Board of Regents, NSHE, obo University of Nevada, Reno

NSF Awards · FY 2025 · 2025-08

This Faculty Early Career Development (CAREER) award will support research that attempts to advance a new computational framework that integrates high-fidelity physics and machine learning to transform earthquake engineering for geostructural systems. By reducing the high computational costs typically associated with accurate numerical models, this research intends to enable rapid and reliable seismic response predictions for geostructural systems. These predictive capabilities are critical for designing and safeguarding infrastructure, minimizing economic disruptions, and building more resilient communities. The approach will intends to pave the way for better sensor data integration, enhancing understanding of complex geologic conditions and improving risk-assessment tools for engineers and policymakers. Through complementary educational initiatives, including hands-on activities, online tools, and curricular enhancements, the project seeks to improve scientific machine learning literacy and train a new generation of engineers equipped to apply machine learning responsibly. Ultimately, this CAREER award supports national interest by promoting public safety, advancing the progress of science, and fostering robust workforce development. This research aims to develop a physics-informed machine learning surrogate modeling framework for geostructural systems under seismic loading. Unlike traditional black-box machine learning approaches that require massive datasets and can yield physically unreasonable results, this framework attempts to embed the governing equations, boundary conditions, and constitutive relations directly into the model training process. By penalizing violations of these physical laws instead of relying solely on data, the project looks to substantially reduce the need for large-scale numerical simulations for training and improve interpretability. Specific objectives include (1) developing scalable machine learning algorithms for accurately modeling the dynamic response of geostructural systems to transient ground deformations due to seismic wave propagation, (2) developing optimal approaches to accommodate uncertainties in uncertainty quantification tasks, and (3) validating the framework and assessing its effectiveness using experimental data. This research looks to promote seamless integration of high-fidelity data into site-specific and rupture-to-structure seismic risk assessment workflows, advancing the fidelity of hazard mitigation and performance-based engineering decision tools. It will strives to enable optimal utilization and assimilation of sensing data at a system level to calibrate high-fidelity models and characterize biases and errors. The methodologies developed here seek to enhance state-of-the-art earthquake engineering and resilience strategies for geostructural systems, and to also be applicable to the dynamic response of other systems in engineering mechanics, cyber-physical systems, and earth 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.

NSF Awards · FY 2025 · 2025-07

As computing demands increase, particularly with the rise of artificial intelligence (AI), understanding which resulting hardware waste poses the greatest threats to human and ecological health is essential for sustainable design. Currently, computer system designers lack quantitative tools to assess the toxicity impact of their design choices. Through an interdisciplinary collaboration, the research team will develop SysTox, a novel data-driven framework that quantifies the toxicity impact of system design choices. This approach serves the national interest by advancing computing technology, reducing harmful e-waste, and providing a foundation for responsible technological advancement. The project will develop methodologies to analyze computing systems at the component level, identifying the elemental composition of various components and assessing their impacts in the waste stream. The research team will create new datasets through experimental measurements, develop models to quantify toxicity impacts, and establish an optimization framework that can guide computer hardware designers in making less toxic design choices while maintaining performance. The resulting framework will enable computer system engineers to make quantitative tradeoffs between performance, efficiency, and footprint, transforming how architectures and systems are designed across scales from microprocessors to data centers. 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

There is a persistent need for reliable and affordable technology for early detection of disease that can spread rapidly to others to manage effectively disease spread. The present project addresses this need by developing and commercializing a highly sensitive, cost-effective, portable, user-friendly sensor system for the detection of bacterial disease, with the goal of enabling rapid, easy testing for the presence of the disease in all environments, including low-resource settings. The new system, which combines advanced quantum-dot sensor technology with a proven rapid detection assay and a specialized user-friendly reader technology, will be developed using tuberculosis, a bacterial disease caused by Mycobacterium tuberculosis that can be lethal if not detected early, as a test case. In recent years, the prevalence of the disease has increased within the U.S. and worldwide, and significant resources are being invested to help physicians combat tuberculosis; thus, there is an urgent need to develop and make available a highly sensitive sensor system that frontline healthcare professionals can use to diagnose tuberculosis readily and reliably. The long-term goal of this project is to enhance public health by reducing the spread of tuberculosis. In addition to developing and commercializing a superior sensor system to fill a market gap in tuberculosis diagnosis, the project includes activities aimed at developing a skilled workforce to tackle regional and national disease challenges that pose a threat to societal health and U.S. economic strength. The QueueD Insights team, which consists of two industry partners (InBios and C2Sense) and several academic institutions (University of Nevada Reno, University of Illinois Chicago, University of Wisconsin–Milwaukee, and Duke University), will develop and commercialize a sensor system that meets internationally accepted standards for sensitivity, cost, speed, and ease of use in healthcare contexts for the early detection of tuberculosis. The sensor of the system involves a lateral flow assay that detects Lipoarabinomanna fragments of M. tuberculosis found in the urine of tuberculosis patients, which makes it suitable for diagnosing both pulmonary and extrapulmonary tuberculosis. By combining the Advanced Quantum-Dots technology patented by QueueD Insights with the lateral flow assay and a custom-built time-resolved luminescence reader, the sensor system adopts three technologies critical to markedly increasing assay sensitivity: preconcentration of scarce antigens, a high-affinity antibody for antigen capture, and a background-free, time-resolved fluorescence detection system. The resulting sensor system will enable significantly enhanced tuberculosis detection. The system’s validity will be established through preclinical studies. Strategies for scaling up the system’s manufacturing and distribution in the tuberculosis diagnosis market will be established with assistance of current and future national and international partners. The goal of this effort is to achieve widespread adoption of the novel sensor system to increase tuberculosis testing rates and detection world-wide, and, ultimately, improve U.S. public health. Additionally, the system offers extensibility to other diseases. 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

Severe drought and explosive forest fires have become a normal feature of life for communities of people living in the western United States. Tree species have evolved adaptations to survive and grow even during times of drought and when fires burn. However, increasingly intense drought and severe fires are pushing trees to their limits. A major challenge for forest ecologists is to predict how western forests will change, but it is currently not known which species will thrive or decline. This research will identify the key adaptations that will help determine the fate of western tree species by measuring how species tolerate drought and fire. The researchers will then predict how forests could change over time. Teaching resources for K-12 will be made publicly available through the Global Vegetation Project to demonstrate the impacts of drought and fire on western forests. This research provides a glimpse into the future to help people prepare for how western forests will respond to more fire and less water and provide guidance to forest managers that steward our nation’s forest resources. Drought and fire are driving widespread tree mortality and limiting tree recruitment in forests across the western United States. Forest ecologists do not fully understand which combinations of drought and fire adaptations will drive changes in tree species abundances in the future, and large gaps in existing functional trait databases prevent researchers from leveraging new analytical frameworks that integrate traits into demographic models. The aims of this project are to 1) quantify adaptive traits of tree species in the West and publish results in an open-access database, and 2) quantify the effects of traits on demographic rates that shape the contours of fitness landscapes in response to changing drought and fire. To achieve these aims, the researchers will measure and compile a comprehensive data set of traits on >100 tree species that capture key dimensions of functional strategies that evolved in response to water limitation and fire regimes, with a focus on bark thickness allometry, resprouting ability, xylem vulnerability to embolism, and rooting depth growth rate. These trait data will be combined with demographic data from the national forest inventory to test our mechanistic understanding of the demographic consequences of traits in multi-species forest communities using structured population models. The goal of this research is to provide a functional explanation for the widespread lack of forest recovery after fire in dry conditions. 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

Language development research often focuses on how communities investigate an ancestral or heritage language in their community. Less theorized within this field are the processes and mechanisms that influence the trajectory efforts over time. The paucity of detailed data and analysis has implications that directly affect ongoing discussions on the nature of language and cognition, and theoretical arguments about the relationship between language and culture. Analysis of the practices associated with the initial stages of language development, therefore, will provide generalizable, empirical data that can test current hypotheses and theoretical discussions on the emergence and formalization of language vis-a-vis their grammatical forms and structures and language as a medium of communication. In this multi-year study, the researcher studies the linguistic practices associated with the early stages of understanding language infrastructure, focusing on how these initial determinants shape future trajectories. The project utilizes a combination of methods that include linguistic analyses, observation and interviews, participatory methods, and digital-network analysis, to track how language is introduced as a mode of communication within the community. The findings from this CAREER project inform predictive models on the potential outcomes from language processes. Findings also enable a detailed account of practices that lead to the emergence of new languages and linguistic varieties. This project is jointly funded by Cultural Anthropology, the Established Program to Stimulate Competitive Research (EPSCoR), and the Dynamic Language Infrastructure Program. 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

Polar firn is multi-year snow that has survived more than one season and is a transition phase in the formation of glacial ice. A comprehensive understanding of how firn densifies into solid ice is important for several reasons: (1) to interpret ice sheet mass balance changes from remote-sensing observations; (2) to determine how the microstructural evolution of firn contributes to the resulting ice sheet microstructure and its ice flow rates; and (3) most importantly for better interpretation of ice-core paleoclimate records by understanding how air becomes entrapped in the firn, and ultimately in the ice. Currently, firn densification is not fully understood, and a physics-based model based on experimentally-observed deformation mechanisms is needed to improve our firn estimates for the important applications described above. This research aims to develop a comprehensive understanding of the compaction and microstructural evolution of polar firn by performing mechanical testing, and characterization of the resulting microstructure on firn cores from four different locations at Taylor Dome, Antarctica. Results from each of these firn cores will provide valuable information about how firn microstructure, and its impact on densification, evolves under varying environmental conditions outside the range of existing datasets. The research will involve a PhD student and several undergraduates. Densification of polar firn involves several different mechanisms including pressure sintering, plastic deformation, grain rearrangement, and, near the surface, temperature gradient metamorphism due to water vapor transport. A key step towards a physics-based firn model is understanding how the microstructure impacts the dominant deformation mechanism(s) at different depths in the firn column. This project will provide valuable data needed to answer this question by performing creep tests and characterizing the microstructural evolution under creep loading on firn cores drilled to pore close-off at four different locations at Taylor Dome, Antarctica. Each of the four firn-core sites vary in their annual accumulation rate (2 cm.a-1, 4 cm.a-1, 8 cm.a-1, 25 cm.a-1), creating different microstructure-depth profiles. We will use a unique combination of X-ray micro-CT imaging coupled with high-resolution imaging of the evolved microstructure using a scanning electron microscope equipped with electron backscatter diffraction, that will provide 3-D orientation information. In addition, we will examine the local microchemistry, particularly around bonds between grains, using energy-dispersive X-ray spectroscopy in the SEM and Raman spectroscopy in an optical imaging system. These results will allow for a full analysis of the role firn microstructure has on densification. This work will ultimately be used to improve the Community Firn Model. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-06

This Faculty Early Career Development (CAREER) award will support research that seeks to (1) establish a methodological and computational framework to harness new knowledge from extensive simulation data for extreme yet plausible natural disaster scenarios and (2) seamlessly integrate it into probabilistic demand and hazard models. Conventional risk assessment approaches rely on the combined use of hazard models and likelihood of damage (i.e., fragility) models. By nature, both models make generalizations starting from the data utilized to develop them. However, extrapolation beyond available empirical data incurs large uncertainties and the chance of misestimating risk for extreme events. The progressive integration of high-performance computing architectures into civil engineering domains helps researchers to simulate all conceivable disaster scenarios. While this is creating extensive catalogs of data, a systematic methodology to extract and apply this knowledge to a cross-disciplinary framework, one that allows for continuous updates as more data become available, is missing. This research will attempt to lay the foundation for the next generation of data-informed probabilistic methods that can reliably assess infrastructure risks posed by extreme seismic loads, which are less understood but highly destructive. The educational component will operate at multiple interconnected levels by incorporating key research outcomes into various educational and K-12 outreach activities and promoting engagement with industry professionals. This award will contribute to the National Science Foundation (NSF) role in the National Earthquake Hazards Reduction Program (NEHRP) 2022-2029 Strategic Plan. The specific objectives of this research are to (i) exploit the potential of machine learning-inspired regression algorithms to map hidden correlations between forcing functions and above and below ground infrastructure response to develop new probabilistic demand metamodels, (ii) create open-source analytical tools to update hazard functions through the convolution principle with complete characterization of uncertainties, and (iii) enable selection of site and application-specific loads to support infrastructure design and assessment. This research intends to answer key fundamental questions, including (i) how newly available simulation data can enable the identification of stable trends traditionally lost in the fog of large uncertainties of sparse information for extreme events and inform the development of new demand models, (ii) how progressive integration of simulation capabilities and machine-learning regression techniques can improve the generalizability of risk assessment methods across multiple hazards and infrastructure, and (iii) how current performance-based approaches can be reliably utilized for extreme yet likely hazard scenarios. This research will foster the theory and practice of resilience engineering, contributing to the general field of risk assessment and mitigation of the adverse effects of natural hazards. Project data will be archived and made publicly available in the NSF-supported Natural Hazards Engineering Research Infrastructure (NHERI) Data Depot (https://www.DesignSafe-ci.org). This project is jointly funded by NSF's Engineering for Civil Infrastructure (ECI) program and NSF's 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 · 2025-05

Biological systems are complex. Layers of reaction and control networks come together to create cellular metabolism. Proteins participate in reaction and control networks. As such, regulating proteins is central to manipulating metabolism. Some proteins serve multiple functions. This makes them interesting targets for modification. The objective of the project is to design multifunctional proteins that can simultaneously affect different pathways. The pathways of interest are related to production of a gel called the extracellular matrix (ECM) that buffers cells from their surroundings. The ECM can also prevent drugs from reaching their target, in the case of tumor cells, for example, which makes understanding the properties of the ECM especially important. Developing educational modules that lean heavily on visual outputs and presenting them to K-12 students will provide an interesting opportunity for interactive learning in a gaming environment. Summer camps and research opportunities for high school students and undergraduates will be employed to promote engagement in STEM careers. Tissue inhibitors of metalloproteinases (TIMPs) are multifunctional proteins that play a critical role in regulating the extracellular matrix (ECM). TIMPs interact with various ECM components and display a range of binding affinity and selectivity. This versatility makes TIMPs great protein scaffolds for regulating complex biological systems and protein networks within the ECM. The main project objective is to engineer and design new multifunctional TIMPs capable of selectively targeting multiple ECM proteins. Directed evolution and high- throughput screening, combined with computational approaches, will address current gaps in protein engineering and design of multifunctional protein inhibitors. Further, the multifunctional inhibitory roles of TIMPs and their engineered variants will be tested in biochemical and cell-based assays to better understand their regulatory functions in cellular processes. These engineered multifunctional protein scaffolds will shed light on the regulation of complex biological systems in ECM. This project is jointly funded by the Established Program to Stimulate Competitive Research (EPSCoR) and the Cellular and Biochemical Engineering Program (ENG/CBET). 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

With support from the Chemical Measurement and Imaging Program in the Division of Chemistry, Professor Borotto's group at the University of Nevada, Reno seeks to characterize the processes driving gas-phase protein folding. This will be accomplished through the development of a laser-based technique that will enable the assessment of fast folding pathways. Better understanding of these processes will improve the capabilities to rapidly assess the solution phase conformation of proteins. As part of this project, Dr. Borotto will partner with local secondary education institutions to better recruit students into science, technology, engineering, and mathematics (STEM) majors. Native mass spectrometry (MS) has shown promise at rapidly assessing the solution-phase conformation of proteins but a thorough understanding of how the structure of protein ions is altered upon entering the gas phase is critical to differentiate the valuable solution phase conformational information from artifacts created during the gas phase measurement. In this work, the Borotto Lab aims to characterize the mechanisms behind gas-phase protein folding. This will be accomplished through the design and development of novel laser-driven activation techniques that will enable the assessment of rapid folding pathways. Additionally, this proposal seeks to increase the involvement of students in STEM disciplines. To accomplish this goal, Dr. Borotto will partner with the local secondary education institutions, demonstrating cutting edge MS-based experiments to inspire increased recruitment to STEM-based degrees, and offering undergraduate research opportunities. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-11

This doctoral dissertation research seeks to explain how centralized leadership and unequal social structures develop in small-scale human societies. Archaeological materials are used to reconstruct patterns of settlement, cultural transmission, and trade, examining how individuals, families, and groups interact to manage competing and mutual interests. In contrast to past approaches, this research decouples inegalitarianism from categories such as chiefdom and state, instead focusing on the relationships between individual and group-level behavior—essential to understanding key issues related to competition, cooperation, privatization, and sharing. The project also engages students in public outreach programs, providing training in archaeology and data analysis techniques. The project examines how leadership and inequality emerged among hunter-gatherers by focusing on three key dimensions of social behavior: cooperation versus competition, provisioning versus prestige, and anarchy versus hierarchy. First, the researcher investigates whether hunter-gatherer communities were more cooperative or competitive by analyzing settlement patterns. Were people working together to share resources, or did competition push them into new areas? Next, the study examines how hunting and resource sharing were tied to social status. Did people hunt primarily to feed their families, or did successful hunters gain prestige and influence by sharing with others? This is explored through a morphometric analysis of stone projectile points to determine whether design traits reflect prestige-based learning. Finally, the project studies the flow of trade goods like tools and beads to understand how social networks formed. As these networks expanded, did certain groups take on more centralized roles, marking a shift toward hierarchy and inequality? By examining these dimensions, the research provides a clearer picture of how relatively egalitarian societies evolved unequal social structures with formal leadership. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-10

Care is a fundamental part of social interactions, but access, levels, and quality of care depend on social conditions. The goal of this study is to assess levels of childhood stress, as well as the frequency and type of care practices experienced and executed by past peoples. Through the analysis of documented dental and traumatic lesions, this study aims to identify and assess evidence of care practices among these peoples. The study reveals the experiences that they were exposed to and the care practices that they performed when faced with traumatic bone lesions. The project provides training opportunities in STEM for a graduate student and informs the public and the community through a series of outreach activities. Using macroscopic analyses of documented linear enamel hypoplasia (LEH) as well as bone fractures this research assesses childhood stress and care practices. LEH is evaluated in terms of presence/absence as well as severity. The frequency, healing and misalignment of skeletal trauma are assessed using clinical standards. The study calculates the correlation between these two types of lesions. Results are integrated with historic information to establish if, how, and to what degree these individuals received care. The study informs the nature of social interactions that past individuals experienced and developed. This project is jointly funded by the Biological Anthropology 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 2024 · 2024-09

This Faculty Early Career Development (CAREER) award supports research that enables a new paradigm for the integration of data with machine-infused physics-based computational models to develop digital twins of dynamical systems, thereby promoting the progress of science, and advancing prosperity and welfare. The advent of the big data era necessitates robust, efficient, and scalable scientific tools that assimilate raw measurements into computational models to enhance the quality and resolution of actionable information in near real-time. Despite the recent prolific trend in the predictive modeling literature, current hybrid data-driven modeling approaches focus on physics-informed machine learning and often face shortcomings in real-world applications due to limited interpretability, limited generalizability outside training domain, and limited training data. Research completed in association with this project will address this critical gap by embedding neural-network components in the core of physics-based models to account for structural errors, simplifications, approximations, idealizations, and unknown physics in the model formulation, and train/update the integrated model with data. The resulting theoretical framework will then be applied to three use-inspired societal challenges: post-earthquake damage assessment of critical civil structures, predictive maintenance of offshore wind infrastructure, and emergency response to wildland fires. In addition to its broad potential applications across various science and engineering fields, the project will improve engineering education and broaden participation in STEM through its robust educational outreach plan that includes K-12, undergraduate, and graduate level engagement with students from underrepresented demographics. This research aims to improve computational efficiency, scalability, and practicality of dynamic models to enhance situational awareness and informed decision-making. It will achieve this goal by developing a Bayesian machine-infused physics-based data assimilation framework to address the fundamental challenge of modeling error in physics-based data assimilation and uncertainty quantification. An inherent part of any physics-based model, modeling error – or model form uncertainty – can disparage the physics-based data assimilation process and the subsequent identification, prediction, virtual sensing, diagnosis, and prognosis applications. The machine-infused physics-based model will be trained using system-level measurements through a Bayesian inference approach. The intellectual merits of the project include (1) theoretical development and implementation of the framework, (2) addressing technical challenges regarding identifiability, robustness, and computational efficiency and scalability, and (3) application to three engineering problems, namely, post-earthquake damage assessment of civil structures using a machine-infused finite element data assimilation, predictive maintenance of wind turbine drivetrains using a machine-infused multibody dynamics data assimilation, and operational prediction of wildfires using a machine-infused coupled fire-atmosphere data assimilation. This project is jointly funded by the Dynamics, Control and Systems Diagnostics (DCSD) 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 2024 · 2024-09

Tropical peatlands are believed to play an outsized role in the global carbon cycle. Despite their relatively small extent, these ecosystems act as some of the strongest long-term terrestrial carbon sinks on Earth. They are also believed to constitute some of the largest natural methane sources. However, these claims are anchored in very little field data. In South America, which may encompass the largest area of tropical peatlands in the world, we do not know where peatlands are, what controls the rate of peat formation, which conditions constrain methane emissions, and whether these ecosystems are resilient to climate change. This project focuses on the PanAm peatlands, which we define as the natural lowland peatlands found across the tropical region of the American continent, from 20 degrees N to 20 degrees S. The selected approximately 80 study sites encompass broad environmental gradients and include distinct climate areas such as the Caribbean coast and the Amazon basin. The main approach involves using a novel field sampling kit, which will allow for consistent field data collection at the continental scale. The ultimate goals of this research are to gain new insights into peatland environments, and better constrain regional and global carbon budgets. Training the next generation of scientists is also a central component of this project. In addition to involving U.S. postdoctoral researchers, graduate students and undergraduate students, in-person field training will be offered to local scientists (including students) through workshops that will be held in the Caribbean and the Amazon. High-quality videos showing how to deploy instruments and take measurements using our field kit will also be developed in English and Spanish, and made available to all. Data analysis training will be offered to peatland scientists and students through hybrid workshops. Overall, this work will contribute to U.S. expertise in Earth System Science, increase capacity through student training, advance the peatland, wetland, and carbon cycling communities’ research agenda, and guide policy and land management decisions. Because tropical peatlands these ecosystems are being lost at a rate about three times faster than forests, it is critical to collect baseline information about their structure and function. This research undertakes a large number of systematic field measurements that will improve mechanistic understanding of plant-soil-water-nutrient-carbon interactions. This information is needed to build holistic representations of the different types of tropical peatlands that exist. These field data and statistical models will allow for predictive assessments of tropical peatland carbon input and output, with an emphasis on methane emissions. Ultimately, the project seeks to (1) identify the hydrological thresholds needed for peatland formation across the tropics, (2) quantify the key constraints on the rate of peat formation over space and time, and (3) document patterns of methane production, consumption, and emission at the ecosystem scale. This field-based research may lead to the development of new theoretical foundations needed to improve multiple modeling efforts. Recurring engagement with modelers is planned to help integrate these field data into peatland mapping algorithms, process-based peatland ecosystem models, and land surface models. A database that combines the new results with a literature synthesis of existing and ongoing measurements will be generated and archived in the Environmental Data Initiative repository. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-09

One of the grand challenges in biology is understanding how organisms respond to environmental change. Advances in genome sequencing technology are allowing unprecedented insight into the role of genetic variation in determining how organisms respond to environmental challenges. Specifically, genome sequencing has revealed substantial variation in the number of copies of ecologically important genes among individual genomes, wherein some individuals have very few copies of a gene while others have many. Yet, we understand very little about how gene copy number variation is maintained in the wild and how it influences survival and reproduction. The goal of this project is to discover how gene copy number variation at genes that aid in processing ingested toxins determines: 1) individual capacity to process toxins in food plants, 2) individual survival and reproduction in the wild, and 3) the ability of populations to persist in the face of environmental change. The PIs will achieve these goals by combining intensive field observations of individual mammalian herbivores (woodrats of the genus Neotoma) with the most modern approaches in genome sequencing, chemical analyses, and computer-based modeling. The students trained through this award will be uniquely positioned to pursue integrative careers with the skills to identify and manage the mechanisms that facilitate or hinder population persistence. The PIs will collaborate with local high school teachers to develop learning modules based on the themes of this research to augment public awareness of organismal response to environmental change, and to create a bridge to higher education. Understanding the genetic basis of phenotypic plasticity and adaptive evolution remains one of the central challenges in evolutionary biology. For vertebrate herbivores, one of the strongest sources of selection comes from exposure to the potentially toxic phytochemicals in the plants they consume. Insect herbivores appear to meet this challenge, in part, by diversification of gene copy number in detoxification loci. The PIs have recently discovered similar expansion of detoxification gene copy number in a group of herbivorous mammals, woodrats of the genus Neotoma. In this proposal, the PIs seek to understand the functional significance of copy number expansion by relating copy number variation (CNV) to detoxification capacity, individual fitness, and population dynamics across a natural environmental gradient. The PIs will examine these critical genotype-phenotype relationships in a hybrid zone between Neotoma fuscipes and N. macrotis. Hybrid zones offer a unique opportunity to study the causes and consequences of CNV because high rates of admixture generate individuals with novel structural variants whose dietary breadth and fitness can be tracked in the wild. In the hybrid zone population, the PIs will identify individual CNV; quantify the capacity of different copy number variants to detoxify phytochemicals; and predict how structural variation in detoxification gene families interacts with other environmental stressors (i.e., drought, high temperatures) to determine individual fitness and the capacity of populations to respond to environmental change. This study will be among the first to directly link structural genetic variation to dietary and detoxification phenotypes and their fitness consequences in the wild. This award was co-funded by Evolutionary Processes in BIO/DEB. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-09

Despite the incredible success of the Standard Model of Particle Physics, there are still significant unsolved mysteries. The PI and co-PI will conduct experimental and theoretical research to investigate the use of quantum sensors to address some of these outstanding problems in fundamental physics. This work will be performed in synergistic collaboration with Laboratoire Aime Cotton, Orsay, France. The students and postdoctoral researchers on the project will be trained in the principles of quantum sensing and work to greatly improve the sensitivity of measurements by using novel quantum states. The research team will investigate alkali-metal atoms trapped in solid cryogenic matrices. Prior work by the PI's group has shown that ensembles of these atoms have excellent spin coherence times, along with the other properties needed for quantum sensing. Additionally, it is possible to create large-number samples which offer extremely good prospects for statistical sensitivity. Even more promising, this prior research has suggested that nonclassical spin superposition states may offer significantly longer ensemble spin dephasing times, which would be advantageous for experiments looking for symmetry violation using spin. The researchers will generate and investigate the properties of these nonclassical superposition states. In collaboration with the Orsay group, the researchers will investigate the potential of this system for measurements of the parity-violating nuclear anapole moment in cesium, as well as possible future experiments searching for time-violating electric dipole moments. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-09

This award supports research that contributes new knowledge related to a compliant actuation mechanism, thereby promoting both the progress of science and advancing the national health and prosperity. Compliant actuators are constructed from soft or compliant materials and can generate shape changes under external physical or chemical stimuli. Compliant actuators technology is crucial to enable dexterous and compact compliant robots, which have a wide application in areas that demand safe interaction with humans in highly unstructured environments. Many compliant actuators, including different types of artificial muscles, have been studied. However, there has not yet been a compliant actuator that exhibits properties or performance metrics comparable to those of biological muscles. This project will address this critical gap by providing fundamental knowledge on the modeling, sensing, and control of a promising compliant actuator, namely the coiled string actuator, which overcomes common limitations of existing compliant actuators. Results from this research will benefit the U.S. economy and society for applications in manufacturing, agriculture, healthcare, and wearable devices. This research involves several disciplines including material science, robotics, control theory, and machine learning. The multi-disciplinary approach will help broaden participation of underrepresented and underserved groups in research and positively impact engineering education. Besides curriculum enrichment, this project will provide research training opportunities for students and a number of outreach activities for students and the public. This research aims to make fundamental contributions to enable the coiled string actuator to produce predicable and desirable outputs, and further realize compliant robotic manipulators with superior compactness and dexterity. The coiled string actuator can overcome common limitations existing compliant actuator technologies have, ranging from small strain or stress outputs, high power requirement, low bandwidth, poor power efficiency, and bulkiness. However, the non-uniform actuation during string coiling, together with sophisticated, coupled electro-mechanical dynamics and time-varying material nonlinearities, presents significant challenges in precisely controlling the coiled string actuators. This project aims to model the coiled string actuators’ non-smooth coiling-induced actuation and its strain self-sensing property, and control them to obtain smooth and desirable actuation. This research will (1) develop a physics-based model, a kinematic model, and a machine learning-based, control-oriented model to capture the coiling-induced actuation, (2) create a model to realize strain self-sensing by correlating the electro-mechanical property and material time-varying nonlinearity, (3) construct new inverse compensation and self-sensing-based adaptive control strategies for controlling systems with non-uniform coiling-induced actuation, and (4) experimentally validate the projected modeling, self-sensing, and control approaches on a compliant robotic manipulator with superior compactness and dexterity. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-09

This award is jointly supported by the Major Research Instrumentation (MRI) Program and the Division of Chemistry Research Instrumentation program. The University of Nevada Reno (UNR) is acquiring a 500 MHz Nuclear Magnetic Resonance (NMR) spectrometer console upgrade equipped with a nitrogen-cooled broadband probe. This instrument advances research in various scientific domains, including the chemistry of biological interactions, materials science, quantum information science, and sustainability. In general, NMR spectroscopy is one of the most powerful tools available to chemists for the elucidation of the structure of molecules. It is used to identify unknown substances, to characterize specific arrangements of atoms within molecules, and to study the dynamics of interactions between molecules in solution or in the solid state. Access to state-of-the-art NMR spectrometers is essential to chemists who are carrying out frontier research. This instrument enhances the educational, research, and teaching efforts of students at all levels including K-12 outreach and collaborations with national and international institutions. The research enabled by the acquisition of this instrument spans a wide range of areas. The upgraded NMR spectrometer provides enhanced sensitivity and throughput, enabling advanced techniques such as NMR-based metabolomics, eco-metabolomics, and structural characterization of natural products, polymers, and catalysts. This project facilitates groundbreaking research in ecological interactions, energy storage, quantum computing, and the development of sustainable materials, thereby driving innovation and fostering interdisciplinary collaborations. The new instrumentation will support over 20 research programs and benefit 20 established researchers, 70% of whom are NSF-funded, and over 60 students and postdoctoral scholars. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-08

The National Science Foundation (NSF) Graduate Research Fellowship Program (GRFP) is a highly competitive, federal fellowship program. GRFP helps ensure the vitality and diversity of the scientific and engineering workforce of the United States. The program recognizes and supports outstanding graduate students who are pursuing research-based master's and doctoral degrees in science, technology, engineering, and mathematics (STEM) and in STEM education. The GRFP provides three years of financial support for the graduate education of individuals who have demonstrated their potential for significant research achievements in STEM and STEM education. This award supports the NSF Graduate Fellows pursuing graduate education at this GRFP institution. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-08

How legislators spend their time is an important factor that shapes congressional operations, legislative outcomes, and representation. Although researchers and the public have some sense of lawmakers’ daily activities, the details and significance of their scheduling choices are unknown. This project will study how legislators allocate their time and the consequences these decisions have on representation and policymaking. Using former legislators’ archived daily schedules, the research will catalog what they do while working. The PI will connect these data to their legislative and political outputs, such as bill co-sponsorships and vote share at the local level. The project findings will show how legislative, technological, social, and political changes affect how elected officials prioritize problems, represent their constituents, and work together. The results will educate citizens about how their elected representatives work on their behalf and advance research in this field by integrating difficult to study concepts, like effort, into theories of representation and lawmaking. This project studies legislators’ time allocation decisions, and their consequences, by creating a new database that catalogues 50 former members’ daily activities from their archived schedules. It will include over 200,000 scheduled activities spanning the 1970s through the 2010s, and be supplemented with qualitative, interview-based data from the schedulers who created these documents and the members who used them. These data will be combined with existing data sources on congressional activity and output to test novel theories about how lawmakers spend their time, as well as extant theories concerning legislative effort, the importance of interpersonal interactions, and the role of access in lawmaking. The database will be made publicly available to facilitate further research on how legislators’ scheduling priorities affect representation, agenda-setting, and policymaking outcomes. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-08

This award supports a collaborative effort between the University of Nevada, Reno and Oxford University in the United Kingdom to study properties of materials under extreme conditions, such as those found deep within planets or in high-energy industrial processes. The materials are studied by imaging the propagation of sound waves through them to determine key properties such as viscosity and diffusion. The project will take advantage of international free electron laser user facilities and will focus on materials such as iron alloys and silicates commonly present in planetary cores. The project will combine these experimental measurements with computational and theoretical methods to develop a comprehensive understanding of the many-body physics within strongly coupled plasmas, with impacts on materials research, studies of planetary evolution, nuclear stockpile stewardship, and potential fusion energy development. The project will also provide year-round research experience for undergraduate students, equipping them with skills in computational physics, data analysis, and experimental techniques. The high-resolution inelastic X-ray scattering technique has been successfully used to study transport in strongly coupled plasmas and warm dense matter. At free electron laser facilities, it has achieved remarkable energy resolutions down to 50 meV and has uncovered key insights into ion dynamics and acoustic modes. This collaborative project between the University of Nevada, Reno and Oxford University aims to further investigate viscosity and diffusion in materials such as iron-alloys and silicates, commonly found in planetary cores. Experimental data will be integrated with computational and theoretical methods, including density functional theory, Bohm molecular dynamics, wavepacket molecular dynamics, and holography. Machine learning techniques, specifically deep symbolic regression, will be used to refine these models, aiming to produce accurate, scalable representations for large-scale hydrodynamic plasma simulations. This comprehensive approach will provide benchmark-quality data and theoretical frameworks, enhancing the understanding of strongly coupled plasmas and their applications in astrophysics and materials science. This collaborative project is supported by the US National Science Foundation (NSF) and UK Research and Innovation (UKRI), where NSF funds the U.S. investigator and UKRI funds the partners in the United Kingdom. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-08

Sergey Varganov of the University of Nevada, Reno is supported by an award from the Chemical Theory, Models and Computational Methods program in the Division of Chemistry to develop a novel computational methodology to simulate the behavior of lanthanide-containing molecules after their exposure to light. The Varganov group will implement a very computationally efficient way to describe electron distribution in lanthanide-containing molecules, called a dynamic ligand field (DLF) method. The DLF method will be coupled with nonadiabatic molecular dynamics (NAMD), a state-of-the-art technique to simulate the combined motions of electrons and nuclei in molecules. The resulting NAMD-DLF methodology will be used to model how the energy of absorbed light is distributed between the electrons and nuclei and how the efficiency of spontaneous light emission in lanthanide-containing molecules is affected by molecular vibrations. The ability to accurately predict light emission efficiency is critical in designing new molecules for solar energy conversion, medical imaging, and optical telecommunication. As part of the broader impact, Varganov will use the 3D-printed potential energy surfaces describing nuclei motions in molecules to enhance the quality of instruction in chemistry courses by providing visual demonstrations of complex chemistry concepts. Sergey Varganov will develop a NAMD methodology based on an extremely computationally efficient ab initio DLF theory to simulate nonadiabatic dynamics in lanthanide complexes on an unprecedentedly long time scale. The direct multiple spawning NAMD will be coupled with the DLF method parametrized using ab initio electronic structure calculations. The specific objectives are 1) develop a novel DLF method capable of accounting for both ionic and covalent contributions to metal-ligand bonds at equilibrium and non-equilibrium molecular geometries, 2) implement NAMD-DLF with analytical nonadiabatic coupling and energy gradients, and 3) apply new NAMD-DLF to elucidate complex mechanisms of nonradiative relaxation responsible for luminescence quenching in lanthanide complexes. This project will support the continued use and expansion of the set of 3D-printed potential energy surfaces (PES) to enhance the quality of instruction in chemistry courses and the effectiveness and impact of outreach programs by providing simple visual demonstrations of complex chemistry concepts, such as a transition state, a minimum energy reaction path, and the relation between the PES curvature and vibrational frequencies. The proposed visits of Prof. Chibotaru, a world’s leading expert on electronic structure and properties of lanthanide compounds, will facilitate the professional development and training of graduate students and postdoctoral scholars in Nevada, which is an EPSCoR state. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-08

Environmental issues like wildfires can serve as effective science learning contexts to promote scientific literacy and citizenship. This project will partner with teachers, teacher educators, and disciplinary experts in data science, fire ecology, public health, and environmental communication to co-design a data-driven, justice-oriented, and issue-based unit on wildfires. In the unit, student will engage in various data practices to gain insights into the issue of wildfires and how it affects their lives and communities. They will create data stories targeting specific stakeholders as a culminating activity. This project will contribute to the field by examining how data science can be meaningfully incorporated into K-12 science education to prepare informed and responsible citizens. It will directly impact approximately 15 middle school teachers and 1500 middle school students in Northwestern Nevada, including students from low socioeconomic status backgrounds and underrepresented groups. This project seeks to theorize how learners can leverage disciplinary knowledge and practices in environmental and data science as a foundation for making data-informed actions towards a more just and sustainable society. The construct of environmental science data literacy will be developed to include three interconnected components: 1) understanding environmental science and/or data science ideas and practice, 2) identifying areas of own expertise within environmental science and/or data science, and 3) using environmental science and/or data science as a foundation for change. Accordingly, student learning outcomes examined in this project include proficiency in science and data practices, identity formation, and agency development. The project will also investigate teacher professional learning during curriculum co-design and enactments through a cultural-historical activity theory lens. This project is funded by the Discovery Research preK-12 program (DRK-12) that seeks to significantly enhance the learning and teaching of science, technology, engineering, and mathematics (STEM) by preK-12 students and teachers, through research and development of innovative resources, models, and tools. Projects in the DRK-12 program build on fundamental research in STEM education and prior research and development efforts that provide theoretical and empirical justification for proposed projects. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-07

Many insect pests that destroy crops or transmit diseases to millions of people each year rely on their sense of smell to navigate toward their plant or human hosts. As they navigate, crawling insects assess their local odor environment by sweeping their heads before deciding to steer to the left or right. These navigational decisions are influenced by the insect’s hunger state. This project aims to study how an insect’s hunger state affects its smell neurons, influencing navigational decisions. It takes advantage of the fruit-fly larva as a model system, which allows detailed analyses of smell neurons and their functions. Researchers will use lab-based behavior experiments and advanced molecular and imaging techniques to gather and analyze data. Expected outcomes of this project include: 1) Enhanced understanding of how hunger affects smell neurons and shapes navigational decisions. 2) Development of an investigative framework to study how multiple satiety hormones, such as insulin and leptin, work together to convey hunger information to smell neurons and influence navigational decisions. 3) Increased understanding of insect navigation strategies, potentially leading to intelligent solutions to combat insect vectors of disease. Additionally, this research will include educational outreach, such as summer workshops for K-12 students from rural counties in Northern Nevada. These activities are designed to raise awareness of the importance of insects to society and demonstrate how basic science research using insect models can have far-reaching implications for human health. The outreach efforts aim to inspire K-12 students to consider STEM-related careers. To navigate natural odor environments containing the smells of food and predators, an insect must make navigational decisions balancing the possibilities of reward and danger. Such adaptive decisions are further modulated by the insect’s satiety state. However, the mechanisms modulating the insect’s navigational decision-making remain poorly understood. Before deciding to navigate to the left or right, a Drosophila larva assesses its odor environment by sweeping its head. Head sweeps, a behavior critical to the larva’s olfactory decision-making, depend on the activity of a pair of inhibitory local neurons in the larval antennal lobe known as Keystone-LN. Keystone-LN-induced head-sweep behavior is affected by the larva’s satiety state; mediated in part via insulin signaling. This project builds upon these findings: The main objective is to ask how insulin mediates the satiety-dependent changes in Keystone-LN-induced head-sweep behavior in the Drosophila larva. Using state-of-the-art behavior, imaging, and molecular approaches, researchers will test a hypothesis that insulin mediates satiety-dependent changes in larval head-sweep behavior by influencing Keystone-LN’s input and output properties. First, the investigators will test insulin’s ability to affect Keystone-LN’s input property (odor-evoked response). Next, they will test how and to what extent insulin affects Keystone-LN’s output property, the release of the neurotransmitter GABA. Finally, the downstream components of insulin signaling in Keystone-LN will be identified. The results will advance our understanding of how adaptability to an internal state is enforced in an inhibitory neuron to shape an insect’s navigational decisions. Understanding these adaptive mechanisms is critical for decoding how neural circuits support animal cognition and behavior. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-06

People do not always notice the same things when they look at the same scene, because perception depends on the goal. At the neural level, many perceptual regions in the brain do not necessarily respond the same way to the same stimuli-activity depends on both the stimulus and on task goals. The ability to focus on task-relevant features and thereby change activity in the brain is called top-down attention. Attention is affected in many neurological disorders, including ADHD and Schizophrenia, and varies across the healthy population. Despite its importance, brain mechanisms of attention are not well understood. One hypothesis holds that a network spanning frontal and parietal cortex supports task-general functions including attention and accumulating or weighing evidence. However, parts of this network respond to visual features of stimuli including motion and shape, and the dual influence of task and stimulus are seldom studied in the same experiments. The goal of this project is to integrate these findings into a coherent computational model. Instead of trying to attribute one function to each parcel of the brain, the researchers aim to quantify the degree to which brain responses across the brain vary with the stimulus, with the cognitive components of the task, and/or with task difficulty. The central aim of the research is to determine the degree to which different tasks change the pattern of activity within and across brain regions. The broader impacts include plans to widen the opportunities for undergraduates to develop computer and data science skills through course development and communication. The project seeks answers to these questions in a cumulative series of behavioral and functional magnetic resonance imaging (fMRI) experiments. Participants view moving stimuli on which they perform different visual tasks while their brains are measured with fMRI. For example, they judge whether one object will collide with another object or whether the participant (proxied by the point of view of the camera) will collide with a static object. These tasks serve as an experimental proxy for many natural tasks. For example, driving, walking, and many sports involve estimating one’s own motion relative to static obstacles and moving objects. Importantly, among stimulus-based factors, self-motion is a particularly strong influence on responses in many areas also driven by task-related factors. The project aims to analyze the data using cutting-edge encoding models based on labels for task conditions and motion parameters derived from deep neural networks. Models are compared with variance partitioning, which assesses the relative effect of each factor on brain responses. The models developed in this project can provide a detailed quantitative baseline that enables sensitive measurement of individual differences in attention and task processing in future studies. Finally, since the analytic approach of this project is computationally intensive, another aim is to improve data science education at University of Nevada Reno by developing and teaching an introductory applied research computing course. This course is also intended to teach the “hidden curriculum” of research computing, including use of the command line, version control, and dependency management. The aim is to help build a community of practice in data science at both graduate and undergraduate levels. This project is jointly funded by Cognitive Neuroscience 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 2024 · 2024-05

This three-year REU Site: Exploring Intelligent Technologies Shaping Future Smart Cities hosted by the University of Nevada, Reno is designed to engage undergraduate students in unique research experiences with cutting-edge technologies and the future of smart cities. Ten students each year will investigate technological advancements in mobility, management of solar energy, infrastructures, and operations and system security. The goal is to create young researchers who understand and can contribute to the progression of scientific knowledge by solving future technological, environmental, and social challenges in transitioning to smart and sustainable ways of living. Participants will engage in experiential learning in research labs, working closely with a faculty mentor and the research team. Students will also participate in professional development about STEM careers, the ethics of research, communication of research results, and field trips to local companies. This three-year REU Site: Exploring Intelligent Technologies Shaping Future Smart Cities hosted by the University of Nevada, Reno is designed to engage undergraduate students in unique research experiences with cutting-edge technologies and the future of smart cities. This unique 10-week summer program will offer students opportunities to engage in research and explore technological advancements in four key areas: mobility, management of solar energy, infrastructures, and operation and system security. The diverse expertise of faculty mentors coupled with state-of-the-art resources offers students access to 10 unique projects with hands-on in a research-focused, experiential learning environment. Research activities will be complemented by field trips to local companies featuring cutting-edge technological advancements being implemented in different sectors of the industry. A variety of professional development activities will enable participants to address the non-technical and ethical aspects of research. This REU program will give students the full experience of how research is conducted ethically, communicated effectively, applied in practice, and how it can be made accessible to others through educational outreach activities. This project is jointly funded by the EEC REU 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.