University Of Nebraska Lincoln

NSF Awards · FY 2025 · 2025-07

This project aims to serve the national interest by investigating conditions under which innovative teamwork practices can improve metrics related to students' success in engineering programs. Teamwork is key to engineering success, but little is known about how faculty can effectively facilitate teamwork experiences. Therefore, this Level 2 Engaged Student Learning project plans to advance understandings of practices for effective teamwork for all students. By studying the effectiveness of these innovative practices, the project seeks to contribute to increasing the numbers of students who graduate with engineering degrees. It is anticipated that the research will provide vital and explicit evidence for how specific teamwork facilitation practices influence persistence in engineering by generating empirical evidence of the multidimensional effects of these innovative teamwork practices on engineering students' experiences and perceptions. This has the potential to contribute to a future in which undergraduate engineering students are fully engaged through development, testing, and use of teaching practices and curricular innovations that aim to engage students and improve learning and persistence in STEM. Analyzing how these practices affect factors related to persistence in engineering, and understanding faculty members' experiences utilizing these practices, could lay the groundwork for large-scale institutional improvement in engineering education. The goals of this project are to implement and assess a teamwork intervention and document the effects on students relative to factors influencing persistence in engineering. The project team plans to conduct a nationwide survey and interviews with engineering faculty members about their perceptions of and experiences with facilitating teamwork. Control data will be collected by administering a survey at the beginning and end of the semester in ten courses; the same courses will then receive a teamwork intervention, with the survey repeated to compare pre- and post-intervention results on student success and persistence metrics. Additionally, faculty will be interviewed to examine their experiences, perceptions, and challenges related to implementing the intervention. It is anticipated that characterizing the experiences of faculty will result in research-based plans for refining and scaling up the intervention and associated materials in the future. By developing research-based materials to improve engineering learning environments, partnering directly with faculty developers from around the country, and broadly disseminating the results, this project has the potential to have both national and local impact. The NSF 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 program supports the creation, exploration, and implementation of promising practices and tools. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-07

The “Quantum Measurement Problem” is an unsolved mystery that stops us from bridging the gap between the microscopic and the macroscopic world. To solve this mystery, microscopic objects, electrons, are placed close to a macroscopic everyday world object, a wall. From a practical perspective, it has been proposed that the interaction between electrons and walls limits how small an object an electron microscope can see. If the effects of this interaction can be overcome, we may improve electron microscopes. A second objective of this project is to look at the microscopic, quantum properties of a group of electrons close to each other. Now we have changed the interaction between an electron and a wall to the interaction between electrons. It is predicted that these quantum properties can be used to further improve electron microscopes. The research project provides training for graduate students, undergraduate students and high school students in quantum science, which is an area of national need. The technologies we develop, which include visualization of our work with Augmented Reality headsets, are aimed to strengthen the national economy. The work is done by using electron diffraction from a nanofabricated grating to make a coherent electron quantum wave. By placing a gold coated wall close to the electron wave, we not only decohere the wave, but Caldeira and Leggett also predict an electron energy loss. If we can find the decoherence-energy loss relation, we have demonstrated Quantum Dissipation Theory, and our understanding of the Quantum Measurement Problem has deepened. Using three nanofabricated gratings we have pioneered, we have built an electron-wave, Mach-Zehnder interferometer. With this device we will add even more to our understanding of quantum effects by searching for a newly proposed space-time topology of the celebrated Aharonov-Bohm effect. This quantum effect is especially profound, as it combines relativistic space-time with quantum waves. Finally, a 10-femtosecond laser pulse is used to pull pairs of electrons from a sharp nano-needle. Electrons with the same spin cannot be emitted in the same state, or in other words, cannot be quantum degenerate according to the Pauli Exclusion Principle. This affects the arrival time relation between the electrons. The arrival time cannot be the same, but only when the electrons start very close to each other. Our goal is to push electron-electron quantum degeneracy to a level where it could be used for a new type of imaging: electron-electron quantum correlation imaging. 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 The view of the ribosome as a static automaton is undergoing a paradigm shift to a heterogeneous molecular machine with specialized forms yielding a ‘ribosome code’ for translation regulation. However, the full extent of ribosome heterogeneity and its biological role remains largely unknown. Despite decades of study, this knowledge gap persists due to the lack of technologies that bridge traditional biophysical methods and high- resolution structure determination approaches. Consequently, it is technologically very challenging to decipher which specific ribosomal components are (1) present, (2) their modifications, and (3) their stoichiometry as a function of cell type, localization, and state. There is a critical need for novel top-down and native mass spectrometry approaches for direct, rapid, and extensive characterization of large, heterogeneous molecular machines, such as the ribosome, to fundamentally understand the mechanisms of biological function. The goal of the proposed work is to directly characterize and quantify intact ribosomal proteins using top- down proteomics and determine the exact set of components that assemble to form specialized ribosomes using native mass spectrometry. To achieve this goal, the foundational technologies must first be developed for (1) accurate mass determination, (2) efficient and informative dissociation, and (3) spectral decongestion techniques which will enable more complete characterization of the intact ribosome and its individual components. This research program will develop and integrate novel (1) gas-phase charge reduction reactions, (2) hybrid tandem mass spectrometry methods, and (3) ion mobility separations that will enable production and interpretation of information rich top-down proteomics and native mass spectrometry data. These advancements will enable rapid measurements of the intact ribosome, gas phase dissection of the intact ribosome, and characterization of the sequence and modification of the individual ribosomal components. These broadly applicable technologies will immediately enable previously intractable studies in a wide range of biological systems. Long term, development and dissemination of these foundational technologies will enable more complete characterization of ribosome heterogeneity, advances in basic understanding of biology, and discovery of novel therapeutic targets for human health issues ranging from antibiotic resistance to cancer.

NSF Awards · FY 2025 · 2025-07

Rain falling on snow, known as rain-on-snow (ROS), can cause rapid snowmelt, which often leads to exceptional runoff causing flooding and destruction in the communities impacted. Due to their effects, ROS events are recognized as one of the 23 unsolved problems in hydrology. In the Midwest, the effects of ROS is significantly underexplored. In this research, the investigator will carry out a comprehensive assessment of rain-on-snow flood risk in the U.S. Midwest. They will use machine learning and other methods to investigate the factors that control ROS events, their characteristics, and how they vary in space and time. The investigator will integrate the research with Indigenous knowledge and education to create ROS education curricula, and address water issues in Nebraska and the Midwest. The scientists will produce readily available educational resources, and useful, interpretable ROS flood information for local communities. The outcomes will fill a gap in knowledge and generate information that will be relevant to other regions. In addition to the significant benefit to society in contributing to solutions that will improve flood resilience, save lives and mitigate costly damages, this researcher will train and mentor undergraduate and graduate students in research at the Nebraska Indian Community College and University of Nebraska. ROS flooding is significantly underexplored in the Midwest. To advance the fundamental understanding of these events in the region, this NSF CAREER project will conduct a comprehensive assessment of ROS structured around three research objectives closely tied to three educational objectives. First, estimate ROS flood risk in the Midwest by investigating its natural and anthropogenic drivers and integrating hazard, exposure, vulnerability, and response dimensions. Second, investigate how Midwest ROS events are teleconnected to large-scale climatic oscillations and how they relate to flood characteristics in the region. And third, research the intersection of climate and ROS flood risk. The research analyses will leverage data from different sources and employ machine learning, causal analysis, and land surface modeling. The education goals include creating ROS flood education curricula that incorporates Indigenous knowledge and promotes an understanding of water-related problems in the Midwest. In addition, the investigator will organize workshops and create Open Educational Resources to make ROS flood education more accessible and readily available to communities. Finally, the team will design and develop outreach activities to make ROS flood information more useful and interpretable for community leaders and city managers and foster a two-way interactions and sharing of ideas, between communities and the scientist to advance understanding of water challenges in the region. The researcher will also provide research mentoring opportunities and training to local community college students, and student at the University of Nebraska. This project is jointly funded by the Hydrologic Sciences program, and the Established Program to Stimulate Competitive Research (EPSCoR). This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-07

With increasing demand for high data-rate wireless communication systems, this project will explore new methods of radio-frequency transmission and reception using novel electromagnetic wavefronts with spin and angular momentum. These wavefronts provide a means to multiplex many different data streams at the same frequency in the air to significantly increase the data rate without increasing spectral bandwidth demand. However, practical demonstrations of such communication links in the microwave to millimeter-wave frequency band have been limited. This CAREER project explores new methods to generate and multiplex data channels using spin and angular momentum modes of electromagnetic wavefront. The research will develop enabling technologies based on electromagnetics solutions to bridge this gap through convergence of theory, design, fabrication methods, and measurement testbeds. Broadly, the proposed solutions tie into 5G wireless networks and the forthcoming 6G wireless systems. On the education part, this project will simultaneously develop industry-facing pedagogical methods, which are also inspired by the goal of increasing student engagement and integrating the research thrusts. Community outreach activities in combination with industry-academia collaborations will be pursued to support research and education goals of this project. The project will adopt a technical approach which moves away from energy-demanding signal-processing, MIMO-processing, or data-encoding techniques and introduce power-efficient passive electromagnetic guiding structures for multiplexing vortex wave modes simultaneously. Towards that goal, the project will introduce new antenna structures, underlying theory, design methods, and associated passive circuits for simultaneous generation and multiplexing of modes in free space. Specifically, the designs will enable coaxially aligned, multiplexed spin and angular momenta electromagnetic beams, which can be scaled to larger apertures and for longer distance wireless communication links. The research will also explore key features such as dynamic configuration of beam directions, focus distances, divergence angles, and suitable positioning of central-null angle to avoid link breakages. In addition, the research will bridge theory and practice by leveraging metal and dielectric 3D fabrication methods. Finaly, an experimental framework will be developed for characterization, analysis, and validation of components and for exploring novel properties of vector vortex waves. This project is jointly funded by the Communications, Circuits and Sensing Systems (CCSS) Program and the Established Program to Stimulate Competitive Research (EPSCoR). This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2025 · 2025-06

Glycan Utilization Profiling in Human Gut Microbiomes of Common Funds Data Healthy diets are key to prevent various metabolic diseases (e.g., cardiovascular disease, intestinal bowel disease, and obesity). The western diets are known to be unhealthy as it lacks sufficient dietary fibers, which are critical to nurture a healthy gut microbiome. Furthermore, not only the amount but also the types of dietary fibers have a significant impact on the healthy gut microbiome. Personalized dietary intervention, by giving different dietary fibers as prebiotics to different individuals, is an effective strategy to enable personalized nutrition for disease prevention. However, microbiome-based personalized nutrition demands a better understanding and a capability to computationally profiling glycan utilization in gut microbiomes of any human individuals from different populations, lifestyles, and diseases. To fill this research gap, this R03 project aims to develop a bioinformatics workflow to automatically retrieve CAZyme (carbohydrate active enzyme) gene clusters (CGCs) from publicly available human gut metagenomes. These include microbiome data generated in three NIH Common Fund programs: the Human Microbiome Project (HMP), the Integrated Human Microbiome Project (iHMP), and the Human Heredity and Health in Africa (H3Africa) project. Other microbiome data that were not funded by NIH will also be included to have a better representation of more diverse human populations. The genomes from HMP, H3Africa, and other microbiome projects will be used to identify fiber degrading CAZymes and CGCs, forming two reference databases (refCAZymes and refCGCs) that can be used to map sequencing reads from any individual’s microbiome sample to infer personalized fiber utilization. To demonstrate this utility, metagenomic and metatranscriptomic reads of 791 samples of iHMP Inflammatory Bowel Disease Multiomics database (iHMP-IBDMDB) will be mapped to refCAZymes and refCGCs to compare the glycan utilization abundance and prevalence between IBD patients and healthy people. The significance of this project is that it will contribute to a better understanding of the diversified glycan utilization among different human populations, lifestyles, and disease status. The workflow developed in this project will be implemented as a new software package named GLUP (glycan utilization profiling, code and documentation will be on GitHub) using the popular workflow manager Nextflow to facilitate the emerging microbiome-based personalized nutrition and health industry. The innovation is that it will be the first global CGC-based glycan profiling across different human populations, especially in the under-represented and only recently available African microbiomes. This project is built upon our highly cited CAZyme bioinformatics tool suite named dbCAN that has been continuously developed since 2012.

NSF Awards · FY 2025 · 2025-06

Melanin is a pigment that protects organisms from ultraviolet (UV) radiation and environmental stresses. Polyextremotolerant fungi found in cold deserts, naturally produce melanin, incorporating it into their cell wall and releasing it under certain conditions. In nutrient-poor environments, where organic material is scarce, these fungi may form symbiotic partnerships with photosynthetic organisms like algae and cyanobacteria. Within these partnerships melanin may be exchanged for essential nutrients. Therefore, melanin could be available to help protect partners from UV radiation, cold temperatures, and water scarcity, enabling the survival of biological soil crusts. However, the genetic or metabolic triggers for melanin excretion are not well understood. This project investigates the symbiosis between a polyextremotolerant fungus and its photosynthetic partners, aiming to uncover the triggers regulating melanin production. It also develops predictive models of the metabolic interactions of the entire microbial community and validates these findings using genetic tools. Additionally, the fungus could serve as a valuable resource for large-scale melanin production, with applications in UV-protective products and advanced materials for the aerospace and other industries. Beyond scientific discovery, the project enhances the STEM workforce by implementing educational, outreach and mentoring activities. This project aims to address two key deficiencies in understanding the role of a melanized fungus (Exophiala viscosa) in biological soil crusts: the mechanisms regulating melanin production and excretion, and the lack of experimental evidence for metabolite exchange between the melanized fungus and photosynthetic microbes. To tackle these knowledge deficiencies, the proposed research: 1) utilizes ‘omics’ data to discern the molecular mechanisms regulating melanin production in the fungus, when tri-cultured with an algae and a cyanobacterium; 2) develops and refines microbial community metabolic models to explore inter-species metabolic interactions and assess how carbon and nitrogen sources from the algae and cyanobacterium impact melanin production; and 3) employs genetic tools developed for the melanized fungus to investigate the interactions driving melanin secretion, applying targeted gene editing informed by the modeling framework. Aided by mathematical modeling and experimental validation, this directed experimental design investigates specific one-on-one interactions among “keystone” microbial species to understand the community dynamics and foster symbioses critical for community survival. This approach promises a deeper understanding of microbial interactions, which differs from typical microbial ecology metagenomics-focused studies that do not provide detailed molecular insights into microbial interactions. 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

This Research Experiences for Undergraduates (REU) site award to the University of Nebraska Lincoln, located in Lincoln, NE, supports the training of 10 students over 10 weeks during the summers of 2025-2027. The program, funded by the Division of Chemistry and the Established Program to Stimulate Competitive Research (EPSCoR), offers undergraduate participants the opportunity to carry out original research projects in the areas of biochemistry, organic chemistry, chemical biology, and chemical engineering. A Graduate student mentor works with each REU student throughout the program. Participants also learn about science communication, careers in the chemical industry, the graduate school application process, and how to disseminate research results through publications and presentations to a variety of audiences. By the completion of this program, students develop complex analytical and communication skills and have a deeper knowledge of science and engineering careers. This REU site offers a range of research projects in chemical assembly, including neuropeptide receptor interactions, microplastics in the environment, perovskite semiconductors, rapid analysis of drug-protein interactions, electrochemical sensors using biomolecules, adaptive surfaces, organic radicals for MRI contrast agents, and the synthesis of 2D materials such as graphene. Students develop communication skills by presenting written, oral, and graphic interpretations of their work to an array of audiences, including REU students from other programs, graduate student mentors, faculty members, and the public through social media. Participants take two field trips to explore industrial careers, learn about careers in academia, and are trained in the responsible conduct of research, including workshops on safety and authorship ethics. 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

This project supports the International Indian Statistical Association (IISA) conference, 2025 - a four-day international conference that will take place at University of Nebraska-Lincoln, Lincoln, Nebraska from June 12 to June 15, 2025. This conference is the official annual meeting of the International Indian Statistical Association (IISA). It provides a platform to exchange ideas and showcase theoretical, methodological, and application of Statistics and Data Science across many scientific domains. The conference features plenary sessions, invited talks, panel discussions and workshops that will provide attendees with invaluable insight into real-world applications of Statistics and Data Science. It will promote education, research, and foster exchange of information and scholarly activities thereby facilitating the emergence of new ideas that will guide future research directions. The conference will also host student paper and poster competitions which will highlight the best research among emerging scholars. IISA 2025 spans topics from statistical theory to advanced computational methods and application-focused modeling of complex data. It will bring together leading experts and emerging scholars in statistics, biostatistics, probability, data science and offer a forum to discuss recent progress in statistical theory and data science. The conference will provide a valuable opportunity to nurture emerging talents and foster collaboration. It has two hands-on workshops, multiple panel discussions, student paper and poster competitions that will make the experience engaging and impactful, leaving attendees better equipped to thrive in their research journeys. Plenary talks and special invited talks will be given by leading experts in Probability, Statistics, Machine Learning, and Data Science. Junior researchers will have the opportunity to present in invited sessions. Doctoral students will also have the opportunity to give oral presentations on a competitive basis. The official website of IISA 2025 is https://www.intindstat.org/conference2025/index. 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 · 2025-04

SUMMARY Measuring immunospecific binding of target nucleic acids (NA) to single stranded DNA (ssDNA) probes immobilized on solid surface has revolutionized molecular biology analysis from basic science to medicine. However, microarray-based technologies including Next Generation Sequencing can detect mutations with high precision but have limited ability to quantify copy numbers. The gold standard for NA quantification, qPCR is limited to meet the current need to quantify small NA, such as miRNA at required sensitivity in attomolar (aM) range for applications such as liquid biopsy for diagnosis and prognosis. Label-free, enzyme-free electro- chemical approaches, particularly Electrochemical Beacon (EcB) is a promising technology to make microarray quantitative. However, the limit of quantification (LOQ) is only ~10 pM due to the complexity of the probe design which is a folded structure with complex conformation. In the proposed EcB study a simple binary probe is designed comprising of short reporter DNA (R) tethered to the electrode with longer probe (P) complimentary to the target (T) of interest. The R has a redox active dye, methylene blue (MB) tethered to the free-end. On P-T binding the released R brings MB in proximity to the electrode resulting in a signal. The research strategy is to design an R-P probe such that there is no signal from the binary probe and a signal is generated on each P-T binding, i.e., positive contrast. The background is well controlled to have low false positives and negatives. The goal is to use a special opto-electrochemical tool called SEED to read P-T binding to a microarray of binary probes on a monolith electrode by measuring local redox on 10 micron spots to obtain LOQ of 10 attomolars with dynamic range of over seven orders of magnitude to profile a mixture of ~25 miRNA sequences on a chip. The premise is based on previously published result showing SEED has the required sensitivity. The study will be organized in two specific aims: Specific Aim 1: Binary Probe design verification. The goal will be to, (a) create a “perfect switch” where before binding all (R-P) probes show no signal and, on each P-T binding the released R contributes to a redox signal. and (b) establish calibration curves to quantify mixtures of up to 25 synthetic ssRNA (or analogous ssDNA) targets with background of non-specific NAs. Specific Aim 2: Validation of binary probe design. The goal will be to quantify ~25 miRNA in cell line and exosomes suspended in its media. The cells will be cultured with/without UV exposure. The success will be a quantitative profile of ~25 miRNA sequences in biological sample with [c] ranging from ~10 aM to ~1 nM. On success, collaborations will be initiated to translate the application to 3D culture (organoid/spheroid), stem cell differentiation and liquid biopsy applications.

NSF Awards · FY 2025 · 2025-04

The theory of C*-algebras, which originated in the 1930s in the study of quantum mechanics, is now a vital part of modern mathematical analysis, with applications across the mathematical sciences. C*-algebras arise naturally in connection with a variety of mathematical objects of interest, including groups, dynamical systems, and discrete graphs. This project concerns the structure and properties of C*-algebras associated to quantum graphs. A relatively recent generalization of the classical notion of a discrete graph, quantum graphs have proven to be useful in quantum information theory: just as classical discrete graphs encode confusion due to noise in a classical communication channel, quantum graphs encode confusion due to noise in a quantum channel. The project will generate new methods for analyzing the structure of quantum Cuntz-Krieger algebras and their underlying quantum graphs, and explore their interplay with quantum information theory, a topic of growing global interest. Educational opportunities for undergraduates will be provided through research projects, and a new, interdisciplinary certification program in introductory quantum information theory at the PI’s home institution. Student researchers and visiting speakers will be recruited with a focus on diversity and representation. Given a simple discrete graph, the Cuntz–Krieger algebra for the graph is a universal C*-algebra which encodes the graph’s edge relations. The Kubo-Martin-Schwinger (KMS) states on a C*-algebra can be physically interpreted as states of thermal equilibrium for a quantum system. The KMS states on the Cuntz–Krieger algebra of a simple discrete graph were classified by Exel in 2003 using an isomorphism between the Cuntz–Krieger algebra and the graph’s Exel crossed product, which is a universal C*-algebra that encodes natural dynamics on the graph’s infinite path space. For a quantum graph, an analogue of its Cuntz–Krieger algebra, called a quantum Cuntz–Krieger algebra, was defined in 2021. The principal investigator and her collaborators have since constructed Exel crossed products for some classes of quantum graphs and shown these Exel crossed products to be isomorphic to a quotient of the corresponding quantum Cuntz–Krieger algebras. The first major objective of this project is to design a canonical construction of an Exel crossed product for an arbitrary quantum graph and study its relationship to the corresponding quantum Cuntz–Krieger algebra. The second major objective of this project is to classify the KMS states on the Exel crossed product for a quantum graph and, following Exel’s techniques in the classical setting, use this relationship established in the first objective to classify the KMS states on the associated quantum Cuntz–Krieger algebra. 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 · 2025-04

This project will identify specific roles that zinc plays in the sensation of taste. Zinc is a micronutrient essential for all life forms. Zinc supplementation is a proven method to treat taste loss, and zinc is a potent inhibitor of sweet taste, but the underlying mechanisms are not known. Zinc transporters are ‘chokepoint’ proteins located in the cell membrane that mediate the movement of zinc into and out of cells and subcellular compartments, thereby regulating intracellular zinc concentration. The key zinc transporters and other zinc-regulated proteins that regulate taste cell regeneration will be identified using gene expression studies in taste buds of mice fed a zinc-deficient diet and by targeted gene deletion of zinc transporters in mouse taste organoids, a tissue culture system that ‘mimics taste buds in a dish.’ To determine how zinc inhibits sweet taste, the changes in behavioral, neural and cellular responses to sweeteners in knockout mice and in taste organoids that lack a sweet taste-cell-specific zinc transporter gene will be determined. The integrated educational and outreach programs aim to train the next generation of STEM workforce and promote nutritional literacy to the public. To this end, summer internships and mentoring will be provided to community college students wishing to transition into four-year colleges, and nutrition literacy will be provided through scientific demonstrations at community outreach programs and at classes for adults. Ultimately, this research will help develop improved methods to treat taste loss and prevent overconsumption of calorie-dense, sugar-sweetened foods and beverages in humans. This project uses gene expression, cell physiology, histology, taste nerve recordings and taste behavior analysis to advance understanding of the roles of zinc transporters and other zinc-regulated proteins in taste cells. CRISPR knockout array screens of zinc transporters in mouse taste organoids, followed by single cell gene expression, will be used to identify key taste-stem-cell-expressed zinc transporters and zinc-regulated genes ex vivo. The results of these experiments will help identify key zinc transporters, zinc-regulated proteins and signaling pathways that are required for taste cell regeneration. In addition, zinc-regulated genes will be identified in vivo by single-cell and spatial gene expression of taste papillae of mice fed a zinc-deficient diet. The roles of the sweet-taste-cell-expressed zinc transporter Slc39a8 in behavioral responses and taste nerve responses to sweeteners will be determined by comparing the Slc39a8 conditional knockout mouse strain and control strain. Sweetener-stimulated zinc and calcium fluxes in taste organoids cultured from both mice strains will be measured, and the roles of extracellular versus intracellular zinc will be studied using cell membrane -impermeant and -permeant zinc chelators, respectively. Together, the combination of behavioral, electrophysiological and mechanistic studies will shed light on how zinc inhibits sweet taste signaling. The findings from both tasks will be highly translatable to related tissues such as the skin and olfactory and intestinal epithelia where zinc plays important roles in development, regeneration, and metabolism. This project is jointly funded by the BIO-IOS-Physiological Mechanisms and Biomechanics Program, the BIO-IOS-Activation Program, and the Established Program to Stimulate Competitive Research (EPSCoR). This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-03

NON-TECHNICAL ABSTRACT This project will investigate the twisting behavior of biological molecules. Many biological molecules are chiral—like our left and right hands, they exist in two mirror-image forms that are chemically identical but structurally distinct. DNA, with its helical shape is chiral. Amino acids, and sugars, all have “right-handed” and “left-handed” versions; as do screws, Slinkys, and springs. In principle, objects with this kind of mirror asymmetry can produce a voltage when we twist them, or twist when we apply a voltage. The catch? It works best when the chiral objects are very small, on the scale of molecules. This project will exploit the electric potential of common biological molecules to create cheap, abundant power sources and microscopic motors. Through publications, demonstrations, and interactive presentations, the team will share the exciting properties of chiral molecules with the scientific community, K-12 students, and the science-curious public. TECHNICAL ABSTRACT This project will explore how solids made of randomly-oriented chiral molecules act as torsional piezoelectrics, generating voltage in response to a twist and twisting in response to a voltage. The majority of biological molecules are chiral, allowing for a range of cheap, abundant materials to explore for power related applications . Because the symmetry breaking arises from the molecular chirality rather than crystallinity, even amorphous and polycrystalline solids made of chiral molecules can, in principle, exhibit torsional piezoelectricity. Arguments based only on mirror symmetry indicate that non-zero components of the piezoelectric tensor exist and must switch sign for chiral enantiomers This two-year project will study both left- and right-handed enantiomers of at least three different molecules to establish a general relationship between chirality and torsional piezo-electricity. Amorphous and crystalline solids of chiral molecules will be fabricated via melting with fast and slow cooling, and sintering. The direct piezoelectric effect will be measured by subjecting the solids to torsional stresses in a variety of geometries and measuring the resulting voltage. The inverse effect will be obtained by subjecting the solid to different voltage configurations and measuring the motion using optical methods. The intellectual merits of this project include the elucidation of novel piezoelectric symmetry breaking mechanisms that may result in functional materials as well as torsional piezoelectric mechanisms for biological energy transfer/mechano-sensing. This work will also provide the community with new insights into the general theory of piezoelectricity in the context of chiral solids. 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 · 2025-03

This workshop aims to bring together experts across EPSCoR jurisdictions and institution types to achieve breakthroughs in fluid mechanics and prepare the next generation of STEM researchers. Fluid mechanics significantly impacts our daily lives in numerous ways. For example, gas dynamics in the oil industry plays a major role in the economies of Louisiana and Oklahoma. Another example is extreme weather data analysis, which is essential for predicting events like tornadoes in Nebraska and hurricanes forming in the Gulf of Mexico. Additionally, aerospace engineering research into turbulence holds potential benefits for national defense. This gathering of diverse researchers is expected to spark innovative ideas and foster collaborations among experts with complementary strengths. It also aims to inspire young institutions to actively contribute to scientific advancements. Furthermore, the workshop will provide opportunities for early-career researchers, including those from historically underrepresented groups in STEM, to network with experts outside their immediate fields of study. The University of Nebraska-Lincoln will host the workshop, in collaboration with a steering committee from Louisiana, Arkansas, and Oklahoma. Confirmed speakers include experts from other EPSCoR states, such as Kansas, Kentucky, and Alabama, who are renowned for their research and ability to effectively communicate complex concepts to young audiences. This multi-day, multi-jurisdictional workshop will convene researchers across all career stages to discuss recent advances, new techniques, building regional capacity, and generating partnerships to solve challenges in fluid mechanics, including: (1) the breakthrough technique of convex integration, which demonstrates the non-uniqueness of solutions to the equations of fluid mechanics; (2) the novel grid-overlay finite difference method, capable of incorporating the fractional Laplacian on arbitrary bounded domains; (3) stochastic partial differential equations of fluid mechanics, which better model small-scale phenomena; (4) recent advancements in continuous data assimilation; (5) the application of boundary layer analysis techniques in fluid mechanics to semi-analytic physics-informed neural networks (PINNs); (6) learning strategies for multi-scale methods aimed at significantly reducing computational costs; and (7) a new approach to quantify ecological resilience by incorporating spatial dimensions. Since a key motivation behind this workshop is contributing to STEM education and training, each day will begin with engaging speakers delivering introductory tutorial lectures designed for students and non-experts. Graduate and undergraduate students will present research talks. To foster collaboration, breakout sessions will be held at the end of each day to promote creative problem-solving skills and encourage partnerships. Additionally, a special session will feature senior professors offering career advice to the next generation of scientists. Through input from these groups via surveys and recruiting efforts, sessions will also promote the involvement of underrepresented groups in STEM, such as women, those from two-year colleges, primarily undergraduate institutions, and minority-serving institutions in Nebraska and neighboring states. 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 · 2025-03

This three year renewal Research Experience for Undergraduates (REU) site will support the training of nine students for 10 weeks during the summers. REU students will be engage in biomedical engineering, a crucial area of importance in the U.S. Students will learn how to conduct research by working in teams which combine the fields of engineering and medicine. The project addresses current needs in the workforce by providing exposure to biomedical engineering devices as well as the academic and industrial skills necessary to produce them. Participants will receive hands-on experience with biomedical engineering devices such as wearable sensors, nanomedicine creation, and medical imaging equipment. The overall goals of the project are to increase the number of students entering the engineering workforce, advancing engineering training, and expand opportunities in engineering and research education. This project will provide an enriching, intensive research experience in biomedical engineering (BME) devices for undergraduate students. Structured research activities will be complemented by professional development training tailored to prepare participants for successful careers in STEM research. Students will have opportunities to engage in cutting-edge research projects covering areas such as medical instrument design, implanted devices, nanomedicine, and tissue engineering. Hands-on research in laboratories, mentorship from faculty with broad expertise in both academics and industry, weekly educational seminars, and collaborative project work will comprise the program. This approach ensures that students not only gain practical skills but also develop a comprehensive understanding of the complexities and challenges in biomedical engineering. REU participants will actively contribute to publishing their research findings through presentations and publications in engineering research and education journals. This project is jointly funded by EEC Workforce Development and the Established Program to Stimulate Competitive Research (EPSCoR). This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-03

Autonomous driving is the much-anticipated transformative technology to revolutionize conventional human driving toward full automation, high safety, and versatile intelligence. In pilot commercialization efforts (e.g., Waymo), autonomous vehicles rely on onboard sensors, hardware, and AI-based software to perceive, understand, and react to complex surrounding environments. Existing autonomous vehicles are manufactured under tightly coupled hardware and software, with limited future upgradability throughout their life cycles. This project’s novelty is to advance existing autonomous driving by integrating the available information from proximate vehicles and roadside infrastructures to seamlessly incorporate into the autonomous driving software, substantially improving driving performance and safety. The project's broader significance is empowering existing vehicles with ever-evolving autonomous driving capability and continual upgradability. Furthermore, the project involves cyber workforce training activities and industry collaboration. This project aims to democratize autonomous driving technologies to every connected vehicle via designing a new connected autonomous driving as a service (CADaaS) paradigm. The fundamental idea is to enable adaptive vehicle-edge collaboration to obtain the latest autonomous driving stacks, including perception, prediction, and planning. First, new network threading techniques are designed to achieve user-initialized resource reservation and user-grained performance assurance in the wireless network. Second, new deadline-aware inference frameworks are designed to assure the percentile constraint of inference latency under multiple deep neural networks (DNN) concurrency in the edge server. Third, CADaaS is deployed and evaluated under real-world at-scale network and computing scenarios, including the University of Nebraska-Lincoln Husker-Net and the University of Delaware D-STAR. This project is transformative in defining, reshaping, and catalyzing the on-the-horizon connected autonomous driving technology, software-defined vehicles, and vehicle computing paradigm. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-02

The 2025 Midwest Numerical Analysis Day (MWNADay) is scheduled to take place on April 5-6, 2025, at the University of Nebraska-Lincoln. This conference will provide a collaborative platform for researchers from the Midwest region and beyond to share knowledge and build connections in numerical analysis, computational mathematics, and related applied fields. By fostering a welcoming and low-pressure environment, MWNADay has become particularly valuable for early-career researchers, enabling them to present their work, exchange ideas, and explore collaborations. This event will also emphasize and encourage participation from individuals across many types of groups and institutions with limited access to major conferences. As computational technologies continue to impact various disciplines, MWNADay serves a crucial role in strengthening the community and advancing the frontiers of computational and numerical sciences. MWNADay 2025 focuses on promoting advancements in numerical analysis and scientific computing by providing a forum for researchers to discuss cutting-edge developments, exchange methodologies, and explore interdisciplinary applications. The conference will draw participants from various academic and research institutions, including (according to the Carnegie Classification System) the 26 R1 (very high research activity) and 32 R2 (high research activity) universities in the Midwest, to share insights and engage in collaborative discussions. Central to its mission is the development of efficient numerical algorithms and their theoretical foundations, which are essential for addressing challenges in fields such as fluid dynamics, biomechanics, and geophysics. The local organizing committee aims to facilitate the participation of a broad audience, particularly early-career researchers, as well as faculty from institutions where access to major research conferences may be limited. MWNADay will offer a range of activities, including technical presentations, poster sessions, and networking opportunities, to inspire innovation and foster partnerships among attendees. Additionally, the conference aims to bridge gaps between research communities by integrating junior and senior researchers and advancing knowledge transfer across disciplines, ultimately contributing to the broader fields of numerical analysis and computational mathematics. The conference website is https://math.unl.edu/midwest-numerical-analysis-day-2025 This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-02

Machine learning (ML) is projected to be essential in future autonomy development. However, ML components such as deep neural networks (DNNs) may react unexpectedly to even tiny input variations. Recent incidents in ML-powered systems, such as Tesla and Uber autonomous vehicles, raise an urgent need for techniques and tools to formally verify the safety and robustness of DNNs before utilizing them in safety-critical applications. The state-of-the-art research, focusing on the safety, robustness, and fairness of deep neural networks and neural network control systems, is powerful and promising for some parts of ML-powered autonomy development. However, enabling trustworthy, complex, learning-enabled autonomy is still challenging due to the need for verification technologies for system-level reactive behaviors involving complex interactions between multiple components. This project's novelty is in creating new formal method foundations in modeling, specification, verification, and toolchains to address this grand challenge research problem beyond the state-of-the-art. The project's impact is the substantial enhancements of safety, reliability, and explainability of various learning-enabled unmanned systems. Additionally, the project will strengthen the research and study in autonomy-focused topics in Nebraska by recruiting undergraduate research assistants and integrating research findings into ML and autonomy verification courses. It will also increase the interest and engagement of K-12 group students in STEM majors and science literacy through outreach events. The project team will also collaborate with the University of Nebraska-Lincoln Osher Lifelong Learning Institute to give lectures and discussions for adults on how autonomy concepts and technologies may impact their lives. This project involves two foundational research thrusts, modeling and specification and quantitative verification, along with software and trustworthy autonomy testbed development and rigorous evaluation. The project's expected research outcomes include 1) a new generic graph-based modeling approach for complex, learning-enabled autonomy (CLeA) in which CLeA's components and their interaction are represented using nodes and edges, respectively, 2) a new set-based algebra, built upon the concepts of probilistic star (or shortly ProbStar, a new variant of the well-known star set) and containing a collection of mathematical propositions and important operators such as parallel composition, decomposition, Minkowski sum, etc., that allow users to discover and keep track of the dependency between multiple reachable sets produced by different components in a CLeA and compose/decompose precise inputs for these components in the analysis, 3) a new set-based specification language to specify CLeA's temporal behaviors based on the concepts of ProbStar set representations, 4) a suite of algebra-based depth first search (DFS) reachability algorithms to construct the reachable set traces of all components in CLeA over multiple steps. and 5) a suite of scalable quantitative verification algorithms to quantify the satisfaction of CLeA's temporal properties under uncertainties. Results from proposed research thrusts will be integrated into StarV, a new quantitative verification tool, to enhance its verification capacity for various applications. Indoor and outdoor learning-based autonomous driving testbeds using the F1Tenth platform and Robify robot will be developed to evaluate the proposed verification framework. CARLA simulator, SCENIC, and hardware-in-the-loop (HIL) simulation will also be used to assess the proposed research in various project phases. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-02

This project is jointly funded by the DMR Polymers Program, and the Established Program to Stimulate Competitive Research (EPSCoR). NON-TECHNICAL SUMMARY Organic photovoltaics (OPVs) made from conjugated (alternating single and double bond containing) polymers are promising materials for application as light-harvesting devices because they are lightweight, flexible, and can be made by easy, low-cost processing. Conjugated polymer-acceptor blends have great potential as active layers in OPVs. The final way the polymer-acceptor blends are organized is controlled by processing conditions and built-in molecular characteristics that dictate the final properties and performance of the critical, active layer. Polymer-acceptor layers are typically made in a complicated process using organic solvents and thermal annealing; in contrast, melt-processing, a simpler, low-cost process which is used to make most inexpensive plastic parts, has remained largely unexplored, so its potential is virtually untapped for OPVs. This research project will enable novel melt-based processing methods of polymer-acceptor films for OPVs and has potential to achieve previously inaccessible morphologies and performance for light-harvesting applications. This project will train graduate students for STEM jobs in both academic and industrial settings and will also educate undergraduate students through research and mentoring opportunities. Outreach efforts in preschool and K-5 will motivate young students and educate their teachers and will seek to broaden participation of underrepresented students in STEM, ultimately contributing to development of the future STEM workforce. Results will be broadly disseminated through specialized journals, publications for broader audiences, at scientific conferences, and by making materials and publications available online. TECHNICAL SUMMARY The objective of this project is to understand and direct morphology development in conjugated polymer-acceptor blends through melt-processing. The bulk heterojunction structure of semicrystalline polymer-acceptor films is determined by an intricate interplay between crystallization and phase separation, depends strongly on material components and processing conditions, and ultimately dictates final photoelectric, mechanical, and stability properties. Polymer-acceptor blends are typically solution-processed and annealed to change structure and improve properties, but the fundamental mechanism of structure development during melt-processing has received little attention and remains poorly understood. This research project will investigate how manipulating melt-crystallization through material and processing parameters can be used to tailor the complex interaction with phase separation and, consequently, morphology development and final properties. The significance of this research is two-fold. On one hand, it will generate fundamental knowledge in structure development of conjugated polymer-acceptor blends of interest for organic photovoltaics. On the other hand, this project will open up the way to rational design of new materials and processing methods to yield previously inaccessible morphologies and properties in polymer-based active layers. 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

Master’s-level engineers are critical for the technology workforce as the nation seeks to advance national health, prosperity and welfare and to secure national defense. While there are four times as many engineering master’s recipients as PhDs in the United States most prior research on engineering graduate students has focused on doctoral students. As a consequence, we know almost nothing about the experiences, motivations, career planning, and skills required by industry of master’s degree students. This project will focus on this critical segment of the workforce with an initial focus on mechanical engineering. The work will help us to systematically understand how to better prepare master’s students for their jobs so that they can make contributions in their careers from the outset. To help inform graduate curricular offerings, we will use cutting-edge generative artificial intelligence techniques to illuminate the specific skills employers want from employees who have engineering master’s degrees. Our research will help identify potential strategies for recruiting more students to engineering master’s programs, in particular domestic students, which is a critical need for the future workforce. The findings of this project will better inform students, employers, administrators, and those considering master’s degrees about the skills desired and expected of mechanical engineering master’s recipients. This project will advance novel applications of natural language processing (NLP) coupled with interview research to understand the skills and benefits of terminal engineering master’s degrees. The quantitative element of the project will involve analysis of over a decade of engineering job postings. We will develop and apply an algorithm to extract skills from this substantial set of data to advance our understanding of the engineering workforce and make methodological advances in NLP. The qualitative element will involve collection and analysis of interviews with current master’s students about their reasons for pursuing a master’s degree, including desired skills. The project will mix these qualitative and quantitative analyses to identify mis(alignments) between what is communicated from the workforce about desired skills via job advertisements and current perceptions of the workforce from current master’s students. This research will fill an important gap in research on master’s-level engineering students, building knowledge about motivations for pursuing a master’s degree and employer expectations, including the most marketable skills. The NLP approaches developed in this project will apply to other employment sectors, disciplines, education research questions, and fields beyond engineering education research. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-01

----------------------------------------------------------------------------------------------------------------------------- This award is funded by NSF Global Centers program, an innovative partnership with international funding agencies to jointly support use-inspired research addressing global challenges through the bioeconomy. This project is supported by NSF, the Research Council of Finland, and Business Finland. It supports U.S.-based and Finland-based researchers developing international partnerships and building multi-stakeholder engagement while further developing their project toward a global multinational effort. The global food system is environmentally, economically, and socially unsustainable. Current practices contribute to significant greenhouse gas emissions and resource consumption. They incur hidden environmental, health, and poverty-related costs totaling US$20 trillion worldwide. Importantly, they are insufficient for feeding a growing global population with one in three people suffering from hunger or malnutrition. These alarming outlooks demonstrate an urgent need for nutritious, environmentally and socially sustainable, and economically viable food production. This project - Food Innovation and Diversification to Advance the Bioeconomy (FoodID) - promote an international collaboration among investigators from academia and the industry notably in the United States and in Finland. The vision of FoodID is to transform the future of food where innovative solutions drive a sustainable and resilient bioeconomy. The research focus is on alternative protein and lipid sources from specifically designed plants and microorganisms. FoodID also informs policymaking, enhances consumer understanding, drives economic development, and trains the next-generation workforce. Partnerships with community colleges and minority-serving institutions provide notably training opportunities to students from underrepresented groups in STEM. Taken together, these efforts will improve global food security and create high-quality, sustainable, nutritious food ingredients that meet consumer needs. FoodID is a transformative approach to advance knowledge of plant-based food systems. Leveraging biodiversity and biofoundry practices, FoodID drives innovation and discovery across four research thrusts: (1) a biofoundry approach for manipulation and production of food ingredients in plants and microbes; (2) sustainable biorefineries; (3) novel food design; and (4) environmental and socio-economic discovery. FoodID uses the design-build-test-learn process to develop advanced plant and microbial platforms to produce complex biomolecules. This include using high throughput automated bioengineering and biofoundry approaches to develop and scale new crops and microbial strains. As a secondary focus, FoodID Leverages biodiversity to predictably control complex phenotypes in plants and microbes. The goal is to develop novel ideotypes that direct carbon into high-value functional food components. Sustainable biorefineries, together with novel food design, optimizes the production and utilization of new ingredients, enhancing their functionality. Crosscutting these efforts is the development of new computing methods to improve bio-design, process development, and data management. The team also develops an integrated sustainability assessment model to analyze environmental, social, and economic impacts of value chains. Through these approaches, FoodID serves as international innovation ecosystem driving sustainable food production. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-01

The lack of network infrastructure in the Midwest and EPSCoR states has widened the digital and economic divide between rural and urban America. Open radio access network (O-RAN) initiatives have gained significant momentum in revolutionizing, defining, and shaping next-generation mobile networks, including Beyond 5G and 6G. In O-RAN mobile networks, network management plays a critical role in overseeing various aspects of network infrastructure, including service orchestration in non-RT RICs and resource allocation in near-RT RICs. Existing approaches generally rely on offline-train-online-deploy strategies using homogeneous AI/ML agents, which face challenges such as simulation-to-reality discrepancies and non-stationary learning environments in real-world, large-scale networks. The long-term vision of this project is to achieve autonomous mobile networks for 6G by designing novel AI/ML techniques to address real-world network management challenges, including, but not limited to, safety, scalability, robustness, and practicality. The project's outcomes are expected to significantly reduce the operating expenses (OpEx) of current mobile networks, thereby facilitating the widespread deployment and cost-effective operation of mobile networks across Nebraska, the Midwest, and EPSCoR states, ultimately contributing to bridging the digital divide between rural and urban America. This fellowship project outlines a first-of-its-kind safe zero-touch network management system by designing a new safe online hierarchical learning framework for O-RAN mobile networks. Leveraging the city-scale network infrastructure at the host institution, Iowa State University, the project will focus on three research objectives. First, the project will focus on online resource allocation at near-RT RICs using safe deep reinforcement learning. Another focus will be on the online service orchestration at non-RT RICs through robust Bayesian learning. Additionally, the project will conduct extensive field testing and evaluation within the O-RAN mobile network at both the home and host institutions. This fellowship will generate long-lasting benefits for the home institution, the University of Nebraska-Lincoln, by strengthening the PI’s research portfolio, enhancing the existing educational platform, and fostering a collaborative research group. This project holds the potential to revolutionize current practices in the acceptance, adoption, and deployment of AI/ML techniques for managing nationwide mobile networks within the telecommunications industry. 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

Integrated Photonics, encompassing functional devices like light-emitting diodes (LEDs) and lasers, waveguides, filters, modulators, and photodetectors miniaturized onto a single chip, holds transformative and revolutionary impacts on signal sensing, processing, and communication. This EPSCoR Research Fellows project focuses on a family of emerging wide-bandgap crystals, namely layered cesium lead halide perovskites, and explores their potential in integrated photonics. Supported by the Fellowship program, the principal investigator (PI) and her graduate student from the University of Nebraska-Lincoln (UNL) will carry out the project via extended visits and collaboration with researchers at the National Institute of Standards and Technology (NIST). The successful implementation of the proposed research will not only advance the understanding of the crystal growth and patterning synthesis of wide-bandgap perovskites from solution processes, but also empower cost-effective integrated photonic platforms that can be utilized for future exploration of light-matter interaction in both classical and quantum regimes. Moreover, the Fellowship will provide unique opportunities for an Assistant Professor and her graduate student to access the world-class cleanroom and nanofabrication facilities at NIST, to establish long-term collaboration between UNL and NIST, and thus to shape PI’s career trajectory and the quantum research and education landscape at UNL and Nebraska Jurisdiction. This Fellowship project aims to transform the field of integrated photonics through interdisciplinary research on wide-bandgap (WBG) cesium lead halide perovskites (CLHPs) and their device design and fabrication to realize high-quality photonic devices in a cost-effective manner. Distinguishing from conventional WBG materials (e.g., diamond and silicon carbide) that require complex and expensive synthesis and patterning processes, CLHPs possess tunable bandgap energies while enjoying the easiness of hydrothermal synthesis in solution. The proposed research is strategized to integrate numerical design and experimental investigation and leverage the state-of-the-art fabrication and optical characterization facilities at the host site, NIST. Two primary objectives are outlined as (1) Understanding the solution-based synthesis of inorganic WBG perovskites and developing a methodology to realize direct patterning sub-micrometer features; and (2) Employing non-classical states, such as topological states and bound states in the continuum (BIC), in device designs to achieve fabrication-tolerant photonic devices. The proposed research will engender an innovative direct-patterning methodology and advance the understanding of crystal growth in solution synthesis and capillary force-driven self-assembly, providing invaluable guidelines for other solution-processed inorganic and organic crystals beyond WBG perovskites. The topological device designs, of which properties are not sensitive to local defects, will elegantly compensate for the possible deficiency in the patterning precision and uniformity of the solution process and promise robust device performance. The integrated photonic platform developed here can serve as a steppingstone toward all-perovskite photonic integrated circuits (PICs) and a unique testbed to investigate the macroscopic quantum phenomena. 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

Images are a powerful tool for observing and understanding the natural world. Ground-based imagery, such as from time-lapse and trail cameras, captures short- and long-term environmental processes that cannot be measured in other ways and has the potential to contribute visual information to multiple fields. Applications include understanding a changing water cycle, developing new technologies to monitor streamflow and river depth, and measuring changes to vegetation. These data can then be used in computer simulations that are enabled by artificial intelligence and machine learning (AI/ML) to model and predict different conditions and scenarios. Imagery is well-suited for environmental monitoring that integrates data from different sources, where variables extracted from imagery complement data derived from other sensors located near cameras. However, there are significant barriers to capturing quality imagery and extracting scientifically useful information from imagery. Computer software and training resources will be developed to lower those barriers, allowing people with different levels of technical expertise and backgrounds to advance science using image-based methods. This project will develop a robust scientific community and accompanying cyberinfrastructure (CI) for using ground-based imagery to study environmental processes. This CI will complement existing remote sensing capabilities using satellite or airborne imagery, where tools such as ArcGIS and QGIS have opened new measurement capabilities and pathways to scientific discovery for a wide range of users. Ground-based imagery has potential for enabling new ecohydrological discoveries, and well-designed CI can empower people with the skills and tools needed for impactful and reproducible science. The specific goals of this Geoinformatics project are to develop: (1) Open-source software (GaugeCam Remote Image Manager Educational – Artificial Intelligence; GRIME-AI) that streamlines and documents reproducible workflows, (2) Benchmark data products (including data from PhenoCam archives) that promote method development and data standards, and (3) Training resources for broadened participation in the emerging scientific community that uses ground-based fixed cameras in ecohydrological research. The CI, available through GaugeCam.org and other public repositories, will be inviting and educational for a broad range of users, including those who may not currently have a strong STEM or data science identity. The project will focus on building community across disciplines through training for new users and increasing the ease of scientific discovery. 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

Being able to talk to family, friends, and at school is very important for children. Communication devices can help people with physical disabilities. However, many communication devices need someone to touch a screen or look at words to work. This can be hard for children with cerebral palsy and other movement disorders. New brain-computer interfaces that do not require surgery could help. These could help children communicate and feel less alone. But the grid layout of these interfaces can be confusing. Real-life pictures, like photos, could make it easier. However, it is unknown how to best use them, which is the goal of this CAREER award. Thjs project will redesign brain-computer interfaces to include real-life images. This will make the devices easier for children with physical disabilities to use. College students will also learn about new brain-computer interfaces through this research. In the end, this work will help people understand and accept children with disabilities. Improving their communication will also help enhance children's quality of life. This CAREER award helps make new communication tools for people with physical disabilities. Brain-computer interfaces often use a brain signal known as the P300 for control. Many P300 tools use grids with letters, like a 6x6 grid. Showing items in real-world scenes can help children who do not understand single grid symbols. These scenes keep real-world sizes and spaces of items, which can help with talking, and learning to use the P300 tool. This research looks at using real-life pictures in P300 brain-computers. It studies how brain activity is different between grids and scenes. It also checks how real pictures and highlighting change brain signals. Finally, researchers will find out what designs children like best. Building new communication tools can help improve children’s quality of life. It also teaches students about new communication tools, preparing them for future jobs. 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.