University of Colorado at Boulder

NSF Awards · FY 2024 · 2024-08

The geographical ranges of most species across the Earth are made up of multiple populations (all individuals of that species in a defined area) and these populations often experience quite different conditions. In addition, how the environment will change in the future will also differ among populations and will ultimately determine whether each population persists, which in turn will determine whether the entire range of a species will contract, expand, or shift in space. In the face of environmental change, such shifts in geographical ranges can have important implications for ecosystem health and human well-being. Given the variation in climate across time and space for populations of even a single species, to understand and predict how geographical ranges will shift with changing climate requires both long-term and multi-population studies. This project will synthesize data collected over decades (up to 29 years to date) for a total of 40 populations of two species of tundra plants that are widely distributed across western North America. Tundra plants are especially suited to studies of geographical range shifts driven by climate warming, because they are adapted to cold climates but can also benefit to some degree from warming. The researchers will use the long-term data to examine how survival, growth, and reproduction of the tundra plants respond to variation in multiple, measured environmental factors (both climatic and non-climatic) at multiple spatial scales (from the scale of individual plants to the scale of entire regions), and will assess how year-to-year variation and long-term trends in those factors are likely to either enhance abundance or increase extinction risk of those plants within populations and thus increase or decrease the geographical ranges of the species. All the data on both performance of individual plants and environmental factors affecting that performance will be made publicly available for any future studies of how environmental changes affect geographical range shifts. The three questions this research aims to address are, first, how does demographic buffering – the evolution of reduced temporal variance in demographic rates (survival, growth, reproduction, and recruitment), especially those that most influence population growth – arise from responses of those rates to specific climatic drivers? Second, what role do differences in demographic responses to environmental variation between individuals within populations, between populations in a region, and between regions play in reducing temporal variance in population growth and thus enhancing species persistence locally, regionally, and across entire geographical ranges? Third, do long-term demographic data reveal evidence of adaptive demographic lability – an increase in the population growth rate due to temporal variation in vital rates caused by nonlinear responses to climate, the opposite of demographic buffering - and if so, does its presence depend on the type of environmental driver (e.g., abiotic vs. biotic drivers)? 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 will focus on the development of chemical mechanisms and models for evaluating the potential effects of atmospheric gases and particles on visibility, human health, ecosystems, and climate. Detailed studies will be conducted in environmental chambers under simulated atmospheric conditions for investigating the reactions of volatile organic compounds and the resulting products and aerosol that are formed. The mechanisms and models developed in this project can be used as modules in chemical transport models for regional and global simulations to predict the fates of organic compounds and the effects of organic gases and aerosol on atmospheric composition, visibility, the hydrologic cycle, climate, and human and environmental health. The primary objectives of this project are: (1) to conduct experimental studies to achieve an improved understanding and quantitative description of the effects of molecular structure and oxidation regime on the gas- and particle-phase products of reactions of monoterpenes with hydroxyl radicals (OH); and (2) to use this information to develop reaction mechanisms and models for predicting the chemical composition, yields, and properties of secondary organic aerosol (SOA) formed over a range of atmospheric oxidation conditions. This project will support the training of students in organic chemical analysis, kinetics, atmospheric chemistry, aerosol science and technology, and data analysis. 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

Silicon is the second most abundant element in the Earth’s crust and the reactions that it undergoes on the surface of our planet controls the biogeochemical cycles of many elements, including carbon. Most minerals in the Earth’s crust are composed of silicate minerals and the dissolution (or weathering) of these minerals on land can be driven by carbonic acid, which forms by carbon dioxide and reacting with water. The drawdown of atmospheric CO2, a greenhouse gas, via these forward weathering reactions can modulate the C cycle and regulate the temperature of the surface of our planet. At the same time, clays and silicate minerals can form in the ocean seabed (reverse weathering), mostly in tropical deltas, and these reactions produce carbon dioxide. The magnitude of both forward and reverse weathering reactions can be traced through the ratios of Si isotopes and the ratios of an associated product of Si mineral weathering, lithium, over different time scales provided we can differentiate the signatures of these distinct reactions. This project will study reverse weathering signatures within major sediment depocenters that can impact C cycling and the coupled cycles of Si and Li. It seeks to develop Si and Li isotope ratios in different biogeochemical reservoirs as proxies of modern reverse weathering reactions. Further, the project aims to develop models that provide a basis for reconstructing ancient C and coupled elemental cycling in the ocean. Measurements and model simulations are an important perspective from which to view modern changes and rates of change in the C cycle. Anthropogenic forcing of the C cycle is driving rapid changes in climate and ocean chemistry, for example, acidification. These changes and impacts are of major societal importance. In this regard, it is critical to check the dynamics of the C cycle over multiple time scales and understand the processes that control them. This project will provide support for two early career scientists, two graduate students, and five undergraduate students. The PIs will prioritize recruiting undergraduate and graduate students from underrepresented backgrounds, two-year community colleges, and the AGU Bridge program which will offer measurable socio-economic benefits to minoritized populations and improve STEM recruitment from two-year to four-year degree granting institutions. The project will also provide STEM learning experiences for more than one hundred local 4th and 5th grade students through a partnership with the Girls at the Museum Exploring Science (GAMES) program run by the CU Museum of Natural History. Stable isotope ratios of Li and Si in marine geological records are promising proxies of the many mechanisms through which the Si and C cycle are coupled on the surface Earth. Advances in instrumentation and technological innovation over the past ~20 years have allowed us to resolve isotope ratios of silicate mineral weathering products Li and Si with enough precision to render their compositions in marine geological records as integral tools to deciphering past and present biogeochemical cycling, weathering regimes, secondary mineral formation, and low and high temperature geochemical reactions across the surface Earth. These proxies are used with a presupposition of isotopic mass balance which may not be valid for all spatiotemporal scales (e.g., embayment scale versus global ocean, or glacial-interglacial cycles versus geological time scales of 106 years). Reverse weathering reactions, or the neoformation of alumino-silicate phases that consume alkalinity and produce CO2, in deltaic systems are the second largest estimated silica sink in the modern ocean and are unconstrained in isotope mass balance summaries. Archived samples of porewater and sedimentary reactive Si reservoirs from three major deltas (Amazon delta, French Guiana mobile mud belt, and the Gulf of Papua), the depocenters where the majority of reverse weathering reactions apparently occur in the modern Earth, using a multi-collector inductively coupled plasma mass spectrometer (MC-ICP-MS) for Li and Si stable isotope ratios. We propose to constrain the isotopic composition of end-members under various sediment transport, geomorphic, and lithological regimes. End-member characterizations and constraints will be applied to a global inverse isotope mass balance model to assess whether Si marine summaries are in isotopic mass balance. The model will be calibrated against available Si isotopic compositions in geological records of biogenic Si through the Last Glacial Maximum. A new inverse isotope mass balance model will be constructed for modern Li marine summaries, a promising proxy for the abiological Si cycle, and also tested. Modeling results will allow us to probe the nuances of CO2 control by the silicate mineral weathering and reverse weathering reactions as relative importance of Si sources and sinks evolve through glacial extent and retreat. Sea level rise, salinization, and subsidence together may act to expand the global footprint of deltaic systems and consequently increase the area over which reverse weathering reactions are favored. Developing a conceptual framework for how these processes are linked to dissolved fluxes of constituents hosted in silicate minerals, CO2, and alkalinity fluxes in deltas is critical to understanding and modeling how this sink will evolve under changing environmental conditions and be incorporated into global biogeochemical cycles. 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

Electromagnetic waves are ubiquitous in space and provide an important means of studying dynamics and energy transfer across space environments. While typical techniques for studying such waves rely on visual inspection or the application of automated algorithms, this team explores sonification techniques (listening to the data rather than looking at it) to better identify and study the complex wave environment in near-Earth space. Sonification has several benefits for analyzing large data sets, many of which still need to be fully realized. The focus of this proposal is very much on the new scientific knowledge to be gained by studying electromagnetic waves through sound, but potential applications to expand upon this work for citizen science projects, education, and outreach are numerous. This project also aims to broaden the pool of people included in heliophysics research (e.g., visually impaired, musicians or artists, non-STEM students, etc.) Electromagnetic ion cyclotron (EMIC) waves, plasma emissions generated by unstable distributions of hot ions, have long been observed in Earth's magnetosphere. These waves have been shown to interact with multiple particle populations in the inner magnetosphere resonantly. They are an important loss process for ring current ions and radiation belt electrons. EMIC waves exhibit several different characteristics, and characterization of their frequency-time structure is critical for understanding wave generation mechanisms and geoeffective impacts. This proposal aims to explore the application of sonification techniques (listening to the data rather than looking at it) specifically for the identification and study of different types of EMIC waves. The team suggested to apply sonification techniques to study EMIC waves in the GOES and Van Allen Probe data sets. The application of this technique for EMIC wave study has not been made before, and there exist both targeted science questions to be answered through these studies (e.g., How often, where, and when do long duration EMIC waves occur? What is the distribution of pearl-type EMIC waves?) as well as discovery level science to be found (e.g., Are there categories or types of EMIC waves that are yet unknown?). Sonification techniques will also enable long-duration historical data sets (e.g., GOES) to be mined in a way that hasn't been possible with current visual inspection analysis approaches. 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

Environmental factors like temperature and precipitation affect mosquito abundance and range. These factors can ultimately affect the transmission and outbreak of vector-borne diseases like West Nile Virus. This disease is responsible for the most mosquito borne illnesses and associated deaths in the continental US. Despite the strong link between virus-carrying mosquito populations and meteorological conditions, West Nile Virus forecasts do not yet incorporate a number critical environmental factors. This research uses observed temperature and rainfall data, along with that predicted by models that calculate weather conditions months into the future, to improve West Nile Virus outbreak forecasts. The work will improve understanding of how this serious mosquito-borne illness will be impacted by climate change and the associated changing rainfall patterns and temperatures. Value of this work is that it links weather and climate forecast models that have inherent complexities and uncertainties to improve West Nile Virus-conducive conditions, providing improved understanding of relationships between weather and disease forecasts which will advance the national health. Other broader impacts include engagement and interaction with public health officials who will help identify critical study factors that will allow them to make more informed decisions about mitigation (e.g., mosquito spraying or other techniques) to prevent or curtail outbreaks of the disease. Development of effective public outreach/warnings/communication will be undertaken to raise public awareness of the conditions and incidences of mosquito infestations that can result in West Nile Virus so communities and individuals can take precautionary measures when conditions indicate a high risk for contracting the Virus. An additional impact is that this work builds relationships between geoscientists who understand the environment and Earth as a system and medical/health professionals who are focused on human health to accelerate advances in the prevention and protection of people exposed to conditions conducive to West Nile Virus. This research builds upon preliminary results that will be extended to (1) strengthen the understanding of how climatic factors impact the transmission of West Nile Virus infections at the regional level; (2) include historical climate data and weather/climate forecasts into models to bolster disease prediction capabilities; (3) establish West Nile Virus disease forecasting model limits; and (4) determine the potential impact of future climate states on West Nile Virus transmission. Model development will use an array of legacy meteorological datasets in multivariate regressions to strengthen understanding of how regional climatic factors impact mosquito population dynamics conducive to disease transmission. Bayesian techniques will be used to develop multivariate disease forecast models that are informed by the climate-West Nile Virus relationships. Climate data from past years will allow quantification of the upper limit of forecast veracity and capability. Sensitivity analysis will be incorporated into operational weather/climate Earth system models from the short-term (i.e., daily to weekly) to long-term (i.e., sub-seasonal-to-seasonal) timescales. Revised climate models will be tested to determine their utility in modeling West Nile Virus outbreaks and infection rates. To this end, ensemble capabilities of numerical weather prediction models will be used to provide a “best” forecast, as compiled from the ensemble mean. Results will be used to identify potential ranges of outbreaks and to determine forecast uncertainties. Results of the research will be used to examine if West Nile Virus forecasts that include climate inputs outperform present baseline transmission predictions that do not include this information. The research will also examine tradeoffs between increased forecast lead-time and disease forecast accuracy and will look at the impact of future climate states on the geographic range of West Nile Virus as a result of increasing temperature regimes driven by climate change. 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

The broader impact of this I-Corps project is based on the development of a minimally invasive, biomaterial filler that augments the current standard of care for patients with cartilage damage. Cartilage damage in the knee inevitably leads to a painful condition known as osteoarthritis which affects over 20 million people in the United States. There is currently no cartilage repair method suitable to repair the tissue following injury, or to halt/prevent progression towards osteoarthritis. Osteoarthritis affects 36% of athletic populations and 94.4% of military populations following injury. For patients who are under 45 years of age, this means there are extremely limited options to delay the progression of the debilitating joint disease. Currently, the only straightforward solution surgeons cite as truly successful is a knee replacement, but this is not recommended until a patient is over the age of 65 due to the lifetimes of knee replacements and complications associated with revision surgery. The novel material explored in this project can be implanted to heal tissue defects, improving quality of life and preventing progression of knee joint degeneration. This I-Corps project utilizes experiential learning coupled with a first-hand investigation of the industry ecosystem to assess the translation potential of the technology. The solution is based on the development of a natural, two-part material for cartilage repair. The first component is a tightly packed slurry of small particles which are composed of the complex proteins found in human cartilage. The second component is a soft gel, that, when mixed with the particles and introduced to the temperature of the body, becomes a strong cartilage-like structure. The novel material forms tissue that closely mimics the natural layered structure of cartilage and the underlying bone. Additionally, the material is ‘flowable’ to allow for easy delivery to the injury. Initial analysis of the material has shown that the natural structure integrates well with the tissue around the injury and performs similarly to cartilage under loading and sliding – the key movements involved in the knee joint during everyday movement. 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 project aims to serve the national interest by generating significant new insights and developing an improved understanding of contexts in which web-based experiments may be more beneficial than physical experiments. Experiential learning, a pedagogical approach that engages students in hands-on experiences, improves students’ conceptual understanding of abstract concepts and enables them to connect theoretical knowledge learned in the classroom to real-world situations. In this IUSE Level 2 Engaged Student Learning project, experiential learning acquired through in-class physical experiments using low-cost desktop learning modules (LCDLMs) is compared with that acquired through web-based interactive digital experiments (WIDEs). Although both LCDLMs and WIDEs offer educational institutions a cost-effective, accessible way to provide high-quality laboratory experiences in engineering, this potentially transformative project seeks to directly compare the efficacy of LCDLMs and WIDEs. The main advantages of WIDEs are that they are inexpensive, highly scalable, easy to maintain, and deployable across digital devices. The collaborating institutions – University of Colorado Boulder and Washington State University - intend to make the WIDEs experiments freely available to engineering and engineering technology students online, through the widely used LearnChemE website and on YouTube. Comparative assessments will be made at five institutions, including a minority-serving institution. The project is expected to generate transformative new insights on teaching through virtual and physical experiments by identifying contexts in which physical (LCDLMs) may be more beneficial, as well as identifying how to improve virtual experiments (WIDEs) to best achieve specific learning objectives. Fifteen WIDEs for each of three engineering courses and two new LCDLMs will be created. The WIDEs will be prepared using JavaScript and/or Python so they play directly in most browsers thus permitting use on Windows-based or Apple computers, as well as iPads and other tablets. For WIDEs, the accompanying screencasts will be recorded and processed using special software (Camtasia), then embedded on pages within the WIDE. Additionally, the same screencasts will also be located on a YouTube channel so that they reach the widest possible audience. The hypothesis that WIDEs will achieve comparable learning outcomes to LCDLMs will be tested through a quasi-experimental design study in which students from a given class will be randomly assigned to use either the simulation (WIDE) or the physical experiment (LCDLM). All students will be given the same pretest before the activity and post-test after the activity to measure cognitive gains. These tests will use the Qualtrics® survey instrument in which students can complete surveys on their mobile devices. The NSF IUSE: EDU Program supports research and development projects to improve the effectiveness of STEM education for all students. Through the Engaged Student Learning track, the program supports the creation, exploration, and implementation of promising practices and tools. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-07

New quantum technologies are poised to transform fields of broad societal impact in computing, sensing, and communications. However, there is a significant gap between fundamental laboratory demonstrations and the fabrication of quantum devices that can be built into instrumentation such as computers, clocks, navigation tools, and optical networks. Simply put, we do not yet know how to build the manufacturable quantum devices of the future, and particularly those that will harness quantum particles like atoms and photons. These quantum building blocks are fundamentally small and new fabrication techniques at the nanoscale will be critical to realize the desired advances in these fields. The National Quantum Nanofab (NQN) at the University of Colorado Boulder will address this shortcoming by developing advanced nanofabrication approaches required to build, control, and connect quantum devices with their supporting infrastructure. In the broader quantum landscape, the focus of NQN will be on the nanoscale fabrication of devices based on atoms and photons with the goal of transitioning quantum discoveries into functioning quantum devices. Importantly, NQN will be an open-access national facility for academic, government and industrial users. It will also be an inclusive hub of education, training, and outreach for diverse populations ranging from K-12 to undergraduate, graduate and community college students. Together, these multiple aspects of NQN will accelerate the scientific breakthroughs, nanofabrication techniques, and workforce development that will strengthen US leadership in quantum science, engineering, and technology. The team will build and implement a facility and essential nanofabrication instrumentation that will comprise the National Quantum Nanofab (NQN) at the University of Colorado Boulder. The NQN facility will advance the fabrication, process development, and integration challenges encountered with quantum devices constructed from neutral atoms and ions that are interfaced and addressed with optical photons. Such atomic-photonic quantum devices are of significant interest to fundamental and applied researchers and are critical to broad reaching technologies such as quantum computing, atomic clocks, electric and magnetic field sensors, and inertial sensors. NQN will address critical nanofabrication needs including those for chip-integrated narrow linewidth lasers, visible wavelength integrated photonics, integrated modulators and frequency shifters, nonlinear integrated optics, metasurfaces and grating structures, integrated photon detectors, and many more. The fabrication requirements for such quantum devices are unconventional, involving materials beyond silicon and complex heterogeneous processes to produce devices that must integrate with atomic systems in high vacuum, and in some cases, at cryogenic temperatures. With its open-access model for academic, industrial, and government partners, NQN will accelerate co-design and development of atomic-photonic quantum devices, positioning the US to lead in this area. Integral to its mission of advancing quantum devices and hardware, NQN will serve as an inclusive hub of education, training, and outreach for diverse student populations and the workforce essential to US leadership in quantum science and engineering. 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

Fluorescent biosensors are powerful tools for visualizing and quantifying metabolites and their chemistry in living cells in real time. Such tools have revolutionized the study of biochemistry and signaling in live mammalian cells, providing new insights into when and where cellular reactions occur. The biggest limitation of the existing toolkit is that most biosensors are made out of the same fluorescent proteins so multiple biosensors can’t be used simultaneously to different metabolites. This project will develop a new class of biosensors based on RNA. The biosensors will be comprised of a series of RNA modules that are like building blocks that can be mixed and matched to create different structures. Biosensors will be built for the two main metabolites in the methionine cycle of one-carbon metabolism. One-carbon metabolism is the main biochemical cycle that drives cellular growth. In this cycle, different nutrient inputs are used to power the synthesis of cellular building blocks, including DNA, polyamines, and amino acids. Biosensors for the two central metabolites of this cycle will enable researchers to study their dynamics and flux through the cycle in rapidly growing cells. Broader impacts of this project include the creation of a toolkit of fluorescent RNA biosensors available to the scientific community, recruitment of multiple undergraduate students and graduate students from diverse backgrounds and diverse disciplines, and the development of a research-based undergraduate class. Additionally, the work has potential to reveal metabolic alterations in various disease states. This project will build a suite of fluorescent RNA-based biosensors for metabolites in the methionine cycle of one-carbon metabolism. One-carbon metabolism is a major pathway that regulates anabolic processes that drive growth of cells and organisms. The ability to monitor metabolic flux over time in live cells has the potential to transform understanding of fundamental biology. Over the past decade a handful of studies have established the proof-of-concept of monitoring metabolites in live cells using RNA-based sensors, however these biosensors have focused upon observing only a few metabolites and do not take advantage of the best fluorescent dyes in current use. This project will take this class of biosensors to the next level by designing and implementing a set of “plug-and-play” modules, systematically defining design principles, standardizing the approach for generating biosensors and establishing a pipeline for validating and benchmarking sensors in mammalian cells. The project will leverage naturally occurring RNA riboswitches as the metabolite sensing domain and couple this domain to a fluorogenic RNA-aptamer that binds and turns on a fluorophore in the presence of the metabolite of interest. The project will generate sensors for the primary carbon donor in cells, S-adenosylmethionine, and its product form resulting from methyl group transfer, S-adenosylhomocysteine. The project will also develop a robust pipeline of design-test-validate in mammalian cells that can be used to expand the RNA biosensor toolkit in the future. 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

Economists and policy makers lack rich data sets and statistical methods to draw valid conclusions given complex human behavior and the statistical models used to study them. This award funds research that will develop new econometrics and statistical methods that allow researchers to draw valid conclusions from data in many settings. This research will yield new theoretical insights for economists and statisticians as well as develop new methods for applied research in economics, statistics, other social sciences, biostatistics, epidemiology, and medicine. In addition to theoretical advances, the research will also produce statistical software that will implement the new methods, thus allowing applied researchers to use these new methods in their research. Results from this research will provide new and improved methods that increases efficient decision making in social, behavioral, and medical sciences. The research results will improve efficiency, increase productivity, economics growth, and wellbeing of citizens not only in the US but also around the globe. This award supports research that focuses on two leading classes of identification-robust inference problems in econometrics: inference robust to weak and partial identification. The current econometrics literature has only developed identification-robust subvector inference methods that are either specific to a small number of models or produce tests and confidence sets that are uninformative and computationally costly for applied researchers. This research will develop a generally applicable reparameterization procedure that allows informative identification-robust subvector inference by allowing researchers to plug estimates of nuisance parameters into test statistics, rather than projecting over them. Additionally, current econometrics literature on partial identification-robust inference lacks tools for inference on treatments or policies chosen by estimating the identified set of best-performing treatments or policies. The proposed research will develop new methods of producing valid confidence intervals for how well one can expect chosen treatments or policies to perform in these settings. The results of this research will provide improved and innovative methods for decision making in social, behavioral, and medical sciences. The research results will improve efficiency in decision making and improve the wellbeing of citizens not only in the US but also around the globe. 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

Understanding the dynamics of radiation belt electrons is critical for our ability to predict the effects of solar storms on the near-Earth space environment and our upper atmosphere. In the radiation belts, these electrons can pose problems for satellite operations. Plasma waves direct these electrons into our atmosphere, where they can influence upper atmospheric chemistry and dynamics. This project will incorporate methods from the weather and climate fields (stochastic parameterization and ensemble modeling) to better model and predict the result of these wave-particle interactions. This project will expose the broader space physics community to these compelling tools from the weather and climate fields. This project will support a first-time early career PI. Lightning-generated whistlers (LGWs) are one of the primary drivers of radiation belt electron precipitation within the plasmasphere. The primary objective of this project is to study the interaction of LGWs with radiation belt electrons using event-specific diffusion coefficients to more accurately capture the rate of pitch angle scattering and precipitation into the Earth’s atmosphere for eventual implementation into a stochastically parameterized diffusion model. Stochastic parameterization is a modeling scheme where the variables representing the sub grid physics (i.e., diffusion coefficients representing wave-particle interactions) are selected randomly from a distribution instead of using a deterministic average. This allows for explicit variance of the sub-grid physics and better error quantification through ensemble modeling. Quantifying and understanding the temporal and spatial scale sizes of the variability of each wave mode is a crucial first step in implementing a complete stochastically parameterized radiation belt diffusion model. This project seeks to accomplish this for LGWs, an important wave mode inside the plasmasphere. 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

Smart textiles provide technological solutions to pressing social problems, especially in domains of healthcare, robotics, and sustainable design. This project is the foundational stages of building an open-source ecosystem that will address and extend the impact of smart textiles by making complex woven structures accessible and understandable to engineering audiences. At the same time, the project will open channels to make engineering practices understandable to weavers, broadening participation and opportunities for grassroots innovation. Such an ecosystem can make positive impacts upon smart textiles research as well as the textile industry of which woven textiles alone account for 47% of the global textile market. This approach to ecosystem development is to transition an existing open-source tool, AdaCAD, to a thriving ecosystem of users and contributors. AdaCAD has been designed and developed in close collaboration with weavers and engineers to address specific difficulties of smart textiles design. AdaCAD overcomes difficulties by applying advances in computer-aided design (CAD) within the domain of textiles design. Specifically, it uses parametric design to provide a standardized and interactive form of documentation that can be shared and extended among diverse researchers. AdaCAD currently supports roughly 100 registered users. This project includes fundamental planning activities that will enable the growth of AdaCAD into an ecosystem with distributed contributors and users. The research team will (1) systematically trace and engage in outreach to smart textiles research teams exploring weaving; (2) pilot a program of monthly virtual AdaCAD study groups focused on onboarding users and transitioning users to contributors; and (3) host a 3-day “governance workshop” that convenes open-source experts from communities of craft and creative code to formulate plans and evaluation metrics measuring the health of the ecosystem. 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

Web-based educational content has evolved into rich, graphical, and highly interactive experiences, becoming an integral resource for Science, Technology, Engineering and Mathematics (STEM) education. While these advances result in more engaging, immersive, and usable content for some, they also establish barriers for a significant portion of the population, including learners and educators with sensory, mobility, or cognitive disabilities. A truly accessible interactive Web learning experience requires the expansion of input modalities (beyond the mouse or touch-based events) and output modalities (beyond visual representations and sound effects) to include a broad palette of multimodal features that match the full span of human diversity. In 2023, the PhET Interactive Simulations project at the University of Colorado Boulder launched SceneryStack, an open-source solution for creating accessible Web-based interactive media, such as interactive graphics, simulations, and games. SceneryStack consists of powerful software code libraries and design patterns, with dynamic visual, auditory, and haptic displays and diverse input capabilities. Vast multimodal capabilities available in one coordinated software development framework create new pathways for unprecedented accessibility, creativity, and innovation through Web-based media. This POSE Phase II project focuses on expanding the SceneryStack community, improving documentation and onboarding processes, and enacting inclusive governance practices. Through strategic partnerships, community events, and a focus on sustainability, this project fosters a robust ecosystem of developers, educators, and researchers. SceneryStack facilitates accessible implementation through a growing list of accessible design patterns, to support a more inclusive, interactive, online experience. This collaborative environment not only advances the accessibility and innovation potential of interactive media, but also serves as a model for inclusive design in educational technologies. By advancing an open-source ecosystem approach to invite and engage a global community of designers and developers, this project will impact the growing community of designers, developers, researchers, and content creators putting diversity at the center of educational technology design, and in-turn benefit a multitude of end-users with inclusive Web technologies. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-06

This project continues a multi-generational atmospheric observing system that is tracking large-scale changes in Arctic marine and terrestrial biogeochemistry. The investigators will measure key carbon and oxygen cycle tracers that have large-scale significance because the atmosphere tends to mix rapidly, integrating flux signals over large areas. The regular collection and analysis of flask samples at three Arctic sites (Cold Bay, Alaska; Utqiaġvik, Alaska; and Alert, Nunavut, Canada) will continue time series of 1) atmospheric CO2 concentration, which resolves large changes in seasonal CO2 cycling related to changes in terrestrial photosynthesis, respiration, and carbon storage; 2) the isotopic composition of CO2, which provides insights into large-scale linkages between water and carbon, such as shifts in leaf-level water-use efficiency; and 3) the ratios of O2/N2 and Ar/N2, which help quantify the magnitude of the seasonal air-sea exchanges of O2 and heat as key indicators of ocean biogeochemical change. Collectively, these data serve as key indicators of large-scale ecological changes, land and ocean carbon sinks, and carbon-climate feedbacks. The data generated by this project will be freely available to the scientific community and the public. This project will train undergraduate and graduate students and leverage international collaborations for improved observing of Arctic Ocean biogeochemical change. Flask samples from each of the three stations will be analyzed for O2/N2 ratio and CO2 concentration. Additional flask samples at Alert and Utqiaġvik will be analyzed for the 13C/12C ratio and 18O/16O ratio of CO2. The project also supports in situ measurements of atmospheric O2/N2 at Utqiaġvik, which began in 2021, and a new in situ O2/N2 measurement system at Alert. The in situ measurements at Utqiaġvik and Alert address the need for better monitoring of the Arctic Ocean by resolving synoptic-scale fluctuations in O2/N2, driven by air-sea O2 fluxes from the Arctic Ocean, which are not well captured in more sparsely sampled flask data. These two installations, combined with a third installation at Svalbard run by the National Institute of Polar Research, Japan, can potentially yield the data necessary for high fidelity monitoring of O2 fluxes over a large portion of the Arctic Ocean. 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

This award will support students from institutions of higher learning in the United States to participate in the 8th IFAC Conference Series on Analysis and Design of Hybrid Systems (ADHS), to be held at the University of Colorado, Boulder during July 1-3, 2024. The field of hybrid systems explores the intricate interplay between discrete-event and continuous dynamics, an issue which arises frequently in modern technological systems. The award targets providing travel support for up to 20 students to attend this flagship conference. . The conference's overarching mission is to cultivate collaboration and facilitate the exchange of insights among researchers hailing from disciplines such as control engineering, computer science, and applied mathematics. Moreover, it extends its embrace to practitioners across a wide spectrum of application domains, spanning process industries, automotive, avionics, communication networks, energy systems, transportation networks, embedded systems, biology, manufacturing, and robotics. By bringing together these diverse perspectives and expertise, ADHS catalyzes innovation and fosters the cross-pollination of ideas to address complex challenges in the realm of hybrid systems. The conference will provide students with the unique opportunity to engage with field experts through attending session talks, immersing themselves in enlightening keynote presentations, and fostering valuable connections during social events. The diverse array of session talks will span numerous categories, encompassing the latest developments in areas such as specification, verification, control synthesis, machine learning, and more. These sessions serve as a conduit for students to be exposed to the forefront of cutting-edge research and advancements in hybrid systems. Moreover, the social events are conducive to interaction between students and both seasoned and emerging leaders across various domains. These interactions not only broaden students' perspectives but also nurture relationships that can prove instrumental in their academic and professional journeys. The conference will offer undergraduate and graduate students a chance to connect with senior peers, enriching conference interactions. The award places priority on funding travel expenses for students from underrepresented groups. 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

The Arctic is critical to understanding the Earth system, as well as a focal point for environmental, economic, social, demographic and political change. It is intimately connected to the global system via ocean, land, and air masses, as well as the people that inhabit and/or are connected economically to the Arctic region. This is an unprecedented time in terms of rapid changes occurring in the Arctic, and our abilities to understand, predict, and respond to those changes are of US and international strategic interest. No single country is able to support the level of research necessary to keep pace with the changing Arctic, and therefore organizations like the International Arctic Science Committee (IASC) represent vital platforms for discussing scientific issues and promoting international, interdisciplinary, and in some cases large-scale projects in the region. The impacts from melting sea ice on biogeochemical cycles, thawing permafrost and resulting change to the carbon cycle, biodiversity and northward species migration and ecosystem reorganization, and associated human interactions and response all link the Arctic to the global system and represent issues so important and so large that no one country can adequately address them in isolation. Additionally, new opportunities for economic development, fisheries, resource extraction and tourism carry a relatively high risk because Arctic science remains relatively immature. To this end, this project provides national support to maintain and enhance US leadership and partnerships in IASC, as well as our planning and coordinating roles for research efforts in the international arena. The International Arctic Science Committee (IASC) is a non-governmental international membership organization that encourages and facilitates cooperation in all aspects of Arctic research, in all countries engaged in Arctic research, and in all areas of the Arctic region. IASC was established in 1990 and today comprises twenty-two member countries. IASC is an international associate of the International Council for Science (ICSU) and an observer in the Arctic Council and has connections to numerous other international Arctic organizations. The US is one of eight Arctic nations in the Arctic Council, with IASC playing a role in providing scientific input to this international policy organization. IASC assists with science development by providing scientific advice as well as coordination to support international science development. Since the founding of IASC, the scientific, environmental, economic and political realities of the North have changed dramatically. New problems and challenges demand new and improved scientific knowledge and enhanced coordination and communications. This grant provides funds to enable the President of IASC and the US delegates to IASC to implement the activities and execute the responsibilities of IASC. 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 · 2024-01

Speech-based technologies have been heralded as promising solutions to overcome the limitations of existing clinical modalities related to limited healthcare access, non-naturalistic in-clinic interactions, and social stigma. Speech measures combined with artificial intelligence can serve as valuable biomarkers for mental health conditions, such as depression and post-traumatic stress disorder. Yet, in order for artificial intelligence to truly succeed in a future-of-work landscape in which clinicians will be expected to work side-by-side with artificial intelligence systems, both clinicians and patients need to calibrate their trust in the algorithms that power this decision-making process. The goal of this project is to design reliable machine learning, notably for speech-based diagnosis and monitoring of mental health, for addressing three pillars of trustworthiness: explainability, privacy preservation, and fair decision making. Trustworthiness is critical for both patients and clinicians: patients must be treated fairly and without the risk of reidentification, while clinical decision-making needs to rely on explainable and unbiased machine learning. This research program further provides a fertile ground for training high school and college students providing them with the knowledge about (and inclination toward) ethically applying computing research in sensitive populations. The tangible applications developed as part of this research serve as a vehicle to encourage students to pursue careers in Science, Technology, Engineering, and Mathematics, and prepare them to work in transdisciplinary settings for solving real-world problems. This project seeks to design explainable, anonymized, and fair speech biomarkers for mental health, integrating aspects of speech acquisition, transparent modeling, and unbiased decision making. The work is divided into three technical objectives. The first objective designs novel speaker anonymization algorithms that retain mental health information and suppress information related to the identity of the speaker. The anonymization algorithms learn a mapping between the original speech and a latent space, which embeds information about speaker identity, mental health, and phonological sequence through deterministic and probabilistic operations. The second objective improves explainability of speech-based models for tracking mental health through novel convolutional architectures that learn explainable spectrotemporal transformations relevant to speech production fundamentals. The third objective examines how bias in data and model design may perpetuate social disparities in mental health, and designs new machine learning to mitigate unwanted bias in speech-based mental health diagnosis. Through a series of experiments this work further contributes to understanding ways in which human-machine partnerships are formed in mental healthcare settings along dimensions of trust formation, maintenance, and repair. 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.

Other NSERC · FY 2024

signal divergence, heterospecific aggression, mate choice, character displacement, species coexistence, interspecific interactions, whole-genome sequencing, hybrid index, learning, colour pattern