University of Colorado at Boulder

NSF Awards · FY 2024 · 2024-09

This project will broaden participation in engineering by developing learning resources through which Black families have opportunities to engage in engineering practices and to see themselves as part of the engineering community. The research team will co-develop informal learning resources with Black families in which children, ages six to ten, have opportunities to engage in biological, civil, computer, electrical, environmental, and mechanical engineering activities at home. Caregivers will support their children through engineering practices such as empathizing, defining, ideating, prototyping, and testing, while also educating them about Black engineers and scientists who made significant advancements within each field. Research will explore whether and how the identity-affirming informal learning resources fostered the children’s engineering identities and interest. The resulting deliverables include video workshops for caregivers, to support them in using the resources, as well as a suite of easy-to-use engineering activities that will be disseminated via national homeschool networks, through public media, through high-traffic repositories with engineering lesson plans, and through professional networks of science and engineering educators. Research will explore how identity-affirming engineering educational resources impact children’s engineering identities and interests. To investigate whether and how these resources contribute to shifts in children’s engineering identities and interests, the research team will conduct a mixed-method study in which they generate and analyze the following data sources: pre- and post-engagement surveys with the caregivers; video-recordings of caregiver-child interactions as they engage with the informal learning resources; interviews with children and caregivers; caregiver reflective journals; and artifacts produced by the families, such as children’s sketches. The results from these analyses will provide insights into how informal educators can design at-home learning resources that build children’s interests in engineering pathways, as well as how families can use identity-affirming interactions in engineering to spark their children’s interest in this field. Findings will be disseminated widely via professional conferences, networks, and journals in educational research. Ultimately, this project is likely to broaden participation in engineering among Black people who remain underrepresented in engineering pathways and careers. This Integrating Research and Practice project is funded by the Advancing Informal STEM Learning (AISL) program, which seeks to advance new approaches to, and evidence-based understanding of STEM learning in informal environments. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-09

Solar flares are believed to be powered by magnetic energy released rapidly through magnetic reconnection. The total emission of a flare lasts for tens of minutes, evolving from the so-called impulsive rise phase through the gradual decay phase. Emission at the feet of each of the individual flare loops also often exhibit an impulsive rise followed by a prolonged decay. The goal of this project is to unravel the physical mechanisms that heat the solar atmosphere during flares. The project will combine numerical modeling with analysis of spatially and spectrally resolved observations to advance the understanding of flare heating mechanisms. Two graduate students will conduct the observational analysis and flare modeling, which will comprise a major part of their thesis projects. An improved understanding of flare physics will lead to advances in the accuracy of space weather forecasting. High-resolution observations reveal that a flare is a collection of energy release and heating-cooling events characterized by a multitude of flare loops in the corona and their foot-points in the chromosphere, which occur from the impulsive through the decay phase of the flare. To date the mechanisms heating the flaring atmosphere are not clear. This project takes advantage of several methodologies, leveraging the efficiency of the UFC 0D global modeling and the advanced capabilities of 1D numerical codes. The team will apply the UV Foot-point Calorimeter method (UFC) to modeling and analyzing multi-wavelength observations of flares, yielding the first-order heating rates in multiple flare loops. They will then use those heating rates to drive a one-dimensional radiative transfer model (RADYN) to simulate flaring atmosphere along the loop and test a few heating mechanisms in different phases of flare evolution. The results will be used to synthesize flare spectra observed in the chromosphere to be compared with high-resolution observations by IRIS and DKIST. This project will advance both models, enhancing their utility and efficiency to model flares and therefore maximize the science output of the large amount of flare data. 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

Photovoltaics (PVs) are critical to global climate change mitigation efforts. Robust, reliable, and efficient PV devices have the potential to be transformational for many communities and industries, including healthcare, remote sensing of environmental quality, energy, and security systems. This IRES program will have significant impact on multiple areas, primarily via the energy sector. The program will network students from across Research 1, Hispanic Serving Institutions, Minority Serving Institutions and Primarily Undergraduate Institutions, with the aim of diversifying this field and science more broadly. Development of more robust and more affordable PV devices could address global energy access and justice needs, expanding clean energy options and driving the transition away from fossil fuels. The benefits of an international research experience for students are extensive and provide a wonderful mechanism and opportunity to develop new skills in collaboration, communication, and team science expanding the way they think and approach multidisciplinary challenges. Graduates from this program will distinguish themselves through their breadth of knowledge, international and cross-cultural communication and collaboration skills, and a curiosity to learn from multiple perspectives. While the worldwide PV market continues to grow by ~25% annually, there are significant limitations that need to be addressed in this decade for PV to have increasing impact as a renewable energy source. From a technological perspective due to fundamental physical limitations of silicon, power conversion efficiencies of commercial solar cells will peak at <27%, which will keep land-use and system costs locked at unnecessarily high levels. In the last decade a class of materials called metal-halide perovskites (MHPs) have emerged as a potential solution thanks to their outstanding properties, the possibility to be used in conjugation with existing silicon technologies to improve efficiencies to well over 34% and their ability to be processed from solutions at low energy and material use levels. The stability of MHP-based solar devices and their upscaling are current key challenges to their commercialization. Through this IRES Program we will strengthen a nascent relationship between groups with MHP expertise in the United States and Germany, that will accelerate the progress in photovoltaics research, leading the development of this valuable energy resource. This multidisciplinary IRES program will contribute to developing the understanding that will enable the fabrication of MHP thin-film PV materials. The goals of this collaborative exchange program are to (1) advance the field of MHPs by developing stronger international partnerships through shared student exchanges, and (2) prepare students to collect, curate, study, and effectively disseminate large materials data sets. Through these goals we will train a cadre of researcher adept at interdisciplinary team science. These relationships will support the development of efforts to increase the reliability and manufacturability of MHP-based technologies, and train students for academic and industrial careers. Access to affordable and sustainable solar technologies is a transformative global need. This initiative will create opportunities for the integration of research and education into the development of these technologies of societal significance that will hugely benefit the future careers of this program’s graduates. The broad expertise collected in this IRES team will provide an excellent environment to drive this field forward and foster new research directions. The global significance of the transition to clean energy is one that requires the consideration of multiple perspectives if it is to be done in a just and inclusive fashion. The skills in collaboration, teamwork, and respect of diverse approaches that this international program will impart will enable participants to be significant contributors to the clean energy economy. A diverse set of key personnel for this team include Seth Marder (PI, CU Boulder), Joe Berry (Co-PI, CU Boulder, NREL), Charles Musgrave (Co-PI, CU Boulder), Rebecca Belisle (Co-PI, Wellesley College), and Luis Raúl Castañeda (Co-PI, New Mexico Highlands University). We will leverage existing federal funding (NSF-STC, NSF-DMREF, DOE-EFRC, DOE-PVRD), and logistical support from the Renewable and Sustainable Energy Institute (RASEI; PI Marder is the Director), to provide meaningful international exchanges to, in particular, the Helmholtz Zentrum Berlin, and the Humboldt University-Berlin for a broad cross-section of students. 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 project is developing a probabilistic programming framework for expressing differential equations and hybrid system models that combine the continuous evolution of state with instantaneous state and mode changes through discrete actions. The probabilistic programming framework is being applied to modeling in key areas, including epidemiology, medical devices, and autonomous systems. The project's novelties include the development of the modeling formalism that incorporates models of continuous-time systems, systematic model transformation techniques, and the use of formal techniques to verify these transformations rigorously. The project's impacts include a useful modeling framework that will help the cause of rigorous model-based decision-making in diverse areas ranging from public policy to robotics and medicine. The project is recruiting a diverse cohort of undergraduate researchers through summer research experience for undergraduate programs at the University of Colorado Boulder. In addition to engaging students in the research, the project conducts workshops to help students develop writing and presentation skills to ensure their future success in academia as well as industry. Differential Equations and hybrid system models are exceedingly common across science and engineering applications. However, inference of these models from data is quite challenging. The project develops rigorously verified model transformation methods, including projection operators to remove latent/unobservable state variables, the use of approximate solutions, and the reduction of the complexity of inference by guessing the correlation between posterior parameters. These transformations are formalized and verified using theorem-proving approaches to avoid errors that can often be catastrophic in the context of safety-critical systems. The project develops an integrated programming model and formal methods that will allow the expression of probabilistic models and their transformation in a provably safe manner. The approach is being applied to challenging inference problems involving models and datasets from domains such as modeling epidemics and estimating the impact of public policy decisions on the parameters that affect their spread, developing personalized models for people with type-1 diabetes, and modeling driving strategies for testing autonomous vehicles. 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

Terrestrial land surfaces rise above the ocean surface because they are underlain by crustal rocks, a layer of material with relatively low density sitting on top of the higher density mantle rocks. The thickness of the crust is important for investigations of earthquake hazards, and the growth and evolution of continents through time. Energetic waves caused by earthquakes travel faster through mantle rocks than crustal rocks. Seismologists typically observe a sharp change in wave velocities at a boundary named Mohorovičić discontinuity, abbreviated to Moho, and has been interpreted as an abrupt boundary between the crust and the mantle. Since the crust-mantle boundary is too deep to be observed directly via methods like drilling, such seismic imaging is the primary method to observe it. However, rare surface exposures of crust-mantle boundaries and fragments of rock brought up in volcanic eruptions suggest that the transition from mantle to crustal rocks may be more gradual than suggested by the sharpness of the Moho. This could mean that the seismically observed Moho may sometimes instead be a boundary within the crust and does not represent the crust-mantle transition. This project combines several types of seismic waves to characterize the seismic wavespeeds above and below the seismic Moho and compares them to calculated seismic wavespeeds for different rock compositions to assess the sharpness of the Moho and its relationship to the crust-mantle boundary. The investigation is carried out using already collected seismic recordings across the contiguous U.S. and Tibet, which provide contrasting geological settings. A more accurate characterization of the boundary provides clues to the evolution of continents and distribution of critical elements as well as improved constraints for calculations requiring crustal thickness. Anticipated results are of interdisciplinary interest for tectonic processes of continental crust formation and evolution and will be disseminated in peer-reviewed journals, at conferences, and via an interactive map and calculation tool product. Funding supports a graduate student and all-early career and/or soft money PIs; funds and mentoring for an undergraduate student at CU Boulder who will present their research project at AGU, a high school intern working on the project through the Simons Summer Research Program and a summer undergraduate intern, both at Stony Brook University. The crust-mantle boundary is seismically defined by the Mohorovičić discontinuity (Moho), interpreted as separating shallower seismic velocities representative of continental crust lithologies and higher velocities typical of ultramafic mantle lithologies. Only in the idealized case does the seismic Moho correspond to a petrological Moho juxtaposing crustal against mantle lithologies. Recent research produces paradoxical features such as a brighter Moho indicative of a large velocity contrast in hotter areas where one expects a smaller velocity contrast. Furthermore, sub-Moho velocities beneath large continental portions are significantly slower than expected. These observations could be explained by anomalously fast lower crust, crustal and mantle compositional effects such as hydration, or a diffuse petrological Moho. The proposed research aims to test these hypotheses by interpreting these paradoxical observations through a systematic combination of surface wave tomography, receiver function analysis, and petrophysical modeling. Accurately constraining the depth, width, and physicochemical state of the crust-mantle boundary is important to the transfer of stress from the mantle to the surface, topographic support, inversion constraints in seismology, and the composition and structure of the crust. The proposed work is to combine surface waves and receiver functions with complementary sensitivities to determine 1. Shear velocity contrast at the Moho; 2. Absolute shear velocities in the lower crust and uppermost mantle; 3. Moho depth and character, all at existing seismic stations with available data under the contiguous U.S. and Tibet. Method development includes modeling of absolute receiver function Moho amplitudes with near-surface corrections and reduction of the nonuniqueness of gradients near the Moho in surface wave inversions by adding receiver function constraints. The scientific product from this proposal will be an interactive map and tool to calculate and display tradeoffs between shear wave velocity, temperature, Moho depth, and compositional structure, in agreement with geophysical data beneath each analyzed seismic station in the contiguous US and Tibet. This product will elucidate the tectonic evolution of continental lithosphere from the Archean to the present as well as provide improved constraints and parameterization necessary for future geoscience studies on the continental lithosphere. 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 grant supports the conference "Dynamics Days US 2025", which will take place in Denver, Colorado, January 3-5, 2025. Dynamics Days is an annual international conference focused on nonlinear dynamics and its applications that has been running in the US for more than 40 years. The conference will provide a venue for young researchers to present their ideas and learn about cutting-edge results in the field. The conference will have 16 invited speakers, a similar number of contributed talks, and two poster sessions. The majority of the funds will be used to provide travel support to students, postdocs, and other individuals without other sources of support. The participation of students and young researchers from underrepresented groups will be particularly encouraged. Dynamics Days is one of the premier conferences in nonlinear dynamics in the US, with more than four decades of history. During this time, it has established itself as an excellent venue for the exchange of ideas and results on nonlinear dynamics, chaos, and their applications. The conference is characterized by covering a wide variety of interdisciplinary topics, promoting the cross-fertilization of ideas across disciplines. Topics covered in the conference include networks, fluid dynamics and mixing, data-driven modeling, modeling of complex systems, nonlinear waves, machine learning applications to nonlinear dynamics, and biological systems. Attendees include researchers from physics, mathematics, engineering, and the biological sciences. The conference webpage is www.ddays.org/2025. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-09

Research in the 1960s revealed that Earth’s outer shell is broken into a dozen or so relatively rigid plates that represent the top of a convecting system in Earth’s deep interior. Motions between and within these tectonic plates create mountain ranges, volcanoes, sedimentary basins, and other major geologic surface features. These features represent vertical relief that, under the force of gravity, is then subject to erosion, landsliding, and other forms of downslope movement of mass. Earth’s topography is thus controlled by the balance between tectonic processes that build relief, and erosional processes that remove and redistribute relief. Conversely, the evolution of topography affects the forces within tectonic plates, influencing subsequent faulting and volcanic activity, and leading to feedbacks over a range of spatial and temporal scales. On million-year timescales, sedimentary basins create natural resource deposits (such as oil and gas reservoirs), and chemical reactions associated with erosion can remove carbon dioxide from the atmosphere, directly influencing Earth’s climate and habitability. On human timescales, the creation of vertical relief promotes landsliding and far-reaching sediment distribution, which is often associated with interacting geohazards including earthquakes, tsunamis, and volcanic eruptions. Building on prior, previously independent work modeling Earth’s interior and surface processes separately, this project develops new computational methods to simulate and advance our knowledge of the dynamic interplay between Earth’s surface and interior and makes these methods available to the scientific community. The computational methods derived through this project have direct societal relevance to studying geohazards and resource exploration. All software developed through this award follows established software engineering practices, is openly available to the public, and is fully documented. Community training activities are used to engage other scientists and promote the adoption of the new methods developed by this project. A major research challenge in the geosciences is understanding how the Earth’s surface and its interior interact to shape one another. Because much of the relevant interactions are inaccessible due to their space or time extents (or both), computer simulations serve as an essential tool for studying interactions in coupled geologic systems. Yet, numerical models have traditionally treated the Earth’s surface and its interior as independent domains. None of the widely used, open-source software packages for simulating mantle convection, long-term tectonics, or short-term tectonics have incorporated surface processes until very recently. Similarly, software for the simulation of surface processes has generally been driven by prescribing vertical uplift rates, even though it is clear that these uplift rates depend on, and thus must be coupled to, erosion rates. This project couples two widely used community codes: (i) ASPECT, a package originally intended for the simulation of mantle dynamics but more recently also used extensively for modeling of long-term processes in tectonic plates, with active development towards incorporating physics (such as compressible elasticity) necessary to capture shorter term processes; and (ii) Landlab, an environment that includes and facilitates the description of surface processes. Since their inception, these codes have transformed the level of complexity of simulations in their respective domains and have gained large user bases. Both codes are backed by large NSF-funded centers: the Computational Infrastructure for Geodynamics (CIG) in the case of ASPECT, and the Community Surface Dynamics Modeling System (CSDMS) in the case of Landlab. The software and workflows developed through this project enable scientific communities that are typically siloed, studying either Earth’s surface or its interior, to initiate new studies of coupled processes with direct societal relevance, including geohazards and resource exploration. Model use cases implemented by the project demonstrate the coupling on different spatial and temporal scales, which can be used by domain scientists to initiate independent research projects. Project training materials are incorporated into long-standing training programs associated with ASPECT (e.g., annual hackathons) and Landlab (e.g., CSDMS clinics), as well as online videos, interactive web visualizations, and at various community meetings and workshops. Finally, a major part of the development effort is parallelizing Landlab, which improves its performance over a wide range of applications, including modeling short time-scale processes such as volcanic eruption cycles, landslides and flooding. This award by the Office of Advanced Cyberinfrastructure is jointly supported by the National Discovery Cloud for Climate initiative within the Directorate for Computer and Information Science and Engineering and by the Geosciences Directorate’s Research, Innovation, Synergies, and Education and Earth Sciences divisions. 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

Given the persistent challenge of racial inequity in STEM, there is a clear need for new models that spur and sustain racial equity change. Successful departmental team-based change efforts demonstrate that change can be created and sustained at the meso level of an institution (i.e., departments, centers, and units as the focus for change). This project will bring together experts in institutional change and experts in advancing racial equity with the goal of combining existing, well tested change models to produce a new, racial equity focused model of change in higher education—the Equity Departmental Action Team (EDAT) model. This model will focus on shifting departmental cultures in ways that benefit, and are grounded in the experiences of, those with historically marginalized racial and ethnic identities. This project will advance the scholarship of racial equity by developing, testing, and refining the EDAT model with STEM departments at a Minority Serving Institution and disseminating the model through partnership with national higher education associations. This project will take place in two major phases: 1) development of the Equity Departmental Action Team (EDAT) model, and 2) pilot of the EDAT model in STEM departments at a Minority Serving Institution, the University of Colorado Denver (CU Denver). The development of the new EDAT model will draw from existing change programs, including the Departmental Action Team (DAT) model and the Dialogues and Change Agent programs. It will integrate multiple theories from systems change, social justice change, social psychology change agency, and intergroup contact. Research activities will focus on both the process and impact of the EDAT model. The project will use surveys, focus groups, interviews, and participant journaling to explore the following research questions. RQ1: To what extent do Foundational Experiences prepare EDAT members for racial equity work? RQ2: What strategies do EDATs deploy when engaging in racial equity work? RQ3: To what extent do EDATs integrate racial equity into departmental culture? Research and program evaluation will be conducted simultaneously with the EDAT implementation so the model can be iteratively refined throughout the project. Dissemination of the model will take place in collaboration with partners from the American Association of Colleges and Universities and the Coalition of Urban Serving Universities - Association of Public and Land-grant Universities. This collaborative project is funded through the Racial Equity in STEM Education activity (EDU Racial Equity). The activity supports research and practice projects that investigate how considerations of racial equity factor into the improvement of science, technology, engineering, and mathematics (STEM) education and workforce. Awarded projects seek to center the voices, knowledge, and experiences of the individuals, communities, and institutions most impacted by systemic inequities within the STEM enterprise. This activity aligns with NSF’s core value of supporting outstanding researchers and innovative thinkers from across the Nation's diversity of demographic groups, regions, and types of organizations. Programs across EDU contribute funds to the Racial Equity activity in recognition of the alignment of its projects with the collective research and development thrusts of the four divisions of the directorate. 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 grant will support research that will contribute new knowledge related to the design of reliable constrained control laws for autonomous systems. Many engineered physical systems are subject to restrictions and constraints that must be satisfied to ensure desirable behavior. When maximizing performance, it is often necessary to push such systems to their limits while being certain that they will not exit a safe and prescribed operating regime. This can be achieved using constrained control, which is a key enabling technology that allows systems to operate at their full potential, while guaranteeing safety. This award supports fundamental research to provide needed knowledge to systematically generate constrained control laws that are guaranteed to enforce safety requirements while also trying to boost performance. Results from this research will therefore benefit the U.S. economy and society. This research project involves expertise in several branches of applied mathematics, including control theory, set theory, and optimization. Control barrier functions have shown great promise for the deployment of computationally efficient constrained controllers. Unfortunately, existing literature does not provide systematic methods for generating control barrier functions, which has led to the deployment of constrained controllers that are unable to formally guarantee constraint satisfaction. To address this gap, this research effort aims to fill the knowledge gap by adapting invariance-based methods found in the reference governor literature to systematically generate control barrier functions. The team of researchers will investigate both continuous and discrete-time systems, address common robustness concerns, and validate the results on robotic applications. 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

Sewers increasingly intersect with the research interests of multiple domains of practice. The large-scale implementation of wastewater surveillance has the potential to provide substantial benefits to the detection and understanding of communal disease prevalence, which can strengthen public health. This advancement shifts the exploration from water as a material to waste as a social indicator. In doing so, the ethical frame and expectations for the research community of practice must evolve. However, guidance, educational materials, and oversight remain underdeveloped within sewer research, requiring a meeting of the community of practice to establish preliminary best practices and educational materials to assist this new evolution within wastewater management. The Ethical Sewer Research (ESR) Workshop Series focuses on assembling the community for two-virtual workshops and one in-person workshop. These events will result in the production of relevant educational materials. The ESR Workshop Series explores the intersectional nature of research informed by wastewater sampling, uniting perspectives from multiple fields. Over the course of virtual and in-person workshops, the discussions supported by the ESR Series are anticipated to develop research guidelines and standards for ethical practice. The two virtual workshops focus on identifying five key thematic areas within the ethical application of wastewater surveillance. The two-day in-person workshop focuses on developing educational materials in support of these identified areas. These education materials will be broadly distributed to assist the sewer research community. Overall, clearly defining the concerns and care surrounding wastewater sampling enables the community to adapt to emerging trends in sewer research. This knowledge and engagement can help to shape more scientifically sound and ethically grounded policies. With the format of the workshop seeking representation from policymakers and community stakeholders, the development of this research community of practice will be directly informed by the desired-use of the data in support of public health, infrastructure, and built-environment resiliency. This project is funded through the ER2 program by the Directorate for Social, Behavioral and Economic Sciences. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-09

Broad evidence in the science of science has shown that the composition of the scientific workforce shapes the pace and direction of scientific discovery and innovation, and that workforce heterogeneity often leads to more innovative science. Recent work has shown that the U.S. scientific workforce is substantially less heterogeneous in socioeconomic backgrounds than the population of doctoral recipients. However, little is known about whether and how this lack of heterogeneity shapes the range and types of scientific discoveries overall or impacts specific fields of study. A lack of comprehensive data on the socioeconomic composition of the scientific workforce has limited empirical work, theory building, and evidenced based policy-making on these questions. This project advances our understanding of how socioeconomic heterogeneity influences scientific careers and scholarship across scientific fields and identifies specific mechanisms to improve the productivity of the scientific community and its capacity to produce innovative research. This project (i) produces a unique, large-scale, individual-level data set that links researcher socioeconomic backgrounds with scholarly topics for U.S.-based scientists spanning more than 100 fields of study; (ii) makes an anonymized version of this data set publicly available for reuse by the research community to investigate related questions; (iii) uses state-of-the-art computational and statistical techniques to quantify the impact of socioeconomic heterogeneity within a field on its scholarly outputs and estimate the impact of socioeconomic heterogeneity on workforce attrition; and (iv) identifies mechanisms by which socioeconomic heterogeneity influences scientific careers and shapes the pace and direction of knowledge production, to inform efforts to improve the results of investments in science and innovation. 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

Predicting how species will be impacted by ongoing and future changes to their environment is critical. Species responses to these changes will determine how well ecosystems function and the ability of the Earth to continue providing food and other resources. Most such predictions focus on changes where a species can occur, but not changes in the numbers of a species, which can be more important for ecology. One major difficulty is that the environment can differ a lot from year to year, which makes it harder to predict how species will be impacted by gradual changes over a long period of time. Also, individuals of widespread species are often adapted to do best in their local environments, which means that the same species in different areas can have different environmental requirements. This project uses transplant experiments and long-term monitoring of wild populations to overcome these challenges, and tests how two common plant species are impacted by environmental change from New Mexico to arctic Alaska. The researchers also team up with educators to create middle and high school curriculum to teach students how to think critically and use real data to investigate ecological and environmental questions. This research relies on a comprehensive dataset spanning 15-28 years documenting demographic trends in two widespread, long-lived tundra plant species (Silene acaulis and Polygonum viviparum) in 29 populations across western North America. By continuing to monitor these wild populations, the researchers will develop a functional definition of rare climate events and assess their demographic impacts. The project also uses common garden transplants and controlled thermal performance experiments to assess local adaptation to climate and the demographic mechanisms driving it. This project will follow the performance of transplants for a total of 9 years, allowing researchers to test the importance of environmental extremes and cumulative abiotic effects on the magnitude and spatial scale of local adaptation. The researchers will integrate demographic and experimental datasets to develop environmentally-explicit and density-dependent demographic models to make range-wide predictions of distribution and local abundance, considering environmental variability and local adaptation. Notably, predictive models will be validated by testing their ability to “present-cast” current patterns of abundance and occurrence in new locales across the species’ latitudinal ranges before forecasting responses to projected climate change. The project’s goals are to both better predict how these particular species will respond to changes in their environment and to develop and test methods for making predictions that can be used for many other species. 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

Drawn by the beauty and recreational opportunities of beaches, communities and economies have sprung up along many coastlines in the last half century. However, coastal environments tend to change more rapidly than other landscapes, posing challenges to maintaining development. Shorelines change position, often moving landward (eroding) over years and decades, bringing the shoreline and storm impacts ever closer to homes and infrastructure originally constructed at a safe distance. The storm impacts that threaten communities range from short term beach erosion to flooding from storm surge—all driven by wind and waves and amplified by increasing rates of sea-level rise. This project will transform the understanding of what causes shorelines to move in such complicated ways, and it will create an opportunity to forecast changes in shoreline erosion and storm hazards in coming decades (and century). The forecasting opportunity arises because winds over an ocean basin produce waves, and because changes in typical patterns of winds in the future can be forecast using global climate models. This project features further development and application of sophisticated techniques for translating forecast future weather over an entire ocean basin into the winds and waves affecting specific shorelines in coming decades. The wind and wave forecasts will, in turn, feed into computer models representing how waves and storm surge move sand around, leading to forecasts of patterns of shoreline erosion ‘hot spots’ as well as storm hazards. This project will bring together experts in shoreline change from around the world to test what is most important in producing shoreline change over days, years, and decades, by comparing model results with observations and with each other. The results of this model intercomparison will be shared broadly and translated into educational materials through the Community Surface Dynamics Modeling System and the Museum of Life and Science. In the coming decades coastlines will move even more rapidly than in the past. Along with the effects of sea-level rise, changing storm behaviors—whether related to decadal-scale climate oscillations or to longer-term trends—cause magnitudes and locations of coastal erosion ‘hot spots’ to shift, compounding threats to coastal communities. This project will revolutionize the scientific community’s ability to understand and forecast coastline change patterns, over timescales ranging from a year to a century. The project starts with the development of new, efficient approaches to downscaling Global Climate Model output to produce forecasts of the waves and winds affecting particular coastlines. This pioneering work will address the Carolina coastline as an initial case study. The downscaled wave data will then drive coupled models addressing coastline shape and position, in response to storm/wave climate shifts, sea-level rise, and inlet dynamics. Model hindcasts will be confronted with historical observations to test the new model and the importance of large-scale coastline dynamics in long-term shoreline change. The downscaled wave, wind, and water-level data will also form the centerpiece of a workshop and community model-intercomparison effort that will bring together contrasting coastline-change models, with the common goal of hindcasting and forecasting changes, comparing the results with each other and with observations, and thereby accelerating coastal science. This project is jointly funded by the Marine Geology and Geophysics program in the Division of Ocean Sciences and the Geomorphology and Land-use Dynamics program in the Division of Earth Sciences. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 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

Forecasting how our environment will change into the future requires the scientific community to understand the processes that shape Earth's surface environments through time. To do this, geoscientists collect images of Earth with satellites, run simulations, and test hypotheses with laboratory experiments. All of these methods improve our understanding of landscape change, but scientists using each of these tools struggle to bring their research together to make new insights. This project establishes a framework of interoperable hardware and software tools, called sandpiper, that enables research products from different teams and approaches to integrate with one another more easily than ever before. Major efforts of the project team include (1) designing and implementing an affordable open-source hardware-firmware system for data acquisition, (2) forging a community-backed data standard, (3) developing a flexible and interoperable data-analysis software library, and (4) establishing a sustainable community of practice. The project team is also advancing science and technology education by creating science museum exhibits that demonstrate fundamental principles in geomorphology and reach a wide audience through an interactive web interface. Recent strides in geomorphology have been fueled by widely available satellite imagery, powerful numerical modeling toolkits, and decades of physical laboratory experiments. Customized algorithms lie at the heart of the discipline because raster data—e.g., photographs, topography—form a fundamental bridge between these complementary modes of inquiry. Transformative insights can arise when researchers apply tools from one mode of inquiry to data from another. However, most innovation at the forefront of geomorphology currently proceeds in silos via ad-hoc algorithms that accumulate “mutations” as they traverse laboratories and graduate-student generations. The problem is particularly acute for experimental geomorphology, where technological barriers have prevented FAIR (Findable, Accessible, Interoperable, Reusable) and OS (open-source) principles from integration into the research process. At present, there is no unifying framework to support collaboration between modelers, observationalists, and experimentalists. The team for this project is creating such a cyberinfrastructure framework and solving these problems at every level. (1) To break down experimental silos, the project team is designing and implementing a modular and extensible open-source hardware–firmware system to affordably and uniformly make measurements and generate reproducible data products in labs across the world. (2) To promote and simplify data sharing, the project team is organizing a community effort to forge a data standard. (3) To mitigate algorithm drift, the project team is developing a flexible analysis library that integrates with this data standard. (4) To establish a community of practice, the project leaders are engaging researchers in their own laboratories and computing environments to facilitate reusing and contributing algorithms to the library. This acquisition-to-analysis toolchain, called sandpiper, will enable the next generation of collaborative research in geomorphology, sedimentology, and stratigraphy; advances could also influence seemingly unrelated fields like dendrochronology, hydrology, and seismology. This award by the Office of Advanced Cyberinfrastructure is jointly supported by the National Discovery Cloud for Climate initiative within the Directorate for Computer and Information Science and Engineering and by the Geosciences Directorate’s Research, Innovation, Synergies, and Education and Earth Sciences divisions. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-08

This project investigates the effects of climate change on human livelihoods including food insecurity, deteriorating health conditions, migration intentions, and changes in traditional livelihoods. Changes in temperatures combined with growing rainfall variability bring unprecedented challenges to rural livelihoods and communities. Rural populations are among the most vulnerable to environmental changes and it is uncertain if pressures on declining resources lead to more reliance on existing cooperative customs or result in increases in violence. This project investigates how environmental change could contribute to an increase in social conflict between local groups and between states, and possibly increase processes of out-migration. Such negative impacts are expected to be most evident in areas with the lowest capacity to adapt to unforeseen shocks and disturbances. This project examines environmental change effects on rural populations that are highly vulnerable to climate change: a diversity of livelihoods, ethnic groups and environmental contexts is examined to allow careful assessment in multiple locations. Rural livelihoods are tightly linked to climate patterns, particularly in areas where proximal agricultural production is central to food security. This research identifies household adaptations to environmental stressors caused by weather variability. The effect of seasonal weather changes on traditional practices of cooperation or new conflict dynamics is examined via a large panel survey of rural households. These survey data are integrated in a data analysis incorporating ancillary data including those capturing institutional dynamics and food support programs. The roles of national and local governmental institutions, as well as traditional customary practices in the determination of natural resource distribution are documented and analysis investigates the effectiveness of food assistance programs as coping mechanisms for rural households. 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

Satellite measurements of the surface snow temperatures on the East Antarctic Plateau have identified an extensive area where mid-winter conditions are frequently below -90°C. A specialized surface observation station has been built and partially tested and is intended to link surface observations with the satellite data. The station is designed to measure surface conditions, including wind and blowing snow. These data will be transmitted during the Antarctic winter. The site will also be instrumented with two cold-rated automated weather stations (AWS). This RAPID award would specifically support the further development and calibration of the existing instrument suite, add an additional sensor (to measure wind and blowing snow), and test the station under realistic field conditions. The project would leverage the logistics of another Antarctic national program and outside, private providers to deploy these sensors. Ultimately, the dataset could lead to public interest, which could in turn provide opportunity to publicly discuss polar climate change. The goal of this RAPID is to establish the coldest temperature that can be reached on Earth’s surface to better understand the weather and climate controls on the lowest-temperature events. The site on the East Antarctic Plateau of the most frequent occurrence of <-98°C conditions is located ~100 km from the Pole of Inaccessibility. At roughly 100 smaller valley sites within this region (typically ~5 square km in area), surface snow temperatures can reach -98°C. Air temperatures at these sites at 2 m height are likely a few degrees above this value due to the intense near-surface gradients that form under clear-sky conditions during polar night, and are estimated to be -94 ± 2°C. The recognized lowest surface air temperature record is -89.2°C, measured at Vostok Station in July 1983. 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

Autonomous systems enabled by artificial intelligence (AI) have the potential to work alongside humans to accomplish previously impossible goals in science, transportation, healthcare, manufacturing, and defense. One critical challenge is designing these systems so that they interact with humans and other AI systems in a predictable and safe way that is beneficial to humans and robust to attacks from bad actors. If this challenge is addressed properly, self driving cars could deliver their passengers quickly through traffic without compromising the safety of other road users, teams of drones could monitor a thunderstorm with limited communication bandwidth to predict tornadoes, and networks of telescopes could monitor low-earth orbit to prevent satellite collisions and protect critical space systems from sabotage. Game theory is a mathematical framework that describes interaction between intelligent human or AI decision-makers called "agents" and prescribes the best strategies for accomplishing goals through interaction. Game theory can model agents with goals that are aligned, opposed, or somewhere in between. It can reveal complex and surprising interaction patterns including ways that agents with disparate goals can cooperate for everyone's benefit, strategies to prevent deception or exploitation by a disruptive agent, and interaction that might result in danger for all agents. In this project, the team will develop decision-making algorithms based on game theory that allow autonomous agents to interact and achieve their goals safely and efficiently even when there is uncertainty about the environment or other agents. In order to disseminate findings rapidly so that they can be applied to real-world problems, the team will develop an open-source programming toolbox, create new university-level course materials, and work with high school teachers to begin developing a diverse next generation of AI engineers. Most recent AI advances, including generative AI systems, are created using an offline training process, meaning that they are trained with vast amounts of data before interacting with the environment or making a decision. This project will focus on a complementary approach called online planning, where reasoning is carried out at the time of interaction with the environment. Online planning has strengths in explainability and ease of composing multiple models, and it can be used to speed up offline learning. This research will focus specifically on developing algorithms for partially observable stochastic games (POSGs), building on recent advances in single agent optimization in partially observable environments. The algorithms will be designed to handle large continuous state and observation spaces that real-world cyber-physical systems act in. The first phase will tackle the cooperative case, where all agents have the same goal, but must still make decentralized decisions with incomplete information, acting in a predictable way to achieve common objectives. The second phase will shift to zero-sum POSGs, where agents' goals are in direct opposition. In this setting, the agents seek to protect information from each other and avoid exploitation by acting in a less-predictable way. Finally, the third phase will focus on general-sum POSGs where goals are neither perfectly aligned or opposed. Here, a key challenge is to use conventions that allow for coordination and negotiation. The result of this research will be a palette of easy-to-use and trustworthy algorithms that engineers can apply to many real-world systems that involve interaction and uncertainty. 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 University of Colorado will engage interdisciplinary groups of college students in creating educational Artificial Intelligence (AI) content for social media. AI literacy is important not just for people studying or working in technical fields, but for everyone. However, not all relevant learning happens in the classroom—especially since in the United States. many K-12 schools still do not have computing courses. Meanwhile, informal learning for young people increasingly takes place on social media, which is also full of myths, misconceptions, and misinformation around AI. Therefore, better public communication around AI is an urgent concern and should involve AI experts who can communicate both about how it works and its limitations. This project seeks to cultivate communication skills in undergraduates and increase the amount of available high quality educational AI content.   Through bringing together computing students with knowledge of AI and non-computing students with knowledge of communication and media production, the ultimate goal of  this collaborative, creative effort towards public AI education is learning outcomes for both groups of students, as well as the broader public. Student participant-researchers will also be engaged with research bookending the project—needs assessment for young learners at the start and evaluation of educational outcomes at the end. The intellectual outcomes of this project therefore include identification and examination of: (1) AI knowledge gaps and interest points for young people; (2) learning outcomes (for both AI and communication skills) for all students engaged collaboratively in creating content; and (3) pedagogical efficacy of this type of public education content. 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

Understanding how the ocean and climate influence each other is important for understanding weather and climate change. Measurements of ocean and air temperature have shown that there are regional climate patterns around the North Atlantic and the Arctic. An important pattern is called Atlantic Multidecadal Variability (AMV), which refers to up-and-down periods of warming and cooling in the North Atlantic Ocean lasting decades. AMV influences regional ocean temperature and marine ecosystems, as well as air temperature and precipitation across the Atlantic Arctic and nearby land areas including eastern North America. Natural patterns of warming and cooling are important because they affect regional weather, and they can strengthen or weaken human-made climate change from greenhouse gases. The general goal of this research is to identify and understand how the ocean and climate interact, through studying regional patterns of climate and how they may change through many centuries. To do this, we will use many different sources of information, including (1) temperature measurements that go back about a century, (2) historical observations that go back a century or two, and (3) longer-term climate records from tree growth rings, mud sediments from lakes and the ocean floor, remains from sea creatures, as well as glacial ice from Greenland, Canada and Svalbard in the high Arctic. We can combine these very different types of information to reconstruct patterns of regional climate through past several centuries or longer. We can then use mathematics to detect any repeating patterns and changes in the behavior of the ocean and climate over the years. One research focus is to identify patterns that last about 20 to 30 years and others that last much longer, about 50 to 90 years. Another focus is to investigate how combining data from different sources, such as precipitation records from Svalbard and ice from Greenland, along with tree rings from Northern Scandinavia, can help us reconstruct changes in extreme weather patterns in the Atlantic Arctic region. We want to figure out how these patterns have changed over the past few hundred years, a time period with both natural and human-made climate changes. This project is not just about science research to learn new things; it is also about teaching others. University students will be involved in this project, receiving training and experience in doing science, and learning about using mathematics to study the climate. Further, we will involve the public through popular science activities. Understanding how the ocean and atmosphere influence each other is crucial for understanding climate change. Natural modes of variability and teleconnections are regional patterns of climate variations, which are important to understand as they can amplify or dampen anthropogenic climate change. An important mode is Atlantic Multidecadal Variability (AMV), which refers to alternating periods of warming and cooling in the North Atlantic Ocean lasting decades. AMV influences sea ice, ocean temperature and marine ecosystems, as well as air temperature and precipitation across the Atlantic Arctic and adjacent land areas including eastern North America. The overarching goal of this empirical research is to quantitatively constrain and understand modes of variability in the climate system in the Atlantic Arctic and Subarctic, in a long-term paleo perspective. This project will study these patterns by using various complementary sources of data, including: (1) meteorological and oceanographic measurements, (2) historical observations, and (3) climate proxy data from tree rings, sediments from lakes and the ocean floor, remains of sea organisms, as well as glacial ice from Greenland, Canada and Svalbard in the high Arctic. By integrating and statistically analyzing data from these different types of natural archives, we aim to reconstruct patterns in regional climate variability over the past several centuries. One key focus is to test the general hypothesis that robust and persistent signals of interdecadal (approximately 20 to 30 years) and multidecadal (approximately 50 to 90 years) variability exist, and can be extracted using advanced statistical techniques applied to a spatial network of data records. Specific hypotheses are: (1) an interdecadal signal will be found primarily in records from the northwestern North Atlantic / Nordic Seas, and may arise from subsurface/surface ocean variability associated with the atmospheric circulation; and (2) a multidecadal signal will be found in marine and terrestrial records across the subarctic–arctic Atlantic, possibly linked to ocean variability such as the AMV and exchange processes between the Atlantic and Arctic. Another key focus is to investigate how combining data from different sources such as precipitation records from Svalbard and ice proxies from Greenland, along with tree rings from Northern Scandinavia, can help us understand changes in extreme weather patterns in the Atlantic Arctic region. Specific hypotheses are: (1) combining these proxies can be used to reconstruct shifts in so-called Scandinavian Blocking teleconnection pattern over the last several centuries; and (2) important shifts in this mode occurred during major climate transitions in the past. Beyond scientific advancements, this work will support a graduate student who will receive training and participate in the research, and also aims to educate students about climate science and statistics, as well as including popular science outreach. 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

Rock glaciers are moving boulder-covered tongues of ice found below alpine rock cliffs. Because they persist at lower elevations and in warmer climates than glaciers, rock glaciers support late season stream flow and ecosystems after glaciers have disappeared. Few glaciers remain in Colorado’s mountains, yet thousands of rock glaciers survive. This research promotes understanding of how these important alpine ice reservoirs are maintained or depleted. Through measuring how long rocks have been exposed on the rock glacier surface and the speed of their conveyance away from the cliffs, the research will constrain the last 10,000 years of climate history and landscape evolution in Colorado’s mountains. The research team will promote a broad distribution of their results through a variety of public outreach venues. They will also connect with middle schools near the study area and the outcomes will be used in a new interdisciplinary climate science minor at CU Boulder. The project expands a pilot study on Mt Sopris to two additional Colorado rock glaciers. Modern velocity fields will be measured by image feature tracking, and surface age profiles will be deduced from cosmogenic nuclides. In addition, the project will monitor stream discharge and quality, measure mass balance, conduct differential GPS surveys to document interannual changes in movement, and survey rock headwalls photogrammetrically. The data will constrain numerical modelling that captures the continuum from pure ice glacier to rock glacier and explain differences in their response to climate changes. The project will deepen our understanding of Quaternary alpine landscape change and reignite thinking about the role of lateral headwall cliff recession. The exposure age profiles on the rock glaciers allows substitution of space for time and should catalyze further use of rock glaciers as conveyor belts on which weathering and biological experiments play out. 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

Oscillators are important to life. They regulate cellular metabolism and control heartbeat, intestinal contractions, and circadian rhythm. This project develops tunable, nontoxic biochemical oscillators. Their behavior in cell-like systems will be studied. It will provide tools for assembling cell-like structures with adjustable dynamic outputs. It could also facilitate the construction of new varieties of devices. A summer biotechnology training program for high school students will be expanded. A modest core facility for droplet microfluidics will be constructed. Finally, a new international partnership focused on the analysis and design of nonlinear biochemical systems will be supported. This project departs from contemporary analyses of out-of-equilibrium reactions that require toxic inorganic catalysts or sustained flows by building biochemical reaction networks that can produce repeated oscillations in small compartments (e.g., droplets). It will develop reagents for building and monitoring batch biochemical oscillators, a kinetic framework (i.e., mechanistic models) for modeling and adjusting oscillatory dynamics, microfluidic methods for preparing and studying multi-oscillator systems, and new designs for the controlled release of functional molecules in complex biological matrices. The ultimate goal of this work is to develop stable, experimentally tractable biochemical oscillators that exhibit predictable dynamics in a broad set of environments. If successful, it could provide a starting point for developing new varieties of protocells, biomimetic materials, and cell-interfacing systems (e.g., drug-delivery vehicles or cellular controllers). 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 Earth’s radiation belts are a dynamic and complex plasma environment. Large amplitude waves can nonlinearly accelerate particles to energies high enough to pose a radiation hazard in the near-Earth space environment. These large amplitude waves are common but only occur in small regions for a short period of time. Therefore, it is unknown if such a drastic but localized acceleration can have an impact on a global scale. By modeling the nonlinear wave-particle interaction in a global scale radiation belt model, this research will conclusively show the importance of these large-scale waves on the whole space environment. The research will promote the development of two early-career researchers. Additionally, undergraduate students at a minority-serving institution will be trained as an integral part of this project. The physics of wave-particle interactions in the Earth’s radiation belts is well understood in the linear and quasilinear regimes, but large amplitude waves create a complex nonlinear problem. Significant theoretical and computational work has been done to understand how nonlinear wave-particle interactions can efficiently energize or pitch angle scatter high-energy electrons. As successful as local studies of nonlinear wave-particle interactions have been in explaining the micro-scale physics of a particle in a large amplitude wave, it has yet to be demonstrated that these nonlinear effects lead to global, macro-scale changes in the radiation belts. In this study, we will use theory, modeling, and data analysis to answer the fundamental science question: Do nonlinear wave-particle interactions affect the radiation belts on a global scale? This will be done by calculating advection and diffusion coefficients from nonlinear wave-particle interactions that can be directly included in the K2 radiation belt model. K2 is a global scale radiation belt model based on the stochastic differential equation (SDE) framework and accurately captures wave-particle interactions at an individual particle level. By simulating real events with K2, the sensitivity of the whole radiation belt system to localized large amplitude waves can be quantified. 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

Autonomous field robots perform increasingly complex tasks under uncertain and dynamic environmental conditions. These robots often rely on sensors like cameras and lidar for perception, but these sensors are hindered in environments where vision is degraded, such as smoke-filled rooms or dust-blown construction sites. This Faculty Early Career Development (CAREER) project supports research that explores the use of millimeter-wave radar, a technology capable of operating effectively in poor visibility, to enhance robot perception and navigation. Millimeter-wave radar can penetrate through obstructions in vision and function irrespective of lighting conditions, making it ideal for tasks in search and rescue, firefighting, and construction. This research has the potential to significantly advance robotic autonomy in challenging environments, benefiting society by improving operational efficiency in critical industries and contributing to the education and diversification of the future workforce in robotics. This research project aims to develop algorithms for interpreting and processing radar data, translating raw signals into a comprehensive understanding of the environment, including object detection, classification, localization, and mapping through feature learning on dense radar data. The integration of radar-based perception capabilities with robotic navigation will create a robust system for navigating through VDEs. Research activities will include developing high-precision metric localization and mapping techniques, learning representations for direct navigation through learning over partially observable environments, and validating these techniques on experimental testbeds. By leveraging advancements in millimeter-wave radar sensors, this project will enhance robotic autonomy and contribute to the broader scientific community by providing new tools and data for further research. The outcomes will be integrated into educational programs to train the next generation of scientists and engineers, fostering innovation in the field of robotics. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-08

Prof. Michael Shirts of the University of Colorado Boulder is supported by an award from the Chemical Theory, Models and Computational Methods program in the Division of Chemistry to improve approaches for predicting molecular crystal properties using generative modeling that can accurately explore molecular conformational ensembles. Understanding the thermodynamics of small molecule crystals and quantitatively predicting their properties is vital for faster and cheaper pharmaceutical development pipelines, as most drugs are distributed as pills in crystalline form. Dr. Shirts’s group will use generative machine learning models to augment physics-based methods to estimate crystal thermodynamics. The methods developed will also be useful for computational studies of a broad range of other complex crystalline materials such as pigments, agrochemicals, food additives, and electronic materials. These methods will be developed in concert with open-source molecular mechanics and machine learning software, with extensive tutorials provided for easier adoption. Dr. Shirts will also work to expand the Living Journal of Computational Molecular Science (LiveCoMS), a free open-source journal for scientific articles which can and should be updated, by incorporating coverage of machine learning best practices and reviews in molecular science. Dr. Shirts will also work to improving the inclusiveness of LiveCoMS effort and expand existing online educational resources for computational drug design techniques. Dr. Shirts will explore ways to use generative machine learning methods, such as Boltzmann generators, to model crystalline systems and capture the relevant configurational ensembles. Crystal thermodynamics provides ideal test cases for developing better generative models for molecular ensembles, due to their relative simplicity, but are still of significant scientific and practical interest. The planned work will introduce the use of sampling from multiple thermodynamic states to improve configuration space coverage for generative models, as well as the development of molecular crystal-specific mapping approaches that learn the differences from physically relevant mappings rather than being forced to learn the mappings from generator latent space de novo. The proposed work also adapts these approaches to bridge between simulated polymorph ensembles, including learning the differences between configurational ensembles generated with differing levels of chemical theory, such as from force fields to quantum mechanical potentials. Finally, work is proposed to extend these generative methods to model systems with differing degrees of freedom between polymorphs such as hydrates. These methods will be distributed to practitioners to be of practical use via existing open-source molecular mechanics and machine learning tools, as well as in stand-alone Python implementations with extensive tutorials for easier adoption. 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.