enable effective strategic planning in various domains such as space exploration, autonomous transportation, sustainable water future, smart manufacturing, critical mineral mining, alternative power generation, and healthcare to name a few. This project focuses on applications to important problems in healthcare sciences related to data-informed decision-making exploiting virtual representations of human physiology and has implications for the development and evaluation of new therapies and treatments. One compelling example application is glucose metabolism in people with Type 1 diabetes (T1D). Patients with T1D must replace insulin exogenously as determined by multiple daily measurements of the blood glucose concentration, to maintain glucose homeostasis and avoid hypo / hyper-glycemia and life-threatening diabetic ketoacidosis. As a result, the person with diabetes has to make multiple, complex decisions each day based on food composition, exercise, hormonal cycles and other behavioral factors. Personalized glucose metabolism digital twins developed through this award will be used to devise new ethical treatment modalities and evaluate safety and effectiveness of automated insulin delivery systems without risk factors. Digital twins as such can also feed essential knowledge about system safety and effectiveness to regulatory agencies through assurance cases and advance regulatory science in profound ways. Both study sites University of Houston and Arizona State University are Hispanic serving institutions and the research is integrated with educational and outreach activities to create awareness, especially among youths, and understanding of diabetes and its management, broaden participation of groups traditionally underrepresented in STEM and contribute positively to engineering education. The first-principles informed data-enabled framework seeks to advance foundational techniques underpinning the development and use of digital twins and synthetic data in biomedical and healthcare domains, by combining advances across mathematical modeling, machine learning (ML) and systems’ theory with human physiology. This research will (1) develop advanced structures based on neural networks (NNs) for the recovery of an underlying physics-based model that are capable of operating in real-world conditions characterized by limited data availability, low and non-uniform sample rate and spatial and temporal noise, (2) develop novel parametrizations of black-box dynamics using NNs from a class of models with "built-in" properties of stability and robustness to perturbations, (3) integrate real-world physical twin generated data which are heterogeneous, scarce and noisy, into its virtual first-principles based mathematical representation, (4) develop a novel framework for learning unmodeled dynamics due to e.g., unaccounted for inputs, inter- and intra-individual variability. Extensive evaluation of this methodology will be conducted using publicly available datasets specially for T1D patients. 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

There is an urgent need to reduce energy use in catalytic chemical processes and to transition these processes toward use of plentiful natural gas reserves available in the United States and around the world. By advancing novel reactor configurations, this project will provide a template for that transition. As part of this project, the PIs will also advance workforce development in the Gulf Coast region by organizing and hosting workshops for working professionals and students focused on decarbonization of the chemical industry, and by developing and teaching a short course on modular processes to be offered to working professionals. Dynamic reactor performance will be investigated by combining catalyst synthesis and characterization, transient kinetic studies including temporal analysis of products (TAP), and kinetic and reactor modeling to understand the impact of molecular-level features such as oxygen speciation and reactivity on reactor performance. Reactor models will, for the first time, be developed while taking into account different types of oxygen species and their reactivities in both desired and undesired reaction pathways during dynamic operation. Rather than using chemical looping merely to minimize mixing of reductant and oxidant, this research will design from the bottom-up. The designed catalysts and reactors will provide inherent advantages in product selectivity conferred by non-steady state operation. A detailed, molecular-level understanding of oxygen speciation and reactivity will be developed using transient kinetic experiments including TAP. This will be integrated into reactor scale models that account for coupling between reaction and transport. The investigators will also exploit mass transfer limitations – typically detrimental in sequential reaction networks – through forced dynamic operation enabled by the selective transient depletion of unselective oxygen relative to selective oxygen. This multiscale, kinetically-guided approach will lead to the identification of catalysts and reactors with performance exceeding the state-of-the-art. It will also provide a framework for quantitatively analyzing chemical looping and non-steady state reactor performance that could be applied to partial oxidation reactions more broadly. 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

Hydrogels possess promising properties, such as biocompatibility and mechanical flexibility. This makes them ideal for various biomedical and energy applications. Currently, hydrogel fabrication relies on laser-assisted printing. In this process hydrogel jet formation is driven by the generation, expansion, and collapse of laser-induced bubbles. Although there is extensive research on laser interactions with liquids, the fundamental dynamics of laser-induced bubbles in hydrogels is not well understood. This research aims to combine modeling, simulation, and experimentation to deepen understanding of laser-induced bubble dynamics and the fluid mechanics of hydrogel jet flow in laser-assisted printing processes. Additionally, the project includes a comprehensive educational framework to foster partnerships with local K-12 schools and the University of Houston’s STEM Zone program. It will also introduce new technical elective courses through an inter-college educational collaboration. The goal of this research is to integrate experimental and numerical methods to elucidate the complex, multiscale dynamics of laser-induced bubbles in shear-thinning hydrogels and the mechanisms driving hydrogel jet formation. To capture initial bubble formation from laser interactions, the researcher will perform first-principles molecular dynamics simulations at the nanoscale. At the microscale, a discrete Boltzmann model will be developed to investigate bubble behavior within the hydrogel layer, accounting for viscoelastic effects. A computational fluid dynamics model will also be created, incorporating critical bubble size and hydrogel viscoelastic properties to explore the multi-physics mechanisms of jet formation, growth, and collapse. Experimentally, a customized high-resolution, time-resolved imaging system with 100-picosecond resolution will be built to observe bubble dynamics and jet formation in the hydrogel. These images and videos will serve to validate the numerical models across scales. The findings from this research will address key knowledge gaps in bubble formation and jet flow mechanisms, laying a theoretical foundation for advancing next-generation 3-dimensional printing technology for multifunctional soft materials. Additionally, this project has potential applications in diverse fields, such as drug delivery, soft robotics, sensors, and supercapacitors for energy harvesting and storage. 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.