Regents of the University of Idaho

NSF Awards · FY 2025 · 2025-01

The goal of this fellowship project is to enhance the lead researcher's career trajectory in data science and geoinformatics through a six-month visit to the Carnegie Institution for Science in Washington, DC (CIW), a leading laboratory in the field. The fellowship will establish a reusable methodology for efficiently deploying data science in geoscience studies to accelerate scientific discoveries. Focusing on mineralogy, the project will implement an empirical approach to integrate existing software and data elements for data exploration. The lead researcher, who has developed software packages for knowledge graphs and data visualization, will collaborate with scientists from the Deep-time Data Driven Discovery (4D) initiative at CIW. This established long-term collaboration with 4D and CIW will enhance the researcher’s research capabilities at the University of Idaho (UI), including software development, data curation, and leadership in cross-disciplinary research. The resulting data science methodology will have a sustained impact on the researcher’s future career. As researchers across many disciplines seek methods to efficiently deploy data science in their work, this fellowship will open numerous opportunities for the researcher to collaborate with researchers both within and outside UI. This fellowship will provide an innovative approach to improving the interconnection and usability of existing software building blocks in open data and open science, facilitating data-driven scientific discoveries within the 4D initiative, a community of over 220 earth, space, life, and data scientists from several countries. The project will significantly enhance the researcher's research capacity, progressing from the development of data and software elements to constructing comprehensive data science frameworks. These efforts will ultimately create workflows that bridge the gap between data scientists and geoscientists. Through the established long-term connection with the 4D network, the researcher will explore cutting-edge topics in data science applications, contribute to the international research community, and cultivate a pioneering professional path that impacts various cross-disciplinary studies. More significantly, the project will provide a foundation for the researcher to contribute to the theoretical underpinnings of data science and develop methods that improve the efficiency of data science application and education. This project will enable the researcher to incorporate new examples and methods into the data science curriculum at UI. Additionally, the software tools and workflows developed will be broadly applicable to researchers and students in geosciences, agricultural science, physics, and chemistry at UI and beyond. The planned conference sessions and presentations aim to promote a shift towards more open practices in data and code across geoscience and other disciplines, fostering a growing acceptance of open data and open code as standard practices in data-intensive research. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-01

Commercial semiconductor chips and their packages are designed to work at temperatures up to 250℃. They will fail to work after a few hours at temperatures above this threshold. In recent years, research has been conducted to develop special chips made of different semiconductor materials that can operate in harsh environments for long durations, up to a year. This project aims to address the challenges of integrating and packaging chips made from different semiconductor materials that can operate flawlessly at high temperatures for months. The goal is to create a package where the chips can continue to work for extended periods at temperatures up to 600℃. This microelectronic system in a package will find broad applications in electric suspension and brakes of electric vehicles, turbine engine sensing and control systems, deep-well drilling telemetry, and Venus and Mercury exploration, where temperatures far exceed 250℃. The technology and management skills gained during this NSF Research Fellowship at Georgia Tech, the host site, will help the principal investigator lead the effort to revitalize semiconductor education and research at the University of Idaho, meeting the workforce needs of the semiconductor manufacturing reshoring to the U.S. in the coming decades. In the realm of electronics, the pursuit of devices capable of enduring extreme temperatures has led to significant interest in wide bandgap (WBG) semiconductors. These materials, known for their ability to operate in harsh environments, are pivotal in advancing compact electronic systems for specialized applications. However, a key challenge lies in the efficient integration of chips fabricated from various WBG materials onto one substrate. This integration is crucial for the development of robust electronic systems that can withstand high-temperature conditions. Addressing these challenges requires specialized knowledge and training in advanced semiconductor manufacturing techniques. The proposed project aims to enable the Principal Investigator (PI) to receive specialized training and access to state-of-the-art semiconductor fabrication and packaging facilities at Georgia Tech. The project focuses on investigating the integration and packaging of chips and sensors based on different WBG semiconductor materials such as silicon carbide (SiC), gallium nitride (GaN), and diamond for extremely high-temperature (up to 600°C) applications. The PI has been working on the high-temperature 3D packaging of SiC chips for three years. Dr. C. P. Wong, the primary research collaborator at Georgia Tech, specializes in packaging material synthesis and characterizations, especially flip-chip underfill, an essential part of the proposed research. After the completion of the project, the PI will continue collaborating with the faculty at Georgia Tech, obtain research funds in this advanced packaging area, and expand the University of Idaho’s research and education capability for semiconductor workforce training. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-12

Changing climates are a major challenge for humans and nature. One of the most important scientific questions of the 21st century is "What will determine the vulnerability of species and biological communities to environmental disruption?" Addressing this question, however, is extremely challenging because changes in climate are complex, evolutionary history will determine species' sensitivity, and species' responses will be affected by the complex food webs of which they are a part. For example, even if a predator species is relatively insensitive to warming temperatures, it may still be vulnerable if the prey species it feeds on are sensitive and disappear. The research team will investigate thermal vulnerabilities of cutthroat trout predators and tailed frog tadpole prey in mountain streams of the U.S. Pacific Northwest using a multidisciplinary approach that combines cutting-edge DNA sequencing technology, physiology experiments, and high-performance computer modeling. This is an excellent predator-prey case study to investigate this question, as cutthroat trout are important predators of tailed frog tadpoles, both species are cold-water specialists that are sensitive to warming, and both species are of conservation concern. Thus, results of this study will be important for informing their conservation and management. The research team will work closely with federal and state natural resource agencies to make sure that results are useful for informing decisions about the conservation and management of stream biodiversity. The team will develop a web-based tool to help managers assess environmental vulnerabilities. The project will also provide training for four early career researchers. It is essential to integrate organismal, ecological, and evolutionary perspectives to predict the vulnerability of species and biological communities to global change. The proposed research will use a highly interdisciplinary approach to test several novel hypotheses about the mechanisms underlying environmental vulnerability in a predator (cutthroat trout) - prey (tailed frog) system in streams of the U.S. Pacific Northwest. In Aim 1, the research team will test whether predators (cutthroat trout) are more sensitive to increasing temperatures than their prey (tailed frogs) using a combination of environmental characterization, genomics, and physiology to quantify thermal adaptation. In Aim 2, they will test whether different dimensions of adaptive capacity (dispersal, evolutionary potential, and acclimation ability) differ between predators and prey by using genomics to characterize patterns and rates of connectivity across the landscape and evolutionary potential, and physiology to quantify acclimation. In Aim 3, the team will determine how predator-prey interactions respond to temperature and flow by quantifying the diet of cutthroat trout and energy content of tailed frogs, and by using a mechanistic food web model to predict how climate change-driven changes in energy flow and food web dynamics will affect cutthroat trout-tailed frog interactions. In Aim 4, they will integrate the results from Aims 1-3 using an individual-based, eco-evolutionary model to predict how species interactions, intraspecific variation in sensitivity, and adaptive capacity act synergistically to determine spatial patterns of thermal vulnerability in cutthroat trout and tailed frogs. 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 award supports research in relativity and relativistic astrophysics and it addresses the priority areas of NSF's "Windows on the Universe" Big Idea. Einstein’s theory of general relativity (GR) provides science's current best understanding of gravity. It predicts the existence of bizarre objects like black holes and neutron stars, and ripples in spacetime called gravitational waves. These predictions motivated the construction of NSF's Laser Interferometer Gravitational-wave Observatory (LIGO), which has detected several gravitational wave signals from colliding black holes and neutron stars over the past years. For their efforts in making these detections possible, the leaders of LIGO were awarded the 2017 Nobel Prize in Physics. Much of gravitational wave (GW) science depends on GW observations being compared with millions of theoretical predictions, which must be built upon GW catalogs extracted from numerical relativity (NR) simulations. NR simulations solve the GR equations in full on the computer, and to date each of these NR simulations has required a small computing cluster, which has limited throughput to only about 3,000 GWs in 15 years. Given the vast number of possible scenarios for even the simplest and most commonly observed GW source, binary black holes (BBHs), such a small GW collection threatens potential science gains from future GW observations. BlackHoles@Home is a proposed citizen-science project leveraging new techniques to fit NR BBH simulations on a consumer-grade desktop computer, enabling new GW catalog generation with unprecedented throughput using volunteer computers. Such throughput will enable far more detailed analyses of observed GWs from current and future GW detectors, maximizing the science gained from hard-fought observations. To educate the public and advertise this volunteer computing project both locally and globally, convocations will be given in underserved high schools, and updates will be posted to a widely disseminated BlackHoles@Home email newsletter. Improvements to the algorithmic and mathematical underpinnings of NR codes have recently culminated in a coming-of-age for the field, moving it beyond proof-of-principle calculations and into the realm of predictive astrophysics. Over the past six years, NR-based theoretical predictions of gravitational waves (GWs) were central to uncovering the binary parameters in LIGO and Virgo's recent GW discoveries. Looking ahead, GW catalogs generated by NR simulations of compact binaries will need to grow greatly to ensure that parameter estimation accuracy can keep up with increased sensitivity of GW interferometers. BlackHoles@Home is a proposed BOINC project that aims to fit binary black hole (BBH) simulations on the consumer-grade desktop computer. In doing so the general public can be enlisted to help generate the large GW catalogs that form the foundation for a great deal of GW science. Traditionally, these BBH simulations have been performed on supercomputers. BlackHoles@Home implements new approaches for robustly solving Einstein's equations of general relativity in highly efficient coordinate systems, so that these simulations will fit on consumer-grade desktop computers in only a few gigabytes of RAM. BlackHoles@Home's core infrastructure provides a firm foundation for compact binary simulations beyond BBHs. To this end, the dynamical-spacetime GRMHD code IllinoisGRMHD will be incorporated into this infrastructure to enable state-of-the-art binary neutron star simulations on supercomputers. These simulations will leverage both recent advances in solving the GRMHD equations in spherical-like coordinate systems, as well as recent improvements to IllinoisGRMHD that add both advanced nuclear equation of state support and basic neutrino physics. Monte-Carlo-based photon and neutrino feedback will also be incorporated to enable state-of-the-art realism in these binary neutron star simulations. 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

Quantum computers can perform calculations much faster and with less energy than classical computers. However, they generally require extremely low operating temperature, noise, and humidity conditions and large device footprints. Conjugated organic molecules, such as dyes, that absorb and emit light are potential candidates for room-temperature quantum computing due to their unique optical properties. The design of such dyes requires efficient high-throughput screening. A new autonomous molecular design cyberinfrastructure (CI) is developed for forward and inverse de novo materials designs. Forward design discovers the most predictive features for a target property or performance called the latent space. Inverse design predicts an optimal structure or compositions leveraging the latent space, given the desired properties and / or performance. The technologies developed in this project are integrated into a STEM education effort through course module development, and participation in the Democratizing Data Science for Climate Resiliency and Social Mobility (ClimB) project - a modular, fully online, automated, and personalized data science certificate program – and a series of K-12 outreach activities. The project aligns well with NSF’s 10 Big Ideas. The project goal is to develop a visual artificial intelligence (AI)- and knowledge-driven machine learning (ML) toolkit (MatFlow) for multi-source and multi-formatted data analysis capable of both forward and inverse designs. Research objectives are to: (1) conduct ab-initio density functional theory (DFT) and time-dependent (TD) DFT to generate dye structure-property datasets; (2) create a visual CI toolkit using a user-friendly graphical interface to conduct both forward and inverse dye designs; (3) validate and test the CI toolkit by primary end users; and (4) expand end-user communities beyond quantum computing. The developed CI accelerates and advances the exploration of new molecular science and broadens the interface of organic chemistry with quantum physics, materials science, biochemistry, biophysics, computer science, and data science. This award by the NSF Office of Advanced Cyberinfrastructure is jointly supported by the Division of Chemistry and the Directorate for STEM Education. 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 project addresses the urgent problems caused by hazardous industrial waste and climate change, disproportionately affecting low-income and BIPOC (Black, Indigenous, People of Color) communities due to social and economic inequality. It aims to transform biomass and hazardous industrial wastes into inexpensive, green construction materials using Negative Emission Technology (NET). This approach promotes environmental justice and enhances resilience to climate-related issues. By incorporating CO2 into building materials from waste, the project reduces atmospheric carbon levels and provides a sustainable waste management solution. It also provides a long-term solution for handling biomass and industrial waste, which is presently disposed in landfills and causes health issues. The project also intends to create better building materials by combining pozzolanic reactivity, CO2 capture and mineralization, which will speed up scientific and technological advancement. It fosters diversity and education by involving researchers from one Historically Black College and University (HBCU) in Alabama and three public universities in New Mexico, Alabama, and Idaho. Comprehensive workforce development programs include early career faculty development, undergraduate and graduate training, K–12 education, and teacher training, supporting STEM jobs and education in underrepresented areas. Benefits include better public health from reduced hazardous waste, safer housing options, and improved well-being through economic opportunities and infrastructure enhancement in the targeted communities. The Fifth National Climate Assessment highlights the vulnerability of low-income and BIPOC communities in Alabama, Idaho, and New Mexico to climate change and land-filled hazardous industrial wastes. In response, a collaborative effort involving four institutions from these EPSCoR jurisdictions aims to develop a novel pathway to alleviate these effects. The project revolves around a Negative Emission Technology (NET) designed to convert alkaline industrial and biomass wastes into low-cost, low-carbon construction materials. This transformative solution provides an economically viable means of enhancing the climate resilience of these communities and sequestering CO2 into concrete. Using alkaline industrial wastes as feedstock, the process produces calcium-rich leachate for CO2 capture, creating CaCO3 precipitate. The filtered solid residual, with higher pozzolanic reactivity, can partially replace ordinary Portland cement in concrete. This method generates new revenues and addresses climate change through carbon capture and utilization in concrete. It is estimated that the NET method could sequester 2.9-8.5 billion tonnes of CO2 per year by 2100. The project is a consortium of researchers from The University of Alabama, University of New Mexico, University of Idaho, and Alabama A&M University, a Historically Black College and University. The diverse team includes nine assistant professors. Workforce development and outreach activities are extensive, covering K-12 education for teachers, undergraduate students, early career professors, industry partners, and communities. These initiatives promote STEM education in sustainable construction materials, circular economy practices, and the emerging interdisciplinary field of decarbonization of the built environment, directly benefiting the targeted low-income and BIPOC communities. This project is funded by the EPSCoR Research Infrastructure Improvement-Focused EPSCoR Collaborations (RII-FEC) program. The RII-FEC program builds inter-jurisdictional collaborative teams of EPSCoR investigators in focus areas consistent with the NSF Strategic Plan. RII-FEC projects include researchers from at least two EPSCoR eligible jurisdictions with complementary expertise and resources necessary to address challenges, which neither party could address as well or as rapidly independently. 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 project aims to significantly enhance post-award research administration by developing open-source tools that integrate artificial intelligence (AI) and data science. Research administration is crucial for supporting scientific progress, but many institutions face challenges with inefficient processes that hinder their ability to effectively manage grants and contracts. Recent advances in generative AI provide a unique opportunity to augment research administrators' capabilities. By creating accessible, innovative tools, this project will help level the playing field for emerging research institutions, minority-serving institutions, and primarily undergraduate institutions. Improved research administration will accelerate scientific discovery across disciplines by reducing the administrative burden on researchers, allowing them to focus more on their work. The project will also contribute to workforce development by training research administrators in data science and AI skills, preparing them for the increasingly technological future of the field. By making these tools open source, the project promotes collaboration and innovation in the broader research management ecosystem. The project has three main objectives: 1) Develop open-source data models and workflows that adhere to FAIR (Findable, Accessible, Interoperable, and Reusable) data principles to improve data accessibility and interoperability in research administration. 2) Create trustworthy AI-powered tools to automate manual processes, reduce errors, and augment the capabilities of research administrators. These will include natural language processing and machine learning models for tasks like data extraction, compliance verification, and decision support. All tools will undergo rigorous testing for accuracy, reliability, and reproducibility. 3) Implement and assess the impact of these tools at partner institutions, starting with the University of Idaho and Southern Utah University, then expanding to additional collaborators. The project will use an iterative, user-centered development approach, incorporating feedback from partners to ensure the tools meet real-world needs. Importantly, the project features an independent external evaluator to ensure the research administration community can learn from both successes and challenges, a critical approach given the rapid advancements in AI capabilities. By integrating data science and AI in a thoughtful, tested manner, this project aims to create a transformative framework for efficient, data-driven research administration that can be adopted widely across diverse institutions. 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

The increase in the frequency and magnitude of extreme weather events is a significant threat to the sustainability of food and fiber production and of rural communities. Land management practices have the potential to remove carbon dioxide from the atmosphere and store it in forests and soils. These land management practices create more resilient, rural communities. This project leverages the natural resources of Idaho and Montana to research and implement smart forestry and agricultural solutions. The team will implement these practices in both jurisdictions to 1) study the carbon and environmental impacts of the practices, 2) research the barriers to implementation of these practices, and 3) determine the economic impacts of the adoption of these practices on local communities. The project's aim is to reduce gas emissions while partnering with local communities to effectively promote community resilience and adaptation to extreme weather events. The team's activities will enhance the resilience of their partnering communities, contribute to the expansion of the STEM workforce, and produce measurable reductions in emissions. This work will offer policymakers in rural states a road map for successful, locally supported, forward-looking infrastructure implementation. Environmental changes, pollutants, or other phenomena originating from human activity have led to increasingly negative events impacting rural Idaho and Montana. These events include intensifying wildfires and persistent drought that are decreasing both ecological and community resilience. Smart land management solutions offer a path to both environmental benefits and increased ecological and community resilience. Through new and existing partnerships the team will experiment with the implementation of smart land management practices in forests and farms in the region. The team will quantify the ecological, social, and economic impacts of such practices. Through this interdisciplinary and community-based effort the team will provide: 1) improved knowledge on the efficacy and biogeochemical impacts of potential smart management solutions, 2) novel, state-of-the-art methods for quantifying and predicting carbon storage potential (including field-based sample collection and process-based ecosystem modeling) for individual regions, 3) training and education programs for undergraduate, graduate, and early career scientists, 4) models for effective partnerships with practitioners and communities, 5) integration of local knowledge, and 6) pathways that reduce barriers and risks identified by local populations to the adoption and implementation of smart land management solutions. 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

Transitioning from high school to university presents various challenges for first-year engineering students, particularly as they adapt from a high school mindset to the rigors of higher education. These challenges are not just academic; they include social issues, the need for independent study, cognitive adjustments, and demanding coursework. Research has shown that certain social factors play critical roles in helping students navigate this transition successfully. These social factors include engineering identity, sense of belonging, and self-efficacy, all of which are essential for students to feel integrated and competent in their new academic environments. The COVID-19 pandemic has further complicated the educational landscape, prompting the creation of diverse learning environments to cater to the varied needs of students. These environments range from fully online formats, asynchronous and synchronous, to hybrid models, combining online and in-person instruction, such as HyFlex formats. The HyFlex model, in particular, offers a blend of hybrid and flexible learning, allowing synchronous participation both online and in person. Despite the proliferation of these innovative learning models, there is little research on how the HyFlex format impacts student learning and engagement in engineering education compared to traditional in-person methods. In response to this gap in research, a quasi-experimental study is proposed to assess the efficacy of the HyFlex learning environment within the context of drone education at a rural public university. The project will involve a new course titled "iDrone 101," which will be available in both HyFlex and traditional in-person formats for first-year engineering students at the University of Idaho. This course aims to provide students with foundational knowledge in automatic control, sensors, ground robots, and drones. Moreover, it will seek to integrate students' cultural experiences and values into problem-solving exercises that address real-world issues, such as wildfire monitoring, river restoration, and animal migration. The overarching goal of this project is to explore how the HyFlex model influences student perceptions and behaviors concerning emerging technologies when compared to a conventional in-person classroom setting. The research objectives include (a) increasing student engagement with emerging technologies through the "iDrone 101" courses, (b) fostering positive attitudes towards engineering, reflecting strengthened engineering identity, enhanced sense of belonging, and increased self-efficacy, and (c) evaluating the specific impacts of the HyFlex learning environment relative to the traditional in-person format. The mixed-methods research from quantitative and qualitative methods including pre-post surveys and semi-structured interviews will evaluate the influence of HyFlex and in-person learning on students' perceptions and behaviors, regarding rapidly advancing technologies, particularly autonomous unmanned vehicles, including unmanned aerial vehicles (e.g., drones), unmanned ground vehicles (e.g., ground robots), and unmanned surface vehicles (e.g., drone boats). This project is designed to be easily scalable and implementable. Therefore, the HyFlex and in-person learning modules developed for the iDrone 101 course curricula from this project will be available to a broader audience across different states through the project's website. 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

In this project, funded by Chemical Structure, Dynamics & Mechanisms-B Program of the Chemistry Division, Professors Kristopher Waynant and James Moberly of the Departments of Chemistry and Chemical and Biological Engineering, respectively at the University of Idaho along with Professor Elliott Hulley of the Department of Chemistry at the University of Wyoming are developing a novel mild method for the sustainable recycling of e-waste metals from pure and mixed metal systems. The goal of this project is to create a series of unique chemicals that dissolve e-waste metals and study the digestion and recovery processes. This project could develop a unique understanding of the essential components critical to develop an innovative technology for sustainable recovery of metals. Outreach activities include a series of course-based undergraduate research experiences to allow students to take part in the project goals. It will also incorporate sustainable chemical principles into undergraduate teaching laboratory. Redox-active ligands, chelators that induce redox events, will be designed for the oxidative dissolution of zerovalent metals from e-waste. A series of ligands based on the azothioformamide 1,3-heterodiene framework will be studied. Both computational and experimental approaches will be coupled to investigate ligand induced dissolution and recovery of e-waste metals. Additionally, valorization, host-guest mechanism deduction, and the development of new reactions and green chemistry will be explored to build a critical materials and precious metals recycling program. Preliminary data has identified 1) a series of ligand dissection points that may govern the efficacy of oxidative dissolution; 2) that substitution patterns on ligands affect coordination of metal salts, giving clues to which ligand modifications may be most selective for dissolution / coordination; 3) recovery of metals is viable by electrochemical means and may be possible by chemical reduction to regain ligands and purify metals toward a circular, sustainable process. 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 incidence of extreme heat events has increased in frequency and intensity in the last century as global temperatures have risen, driven by anthropogenic greenhouse gas forcing. When extreme heat occurs at the same time as drought, the impacts are exacerbated. These "hot drought" events have complex consequences for communities across North America, including altered water resource availability and fire regimes, as well as the magnitude of the uptake of carbon dioxide by forests. This project will compile new and previously collected temperature reconstruction data from tree cores from across North America into a "North American Temperature Atlas," which will allow for the analysis of relationship of heat and drought at a range of time and spatial scales. The goals of this project are to make new blue intensity measurements on previously collected tree cores from North America, compile existing blue intensity and maximum latewood density tree ring chronologies from North America, and combine the new and existing datasets together to create the “North American Temperature Atlas” (NATA), a gridded reconstruction of warm season surface air temperature. The NATA will be compared to a gridded North American drought atlas and a gridded North American seasonal precipitation atlas to determine the contribution of temperature to past droughts, evaluate the temperature-drought relationship, and place the modern occurrence of drought in the context of the last several centuries. The Broader Impacts are to create a web interface for public access to the NATA, support for graduate students at University of Tennessee, Knoxville, and University of Idaho, development of outreach to water and natural resource managers, creation of K-12 STEM activities for middle school students, tours of tree ring lab for K-12 students, mentoring high school and undergraduate students underrepresented in STEM on projects related to this work. 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

A challenge in mathematics education is that students often do not see math as an opportunity for exploration and creativity. However, these tenets are central to how mathematicians engage in mathematics and could prove transformative in how students experience mathematics learning. Mathematicians identify and formalize patterns, develop and prove conjectures, and explore mathematical structures in creative and playful ways. Mathematical play can also be used to help students explore new ideas, experiment with different solutions, and develop mathematical reasoning. Mathematical play can also bolster motivation and engagement, which are critical factors in supporting students’ abilities to understand and persist in mathematics and the STEM fields. The project will investigate (a) how to meaningfully incorporate playful elements into the foundational secondary and undergraduate mathematics topics of algebra and calculus, and (b) the potential outcomes of “playifying” classroom mathematics for students’ learning and enjoyment. The project will investigate tasks that can be used for students to explore mathematical ideas such as rates of change, functions, derivatives, and integrals. This study will examine the following traits of mathematical play: (a) exploration, (b) self-selection of goals, and (c) immersion, investment, and/or enjoyment. The research questions address students' mathematical activity, reasoning in algebra and calculus, the nature of mathematical play, and the learning benefits for students. In parallel to the student experience, the research questions also examine task design elements, pedagogical moves, and classroom features that support mathematical play. The project will implement a multi-phase design experiment model, leveraging clinical and stimulated recall interviews, small-scale teaching experiments, and whole-class teaching experiments, with each phase building on the findings of the prior. The research activities will produce a set of findings about the aspects of task design, instruction, and classroom interactions that support mathematical play, as well as the learning benefits of mathematical play for adolescents and undergraduates. This project is supported by NSF's EDU Core Research (ECR) program. The ECR program emphasizes fundamental STEM education research that generates foundational knowledge in the field. Investments are made in critical areas that are essential, broad and enduring: STEM learning and STEM learning environments, broadening participation in STEM, and STEM workforce development. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-08

The Sierra Nevada of eastern California is a rugged mountain range home to deeply incised canyons and the highest peak in the conterminous United States. These features and a variety of studies have led many researchers to conclude that the high elevations are relatively recent (young tectonic uplift model). Other datasets, however, indicate a long-standing Sierra Nevada that has retained high elevations for the past 70 million years or more (old tectonic uplift model). Most studies favoring the young uplift model are from the southern part of the Sierra, whereas those favoring the old uplift model stem from studies of ancient gold-bearing river gravels and volcanic rocks only present in the north. This project will use low-temperature thermochronology (which can reveal the timing of exhumation), together with study of these ancient river deposits preserved on the western range flank, to address discrepancies between uplift models. Importantly, this work will create a range-wide thermochronologic dataset that will test whether conflicting interpretations are due to fundamental north to south changes in the geology of the mountain range or if the range shares a unified uplift history. Undergraduate and graduate students from three universities will be supported by this project and will receive mentoring from both their peers and principal investigators from all involved institutions. Three cohorts of high-school students will also be engaged in this research through a TRIO-INSPIRE STEM-Access summer internship program. This project aims to constrain the exhumation history of the northern Sierra Nevada and its Cenozoic sediment sources to test hypotheses for possible along-strike variability in the history and causes of topographic uplift. Most thermochronologic and tectonic geomorphology studies are focused in the southern Sierra and support a model of recent (post-Miocene) tectonic uplift. In contrast, paleoaltimetric and detrital zircon (DZ) studies of Cenozoic strata preserved in the northern Sierra Nevada suggest development and maintenance of high topography since the Late Cretaceous. These spatially separated and often contrasting data have hindered agreement on an uplift theory for either part of the range. This study will use 1) basement (U-Th)/He data along two range-perpendicular transects in the northern Sierra, with a focus on sampling both modern valley and paleovalley bottoms, the latter immediately below the Eocene fluvial gravels, and 2) laser-ablation (U-Th)/(He-Pb) double dates coupled with Hf isotope data on targeted DZ sub-populations in the basal Eocene gravels to constrain incision timing and discriminate local vs. extra-regional sediment sources, which is not possible with the DZ U-Pb data alone. The integrated basement-detrital datasets will determine whether (U-Th)/He patterns are similar or different across the range, implying shared or separate uplift histories, and the Eocene position of the northern drainage divide, which informs the large-scale geometry of topography and fluvial drainages from the Sierra eastward to an elevated plateau. The researchers will test competing hypotheses for the uniformity, timing, and causes of Sierra Nevada uplift. 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

Seismic mapping of Earth’s lowermost mantle reveals the presence of at least two continent-sized structures located just above the outer-core within the mantle. Due to these structures slow seismic wave speeds, they are commonly referred to as Large Low Velocity Provinces (LLVPs). LLVPs potentially influence deep-Earth dynamics, alter lower mantle circulation, decrease core heat flow, and consequently modify the planet’s magnetic field. The extent of these potential impacts depends on LLVP material properties, including their composition and density, among others. However, current geophysical methods do not directly measure composition and density. To address this, this project will leverage low-mass, elementary particles called neutrinos that have direct sensitivity to the composition and density of matter using a technique known as Neutrino Oscillation Tomography of the Earth (NOTE). The NOTE method can infer properties of the lower mantle by analyzing the characteristics of Earth-crossing neutrinos arriving at a detector (e.g., angles of arrival, energies, and neutrino types). To enhance the sensitivity of NOTE and complement existing geophysical methods, the researchers will create a new NOTE framework capable of integrating data from multiple neutrino detectors into a single analysis, providing new, independent estimates of the composition and density of LLVP. With several neutrino detectors online or in development, this project will develop tools to harness their collective capabilities, while also leveraging existing geophysical data toward new breakthroughs in understanding our planet. The goal is to build the tools necessary to demonstrate that with the use of multiple detectors, NOTE can provide a robust determination of LLSVP density and composition within a decade. This project will establish and strengthen international collaboration, while providing and training and mentoring for students, and facilitating outreach opportunities. The composition and density of an LLVP impact its viscosity and relative buoyancy, thereby governing its dynamic behavior and lifetime in the mantle. By developing a new tool to constrain the density and composition of LLVPs, this project will open new approaches for exploring the Earth’s deep interior. The researchers will create a novel multi-detector framework for NOTE and integrate it with existing geophysical constraints, yielding a unified method for estimating LLSVP properties. The project has three primary objectives: 1) incorporate multiple detectors into the NOTE code EarthProbe, 2) systematically assess the sensitivity of multi-detector NOTE to LLVP properties, and 3) integrate geophysical constraints into the proposed framework to improve data statistics and decrease required data collection times. To achieve Objective 1, the project team will modify the EarthProbe code and data structures to account for neutrinos arriving at multiple detectors, as well as the covariance matrix used by existing minimization algorithms to yield estimates of LLVP properties. Objective 2 will be accomplished through a series of systematic sensitivity tests of the new multi-detector framework across a range of anomalous LLSVP densities and compositional contrasts. Finally, for Objective 3, the team will incorporate existing geophysical estimates of either composition or density to quantify any reduction in data collection period required for NOTE analysis. This project will establish and strengthen international collaboration with a PhD student at the center, while providing and training and mentoring for students, and facilitate outreach opportunities. This project is jointly funded by Cooperative Studies for the Earth’s Deep Interior (CSEDI) 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 · 2024-07

This research explores the world of chemical communication among land snails forming communities in the Peruvian Andes, an area particularly rich in biodiversity. Chemical interactions, such as those mediated by snail mucus, are crucial yet understudied components of community assembly and biodiversity. By focusing on the chemical properties of snail mucus, the project aims to uncover how these traits related to communication between organisms influence species interactions, which in turn are shaping the assembly of biological communities. This study is addressing an important gap in knowledge by merging the fields of chemical ecology and evolutionary biology to better understand how species interact and coexist at a local scale. Understanding species interactions is crucial for biodiversity conservation, as it can reveal how species adapt to their environments and maintain stable communities, especially in the face of environmental changes. The project will support educational workshops for undergraduates and early graduate students in the USA and Peru, emphasizing research skills and leadership, particularly for participants from underrepresented groups, thereby fostering a diverse scientific community and international collaborations. This project integrates chemical ecology, phylogenetics, and morphometrics to quantify chemical (mucus), morphological (shell), and genetic (DNA) variation in Andean land snails and how this variation contributes to community assembly and species diversification. In particular, using mass spectrometry and proteomics, the research will identify and quantify mucus components across different snail species. Phylogenetic analyses will be conducted to understand the evolutionary relationships among these species, while morphometric techniques will assess shell characteristics. Together, these approaches aim to test hypotheses regarding the role of mucus in species recognition and community dynamics, providing insights into the mechanisms of species coexistence, competition, and ecological adaptation. By combining these interdisciplinary approaches, the research seeks to advance our understanding of the chemical basis of species interactions and its impact on community structure and evolution. Ultimately the research will provide insights into the mechanisms driving community dynamics and species diversification in this biodiversity hotspot, and serve as a model to understand these processes in other systems. The award is co-funded by the Population and Community Ecology 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.