Texas Tech University

NSF Awards · FY 2026 · 2026-07

Developing new manufacturing capacities for semiconductor materials is crucial for optoelectronics, sensing, computing, and energy conversion technologies. Metal chalcogenides are semiconductors with unique lattice structures and intriguing material properties; however, additive manufacturing of high-quality chalcogenides remains challenging. Most printed chalcogenides rely on organic surfactants that aid printability but leave insulating residues that hinder performance. This Faculty Early Career Development Program (CAREER) award supports research that advances the additive manufacturing of chalcogenide-based semiconductors by manipulating their interfacial structures and transport properties. By advancing the understanding of interfacial interactions between metal chalcogenides and emerging inorganic additives, this award will establish fundamental structure-property relationships and accelerate innovations in printed electronics and energy devices. By enabling new manufacturing capacities for semiconductor chalcogenides, it strengthens U.S. leadership in next-generation manufacturing through innovative strategies that enhance the performance, precision, and reliability of emerging semiconductor technologies. In addition, this project will extend its impact beyond campus to serve local and surrounding rural communities by creating accessible, hands-on opportunities for K-12 students and inspiring pathways into Science, Technology, Engineering, and Mathematics (STEM) careers, contributing to the future U.S. workforce.   While the printing of semiconductor chalcogenides promises novel energy and sensing electronics, a lack of understanding of interfacial interactions and subsequent difficulty in controlling undesired film porosity pose considerable manufacturing challenges, leading to poor conductivity and device performance. To overcome these limitations, research enabled by this award aims to establish interfacial design principles that enable pore-free, high-performance chalcogenide films for printed electronics. This research plan investigates key characteristics of nanoparticle-based additives and colloidal surfactants in determining interfacial structures and develops effective ink design strategies to enhance device performance. Unlike traditional organic surfactants that aid printability but leave insulating residues, this research studies polymer-free nanoinks to reduce film porosity at mild temperatures and establish quantitative structure-property relationships linking microstructure to charge transport and mechanical durability. This project will identify the underlying densification attributes of nanoparticle additives for semiconductor chalcogenide inks and manipulate key factors limiting the electrical and mechanical properties of printed semiconductor devices. The tunable band structure of nanoparticle additives, in combination with nanofiller-driven densification mechanisms, will advance the understanding of the interfacial physics of additively manufactured structures and enable a new generation of ink formulation strategies for printed electronics.  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 · 2026-06

Title: Microfluidic Isolation and Deep Learning-based Profiling of Subtypes of Circulating Tumor Cells during Epithelial Mesenchymal Transition Project Summary/Abstract Circulating tumor cells (CTCs) are highly heterogeneous and contain many cellular subpopulations. It is known that specific CTC subpopulations, rather than the whole, are responsible for cancer metastases. Recently, epithelial mesenchymal transition (EMT) of adherent epithelial cells to a migratory mesenchymal state has been implicated in tumor metastasis. CTCs isolated from cancer patients exhibit dynamic changes in epithelial and mesenchymal composition, and serial CTC monitoring in those patients suggested an association of EMT in CTCs. Emerging microfluidic technologies have shown great promise for the complete capture of the CTCs population with high yield and enhanced purity. However, most existing devices simply isolate all CTCs in blood without resolving them into distinct subpopulations: isolation of CTC populations with specific EMT markers remains a significant challenge. Consequently, epithelial-mesenchymal plasticity of CTCs during cancer progression is largely unknown, preventing researchers from acquiring true insights into the metastatic potential of CTCs. In the R15 project, we propose to develop the HU microchip platform for isolation and profiling of CTCs in pre-clinical settings, with the focus on detecting and understanding features associated with CTC subtypes during the course (or intermediate states) of EMT. To do so, we will isolate CTCs and explore deep learningbased profiling platforms using both micro and nanometer-scale features found from microscope images obtained from our HU microfluidic devices. This is expected to achieve super-high accuracy for profiling CTC subtypes with varied cell plasticity (i.e., more “epithelial” or more “mesenchymal” type). Building on the success of this project, we expect to develop an integrated system comprising 1) a user-friendly HU microchip for highly efficient isolation of CTCs from blood samples, and 2) an AI-based image analysis platform that can identify CTC subtypes for monitoring cancer progression and support cancer diagnosis and treatment. CTCs with defined EMT stages generated from the NSG mouse model will used to provide critical ground-truth information for validation of the proposed AI model, which cannot be accomplished using other alterative models. This project is strongly integrated with an educational plan to expose undergraduates to advanced experiments in the areas of biomedical engineering and cancer research. It is our belief that involving all students in cutting-edge research will have a significant positive impact on attracting and retaining students in science and healthcare fields. In this grant, we will provide research opportunities for 6 undergraduate students and 1 graduate to learn advanced knowledge on cancer biology, obtain hands-on experience in microfabrication and biological assays. Texas Tech University (TTU) is located in Lubbock, a rural area in northwestern Texas and TTU has been classified as "Tier 1" status in 2016. Due to historical and geographical reasons, life sciences research is not a strength for TTU, and TTU is not a recipient of major NIH grants. This R15 grant will strengthen our research and training at TTU and help us to reach our goal to be one of the best bioengineering programs in Texas.

NIH Research Projects · FY 2026 · 2026-06

PROJECT ABSTRACT Membrane integrity is key to the survival of pathogenic protozoa, but their mechanisms of maintaining membrane integrity are poorly understood. For example, the causative agent of cutaneous leishmaniasis, Leishmania major, maintains membrane integrity using distinct lipids compared to mammalian cells. Targeting key functional differences between mammalian cells and pathogenic eukaryotes in maintaining membrane integrity represents one strategy to developing new and improved therapeutics. There is a critical need to determine the mechanisms by which Leishmania sense damage and reseal damaged membranes. Without this information, the full potential of anti-ergosterol drugs (which compromise membrane integrity) may not be realized, and new membrane- disrupting strategies will not be identified. The long-term goal is to exploit the unique membrane architecture and repair mechanisms in Leishmania to develop highly selective therapies that limit infection. The overall objective for this project is to determine the mechanisms Leishmania uses to prevent and reseal membrane damage. The central hypothesis is that Leishmania senses the influx of oxidizing agents, which triggers Endosomal Sorting Complexes Required for Transport (ESCRT)-mediated membrane resealing and shedding via a Ca2+- independent mitochondrial pathway. The rationale for the project is that Leishmania are evolutionarily distinct from mammals, yet genetically tractable organisms that have unique systems of membrane repair. Determining the differences in membrane resealing between Leishmania and mammals will provide a strong scientific framework in which existing anti-Leishmania therapies can be improved, and new therapies can be developed. To attain the objectives, these specific aims will be pursued: 1) Determine the mechanisms by which Leishmania senses membrane damage, and 2) Determine the contribution of ESCRT to membrane repair in Leishmania. In Aim 1, the working hypothesis that Leishmania sense damage via the influx of reactive oxygen species that depolarize the mitochondrion and alter lipid trafficking will be tested in lipid-deficient L. major by flow cytometry and live cell imaging after challenge with multiple toxins. In Aim 2, the working hypothesis that Leishmania reseal their membrane using Ca2+-independent, ESCRT-dependent shedding of damaged membranes will be tested by measuring repair responses to L. major expressing tagged ESCRT proteins using flow cytometry and high resolution imaging and transiently deleting ESCRT proteins. The expected outcomes are to have determined the mechanisms by which Leishmania sense membrane damage, and to have determined the contribution of Leishmanial ESCRT to membrane repair. The proposed research is innovative because it departs from the status quo by revealing the mechanism of damage-sensing in Leishmania and providing a new paradigm of membrane repair. These results are expected to have a positive impact because a better understanding of how Leishmania sense damage and repair their membrane will provide new drug targets and new paradigms of membrane repair.

NSF Awards · FY 2026 · 2026-06

Understanding how the brain functions require instruments that can measure neural activity safely, accurately, and in everyday environments. Existing brain-imaging systems that detect magnetic signals from neural activity are large, expensive, and require cryogenic cooling, limiting their accessibility and preventing studies of natural behavior. This project will develop a new generation of wearable brain-imaging technology based on flexible magnetic sensors that operate at room temperature and conform to the scalp. These lightweight sensor arrays are designed to measure weak magnetic signals produced by the brain while individuals or animals move freely, enabling more realistic studies of cognition, sensory processing, and behavior. Beyond imaging, the project also explores an integrated approach in which magnetic sensing and noninvasive magnetic stimulation operate together in a closed-loop system, opening the possibility of precision neuromodulation for neurological disorders. The research advances new computational approaches that compress and analyze large volumes of neural data directly within the sensing hardware, enabling real-time interpretation of brain activity without overwhelming data-storage requirements. In addition to advancing neuroscience and medical technology, the project provides interdisciplinary education and workforce training in quantum sensing, artificial intelligence (AI), and biomedical instrumentation. Undergraduate research courses, graduate training programs, and K-12 outreach activities will broaden participation in science and engineering, while industry partnerships will help prepare students for careers in emerging sensing and neurotechnology industries. By enabling accessible, wearable brain-imaging systems and training the next generation of engineers and scientists, this work supports national priorities in advanced sensing, health technologies, and STEM workforce development. The project establishes a flexible quantum magnetoencephalography (MEG) platform based on tri-axial magnetic tunnel junction (MTJ) sensor arrays integrated with physics-aware compressed sensing (PACS) and edge artificial intelligence for scalable, real-time neural imaging. The central hypothesis is that dense, scalp-conformal MTJ arrays, combined with hierarchical in-sensor preprocessing and PACS-enabled reconstruction algorithms, can achieve high-resolution, room-temperature magnetic neuroimaging while maintaining manageable power and data throughput. Task 1 develops strain-tolerant MTJ thin-film stacks, tri-axial magnetic flux-guide architectures, and hierarchical sensor electronics that enable vector magnetic-field reconstruction with low-latency analog preprocessing. Task 2 investigates nanoscale magnetic-to-thermal transduction using integrated micro coil systems to enable magnetothermal neural stimulation, establishing a bidirectional magnetic neural interface that combines stimulation and sensing in vitro. Task 3 validates wearable flexible MEG arrays for in vivo neural imaging in freely moving animal models, benchmarking performance against multimodal ground truth measurements, including electrophysiology, behavioral readouts, and complementary imaging modalities. Task 4 develops PACS-AI algorithms that exploit neural signal sparsity and spatiotemporal structure to enable dynamic sampling, adaptive reconstruction, and real-time inference across large-scale sensor arrays. These efforts jointly advance reconfigurable quantum sensing architectures, closed-loop magneto genetic neuromodulation, and AI-integrated cyber-physical bio interfaces. The resulting framework establishes a scalable pathway toward wearable, label-free neural imaging systems capable of real-time operation, providing new tools for studying distributed brain dynamics and enabling future adaptive brain-machine technologies. 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 · 2026-04

Project Summary/ Abstract Eastern equine encephalitis virus (EEEV) is an alphavirus (family Togaviridae) transmitted among birds by mosquitoes, primarily Culiseta melanura. Transmission foci of EEEV exist throughout the eastern U.S. and occur in a mix of hardwood and cedar forested wetlands, which are the main habitats of Cs. melanura. Patterns of EEEV detections in Cs. melanura mosquitoes in the northeast US are typified by multi-year interepidemic periods of minimal detections punctuated by intense, single season epidemics. These dynamics make EEEV a difficult zoonotic pathogen to predict. To quantify seasonal probabilities of EEEV epidemics for any given surveillance season in the northeast, our proposed modeling framework combines data on EEEV dynamics from the Connecticut Agricultural Experiment Station (CAES; Connecticut, United States) with Continuous Time Markov Chain models (CTMCs) and Galton-Watson (GW) branching processes. CTMCs are capable tools for analyzing the stochastic nature of pathogen dynamics, while GW processes are capable of estimating probabilities of epidemic extinction based on observed environmental conditions. CTMCs are well suited to represent pathogen dynamics that exhibit small levels of infection and/or acute epidemics, and GW process models work well in combination with stochastic processes based on data that showcases overall low prevalence of disease. Together, the combination of CAES data with these modeling frameworks will examine three main hypotheses: EEEV epidemic probabilities are a function of vector population size (H1); Pathogen introduction drives EEEV epidemic probabilities (H2); Avian herd immunity drives EEEV epidemic probabilities (H3). For H1, we will explore vector population growth dynamics estimated from CAES data or approximated using periodic functions at different vector: host ratios. For H2, we will explore how pathogen introduction through long distance avian migration or passive dispersal from nearby transmission foci influences the timing and magnitude of EEEV epidemics. For H3, we will explore hybrid models that track exposure and immunity in hosts over multiple seasons, which will provide estimates of the interepidemic periodicity of EEEV. H2 and H3 build on models developed in H1, such that while all three hypotheses are likely interrelated and not mutually exclusive, we expect our results to identify the relative contribution of each factor to overall risk of seasonal EEEV epidemics. At the conclusion of our research, we expect to deliver a predictive framework that quantitatively measures seasonal risk of EEEV in the northeast. This would be a significant advancement over current EEEV risk assessments in Connecticut and beyond which are reactive (i.e., management decisions occur as information, such as positive mosquito pools and/or identified infected human cases, are received) and ill-equipped at keeping pace with rapidly developing epizootics as typified by EEEV.

NSF Awards · FY 2026 · 2026-04

Carbon dioxide (CO2) capture from air is considered an important negative emissions technology to mitigate global warming. Current sorbent-based CO2 capture processes are prohibitively expensive because the sorbent must be regenerated via an energy-intensive step to release the captured CO2. Once released from the sorbent, the captured CO2 must also be compressed and transported to sites for storage, which further reduces efficiency. An ideal CO2 capture technology would be both high-capacity and energy-efficient, while simultaneously converting the waste CO2 stream into a value-added product. This project will advance the fundamental science of integrating CO2 capture with CO2 conversion in a single unit process using a newly-envisioned bifunctional catalytic membrane. The membrane would act as both the CO2 separation and conversion medium, providing an energy- and atom-efficient alternative to sorbent-based CO2 capture, compression, transport, and storage. Such a membrane could revolutionize the way fuels and chemicals are produced, leading to transformative change in the chemical process industries and long-term stimulation of the United States economy. The students supported by this project will receive interdisciplinary training in catalysis, membrane separations, and advanced spectroscopic characterization. This CAREER project will also broaden participation in STEM through outreach activities at Navarre Middle School in South Bend, Indiana, and research opportunities for undergraduate and graduate students from Puerto Rico. This CAREER project aims to advance the fundamental science underlying the integration of CO2 capture and conversion into a single unit, membrane-based process. The envisioned bifunctional CO2 capture and conversion membrane is polymer-based with: (1) amine groups attached to the polymer chain to selectively capture CO2 from air, or concentrated point sources, and facilitate CO2 transport across the membrane; and (2) catalytic groups (e.g. amines, halides, metals) to catalyze conversion of CO2 on the permeate side of the membrane. The research goal of this project is to identify the key factors (mass transport, reaction kinetics, and stability) that limit the overall rate of integrated CO2 capture and conversion. The project will examine cyclic carbonate synthesis as a model CO2 conversion reaction. Knowledge of the rate-limiting factors will be used to develop strategies for circumventing these performance-limiting processes. The project is innovative in its use of unique operando spectroscopy techniques developed by the investigator to probe the structure, performance, and dynamics of the catalytic membranes under realistic operating conditions. The research outcomes will provide foundational knowledge of the kinetics, mechanisms, and stability of the catalytic membrane, which will provide a framework for developing a practical membrane system for integrating CO2 capture with CO2 conversion to a wide range of fuels and chemicals. The research will also advance the state-of-the-art of operando membrane characterization, enhance membranes for CO2 separation, and enhance catalysts for CO2 conversion. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2026 · 2026-04

The “XX Red Raider Mini-symposium: Geometry Analysis and Applications” will take place at Texas Tech University (Lubbock, Texas), on April 24-26, 2026. The scientific focus is a dynamic and influential area with profound implications for mathematics, natural sciences, and modern applications such as artificial intelligence and machine learning. The research talks will be complemented by a poster session and social activities. Thus, it creates a platform for mutual scholarly understanding, networking opportunities, and the formulation of a scientific community with a strong sense of camaraderie. Broader impacts include training graduate students, broadening participation in STEM, building STEM infrastructure, and disseminating knowledge. The main objectives of this program are the following: advance understanding of significant recent developments in Geometric Analysis and Applications, facilitate interdisciplinary collaboration, promote student engagement, and stimulate future discoveries by identifying open problems and building research networks. Distinguishing itself from similar meetings, this workshop-style event combines complementary areas of expertise in a cohesive manner, with dedicated sessions for early-career researchers and 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.

NIH Research Projects · FY 2026 · 2026-04

PROJECT SUMMARY Peptide and protein antigen vaccines are very safe compared to live-attenuated vaccines. They can be used to immunize anyone and are not associated with a lot of side effects compared to live-attenuated vaccines. However, peptides and proteins are less immunogenic compared to live-attenuated vaccines. To overcome these limitations, peptide antigens have been inserted/displayed on the surface of virus-like particles (VLPs) to increase their size as well as the valency or density of the peptide. VLPs are morphologically and structurally similar to viruses from which the coat proteins are derived from, except for the fact that they lack the viral genome. VLPs are highly immunogenic even at small doses of antigens. Most peptide antigens can be displayed on VLPs by genetic insertion or by chemical conjugation (to make them multivalent and highly immunogenic). However, these two approaches cannot be used for every peptide not to mention protein antigens. The size of an antigen as well as the composition of the antigen most of the time interferes with these two conjugation approaches. These limitations make in very impossible to use identified antigens with neutralizing and protective epitopes in vaccine design. To overcome the limitations with genetic and chemical conjugation approaches, we propose to explore a novel bio-conjugation approach on 4 bacteriophage VLPs (MS2, PP7, AP205, Q). This approach will use the new Spytag003/Spycatcher003 bio-conjugation system developed from the prototype Spytag/Spycatcher. In the proposed study, we will assess the potential of using the new Spytag003/Spycatcher003 bio-conjugation system to display diverse protein antigens on the surface of 4 bacteriophage VLPs. We will assess the versatility of the new bio-conjugation system on these VLPs using diverse protein antigens that differ in origin, size, and conformation.

NSF Awards · FY 2026 · 2026-04

This award supports the study of cloud electrification and lightning using phased-array weather radar. The research team will deploy multiple mobile radars to the field to observe key aspects of thunderstorms. Phased-array radars provide extremely fast sampling compared to traditional radars, allowing measurement of lightning features that were previously difficult to observe. Improved knowledge of lightning processes supports better hazard prediction and helps reduce risks to people and infrastructure. Students will be trained at the intersection of meteorology, radar engineering, and data science. The research in this award will focus on two core scientific uncertainties in lightning and atmospheric electricity: 1) flash-scale plasma physics, and 2) the meteorological setting of lightning initiation via electric-field changes. These topics will be studied using data collected during thunderstorms in the U.S. Great Plains from the University of Oklahoma’s Horus S-band radar and the X-band RaXPol radar. The research will provide new insight into when and where lightning channels heat and how they interact with the storm’s electric field as observed through ice crystal alignment. The study will also clarify how storm-scale microphysical and kinematic conditions relate to the probability of observing ice crystal alignment and direct plasma scattering. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2026 · 2026-03

Hydrogen is a vital chemical feedstock for the chemical industry. It is essential in applications ranging from fuel production to manufacturing plastics and fine chemicals. Hydrogen can be produced by water electrolysis, which uses electricity to split water into hydrogen and oxygen. However, today’s water-splitting technologies use expensive and scarce metals. They gradually degrade under harsh operating conditions, which reduces performance, increases costs, and slows the adoption of water electrolysis. The reasons why the metals degrade are unclear, which makes it difficult to design alternatives. This project will develop new computational methods to understand how and why metals dissolve during water electrolysis. Machine-learning and AI models will identify pathways leading to material breakdown. The results will support the design of longer-lasting electrodes made from Earth-abundant elements. The advances could reduce costs and accelerate sustainable hydrogen technologies. Reducing reliance on scarce materials and producing affordable clean hydrogen will strengthen U.S. energy security, advanced manufacturing, and economic competitiveness. The project will provide education and outreach that expand undergraduate research opportunities and participation in computational materials design, particularly in West Texas rural communities. This project will establish a predictive understanding of the relationship between catalytic activity and material stability in oxygen evolution electrocatalysts operating in acidic environments. The mechanisms governing catalyst degradation and surface dissolution remain poorly understood and are not reliably captured by existing theoretical frameworks. The project will employ machine-learning–accelerated electronic structure simulations to explicitly model reaction pathways for oxygen evolution and surface corrosion processes at the atomic scale. Density functional theory calculations will be benchmarked against experimental thermodynamic and kinetic data where possible, enabling quantitative evaluation of electrochemical reaction barriers and dissolution energetics. The resulting framework will be applied to identify Earth-abundant oxide catalysts that exhibit improved activity–stability tradeoffs, providing mechanistic insight into electrocatalyst degradation, and guiding the rational design of durable anodes for water electrolysis. The fundamental knowledge generated by this work will advance computational electrochemistry and enable more reliable prediction of catalyst performance in demanding electrochemical environments. The project will develop a new course focused on project-based learning, which will equip students with skills needed to succeed in the interdisciplinary field of computational sciences. The project will also bolster public scientific literacy and public engagement with science and broaden participation in STEM through targeted activities for middle-school students in rural West Texas. 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 · 2026-02

Project Summary/ Abstract The mosquito species Aedes aegypti is the primary global vector of important human pathogens, including dengue and yellow fever viruses. The attributes that make Ae. aegypti an efficient vector of these pathogens – such as small flight ranges and the proclivity for human blood feeding – also impact the genetic structure of populations at local and regional scales. Aedes aegypti has recently re-emerged as a medically important insect in the (semi)arid American southwest (ASW, California, Arizona, New Mexico, Nevada, and western Texas), and populations are rapidly dispersing along a northward gradient. This proposal aims to investigate the structural properties of localized and regional Ae. aegypti population networks in this unique landscape to identify weaknesses and breakpoints in (sub)population connectivity which can inform mosquito control interventions. Our central hypothesis is that Ae. aegypti population dynamics in the ASW are best explained by a core-satellite metapopulation framework in which large urban centers maintain genetically diverse and stable populations (i.e., cores) while connectivity (i.e., gene flow) and invasion of satellites outside of cores is best explained by distance to a source, size of urbanized sites, and density of human populations. To investigate population structure across the ASW, we will systematically sample individuals from urban centers of varying human population densities, genotype individuals using a single-nucleotide polymorphism array (SNP-chip), and then use a combination of landscape genetic approaches and network analyses to quantify population connectivity and determine the influence of commerce (i.e., roads) systems on the structure of ASW Ae. aegypti populations. To investigate population structure within ASW cities, we will first intensely and systematically sample individuals from multiple locations and time points within four urban cores with historical, established, and recently invaded Ae. aegypti populations. We will then genotype individuals using whole genome sequencing and will analyze networks using persistent homology approaches to determine the extent to which population structure at local scales is driven by founder effects (e.g., time since invasion), physical barriers to random mating (e.g., site fidelity), and individual limits of dispersal (e.g., kinship networks). Our results will provide a novel and innovative assessment of Ae. aegypti population biology and invasion dynamics at multiple spatial scales, which can better guide mosquito control intervention efforts in the region.

NIH Research Projects · FY 2026 · 2026-02

Project Summary Alzheimer’s disease (AD) is the leading cause of dementia worldwide, yet there is no effective treatment to slow or halt its progression. AD is characterized by the accumulation of amyloid beta (Aβ) plaques, neurofibrillary tangles, inflammation, and dysfunction of brain-supporting cells. Recent studies suggest that microRNAs (miRNAs), small molecules that regulate gene expression, play a critical role in AD pathology. Our research focuses on miRNA PC-5P-12969, a newly identified miRNA that may influence disease progression by affecting key pathways related to GSK3α and APP, two proteins involved in neuronal damage and AD progression. Our long-term goal is to determine whether miRNA PC-5P-12969 can serve as both a biomarker for early diagnosis and a potential therapeutic target for AD. To achieve this, we propose the following aims: Aim 1.1: Validate miRNA PC-5P-12969 as a biomarker in human AD samples. We will measure miRNA PC-5P-12969 levels in blood, cerebrospinal fluid (CSF), and postmortem brain tissue from individuals with and without AD, using samples from diverse cohorts (TARCC and NIH NeuroBioBank). We will analyze its correlation with cognitive decline, Aβ pathology, and tau pathology to assess its diagnostic potential. Aim 1.2: Investigate the mechanistic role of miRNA PC-5P-12969 in regulating GSK3α and APP. Using iPSC-derived neurons from AD patients and healthy controls, we will examine how miRNA PC-5P-12969 affects the expression of GSK3α and APP and its impact on mitochondrial function and synaptic integrity. Aim 2: Determine the protective effects of miRNA PC-5P-12969 in an AD mouse model (APP NL-G-F Knock-in). By increasing miRNA PC-5P-12969 levels in AD mice, we will evaluate its impact on memory function, Aβ accumulation, and mitochondrial health, exploring its therapeutic potential. Our preliminary data suggest that miRNA PC-5P-12969 is elevated in AD brain tissue and can reduce GSK3α and APP expression, potentially protecting neurons from disease-related damage. By validating its role as both a biomarker and a therapeutic target, this study could lead to earlier AD diagnosis and novel treatment approaches. This project has the potential to advance our understanding of AD and contribute to the development of new diagnostic tools and therapeutic strategies to improve patient outcomes.

NSF Awards · FY 2026 · 2026-01

This project aims to serve the national interest by improving and testing curricula in economics education. Addressing student persistence in quantitative STEM disciplines and the limited number of rigorous randomized control trials (RCTs) investigating instructional practices in economics, this project will examine the effectiveness of a low-touch instructional intervention. A vital part of this project is the partnership among three institutions, Miami University, Texas Tech University, and Ohio State University. Building on a successful pilot study, the project aims to reshape introductory economics courses to emphasize social context. The project team intends to design an instructional intervention of curricular modules and to rigorously test their effectiveness by conducting a randomized control trial study across six public institutions in Ohio. This intervention applies core economics concepts and principles to real world contexts, as an instructional approach to relate economics to students' lives. Economics is well suited for this effort, as the economics curriculum is similar across institutions, which includes a two-semester introductory economics course sequence comprised of a Principles of Microeconomics course and a Principles of Macroeconomics course. These courses are required for students intending to major and minor in economics and are popular among non-majors seeking to fulfill distributional requirements. Courses are structured around a common teaching structure, in which core concepts and principles are taught and then illustrated through examples. Based on context-based learning theories, the instructional intervention will consist of curriculum modules that cover core concepts/principles and examples, which are relevant to the varied lives and interests of students. Each experimental module includes non-technical general news articles, class discussion questions, exam questions, and teaching resources. To support implementation, faculty professional development activities will be scheduled throughout the project, which will include the development, testing, and review of the modules. Based on its design and planned implementation, the project has the potential to significantly understand complex relationships that link instructional interventions with expected outcomes. The goals of the project are to scale-up the approach and assess the effectiveness of this promising curricular intervention, which exposes students in introductory economics courses to the principles and core concepts of economics through relatable, context-based material that connects course content to social relevance. In addition, the project intends to examine how exposure affects students' perceptions of economics and future academic and career choices, paying close attention to heterogeneous effects across population subgroups. Using survey and administrative data, the team will implement a robust plan that compares the outcomes of students randomly assigned to treatment course sections to students randomly assigned to control course sections. Additionally, by linking the state's Higher Education Information dataset with records from the Department of Jobs and Family Services, the team intends to estimate the predicted effect of the intervention not only on students' major choice, but also on their potential future income levels. The IUSE:EDU Program supports research and development projects to improve the effectiveness of STEM education for all students. Through the Engaged Student Learning track, the project 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 2026 · 2026-01

Non-technical Abstract: This project will widen our understanding of a class of materials known as strongly correlated insulators. Among these materials, some exhibit unusual behaviors, such as having a conducting surface at the boundaries, while the interior remains insulating. Motivated by the decade-long work on a flagship system, samarium hexaboride, this project aims to systematically explore other correlated materials to examine the universality of physical properties. By charting correlated insulators, this project will expand a small set of known examples into a broader class within quantum materials. This work will also be the foundation for correlated insulators to become the platform for future electronics devices. In addition, this project actively trains and engages future students, who will become next-generation scientists. This project also helps military veterans build their foundation and career in the STEM field. Technical Abstract: The research team identifies and characterizes emergent surface states and the true nature of the bulk in correlated insulators. The project's goals are to establish a comprehensive understanding of these materials by asking whether more correlated insulators possess surface states, if their bulk insulation is immune to disorder, and how surface state existence depends on the band gap origin. The scope of the research involves single crystal growth and electrical transport measurements. The primary transport method employs the "inverted resistance" technique, which allows for the detection of the surface states and the characterization of the bulk conductivity hidden under the surface. The research team also uses Corbino disk magnetotransport to further characterize the newly discovered surface states. This approach enables the systematic charting of these materials, leading to a generalization of understanding regarding their unique electronic properties. 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 · 2025-12

With support from the Chemical Catalysis program in the Division of Chemistry, Casey O’Brien and William Schneider of the University of Notre Dame are working collaboratively (1) to clarify the mechanisms by which atmospheric pressure plasmas couple with solid surfaces to access and stabilize metastable nitrogen species, and (2) to explore the potential to exploit these metastable states to drive novel reactions. The integration of atmospheric pressure plasmas with heterogeneous catalysts (plasma catalysis) has recently gained considerable attention because of its potential to carry out transformations that are difficult or impossible using conventional thermal catalysis, and in modular units powered by renewable electricity. Realization of this potential would lead to more energy-efficient and environmentally sustainable chemical processes. Nitrogen activation in particular has attracted substantial interest in the plasma catalysis community because of the ability of plasma to activate the strong dinitrogen triple bond. Preliminary work in the O’Brien lab suggests that the types of adsorbed nitrogen species relevant to plasma catalysis may be richer than previously thought. This project focuses on clarifying the nature and reactivity of plasma-generated nitrogen species using spectroscopic techniques developed in the O’Brien lab and computational approaches in the Schneider lab. The theory-informed spectroscopic approach and proof-of-concept catalytic examples will broadly impact both catalysis science and technology. This research will also enhance advanced scientific education through training of graduate students in experimental and computational research, communication, scientific rigor, and the ethical conduct of research. This proposal explores a strategy to access, stabilize, and concentrate metastable intermediates that are thermally inaccessible by coupling plasma and surface chemistry, and to exploit these metastable species for novel surface reactions. Casey O’Brien and William Schneider will collaboratively explore these concepts in the context of nitrogen activation. Recent unpublished work in the O’Brien lab suggests that metastable azides, or N3, are stabilized by metal surfaces during exposure to N2 plasmas. While preliminary experiments indicate that LTP(low temperature plasma)-exposed metal surfaces can accommodate metastable N3 species, there are many fundamental science questions that remain unanswered: (i) What is the identity (N2, N3, or other) and nature of this species? (ii) Is this species formed in the plasma first and subsequently trapped by the metal surface, or does the surface facilitate its formation? (iii) How reactive are these surface-adsorbed species towards other reactants? This project will address these fundamental science questions to develop strategies to exploit metastable species generally, and azides specifically, to drive chemical transformations that cannot be achieved by conventional thermal catalysis or plasma alone. To this end, this project integrates experimental and computational approaches to clarify the mechanisms by which atmospheric pressure plasmas couple with solid surfaces to access and stabilize metastable N3 species and explore the potential to exploit these metastable states to drive novel reactions. The work leverages state-of-the-art in-situ spectroscopy techniques developed in the O’Brien, and density functional theory calculations that predict the relationship between surface composition, structure, products, and reactivity, providing a theoretical framework for understanding and guiding experiments. 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 · 2025-11

Grassland wildfires increasingly threaten human life and property in regions of the USA and worldwide. Managing fuels is a key way to reduce grassland fire risk. However, risk management strategies developed for forests do not translate to grassland systems, which are not as well studied. Grassland fuels vary across the year, vary from year to year, and change dramatically across the landscape. The critical knowledge gaps around how grassland fuel structures vary across space and through time make it difficult to evaluate methods for effectively reducing wildfire risk. In addition, approaches that allow scaling on-the-ground fuel measurements to characterize landscape-scale parcels are needed to assess risk and prioritize mitigation. In this project, a collaborative network of researchers, land managers, and fire practitioners work to fill these knowledge gaps and build the capacity to coordinate across organizations and regions in the Southern Great Plains of the United States. This project aims to fill several critical gaps in understanding grassland wildfire risk. The project forms and coordinates a collaborative network of partners interested in grassland wildfire that range from researchers to land managers and fire practitioners across regions of the Southern Great Plains. In addition, this project organizes coordinated data collection on grassland fuel variation and how the attributes of fuel influence fire behavior. This dataset enables effective scaling from on-the-ground fuel characteristic measurements to landscape scales important for land management planning and risk assessment. This network of field sites provides the ability to test patterns in grassland fuel characteristics across different climates, plant communities, and cultural fire contexts. Together, these advances enable better representation of grassland fuels in large scale fuel models and ultimately improve fire behavior modeling and risk assessments. 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 · 2025-11

This REU Site program at Texas Tech University focuses on advancing knowledge and workforce development in the field of wide and ultrawide bandgap (UWBG) semiconductor technologies. The project addresses critical needs in high-speed electronics, power systems, and photonics, supporting national priorities such as technological innovation, energy efficiency, and national security. By engaging undergraduate students in cutting-edge research and hands-on training, the program aims to prepare a highly skilled workforce capable of contributing to the innovations that underpin modern communications, military systems, and industry. Participants will also develop essential technical and soft skills through seminars, mentorship, and professional development activities, fostering interest in advanced research and graduate studies in electrical and computer engineering. Overall, this initiative promotes scientific progress, and educational excellence with broad benefits for society and the economy. This project develops a research and training environment focused on the synthesis, characterization, and application of wide and ultrawide bandgap semiconductor materials, particularly for nanoelectronics and nanophotonics. The research aims to address fundamental challenges in the growth, fabrication, and integration of high-quality UWBG semiconductors such as Ga2O3 and related heterostructures. The program's core activities include epitaxial growth, device fabrication, and characterization of high-power transistors, light-emitting devices, and memory components. The methodology combines theoretical modeling, materials synthesis, and advanced testing techniques, with an emphasis on translating research into emerging applications like high-power electronics, RF devices, and optical emitters. The project will contribute to the scientific understanding of UWBG materials and accelerate their deployment in next-generation technologies, supporting the U.S. semiconductor industry and national 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.

NSF Awards · FY 2025 · 2025-10

This project will contribute to the national need for well-educated scientists, mathematicians, engineers, and technicians by supporting the retention and graduation of high-achieving, low-income students with demonstrated financial need at Texas Tech University. A total of at least 36 scholars pursuing BS degrees in electrical engineering and computer engineering will receive scholarships of up to $15,000 per year for three years. Scholars will receive faculty and semiconductor industry focused mentoring, and the project will build strong scholar cohorts through experiential learning such as hands-on semiconductor manufacturing experiences. Additional activities for scholars include personalized, AI-informed career preparation support, industry-informed mentorship, laboratory training, and academic and professional mentoring. The overall goal of this Track 2 Scholarships in STEM project is to increase STEM degree completion of academically talented, low-income undergraduate students with demonstrated financial need. There is a significant national need to grow the STEM workforce and nurture key talent that will ensure economic competitiveness and provide domestic leadership across critical sectors. This project directly speaks to this need by supporting STEM student success, which will strengthen the workforce in the semiconductor industry and other key areas of need. The project will be assessed by an experienced evaluator that will help the team gain knowledge (e.g., access to academic and professional development resources, learning gains and learning gaps, industry engagement, etc.) about the specific needs of these high-achieving, low-income students, and the data generated will contribute to the knowledge base regarding effective strategies to support students in STEM. 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 · 2025-09

This I-Corps project focuses on the development of a dynamic circulation system to improve accuracy and reliability of 3D bioprinting process as well as the functionality of fabricated tissues and organs that could be used for transplants. One major problem in 3D bioprinting is that the suspended living cells in the bioink often sink to the bottom and adhere with each other to form clusters with different shapes and sizes during printing. This leads to uneven cell placement, which lowers the quality and function of the printed tissues. This issue is especially important for creating transplantable organs, testing new medicines, and developing treatments tailored to individual patients. Current methods, such as active stirring or using special materials, can harm cells, require precise bioink preparation, and are difficult to use with multiple cell types. The solution is a new circulation system that helps prevent cells from sinking and clumping together in the bioink. A peristaltic pump is used to move the bioink from the bottom of the container back to the top, and the bioink is kept gently flowing and well mixed while the cells remain viable. This technology has the potential to improve human health, support medical research, and reduce the need for organ donors, contributing to the nation’s well-being and scientific progress. This I-Corps project utilizes experiential learning coupled with a first-hand investigation of the industry ecosystem to assess the translation potential of the technology. This solution is based on the development of a dynamic circulation system designed to mitigate cell sedimentation and aggregation in 3D bioprinting. Bioprinting is widely recognized as a promising solution to fabricate functional tissues and organs suitable for transplantation. Homogeneous cell distribution in scaffolds is essential to final functionality of fabricated tissues/organs. However, it is extremely challenging to achieve homogeneous cell distribution, primarily due to cell sedimentation and aggregation during the bioprinting process. The current mitigation approaches using active stirring and functional biocompatible materials have limitations in low cell viability due to mechanical stress, careful bioink formulation required, and difficulty accommodating multiple cell types in the bioink. The technology is a bioink circulation system to significantly mitigate cell sedimentation and aggregation. A peristaltic pump extracts the bioink from the bottom of the bioink reservoir and replenishes it to the top of the reservoir to achieve active circulation. The circulation flow rate is dynamically changing while the total volume of the bioink is decreasing. The technology improves printing reliability and performance of 3D bioprinting in a number of applications in tissue engineering and regenerative medicine, such as personalized medicine, precision medicine, and drug screening. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-09

This I-Corps project investigates the commercial potential of an online biometrics data collection system. Using a webcam, the system collects biomarkers of cognitive effort and attention (heart rate and eye tracking) and emotions (facial electromyography (EMG)). This new solution has many potential applications, including the promotion of message effectiveness for content creators/advertisers. It also has the potential to enhance online training and education activities across education levels and disciplines. The system takes advantage of the naturally occurring signals produced by the body to help content creators and designers understand, in part, what is occurring in the brains of those watching messages using webcam technology. This I-Corps project utilizes experiential learning coupled with a first-hand investigation of the industry ecosystem to assess the translation potential of the technology. This solution is based on the development of an online tool designed to replicate the vectors of biometrics / psychophysiological data typically collected in a fixed lab location, thereby replicating the rigor of traditional in-lab biometric / psychophysiological research without the constraints of geography, time, expense, and heterogeneity. Data collection occurs via browser-based experiments with remote participants, typically yielding message effectiveness results within 24 hours. A user-friendly dashboard visualizes insights, thereby helping content creators refine creative assets quickly. Compared to traditional methods, the approach is roughly 90% faster, significantly cheaper, and easily scaled to a variety of audiences. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-09

3D bioprinting is widely recognized as a promising solution to fabricate functional tissues and organs suitable for transplantation. In 3D bioprinting, the bioink containing living cells is precisely deposited to form 3D constructs based on a layer-by-layer manner. Cell patterning arranges cells in specific spatial distributions to resemble the native architecture of tissues and organs, which is crucial for replicating the complex functionality of biological tissues. However, current cell patterning techniques are either extremely challenging to implement into 3D layer-by-layer bioprinting process or constrained to fixed patterns. This EArly-Concept Grant for Exploratory Research (EAGER) award supports fundamental research seeking to develop an innovative acoustic array-assisted 3D bioprinting technology to enable dynamic, layer-by-layer cell patterning within filaments during 3D bioprinting aiming at significantly improving functionality of fabricated tissue and organ models. Results look to advance engineered tissue functionality for various applications across regenerative medicine, drug screening, and personalized drug therapies. Beyond 3D bioprinting, this technology seeks to introduce a new manufacturing paradigm by enabling precise microscale organization of functional materials (such as particles, fibers, and cells), paving the way for advanced applications in healthcare, energy, and electronics. The objective of this research is to understand effects of the acoustic array properties on planar cell patterns during 3D bioprinting and post-printing viability/proliferation and α – smooth muscle actin (α-SMA) expression of patterned smooth muscle cells. Specifically, the acoustic array-assisted 3D bioprinting system in this project combines acoustic cell patterning and microextrusion-based 3D bioprinting. The key component is a customized acoustic array module consisting of multiple piezoelectric transducers. By exciting specific piezoelectric transducers of the acoustic array, the cells in the bioink are expected to be patterned into the filament center plane. Moreover, by adjusting the array’s operating conditions, mainly the excitation patterns of different transducers and frequencies, real-time change of the pattern orientation is expected to be achieved to meet the dynamic cell patterning requirements for fabrication of tissue models with complex geometry. A physical model based on principles of structural vibration and acoustic wave propagation will be used to simulate formation of 3D acoustic pressure fields and identify the design properties for the acoustic arrays for the desired planar cell patterns for each layer. Experimental cell patterning will be observed using a fluorescence microscope to validate the simulation results. Finally, smooth muscle cells look to be patterned using the proposed technology, and the post-printing assessment focuses on cell viability and proliferation, and α-SMA expression for contractile function. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-09

With the support of the Macromolecular, Supramolecular, and Nanochemistry program in the Division of Chemistry, Professors Hans Lischka of Texas Tech University and Miklos Kertesz of Georgetown University will develop and apply high-level computational methods to elucidate new design principles for the technologically important area of novel carbon nano-aggregates, which are crucial for the development of organic semiconductors for quantum devices, spintronics and biosensors. The work will focus on the discovery of new open shell multiradical polycyclic aromatic hydrocarbons (PAHs), their accurate characterization, and their association mechanisms in complex environments relevant for the understanding of the formation of carbon nanoparticles. For this purpose, combinations of highly innovative theoretical multireference methods available in the COLUMBUS program system of the Lischka group and artificial intelligence (AI) approaches will be applied together with the comprehensive chemical expertise of the Kertesz group. These investigations will lead to important expertise for the control and tuning of spin properties of PAH aggregates leading to the prediction of magnetic, optical and transport properties of new semiconductor materials. These computational technologies will provide new scientific opportunities and will have the potential for significant societal impact. Educational aspects are emphasized at graduate and undergraduate levels with the long-term goal of contributing to the availability of a technically competent new generation of scientists trained in a wide combination of computational methodologies. Effective ways to elevate the public awareness of science will be pursued by specific discovery-based science content incorporated in courses designed for non-science major college students. A new scope of computational investigations will be developed concentrating on complex modeling aiming at the determination of ensemble structures where radicals and biradicals are embedded in an unstructured PAH environment to allow better representation of realistic models like the Yen-Mullins model for asphaltenes or carbon nanodots. The tools for the creation of these ensemble structures are based on meta dynamics and replica exchange molecular dynamics which will be combined with efficient quantum chemical methods to reproduce the main features of the open shell PAH systems. As important new areas, the magnetic properties of single-sheet PAHs adsorbed on gold surfaces will be investigated to explain scanning tunneling microscopy (STM) and non-contact atom force microscopy (nc-AFM) experiments since they provide new possibilities to create a multitude of open shell PAHs with π paramagnetism. The developed multi-reference methods provide ideal possibilities for urgently needed accurate predictions and better understanding of the open shell character of these unique compounds. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-09

This award supports research to improve collaboration between humans and robots in dynamic industrial environments such as construction and manufacturing settings. Environmental factors such as temperature, lighting, and noise can affect each worker's performance and reaction time differently, depending on their unique physical and mental characteristics. These variations can impact on the overall effectiveness and safety of human-robot collaboration, particularly in scenarios where the robot assists without taking over control. Current robotic systems do not adequately account for such differences, limiting their ability to adapt to changing environments or individual workers’ needs. This research will create a new generation of human-centered robotic systems that monitor both environmental conditions and human well-being in real time and adjust their behavior to support smoother and more efficient teamwork. The robots will interpret indicators of workers’ mental states and use that information to adjust how they interact. They will also communicate their status to human partners intuitively. These innovations are expected to enhance workplace safety, task accuracy, and worker satisfaction, especially in labor-intensive jobs. Broader impacts of the project include the integration of research outcomes into university curricula and outreach programs aimed at inspiring and educating students from high school through graduate levels. This research aims to develop an adaptive human-robot collaboration framework. The approach integrates real-time human state with adaptive robot control, allowing the robot to respond intelligently to changes in human and environmental conditions. Human states are inferred through physiological sensing methods and used within a closed-loop system to guide collaborative behaviors. The robot communicates its internal state and intentions through intuitive multimodal feedback, promoting seamless coordination and reducing cognitive effort for the human operator. The control strategy leverages deep reinforcement learning techniques to optimize decision-making in complex, high-dimensional environments. The system will be tested in both laboratory and real-world settings to evaluate its robustness and effectiveness. Results from this work are expected to contribute to the advancement of control, perception, and learning in human-centered robotics, with broad implications for future collaborative systems across various industries. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-09

The Ohio State University and Texas Tech University are collaborating on an EDU Core Research project to identify factors that affect participation in STEM education and the workforce. This project will specifically address access to STEM education and the workforce by examining student debt and its effects on participation in STEM majors. Students who major in STEM fields frequently earn more than their non-STEM counterparts, and STEM is critical for the economy and for addressing today's needs and opportunities. However, the up-front costs of college and the challenges of STEM curricula can be formidable, often leading students to make choices that may lead to increased debt loads. This project undertakes quantitative and qualitative analyses of how student debt and STEM majoring affect each other and jointly shape educational, graduation, and post-graduation outcomes, including decisions to continue in a STEM major, time to graduation, income, debt, and financial burdens. This project addresses causality concerns by testing two key hypotheses. The first hypothesis suggests that college costs and resulting educational debt have become important drivers of student decisions. The second hypothesis proposes that student debt is an important determinant of student behavior, including major choice, major switching, and degree completion. The project's quantitative analysis is using unique, population-level administrative data from the State of Ohio to conduct causal analyses, investigate differences across socioeconomic and demographic groups, study differences by type of public institution, and ask whether and how these relationships have changed as students have taken on higher debt burdens. This work is informed by a complementary qualitative analysis of interviews with students at public universities to understand their subjective decision-making experiences related to debt, STEM majors, and STEM careers. Taken together, the work will greatly advance understanding of the causal and subjective mechanisms shaping the size of the nation's future STEM workforce. This project is supported by NSF's EDU Core Research (ECR) program. The ECR program emphasizes fundamental STEM education research that generates foundational knowledge in the field. Investments are made in critical areas that are essential, broad and enduring: STEM learning and STEM learning environments, broadening participation in STEM, and STEM workforce development. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-09

Microscale control of liquids and suspended particles is essential for next-generation medical diagnostics, environmental monitoring, and for development of miniature soft machines capable of locomotion in complex fluid environments. Yet existing devices steer fluid flow along only a few fixed directions and lose precision when conditions change. This project seeks to create artificial motile cilia arrays (soft filaments slimmer than a human hair) that can adjust their rhythm in real time and move fluid or cargo in any direction. By melding recent progress in soft-composite manufacturing, embedded sensing, and model-based control, the work seeks to emulate the versatility of living cilia while offering greater durability and scalability. The anticipated advance will not only deepen fundamental understanding of microscale transport, but also strengthen national health through faster diagnostics and gentler cell handling. The project also looks to propel economic prosperity by enabling agile soft microrobots for targeted drug delivery and high-precision microassembly of next-generation devices. A coordinated education plan looks to integrate project discoveries into undergraduate and graduate curricula, offer mentored research opportunities for students, and deliver hands-on demonstrations to learners from kindergarten through grade twelve. The research seeks to establish a new class of self-regulating artificial cilia arrays designed to enable precise, energy-efficient manipulation of fluids and suspended particles in three dimensions. Each soft filament in the array will operate under localized actuation and embedded feedback, allowing it to autonomously adjust its motion in response to environmental conditions. Rather than relying on pre-programmed sequences, the system looks to exploit hydrodynamic interactions and internal sensing to generate coordinated, adaptive wave-like patterns that emerge from simple local rules. This self-organization enables robust control over the flow direction and transport behavior, even as fluid properties or external constraints change. A prototype array will be built to demonstrate these capabilities in representative viscous media. In parallel, theoretical and computational studies seek to inform the design of control strategies and guide the scaling of the system for broader application. The resulting framework intends to offer a versatile foundation for next-generation microfluidic devices and soft robotic systems capable of autonomous operation in complex, dynamic 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.