Florida State University

NIH Research Projects · FY 2025 · 2025-10

Project Summary. The prevalence of daily cannabis use and cannabis use disorder (CUD) has increased in the United States over the past two decades. Unfortunately, psychosocial treatments produce minimal long- term abstinence rates and no FDA-approved medications for CUD exist. Thus, identifying novel CUD treatment targets is an increasingly urgent public health need. Stress-elicited cannabis use motivation has been implicated in worse CUD outcomes, but a mechanistic understanding of how acute stress increases cannabis use motivation in CUD is limited. The PI’s work has demonstrated that acute psychosocial stress enhancement of subsequent cannabis cue incentive salience, as indexed by the late positive potential (neural measure of approach-motivated attention recorded using electroencephalography [EEG]), was associated with worse CUD severity and intervention response, independent of subjective craving. Moreover, hypothalamic pituitary adrenal [HPA]-axis, rather than noradrenergic or subjective reactivity to the psychosocial stressor was associated with subsequent potentiation of the cannabis cue-elicited late positive potential. These studies suggest that non-genomic, rapid glucocorticoid effects may be a contributing mechanism in stress amplification of neural drug-cue reactivity, but their correlational designs preclude causal inference. Further, psychosocial stressors are unable to isolate HPA-axis vs. noradrenergic components of stress reactivity. To isolate HPA- axis activation and test causality, pharmacological manipulations, common in animal models but rare in human studies, will be used to produce separate and co-operative glucocorticoid (hydrocortisone) and noradrenergic (yohimbine) activation. We will employ a 2x2 randomized, placebo-controlled double-blind crossover design in 36 cannabis users with severe CUD. Our primary aim is to test the causal potentiating effect of glucocorticoids on neural drug-cue reactivity, and further determine if the effect depends on co-occurring noradrenergic stimulation. Preclinical work indicates that glucocorticoids can potentiate reward motivation via mobilization of endocannabinoid activity (primary target of cannabis). Thus, as an exploratory aim in line with NIDA priorities (NOT-DA-22-048), we will obtain plasma samples to test the impact of pharmacological stress on circulating endocannabinoids and their mediating role in glucocorticoid potentiation of neural drug-cue reactivity. This project represents a highly novel integration of a rigorous pharmacological challenge design with biological markers of drug-cue incentive salience and endocannabinoid system activity. If hypotheses are confirmed, one causal mechanism through which stress increases neural cannabis cue reactivity will be known, which has immediate implications for testing experimental therapeutics. The long-term goal is to understand how a stress- related mechanism predictive of worse CUD phenotype is generated and can be blocked in CUD. Development of this model will provide a valid, efficient and (relative to other neuroimaging methods) low-cost approach to screen candidate medications and optimize psychosocial drug cue exposure therapies.

NIH Research Projects · FY 2025 · 2025-09

Spheroids and organoids are self-assembled, multicellular, 3D structures that better mimic in vivo tissues than 2D cell cultures. They have become an indispensable in vitro tool for processes such as drug assays, disease modeling, and for mechanistic studies. The secretion of specific factors by spheroids and organoids is vital for accurate recapitulation of in vivo function. Methods to measure secretion from these in vitro systems are indirect, slow, laborious, and combine many spheroids / organoids together masking the dynamics of secretion from the individuals. In this proposal, we will develop technology that will enable online measurement of secretion dynamics from many individual spheroids or organoids in parallel. To accomplish this work, we will perform the following specific aims. In aim 1, a centrifugal system will be developed to deliver a continuous flow of perfusion solution to spheroids / organoids as well as immunoassay assay reagents specific for the secreted factor of interest. These solutions will be delivered via centrifugal pumping to a mixing channel where fluorescence anisotropy will be used to quantify the secreted factor as the device spins. The spinning system and fluorescence anisotropy system will be fully optimized. In aim 2, this system will be applied to several model spheroid systems for validation of the technology. The system will enable quantitative measurement of secretory function multiple times per second from each organoid, making a significant and vertical advance compared to current technology. We anticipate the new technology will be a powerful tool in the field of spheroid and organoid research.

NSF Awards · FY 2025 · 2025-09

How can we explain the enormous diversity of life on our planet? For example, there are over 7,000 species of frogs and toads alone. What is the origin of this diversity? Biologists tend to agree that species differ in their basic biology, but how species differences arise is often difficult to study unless one can see catch species just as they form. Social communication in frogs, where individuals produce sounds heard by others, is a key aspect of what makes a species. This project will explore the idea that processes in the brain that influence the choice of mates play a pivotal role in promoting formation of new species (speciation). The work will investigate how neuronal circuits in the brain change in Upland chorus frogs when they encounter other frogs that also produce sounds that are needed for females to choose mates. A primary objective is to better understand what aspects of brain function are particularly prone to change among frog populations and how this divergence promotes the formation of new species. The populations of Upland chorus frogs to be studied are presently undergoing speciation and, therefore, are ideal for this investigation. This project will also train postdoctoral researchers and graduate students to understand brain physiology, animal behavior, and evolution. This project will investigate how ultimate evolutionary forces drive diversification of proximate neural mechanisms of speciation, and how neural divergence, in turn, feeds back to accelerate the engine of speciation. Specifically, the objective is to investigate how the relationship between auditory neural circuits and mating behaviors facilitates reproductive isolation (RI) during speciation. The overarching hypothesis is that divergent selection, acting directly on mating behaviors used in species recognition, can drive differential changes in auditory neuronal circuits, thereby promoting the evolution of RI and the radiation of new species. This project will focus on a species (the Upland chorus frog, Pseudacris feriarum) in which RI has evolved among populations, driven by independent reinforcement of mating behaviors in multiple lineages. Given knowledge of mating behavior and the auditory neurons mediating these behaviors, a series of complementary experiments will characterize the neural architecture of behavioral phenotypes. An empirically informed auditory neural circuit model will be used to generate predictions about the mechanistic neural changes underlying behavioral diversification. These models will then be validated through directed neurophysiological experiments. Finally, integrative modeling will test the evolutionary consequences of this neurodiversity in nature and how this variation contributes to the origin of species. This project is jointly funded by the Evolutionary Processes Program in the Division of Environmental Biology, the Neural Systems Activation Program in the Division of Integrative Organismal Systems, and the Division of Emerging Frontiers, all in the Directorate for Biological Sciences. 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 · 2025-09

Project Summary/Abstract The Lazenby lab is an electroanalytical research group dedicated to introducing and developing new imaging modalities and sensing methodologies for the study of complex biological systems. Our research leverages the capabilities of electrochemical techniques to measure molecules in real time, with spatial resolution across various size scales. Electrochemical methods are suitable for examining redox-active analytes, but many biologically significant molecules don’t exhibit redox activity, meaning they can’t directly undergo electron transfer in a potential window that doesn’t oxidize or reduce the solvent (i.e. aqueous media). Also, selectivity and the number of analytes that can be detected is governed by the formal potential of the analytes of interest and other redox active species in solution. To broaden the scope of processes that can be monitored, our lab is building new methods to map and measure concentrations of various analytes to be applied to a range of cell lines. We are using aptamers as the biorecognition element in a variety of sensor platforms that take advantage of the ability to measure non-redox active analytes. These aptamer-based sensors have high specificity and sensitivity for a diverse range of analytes, including small molecules, drugs, metabolites, biomarkers and toxins. We are advancing the field by integrating these aptamer-based sensors into scanned probe microscopies, wherein the functionalized imaging probes will enable high spatial resolution mapping of both cellular and subcellular structures. Our lab is building a suite of imaging techniques, using different types of scanned probe that include aptamer-modified microelectrodes for mapping large areas, and aptamer-functionalized nanopipettes for probing subcellular features with high precision, and with the ability to measuring redox active analytes using traditional electrochemical methods. These capabilities are particularly well-suited for the study of complex biological processes that are challenging to observe using conventional methods. A key innovation in our lab’s approach is the multiplexing of these imaging probes, that enables the simultaneous real-time detection of multiple analytes. This will enable the concentrations of multiple analytes to be quantified, and even visualized, simultaneously by targeting where aptamers are immobilized on probes with multiple elements and exploiting the fact that a single potential waveform can be uniformly applied to any sensor of this type. Furthermore, we are coupling the sensors with ion conductance probes for topographic measurements of cells, and with electrodes for the detection of redox active species, and with potentiometric probes for local pH measurement. These integrated probes will greatly enhance our ability to study a broader range of biological systems than can currently be studied using existing imaging and multiplexing strategies. The outcomes of the proposed research have the potential to transform our understanding of complex biological processes, which paves the way for novel diagnostic tools and therapeutic strategies.

NSF Awards · FY 2025 · 2025-09

In advanced manufacturing systems, anomalies such as unexpected deviations from normal process behavior can lead to defective products or production disruptions. Detecting these anomalies early is essential for maintaining product quality and reducing waste. However, identifying such faults is challenging, especially when their occurrence is rare and thus there is a lack of labeled data for conventional machine learning methods to recognize. This award supports research looking to address this gap by developing an intelligent system that learns from existing engineering knowledge embedded in texts and images in professional documents to detect new and unforeseen anomalies. The proposed process does not rely solely on expensive or exhaustive measurements needed for traditional fault diagnostic methods. Thus, this project looks to strengthen domestic production capabilities and reduce dependence on manual inspection and expert-only knowledge. Furthermore, the project will engage STEM students in cutting-edge research at the intersection of artificial intelligence, natural language processing and manufacturing engineering. Through engagement with industry partners, students will gain hands-on experience with real-world challenges, preparing them for the advanced manufacturing workforce. Results will be shared broadly with the manufacturing community. In addition, industry seminars with 3D printer suppliers and automakers will support long-term technology transfer. The goal of this project is to establish a scalable and automated approach that combines technical documents with machine learning to detect manufacturing anomalies in zero-shot settings. The documents include publications, experiment reports, and simulation data. A key innovation is the automatic creation of hierarchical knowledge graphs (HKG) that extract and organize domain-specific attributes from texts and images in diverse document sources. These attributes provide context-aware supervision to connect real-world measurements with descriptions of previously unseen faults. Unlike conventional zero-shot learning methods that rely on generic embedding or black-box foundation models, this approach builds a structured, engineering-specific knowledge base. The model uses this base to reason about new failure modes by comparing them to known cases. This approach seeks to enable explainable and accurate anomaly detection without labeled data for each failure type. The method looks to support automatic generation of graph-based model architectures, helping overcome challenges in designing model architectures for physics-informed machine learning. The framework will be demonstrated in two powder bed manufacturing settings - lithium-ion battery electrode coating and metal binder jetting - to identify unseen subsurface anomalies from surface-level data. This research seeks to advance zero- or few-shot learning methodology and supports generalizable, interpretable anomaly detection in diverse manufacturing environments. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-09

Arctic rivers drain vast northern landscapes and flow into the Arctic Ocean, forming critical land-ocean linkages. Studies of these linkages provide data that are influential for U.S. interests in the Arctic, including economic development, food security, community resilience, and weather patterns across the U.S. and the globe. Since 2003, the Arctic Great Rivers Observatory (ArcticGRO) has provided essential time-series data of water discharge and chemical analyses for the six largest Arctic rivers – the Yukon in the USA, the Mackenzie in Canada, and the Yenisey, Ob’, Lena, and Kolyma in Russia – as well as the curation and dissemination of discharge data for nine additional rivers. This proposal supports the continued sampling of water chemistry in the Yukon and Mackenzie rivers, ensuring the continuity of these crucial time-series. The team will curate and disseminate discharge data for all ArcticGRO rivers and will expand the dataset through analysis of archived samples to explore potential causes of documented changes in Arctic river water chemistry over the past 20 years. The ArcticGRO research team will also work with communities in the Yukon River watershed to address questions about local water quality. Data generated by ArcticGRO will be disseminated broadly and used by the scientific community to understand watershed dynamics and ocean processes at local, regional, and global scales. Continued sampling of the Yukon (at Pilot Station) and Mackenzie (at Tsiigehtchic) rivers will extend time-series records of water chemistry, supporting the assessment of watershed-scale changes across wide expanses of the North American Arctic. A broad suite of parameters will continue to be measured, including dissolved and particulate organic carbon concentrations and isotope values; particulate nitrogen concentrations and isotope values; concentrations of dissolved nutrients, major ions, and trace elements; alkalinity; optical properties of dissolved organic matter (DOM) including UV-visible absorbance and fluorescence; molecular-level composition of DOM, and stable isotope ratios of oxygen and hydrogen in water. Additional water samples will be archived to support future research. At the same time, retrospective analyses of archived samples deepen the dataset and provide critical context for hypothesis testing and model development. These efforts will apply advanced techniques, including ultra-high-resolution mass spectrometry to assess DOM composition, and strontium and sulfate isotope analyses to resolve weathering processes and their role in observed increases in river alkalinity. The investigators will also continue to acquire river discharge data from a larger set of international Arctic rivers. Discharge data are essential for interpretating changes in Arctic river water chemistry and are also widely used across the Arctic research community as a standalone metric. The ArcticGRO team will maintain and enhance access to its comprehensive time-series datasets, enabling researchers to investigate watershed processes and land-ocean interactions. To promote broader synthesis and discovery, they will also develop user-friendly visualization tools and foster integration of ArcticGRO data into interdisciplinary Arctic research and synthesis initiatives. This project is jointly funded by the Arctic Observing Network 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 · 2025-09

This project addresses the need for statistical models that help biomedical researchers better understand changes in biological markers across populations and within individual subjects over time, using multidimensional tabular data. The investigators will collaborate with neuroscientists at the University of Iowa College of Medicine to adapt and enhance statistical modeling approaches for analyzing data from real-world biomedical studies involving mice. This work seeks to address some of the fundamental statistical challenges associated with these complex datasets. The investigators will accomplish this by leveraging two types of time-dependent modeling approaches and extending these methods to datasets that contain far more variables than observations. The project is significant because these methods will assist biomedical researchers in tackling key healthcare questions, accelerating scientific discovery, and providing tools for interpreting complex biomedical data. To promote broad accessibility and impact, the methods and tools developed will be released as open-source software, advancing both statistical methodology and biomedical research. The project will also strengthen data science training for graduate students and contribute to curriculum development at the undergraduate and graduate levels, thereby preparing students for careers in academia and the biomedical industry. The investigators are committed to mentoring graduate students in both methodological innovation and the adaptation of statistical tools for biomedical applications. Additionally, an open-source software package will be released on a publicly accessible platform to extend the project’s impact across the broader scientific community. In these ways, the project serves the national interest by contributing to NSF’s mission to promote the progress of science and to advance the nation’s health. This project will develop statistical methods for high-dimensional array-variate data with longitudinal and temporal dependencies, motivated by biomedical applications where data are often structured as multi-dimensional arrays with far more variables than observations. The investigators will design a suite of penalized likelihood and generalized Bayesian approaches for array-variate mixed-effects and autoregressive models, with a focus on inducing sparsity and low-rank structures in the mean, covariance, and precision arrays. Key innovations include the use of generalized likelihoods to broaden applicability beyond traditional Gaussian settings, and random projection matrices to compress mean, covariance, and precision array parameters, thus enhancing the computational scalability of Expectation-Maximization and Monte Carlo-based inference in high-dimensional settings. The proposed models and algorithms aim to produce interpretable results and remain computationally efficient even in applications with large numbers of variables and samples. Theoretical investigations will establish the consistency and optimality of these methods under minimal assumptions. In summary, this toolbox will enable flexible, scalable, and principled inference for array-variate data with complex temporal structure, advancing statistical methodology for structured biomedical datasets. 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 · 2025-09

Environmental change drives the evolution of new metabolic pathways. A key step in metabolic plasticity and innovation is enzyme recruitment — when an existing catalyst is enlisted to provide a new function. Enzyme recruitment fuels the evolution of new metabolism and facilitates real-world, health-relevant processes such as antibiotic resistance and pollutant remediation. For example, a degradative pathway for the toxic herbicide atrazine recently emerged via recruitment of two hydrolases from melamine and cyanurate catabolism. Enzyme recruitment is powered by the inherent functional promiscuity of modern enzymes, which allows them to transform multiple substrates and perform multiple chemical reactions. Indeed, a single bacterial proteome is estimated to contain thousands of promiscuous enzymatic activities “lying in wait” for future recruitment. Such widespread promiscuity suggests a multitude of potential solutions to new metabolic challenges. Yet the solution selected by evolution remains unpredictable, in part, because enzyme recruitment occurs amidst a complex, dynamic physiological backdrop. Indeed, the factors that facilitate or constrain the recruitment process remain unknown, representing a major gap in knowledge. This proposal’s objective is to use a combination of classical biochemistry, adaptive laboratory evolution and experimental -omics methods to understand how specific organismal features impact recruitment outcomes. Why is one enzyme’s recruitment favored over another? Investigating this question requires a model system in which multiple promiscuous enzymes are known to provide multiple solutions to the same metabolic challenge. We have identified three distinct model systems in Escherichia coli that will be leveraged to investigate the genetic, biochemical, and cellular factors that impact enzyme recruitment during adaptive laboratory evolution. The goal of this proposal is to investigate three specific questions related to factors governing enzyme recruitment outcomes: (1) How does local genomic structure and/or context shape recruitment? (2) How do functional pleiotropic constraints alter the trajectory and/or outcome of the recruitment process? (3) How does metabolic plasticity enable recruitment of an enzyme that provides an indirect metabolic bypass via synthesis of a “new-to-nature” metabolic intermediate? The results of the proposed work will be impactful to human health, as enzyme recruitment drives events such as bioremediation of anthropogenic chemicals and drug inactivation. This study will also impact synthetic biology, where a major goal is to design new metabolic pathways from extant promiscuous enzymes.

NSF Awards · FY 2025 · 2025-09

The movement of microorganisms through thick, gel-like materials plays an important role in many real-life situations. For example, Helicobacter pylori swim through the sticky mucus lining the stomach, contributing to ulcers and cancer. Other bacteria move through food gels and medical hydrogels, which can lead to contamination. Even tiny worms called nematodes burrow through wet soil, helping to improve its fertility. Despite the importance of these biological processes, the mechanics behind movement in such resistant, gel-like environments remain poorly understood. This award will fill that gap by combining innovative experiments using a helical corkscrew and self-propelled robotic swimmer with advanced three-dimensional simulations to uncover the physical principles governing locomotion in gel-like materials. Project outcomes could lead to non-antibiotic methods to prevent Helicobacter pylori infections in human, improve microrobot design for safe drug delivery, and optimize bacterial hydrogels for filtration and sensing. Additionally, the developed models will support innovations in 3D printing, drilling, and natural hazard prediction. Finally, the project will contribute to workforce development by building a collaborative fluid dynamics research and education program at FAMU-FSU College of Engineering. Yield-stress fluids behave like solids below a critical stress threshold, creating a mechanical barrier that organisms must overcome to initiate motion. Although this threshold is fundamental to locomotion, the conditions that trigger movement in yield-stress materials and the resulting swimming dynamics remain poorly understood. It is hypothesized that locomotion is governed by a critical yield strain and non-dimensional numbers such as the Bingham, Oldroyd, and Deborah numbers. To investigate this, a modular approach will be adopted. First, the fluid dynamics of the swimmer components will be studied, focusing on drag, thrust, and yield surface analysis. Using Carbopol-based fluids, well-controlled viscoplastic and elasto-viscoplastic environments will be studied to isolate rheological effects. Next, the locomotion of a self-propelled robot will be examined to identify critical thresholds for movement in viscoplastic fluids. Finally, the impact of elasto-viscoplastic properties on swimming performance will be studied. Experimental results will be compared with simulations using viscoplastic and elasto-viscoplastic fluid models. A novel prism flow analysis will further elucidate the governing fluid mechanics behind swimming in yield-stress media. 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 · 2025-09

Type 2 diabetes (T2D) is one of the most prevalent chronic disorders with a substantial healthcare burden. The hallmark of T2D is beta cell dysfunction. High glucose and high lipid levels (herein referred to as overnutrition) are the two most salient environmental risk factors associated with T2D. However, the specific impacts of these factors on beta cells, as well as the mechanisms underlying such impacts, remain unclear. Adding to the complexity is the observation that beta cells exhibit both adaptive and maladaptive responses to overnutrition, contingent on the dosage and duration of exposure. The goal of this proposed research is to determine the molecular mechanisms underlying beta cell overnutrition response. Our central hypothesis is that the transition from glucolipoadaptation to glucolipotoxicity in beta cells is mediated by epigenetic programs. To gain a deeper understand of overnutrition-induced beta cell state changes, we have established an in vitro human beta cell metabolic stress model. We have also generated single- cell transcriptomic maps from primary human pancreatic islets exposed to metabolic stress as well as pharmacological induced endoplasmic reticulum stress. Combined with targeted drug screening, we identified histone H3K9 methyltransferases G9a/GLP to be important mediators of beta cell stress. Building on these findings, we have developed two aims to translate the laboratory discovery into mechanistic biological knowledge of T2D. In Aim 1, we will perform a series of -omic assays with dense time course to delineate the molecular landscape and beta cell dynamic state changes in response to overnutrition challenge. We will connect the T2D variants identified by genome-wide association studies with nutrition-responsive chromatin and transcriptomic changes and experimentally establish causality between variants to functions in beta cells. In Aim 2, we will dissect the molecular pathways linking overnutrition with G9a/GLP, and beta cell cellular molecular changes. Together, our study has significant and broad impacts by providing (1) novel biological mechanisms and therapeutic targets of T2D; (2) a molecular network encompassing epigenetic regulators, nutrition-responsive cis-regulatory elements, and their genetic targets in human beta cells that enables interpreting of T2D pathogenesis in signaling-dependent genetic programs.

NIH Research Projects · FY 2025 · 2025-09

Project Summary/Abstract Motions through a complicated conformational landscape are almost certainly central to enzyme activity. This project aims to test the hypothesis that residue replacements distant from the active site impair activity in human disease-associated missense variants by altering protein dynamics. These proof-of-concept studies will use asparagine synthetase (ASNS) and its variants linked to asparagine synthetase deficiency (ASNSD) as a model system. Leveraging my expertise in mechanistic enzymology, protein dynamics, and the development of high- throughput screening assays, we will combine enzyme kinetics, hydrogen-deuterium exchange mass spectrometry (HDX-MS), and cell-based assays to correlate changes in conformational dynamics with the altered catalytic activities seen for two representative ASNSD-associated variants (ASNSDVs). Time- and temperature- dependent HDX-MS will be used to place the dynamic differences between the wild-type ASNS and the ASNSDVs on a quantitative basis. A cell-based functional complementation assay will also allow us to examine how hydrophobic residue networks permit distal residue changes to affect individual steps in complicated kinetic mechanisms. Project outcomes will clarify how missense mutations that are distal to the enzyme active site lead to activity impairment and will lay a strong foundation for applying this general approach to understand 1,500 human enzymes linked to 2,500 inherited diseases.

NIH Research Projects · FY 2025 · 2025-09

PROJECT SUMMARY/ABSTRACT Children typically have a vocabulary of 150-450 words by 2 years old. However, ~15% of children exhibit expressive language delays, which is referred to as late talking. Late talking can be a precursor to neurodevelopmental disorders, such as developmental language disorder, developmental dyslexia, or autism spectrum disorder, and comes with long-term educational and social consequences. However, not all children who are late talkers experience persistent delays, with some late talkers recovering by early childhood; these children are referred to as transient late talkers. It is difficult to prospectively know which late talkers will persist into future delays, thus studies of the etiology of late talking and persistent language delays are needed. Neuroimaging studies have identified differences in brain structure and function between late talkers and their non-late talking peers. However, these neural differences have only been studied after late talking is diagnosed, thus there is a critical need to prospectively identify late talkers by examining early neural development before observable behavioral differences manifest. Taken together, the current proposal seeks to address this need by prospectively identifying biomarkers of late talking before diagnosis and examining biomarkers that can separate transient late talkers from those who persist into future delays. This proposal aims to address this need by leveraging a large, rich, longitudinal, pediatric neuroimaging dataset collected from over 700 children, the Early Brain Development Study. The proposed study will use measures of structural and functional brain development from 0-2 years of age and measures of expressive language at ages 2 and 6 years to examine biomarkers of late talking and future delays. Cutting edge graph theoretical techniques will be used to estimate functional connectivity of the language and subcortical networks at rest. Trajectories of structure (Aim 1a) and functional connectivity (Aim 1b) of the language network will be estimated across ages 0-2 years. These trajectories will be tested as early life biomarkers that can predict language delays at 2 years. In Aim 2 a hierarchical clustering algorithm will be used to identify brain-based subgroups of children who meet criteria for late talking at age 2. These subgroups will be examined for differences in verbal language skills at age 6 years to test whether biomarkers that parse heterogeneity in late talkers at age 2 years predict persistence, or alternatively transience, of language delays. The findings from this proposal stand to inform our understanding of the underlying biological mechanisms and developmental pathways of late talking, prior to diagnosis, allowing for timely identification of children at risk of language delays, resulting in better outcomes for these children. In accordance with the goals of the funding mechanism, all data derivatives used for the proposed studies will be curated on Open Science Framework to create a novel shared resource for studying late talking.

NSF Awards · FY 2025 · 2025-09

CAR T-cells exhibit some success in the treatment of some cancer types. The acronym CAR refers the fact that the T-cell has been engineered to express a novel receptor protein on its surface. This receptor protein allows the cell to recognize tumor cells and activate the T-cell to attack it. Neutrophils are another type of immune cell that may possess enhanced targeting to glioblastoma. Neutrophils also produce extracellular vesicles (EVs). EVs are membrane-enclosed particles that can carry some material found inside the cell to be delivered elsewhere. If EVs from neutrophils can be engineered with the CAR receptors that can target specific cancers, then the EVs could deliver a toxic payload to cancer cells. This project will develop CAR-engineered EVs, introduce potentially toxic cargos, and test their effectiveness. They will test the CAR EVs against two targets. The first is glioblastoma cells. The second involves brain organoids, which are clumps of neural cells that exhibit features of a fully developed brain. Seeding these organoids with glioblastoma cells will be used to approximate a brain tumor. The effectiveness of the CAR EVs against the "brain tumor organoid" will also be evaluated. Educational outreach activities will complement the research efforts. These will include outreach to high schools, involvement of undergraduates in research, and developing digital platforms targeting the training of industrial employees. The objective is to engineer chimeric antigen receptor (CAR)-associated extracellular vesicles (EVs) of induced pluripotent stem cell (iPSC)-derived neutrophils (Neu). CAR-Neu may possess enhanced targeting ability to glioblastoma using iPSCs engineered with chlorotoxin (CLTX), a glioblastoma-targeting peptide. EVs derived from CAR-neutrophils, i.e., Neu EVs, express a high level of cytotoxic molecules and better inhibit tumor growth, being safer than the CAR cells. The antitumor properties of EVs secreted by CAR-Neu with loaded microRNAs have not been investigated. The central hypothesis of this proposal is that CAR-Neu EVs carrying PDL1 and LMNB2 microRNA deliver their payload to glioblastoma and are able to reduce tumor significantly. Specifically, the following three sub-objectives are proposed: (1) To test the hypothesis that CAR engineered iPSC-Neu EVs from a scalable bioreactor contain anti-tumor cargo; (2) To test the hypothesis that CAR-iPSC-Neu EVs containing PDL1 and LMNB2 microRNAs exhibit strong cytotoxicity again glioblastoma cells; (3) To test the hypothesis that the dual microRNA CAR-iPSC-Neu EVs have strong anti-tumor ability in 3D human brain organoids bearing glioblastoma. 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

High-performance thermal insulation is crucial for preventing heat loss and reducing energy bills in buildings and industrial sectors such as manufacturing, petroleum, and cryogenics. Conventional insulation materials, including glass wool, polystyrene foams, and many recycled cellulose products, degrade in thermal performance under humid conditions. In contrast, specifically designed porous insulators made from cellulose can behave differently, sometimes insulating even better with moisture. Unlike conventional materials, these advanced cellulose-based insulators can show complex thermal conductivity behavior, sometimes becoming better insulators with increasing humidity. This unique behavior happens because moisture can cause the structure of these advanced cellulose-based insulators to swell, which reduces heat conduction through the solid fiber network. While this offers the potential for enhanced insulation performance in humid environments, the underlying mechanisms governing these unconventional responses remain poorly understood, which limits practical applications. This project will close this important knowledge gap by studying how cellulose fiber dimensions, pore structures, and moisture collectively govern thermal transport in cellulose-based porous materials. This research will also create valuable learning opportunities by involving students at all levels in research, training, and curriculum development. The goal of this project is to understand how moisture affects heat flow in cellulose-based porous materials by linking molecular-level water-cellulose interactions to macroscopic thermal behavior. To achieve this, the research team integrates experimental and computational approaches across three specific aims: (1) establishing quantitative relationships for moisture-dependent thermal transport by fabricating a wide range of cellulose materials with controlled fiber sizes and pore structures and measuring their thermal conductivity under various humidity levels; (2) developing multiscale models that links molecular moisture interactions to macroscopic thermal transport; (3) using advanced characterization techniques, including neutron and X-ray scattering and spectroscopy, to observe how water partitions across multiscale pore structures and molecular domains and understand how this partitioning influences thermal pathways. This integrated approach will reveal the fundamental mechanisms driving moisture-dependent heat transfer, enabling the design of novel insulation with enhanced moisture resilience and advancing energy savings in buildings and industries while supporting economic opportunities through value-added biomass products. 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 support from the Chemical Structure and Dynamics (CSD) program in the Division of Chemistry, Professor Oliver Steinbock at Florida State University is investigating the mechanisms by which the motion of nano- and micro-particles driven by concentration gradients of solutes (known as diffusiophoresis) is influenced and controlled by self-organizing reaction-diffusion patterns far from equilibrium. In many biological systems, steep gradients and active transport processes reinforce one another to enable highly organized behaviors. In contrast, this nonequilibrium coupling remains largely unexplored in chemistry, limiting opportunities for developing self-regulating materials and understanding transport phenomena in synthetic media. Professor Steinbock and his students will develop experimental model systems that combine synthetic micromotors with pattern-forming chemical reactions, such as oscillatory and Turing systems, to investigate scenarios in which particle motion and chemical self-organization coexist, disrupt each other, or give rise to emergent order. Their studies could provide important experimental insights into novel types of complex systems involving new forms of chemical self-organization, which would also inform the analysis of similar phenomena in living systems. Educational and outreach components include hands-on research opportunities for students, public science lectures for adult learners, social media engagement through art-in-STEM content, and participation in local science festivals. The proposed research will combine chemically powered Janus particles with classical oscillatory reactions such as the Landolt and Briggs-Rauscher systems, both of which rely on hydrogen peroxide as a key reactant. These particles will catalyze local decomposition reactions that create chemical gradients and drive self-propulsion. Their behavior will be studied in environments that feature large-scale, dynamic concentration structures, including traveling waves, rotating spirals, and stationary Turing patterns produced by the Belousov-Zhabotinsky and chlorite-iodide-malonic acid reactions. Polymer microbeads will be used to probe passive diffusiophoresis and its modulation by chemical structures. The research will further explore the dynamics of mixed particle populations to investigate clustering, segregation, and mobile inhibitory domains, as well as the possibility of particles triggering or suppressing wave formation. These systems represent new forms of active matter with feedback between motion and chemical reactivity. The experimental studies will be guided by simplified kinetic models and numerical simulations to identify key mechanisms and regimes of behavior. The overall goal is to reveal fundamental principles governing particle-pattern interactions in chemically driven, self-organizing systems, and to lay the groundwork for future applications in autonomous materials, microscale transport control, and soft robotics. 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 · 2025-08

This collaborative project explores how a tiny droplet, powered by internal and surface activity, can propel itself—serving as a simplified model for how primitive cells, or "protocells", move. In experiments, such systems can be created by building networks of actin proteins inside and along the membrane of giant vesicles. To understand how this motion arises, the research team develops mathematical models that describe how forces inside the droplet and on its surface interact with the surrounding fluid. A key focus is to understand how this active droplet pushes against its environment to generate sustained forward motion—behavior that is fundamental to many forms of movement in soft materials and living cells. The project supports graduate education at Florida State University and New Jersey Institute of Technology, and promotes collaboration and dissemination of scientific knowledge through scientific workshops and seminars. The project aims to elucidate the role of steric alignment interactions in the nematic fluid on drop propulsion. The project combines analytical theory, numerical simulations, and comparisons with experimental data from active vesicle systems. The primary investigator Young leads the development of mathematical models and analytical methods, including theory of partial differential equations (PDE), dynamical systems analysis, differential geometry, and asymptotic techniques. The primary investigator Quaife develops efficient numerical algorithms for solving coupled surface-bulk PDEs on both rigid and deforming geometries. These numerical methods include solvers for surface PDEs on evolving interfaces and bulk-surface coupling across moving boundaries. A central challenge is modeling steric alignment interactions at the continuum level and calibrating their strength using experimental observations. The resulting framework has broad applicability to active matter systems described by coupled surface-bulk dynamics on moving domains. 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 · 2025-08

PROJECT SUMMARY- Social touch contributes to brain and behavior development in the infant attachment sensitive period. The long-term goal of this project is to understand the sensitive period neural mechanisms of social touch. We do not yet understand the precise mechanisms by which social touch in infancy is encoded in the brain. This proposal will help address this need. Oxytocin (OXT) is a candidate touch encoding factor because of its roles in infant and adult social behavior. Receptors for OXT (OXTR) are expressed in the developing brain in touch circuits, as well as in a subset of peripheral sensory neurons. Sensory experience increases infant OXT production in the paraventricular nucleus (PVN) and social touch increases the release of OXT. Pre-weaning OXTR KO mice have blunted OXT production and blunted PVN activity. Mice with OXTR deletion from peripheral sensory neurons have impaired social behavior and increased aggression as adults. These data are consistent with a hypothesis that OXTR on peripheral sensory neurons enhances the touch- dependent development of the PVN OXT system with lifelong implications for species-typical social behavior. The experiments in this proposal will test the novel hypothesis that OXTR on peripheral sensory neurons refine the PVN through social touch dependent activity. Using bulk and cell-type specific transcriptomics and circuit- based activity, we will determine the role that peripheral sensory OXTR activity has on the development of the hypothalamus. All of the proposed experiments will be performed in infant mice and the potential for sex differences will be explicitly tested. Even if our organizing hypothesis is not supported, the data generated will add new knowledge regarding the impact of touch on hypothalamus development in this sensitive period. The knowledge gained by these experiments will be informative to improve prevention and intervention during experience-dependent infant attachment.

NSF Awards · FY 2025 · 2025-08

Algebraic geometry is the study of varieties, which are geometric objects defined by polynomial equations. Examples of varieties include the surface of a sphere and the surface of a donut. These two examples have something important in common: they are both smooth, i.e., if one zooms in close to these objects, they eventually look flat. Smooth varieties naturally arise throughout the sciences. For example for many physical systems, one expects that small changes to the starting configuration will only lead to small changes in the short-term behavior of that system. Considering the underlying geometric object that determines the behavior of that system, such an expectation is essentially the assumption that this geometric object is smooth. Therefore improving our understanding of smooth varieties, in addition to being a central goal in algebraic geometry, is of broad significance beyond mathematics. Many of our most powerful tools for studying smooth varieties rely crucially on varieties that are not smooth, i.e., those that have "sharp" or "pointy" pieces called singularities. Obtaining a better understanding of these singularities is also a central goal in algebraic geometry. The PI will develop new techniques in the study of singularities with an emphasis on certain invariants that arise in theoretical physics. The PI will also conduct activities in outreach, mentoring, and conference and seminar organizing. More specifically, the PI will conduct research on stringy invariants and Gromov-Witten invariants and will pursue interactions between these invariants and combinatorial algebraic geometry, including connections to toric varieties, hyperplane arrangements, and matroids. The PI will use motivic integration for smooth Artin stacks to study stringy invariants of singular varieties. With a view toward finding a cohomological interpretation for stringy Hodge numbers, the PI will pursue a strategy for describing stringy Hodge numbers in terms of crepant resolutions via Artin stacks. Furthermore, The PI will use related techniques to develop a McKay correspondence for reductive groups. The PI will also study new matroid invariants related to the Gromov-Witten theory of wonderful models of hyperplane arrangements. In particular, the PI will pursue a strategy for defining quantum cohomology rings for arbitrary matroids. This strategy involves an interplay between Gromov-Witten theory, tropical curves, and polyhedral geometry. 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 · 2025-08

Project Summary/Abstract Drug abuse is a serious worldwide public health problem [1-3]. While the majority of research has focused on potential neuropharmacological interventions [2], social connection and social affiliation also have profound effects on drug addiction [4, 5]. Not only do positive social connection and social interaction prevent and/or reduce drug use and dependence, they also facilitate drug reward extinction [4]. Unfortunately, studies on the interaction between drug addictive behaviors and the social environment are limited. This is partially due to the difficulties inherent in studying neurobiological mechanisms in humans as well as the fact that traditional laboratory rodent species show limited adult attachment behavior [6-8]. Recently, the socially monogamous prairie vole (Microtus ochrogaster) has emerged as an alternate and unique rodent model for investigating the role of social environment on addictive behaviors. In prairie voles, social bonding between mating partners (pair bonding) [7, 9] and familiar cage mates (peer bonding) [10, 11] can be tested by using the partner preference paradigm. The rewarding value of the psychostimulant amphetamine (AMPH) can be assessed in this species using an established conditioned place preference (CPP) paradigm [12]. Using the combination of those two behavioral paradigms, interactions between social bonding and AMPH reward have been shown and neurochemicals, such as oxytocin (OXT) and dopamine have been implicated in regulating such interactions [13-15]. These findings illustrate the utility of this unique animal model for such investigation [6, 8]. Recently, our data have shown that, in female prairie voles, the presence of a peer partner facilitates AMPH CPP extinction. This social facilitation of AMPH CPP extinction can be impaired by the peer partners’ own AMPH experience but rescued by brain administration of OXT. Here we propose to use the prairie vole model to systematically study the effects of peer partners on facilitating AMPH CPP extinction and the underlying OXT-mediated mechanisms. Specifically, in Aim 1, we propose to establish and validate the behavioral paradigms and to characterize the sex differences in, and the impact of the partner’s drug experience on, the social facilitation of AMPH CPP extinction. In Aims 2 and 3, we will focus on the nucleus accumbens to examine changes in OXT release and OXT receptor (OXTR) expression associated with AMPH CPP extinction, and then examine how pharmacological OXTR manipulation or viral-mediated chemogenetic inhibition/activation of OXT pathway activity alter social facilitation of AMPH CPP extinction. Together, data from this study will not only enhance our understanding of the social facilitation of drug reward extinction and the potential therapeutically relevant mechanisms that underlie it but will also further establish a much-needed animal model to investigate the profound effects of social environment on drug addictive behaviors.

NIH Research Projects · FY 2025 · 2025-08

Project Summary. Appearance concerns are a common component of several psychiatric disorders including body dysmorphic disorder (BDD), eating disorders (EDs), and social anxiety disorder (SAD)1-3. Given the transdiagnostic nature of these concerns, it is crucial to identify key targets that can improve these concerns. Appearance related safety behaviors (ARSBs), or maladaptive behaviors aimed at checking, concealing, or fixing perceived flaws in appearance have been shown to be strongly related to appearance concerns, symptoms of BDD, ED, and SAD, as well as depression4,7. Additionally, experimental research has demonstrated that instructions to reduce these behaviors led to lower appearance concerns and symptoms of BDD, ED, and SAD, demonstrating their transdiagnostic importance14. Consistent with the NIMH Research Domain Criteria (RDoC) initiative, ARSBs represent a transdiagnostic construct relevant to multiple psychiatric disorders that can potentially be targeted to yield improvements across an array of highly comorbid disorders. While there is a growing body of research on ARSBs, little is known about how these behaviors vary within a day and their potential causes and consequences. This is a crucial gap in the literature that the present study aims to fill. The present proposal will be a novel examination of these behaviors using ecological momentary assessment (EMA). Specifically, this study will (a) use EMA to examine what situational or contextual triggers precede ARSB use, (b) examine how within day ARSB use will precede and predict increases in appearance concerns as well as symptoms of BDD, EDs, and SAD, (3) determine how these relationships vary between individuals who are high in appearance concerns compared to those low in these concerns, and (4) examine how symptoms of BDD, EDs, and SAD moderate these relationships. Results of the present study would advance the knowledge on the processes surrounding ARSB use and provide a better understanding of how these behaviors contribute to the risk for body dysmorphia, eating pathology, and social anxiety. This proposal has important implications for the prevention and treatment of these appearance-related disorders. Through this proposed study, the applicant will acquire additional training and experience in the design and execution of intensive longitudinal study design, advanced statistical techniques, and professional development crucial for an independent clinical researcher. The experience that can be gained from this fellowship will lay the foundation for the applicant to become an independent clinical scientist who investigates transdiagnostic pathology. Coupled with extensive education in computerized treatments gained from the applicant’s lab, the experience from this fellowship will enhance the applicants ability to identify and target malleable mechanisms of psychopathology. This fellowship will provide crucial experience to bolster the applicant’s goal of identifying targets for the development of more efficacious treatment for psychopathology.

NSF Awards · FY 2025 · 2025-08

Glaciers are retreating at rapid rates, yet critical questions remain about their role in the Earth system. As glaciers melt, they release ancient, biologically available dissolved organic matter (DOM) into water. This organic material can support downstream food webs, and release carbon that was trapped in ice to the atmosphere. Recent research shows that glacier DOM is deposited from the atmosphere and also grown on the surface by algae and other microbial organisms. The relative importance of new versus ancient carbon sources driving the observed bioavailability of glacier DOM is largely unknown. This has important implications for understanding bio-feedbacks from glaciers in global carbon cycling. This project provides opportunities for undergraduate students and an early-career researcher with training in biogeochemistry, environmental biology, and Arctic science. The collaboration extends beyond the funded team to include scientists at the National High Magnetic Field Laboratory, enabling knowledge transfer and access to world-leading analytical facilities. Results will be disseminated to the public through science fairs and educational outreach at the U.S. Forest Service Mendenhall Glacier visitor center. The researchers have developed a respiratory carbon recovery system (RCRS) to directly analyze the age and source of glacier DOM consumed by microbes. Preliminary results support the hypothesis that modern microbial production on the glacier drives the high bioavailability of ancient DOM. Using analytical techniques, including ultrahigh-resolution mass spectrometry, radiocarbon dating, endmember source analysis, and RCRS incubation experiments, this research will investigate whether the source of respired glacier DOM varies between glacier ecosystems and through the melt season in the Alaskan Coastal Mountains. This will establish if there is homogeneity in the glacial carbon released to downstream food webs that support ocean fisheries. These results will improve understanding of the quantity, timing and sources of carbon delivery to aquatic ecosystems in glacier runoff. Ultimately, these results will provide critical insights into how glacier ecosystems function and how their loss may affect downstream freshwater and marine ecosystems, and global carbon cycling, with ramifications for resource-dependent communities and fisheries. 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 · 2025-08

In this project funded by the Chemical Mechanism, Function, and Properties Program of the Chemistry Division, Professors Biwu Ma, Bin Ouyang of Florida State University and Professor Lin X. Chen of Northwestern University will investigate how light-induced structural changes in hybrid materials affect their optical and electronic behaviors. By combining material design, ultrafast optical and X-ray techniques, and theoretical modeling, the team will study an emerging class of organic-inorganic hybrid materials, organic metal halide hybrids (OMHHs), with deformable lattices. The project will provide insights to guide the development of next-generation light-responsive materials and devices. Participating graduate and undergraduate students will receive interdisciplinary training in synthesis, spectroscopy, and theory. The project’s findings will also support science education through outreach activities such as summer programs and public exhibitions. This project addresses fundamental questions about the coupling between electronic and atomic motions in photoactive materials, focusing on OMHHs with controllable 0D, 1D, and 2D structures. These low-dimensional systems offer an ideal platform to explore how structural reorganization under light excitation influences exciton dynamics and carrier transport. The research will combine three major strategies: (i) ultrafast optical spectroscopy to track charge carrier and exciton localization and dynamics on femtosecond timescales, (ii) ultrafast X-ray absorption and scattering techniques to reveal structural reorganizations at the atomic level, and (iii) density functional theory (DFT) calculations to model excited-state properties and structure–function relationships. The findings will advance fundamental knowledge of light-matter interactions and inform the rational design of next-generation hybrid materials with tunable optical and 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 · 2025-08

This project will improve the performance of lithium batteries by advancing understanding of polymer electrolytes. A series of experiments will be conducted to formulate polymer electrolytes and discover the key characteristics that determine their suitability in lithium batteries. The project will generate innovative electrolyte materials that can improve ionic conductivity and suppress lithium dendrite formation, enabling safer, longer-lasting battery operation. Outcomes of the research can be extended to other battery chemistries and energy storage formats. The project will support training of graduate and undergraduate students who will be prepared to join the STEM workforce. Local high school students will be recruited to participate in a summer workshop on electrochemistry at FSU and possibly continue undergraduate research at the National High Magnetic Field Laboratory. Improvement in the safety, reliability, and energy density of batteries for electric vehicles will have a significant impact on sustainable energy technologies. This project focuses on polymer blend electrolytes (PBEs) containing a charged polymer (polyanion) and a polar polymer (polysolvent). The charged polymer yields single-ion conduction of lithium ions by immobilizing anions on a macromolecule. The polar polymer facilitates cation dissociation and conduction. This approach builds on prior work that demonstrated the simplicity of formulating PBEs of different chemical combinations and different compositions. The team’s prior experiments agree with theoretical predictions that ion presence and correlation strength have a major impact on PBE phase behavior. However, it is not clear what material properties are most important in determining ion correlation strength. The first aim of the project will resolve this question. An array of material properties, including dielectric constant, functional group spacing, and ion chemistry will be investigated. The second aim will examine ion and polymer dynamics in homogeneous PBEs and hybrid electrolytes comprising PBEs and a sulfide-based inorganic electrolyte. The third aim will experimentally evaluate interfacial behavior, voltage stability, battery cycling, and dendrite formation with 2–3 top-performing PBEs and their hybrid analogues. Interfacial stability will be achieved through a combination of intelligent design of polysolvent chemistry and matching of transference number, which will translate to higher achievable battery cycling rates and slower lithium metal dendrite growth rates, both to be measured. The coordinated effort to develop mechanistic insight into PBE ionic conductivity will study combinations of novel, judiciously synthesized new materials. 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 · 2025-08

With the support of the Chemical Mechanism, Function, and Properties Program of the Division of Chemistry, Professor Lei Zhu of the Department of Chemistry and Biochemistry at Florida State University is developing photosensitive organic compounds with interesting emission or photochemical properties. The goal of this research is to understand the factors, be they structural or environmental, that control the behaviors of these compounds in response to light. In addition to enriching the knowledge base of relevant scientific areas such as photophysics and photochemistry, the discoveries on the fundamental front will likely lead to (1) the development of organic emitters whose emission colors can be tuned by the applied excitation energy in optical devices (e.g., organic LEDs), and (2) new methods of preparing highly functionalized indoles or pyrroles, which are sought after core structures of pharmaceutical drug candidates. This project will provide training opportunities to young scientists who aspire to be well-rounded teachers and innovators in the disciplines of organic and physical chemistry. The special emphasis on sharpening the critical thinking skills of the trainees will help them become problem solvers who will have to address real world challenges with complexity levels that are not defined by disciplines. In this project, Professor Lei Zhu and collaborating students will uncover the principles that govern the photophysical and photochemical properties of two categories of organic dyes, both of which have access in their electronically excited states to either proton transfer or charge transfer process, or both. The subjects of this project will be prepared, while their properties will be characterized by advanced spectroscopic and theoretical methods. The first category of the targeted fluorescent dyes will be capable of excited state proton transfer to produce excitation energy-dependent dual emission. This is not only a challenging property to produce from small molecular emitters but a potentially useful one, as the composite color or the ratio of two emission bands could then be altered by excitation energy in applications requiring color-shifting emitters. The second category of emissive compounds will contain a triazole core, and many of them are projected to possess high brightness in both solution and solid phases. Depending on the substitution pattern, these compounds will be either photoreactive to be converted to highly functionalized indoles or pyrroles, or photostable. The discoveries on the fundamental front of this project will endow predictive power for assisting the accurate design of dye structures that respond to photoirradiation with either proton transfer or charge transfer process. These processes will in turn trigger the emergence of new properties and functions that are targeted in this project – excitation-dependent dual emission, or photostable and photoreactive emissive compounds that can be easily prepared. The new properties encoded in the planned structures are projected to benefit technological sectors that call for versatile emitters with either durability or photo-convertibility. 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 · 2025-08

Statistical models — probabilistic descriptions of the processes that give rise to observed data — are an integral component of modern science across various disciplines, enabling researchers to learn from the data they collect and make predictions about future events. This research addresses the important challenges of model selection and model combination within the Bayesian statistical framework. Model selection involves choosing, from a collection of candidate models, the single model that best describes the data, while model combination involves constructing a hybrid model that outperforms any single model. The Bayesian framework has gained prominence in recent decades because it enables researchers to fit complex models to data, simultaneously account for multiple sources of uncertainty, and combine the information in newly observed data with prior scientific knowledge. This research will develop new, computationally efficient techniques for estimating a model’s prediction accuracy in the Bayesian framework and will apply these techniques to the problems of model selection and model combination. In the process, it will contribute to STEM education by training statisticians at the graduate level. It will also lead to publicly available software for researchers across a broad range of disciplines. This research will include several novel projects aimed at developing Stein’s unbiased risk estimate (SURE) as a practical and computationally efficient tool for Bayesian analysis. SURE has become an established tool for model selection and parameter tuning in frequentist settings. However, SURE requires the computation of a penalty term, sometimes referred to as the generalized degrees of freedom, which adds a significant computational burden for complex estimators. Consequently, SURE has been applied considerably less for more computationally demanding Bayesian and machine learning models. This research will: (1) develop a novel expression of SURE that is straightforward to compute via Markov chain Monte Carlo for Bayes estimators of a Gaussian mean resulting from essentially arbitrary prior distributions, along with extensions to unknown variances and continuous tuning parameters; (2) introduce methodology for fully Bayesian M-open inference for both Gaussian, improving upon existing model combination/selection techniques and allowing for fully Bayesian uncertainty quantification for machine learning models. 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.