Regents of the University of Michigan - Ann Arbor

NSF Awards · FY 2024 · 2024-08

This project aims to serve the national interest by reforming college-level chemistry teaching through an instructional coaching program developed by and for graduate student instructors. Instructional coaching addresses the need for long-term instructor support that attends to the complexities of teaching. This collaboration between the University of Michigan (U-M) and The Ohio State University (OSU) will engage in Institutional and Community Transformation (Level 2) by changing departmental structures that guide, incentivize, and sustain reform-based instruction. The project is significant because of its potential to effect change in chemistry classroom interactions. By working with graduate student instructors and leveraging feedback from undergraduate students, this project will advance our understanding of how teaching and learning can be enhanced within the moment. Expected outcomes will include (1) robust pedagogical training models at both U-M and OSU, (2) the generation and dissemination of a video library that curates authentic chemistry teaching-learning moments, and (3) improved undergraduate learning outcomes and chemistry experiences. This project will study how an iterative research-based intervention supports pedagogical noticing and, as a result, influences undergraduate students' chemistry experiences. The scope of the project will improve how one observes, reasons, and responds throughout instruction via a coaching cycle that uses recorded sessions of graduate instructors' teaching. The project goal is to develop college instructors as reflective practitioner-researchers who can analyze and reinvent their own and their peers' pedagogy. Teacher growth, informed by the Interconnected Model of Teacher Professional Growth framework, will be determined using interviews, surveys, and recorded instruction in naturalistic settings with graduate student instructors and undergraduate students. To evaluate undergraduate students' learning, the project will leverage classroom observations, survey responses to understand undergraduate perceptions, interviews to explore undergraduate insights on research-based practices, and course grades to assess the outcomes of the project. This project will unpack the nuances of effective teaching that can significantly improve how college-level instructors learn pedagogy and undergraduate students learn chemistry. The NSF IUSE: EDU Program supports research and development projects to improve the effectiveness of STEM education for all students. Through the Institutional and Community Transformation track, the program supports efforts to transform and improve STEM education across institutions of higher education and disciplinary communities. 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 plastic industry is a multi-billion-dollar enterprise poised to expand even in light of growing knowledge and public concern about the harm its industrial lifecycle places on the environment, ecology, our health, and by extension on society in general. Working towards a future free from plastic pollution requires a multi-pronged approach across disciplines, industries, and scales. The PI of this Boosting Research Ideas for Transformative and Equitable Advances in Engineering (BRITE) award researches a novel pivot from mechanics to ecology and environmental biology and back to mechanics to infuse state of the art biological knowledge and tools into the study of the bio-macromolecular interface. This pivot will create new knowledge in the area of microbe-mediated biodegradation of polymers. With this knowledge, new degradation strategies for effectively recovering monomers and materials precursors for recycling could be explored. Furthermore, this project will deepen our understanding of how plastics persist in the environment, as well as provide a foundation for classifying the fate of more ecofriendly materials as they emerge. Beyond the expected educational impacts on workforce development, training, and coursework, a number of unique activities are researched, namely, immersive research through the UM Biological Station at Douglas Lake, a Research Table at the Museum of Natural History, graduate student participation in the Science Communication Fellow program, and targeted recruitment efforts from the NSF funded Michigan Louis Stokes Alliance for Minority Participation. This BRITE Pivot will generate fundamental knowledge in the area of bio-macromolecular mechanics and deliver a user-friendly predictive model of polymer biodegradation. Specific innovations include: 1) the creation of a simulation-driven kernel to guide the design of biodegradable materials and advance our understanding of structure-property-performance relationships of degradation due to biotic factors, and 2) the demonstration of a user-friendly bio-chemo-mechanical model framework that will predict the degradation times of a range of polymers and enzymes. Whereas the PI has molecular dynamics expertise in simulating the mechanical behavior of crosslinked polymers and has developed electro-chemo-mechanical models in the past, this project researches a pivot into Biology to leverage state-of-the-art tools and knowledge about enzymatic processes and biodegradation. This new disciplinary perspective will be infused into the bio-chemo-mechanical modeling framework in an effort to improve the predictive capability and expand the usefulness and potency of the model. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-08

Higher organisms package genomic DNA in linear chromosomes, which contain ends that pose “end protection” and “end replication” problems. Harmful internal or environmental factors can break chromosomes, requiring DNA repair to join the broken ends. However, if the DNA repair machinery mistakenly recognizes natural chromosome ends as breaks, it could join entire chromosomes to one another and compromise genome stability. This is the end protection problem. The end replication problem occurs because linear chromosomes shorten when they are duplicated in a dividing cell. Without a mechanism to grow the ends back, cells would ultimately stop dividing. A complex of proteins called “shelterin” helps solve both chromosome end problems. The simplicity of shelterin in a well-studied worm model organism will be leveraged to answer fundamental questions about the structure and function of these proteins and their roles in chromosome end protection and replication, with implications for human genome stability and related health issues such as aging and cancer. The project will allow high school, undergraduate, and graduate students to develop experimental and critical thinking skills, and generate research tools for the scientific community. Finally, a display based on the project at the host institution’s Natural History Museum will make this research accessible to the local community and increase the visibility of science. Telomeres are nucleoprotein complexes containing telomeric DNA and proteins that protect natural chromosome ends from being recognized as breaks by the DNA damage response machinery. The six-protein shelterin complex protects mammalian chromosome ends by binding telomeric double-stranded (ds) and single-stranded (ss) DNA, and the ds-ss junction. Human shelterin protein POT1 binds telomeric ssDNA, and the PI’s group recently discovered that POT1 also protects the telomeric ds-ss junction. In addition, POT1 plays multiple roles in chromosome end replication by regulating telomerase and replicative polymerases. How POT1 performs these functions is not clear. Moreover, how shelterin DNA-binding activities are coordinated to uphold genome integrity is poorly understood, because of the limitations of existing model systems to study telomere biology. Based on biochemical, structural, and cytological data from the PI’s group and collaborators, C. elegans presents an ideal model to address these gaps in telomere biology. This project will reconstitute C. elegans shelterin protein activities, determine their structural basis, and evaluate their physiological significance in the worm, specifically addressing how POT1 homologs in worms coordinate their DNA binding activities and perform multiple functions at telomeres. 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 research will investigate whether, when, and in what ways, if any, the political attitudes, values, and behavior of ordinary citizens in multiple countries are influenced by their understanding of their religion, and by their interpretation of the religion’s prescriptions and codes. What people think about important political and social issues, prominent among which are democracy and governance and the status and rights of women, is important for a variety of reasons, including many with implications for the United States and the international community more generally. Citizen attitudes, values, and behavior have significance for the character, stability, and foreign relationships of a country. Important as well are citizen orientations that predispose individuals to have either more positive or more negative judgments about societies and cultures other than their own. Data with which to examine the connection between the way a person interprets their religion and the political and social attitudes that he or she holds will be collected through original public opinion surveys in multiple countries. The Principal Investigator’s long experience with cross-national survey research, as well as the availability of trusted and highly qualified local partners in each country, ensure that representative, reliable, and valid survey data will be collected.   This research builds on and will contribute to existing research on the relationship between religion and the political attitudes, values, and behavior of ordinary citizens in multiple countries. Additionally, it addresses and seeks to fill an important gap in this literature, one pertaining to the interpretation of religious prescriptions and codes. The research will develop, and administer in original public opinion research, a new multi-item scale that will measure conceptually and empirically distinct dimensions of “illiberalism-liberalism” in the interpretation of religious prescriptions. Factor analysis will be employed to identify and measure the relevant dimensions, and bridging items will be used to establish conceptual equivalence in instances where dimensions are measured by different combinations of items in different countries. Representative public opinion surveys will be conducted in multiple countries. The principal investigators long experience with cross-national survey research, as well as the availability of trusted and highly-qualified local partners in each country, ensure that representative, reliable, and valid survey data will be collected. Prominent among the dependent variables on which the research will focus are democracy and governance and the status and rights of women. Informed by the scholarly literature, as well as by the PI’s own previous research, the study will develop and test hypotheses in which connections between dimensions of religious interpretation and these and other important individual-level orientations are specified. Hypotheses will be tested in regression-based multivariate analyses, which will include pooled analysis with country fixed effects and country specific analyses. 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 Program in Climate and Space Science Observation (PICASSO) at the University of Michigan is a ten-week, site-based, program housed within the Department of Climate and Space Sciences and Engineering. The program particularly seeks to support students with limited access to research opportunities, engaging a diverse cohort of undergraduate students in research projects covering a broad range of climate and space science topics: meteorology, climate variability/impacts, atmospheric chemistry, remote sensing, computational modeling, space weather, and planetary atmospheres. Paired with faculty research mentors with expertise within these fields, participants will collaboratively develop individual research projects that will contribute to our overall understanding within these fields. Through their projects, participants will learn technical skills, such as: (a) project development and management, (b) data collection, analysis, and visualization, (c) computational model development, application and evaluation, and (d) assessment and consideration of uncertainties in the interpretation of research results. The results from these projects will be presented at an “End-of-Program Poster Fair”. The program provides support for participants to present their work at a scientific conference of their choice, as well, which will allow them to develop professional networks to support their personal and professional growth throughout their academic and professional careers. In addition to technical skill-building, community-building and professional development will be core facets of the program. Community-building will take place through both community service projects benefiting those within our local community, as well as through planned activities with faculty, students, and staff within the department. The REU site will support our participants’ professional development through a series of workshops, including understanding the responsible and ethical conduct of research, developing skills for the communication of scientific research results to their peers and the general public, learning the nuances of selecting, applying to, and succeeding in graduate school, and developing a greater sense of self-understanding and self-authorship in support of professional and personal decision-making. Based upon previous participant feedback, this program has demonstrated success in helping to improve/solidify the confidence of participants and their continued participation in science, technology, engineering and mathematics fields. Further, the program activities aim to develop scientists and engineers who are culturally and ethically aware of the impact of their professional activities, and who will use their newfound skills to solve scientific and engineering challenges facing society, including the protection of our environment, and human health and welfare. 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

High-quality data on public opinion is important today for policymakers to effectively address the many challenges to prosperity and well-being in the US. To improve data coverage, this project establishes a new survey research laboratory to produce high-quality public opinion data to policymakers and the public, with new curricula and improved educational and workforce training of undergraduate students. The new academic laboratory has the potential to serve as a hub for research into public opinion, helping such institutions, community organizations, and government policymakers better serve their sometimes hard to reach residents. To expand survey research coverage in the US, the project's research team uses an innovative sampling design for jurisdictions that lack standardized addresses (with address-based sampling being the most common approach to representative sample design). This survey draws on an original, stratified, three-stage area probability sample that maximizes the quality and representativeness of the resulting data. The panel survey data offers covers topics common on public opinion surveys on contemporary issues that impact Americans. The panel survey occurs in tandem with a robust set of programs to support student and faculty research. In doing so, this collaboration creates new possibilities to support student and faculty research and build the next generation of the scientific workforce through investments in technical skills, data tools, and research experiences. 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

Early childhood years are critical for developing the foundational knowledge, skills, and attitudes for later success in STEM. Young children learn science best when they actively engage with topics that are meaningful to their everyday lives. Artificial intelligence can help in developing science learning content and making it more interactive, while also presenting the challenge - and opportunity - of providing unbiased AI-generated materials. This project involves direct participation from local parents in co-designing these AI-based educational materials. University and community partners will jointly work with AI to create interactive science stories that draw on family values, and everyday experiences, and that are adaptable for families in multilingual contexts. The project leverages storytelling - a major form of social capital in local communities - to foster children's scientific curiosity and engagement, while also helping build community members' AI literacy skills. The project will contribute important knowledge about how AI can be effectively harnessed to support science learning across contexts. The project utilizes participatory design with families in California and Michigan to create 24 e-books for children aged 4-7, employing generative AI for rapid, iterative content development. The e-books will feature an AI-powered conversational agent that allows children to dialogue directly with the story characters, as well as family discussion prompts to encourage parent-child interaction. After the 24 interactive e-books are piloted and iteratively improved, a randomized control trial will be carried out with 120 families to evaluate the impact of e-book use on children's science knowledge and engagement and on parent-child science communication. Subsequent improvements will prepare the e-books for free national distribution. Findings will expand knowledge of how AI-powered storybooks support children's science learning and family interaction, and will inform the design of scalable, language-adaptable tools that can strengthen early STEM education across varied home and school settings. In doing so, this project will also demonstrate pathways for promoting the innovation and use of trustworthy AI in educational technologies. This Integrating Research and Practice Project is funded by the Advancing Informal STEM Learning (AISL) program, which supports research on the development and impact of STEM learning opportunities in informal educational environments. This project is also partially funded by the Innovative Technology Experiences for Students and Teachers (ITEST) program, which supports projects that build understandings of practices, program elements, contexts and processes contributing to increasing students' knowledge and interest in science, technology, engineering, and mathematics (STEM) and information and communication technology (ICT) careers. 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

NON-TECHNICAL SUMMARY This project aims to gain a fundamental understanding of the unique deformation behavior of metallic glasses (MGs). MGs possess excellent properties, such as extremely high strength, corrosion resistance, and unique magnetic characteristics, which can be harnessed for potential applications in various fields, including nanotechnology, electronics, and aerospace. Broader manufacturing and application of MGs require a better understanding of the atomic structure of MGs (i.e., how the atoms are arranged inside the material) and how this arrangement changes when force is applied, which defines the way MGs deform when in use, typically under various stress conditions. This project seeks to explore the details of MGs' atomic structure and gain critical insights into how to control this structure to obtain desired properties that can be utilized for many important applications in science and industry. By employing cutting-edge techniques such as time-resolved 4-dimensional scanning transmission electron microscopy (4D-STEM), machine learning-assisted data analysis, and computer simulations, the research team is mapping and monitoring the atomic structures within MGs and tracking how they change over time and under stress. A particular focus is given to understanding what types of atomic arrangements can lead to significant variations in the material's response to stress. The deformation process involves the softening of local volumes of material, a process that makes these volumes easier to deform as the deformation progresses. This research is investigating the detailed mechanism of this softening behavior and how it relates to the local atomic arrangements within the material. This work is providing crucial insights into why some MGs exhibit better ductility and resistance to failure than others, which will pave the way to harnessing this knowledge to design MGs with improved mechanical properties, making them more reliable for practical and industrial applications. The findings from this research are being integrated into undergraduate and graduate curricula, enhancing the educational experience for students. The project also includes outreach activities that are being conducted at local K-12 schools to inspire and educate young minds about materials science. Moreover, the project is offering internships to community college students from diverse backgrounds, providing them with hands-on research experience and encouraging their pursuit of STEM careers. TECHNICAL SUMMARY This project investigates structural heterogeneities and variations in shear transformation zone (STZ) properties to understand the softening behavior, autocatalysis, and strain localization in metallic glasses (MGs). The research integrates time-resolved 4-dimensional scanning transmission electron microscopy (4D-STEM), machine learning-assisted data analysis, atomistic simulations, and mesoscale STZ dynamics modeling. The core hypothesis is that the autocatalytic shear activities and resultant deformation localization in MGs are influenced by intrinsic structural heterogeneities and the softening behaviors of local atomic environments over time. To validate this hypothesis, the project is: 1) Performing 4D-STEM on MGs with slight compositional differences to identify dominant medium-range ordering (MRO) structures, their relaxation times, and evolution pathways. 2) Using machine learning to analyze the angular correlation functions from 4D-STEM data, determining the types, volume fractions, and spatial distributions of MROs. 3) Extending 4D-STEM to the time domain to track thermal relaxation of MRO symmetries and relate these changes to STZ activation energies. 4) Connecting experimental data to atomistic models to reveal atomic arrangements within MROs, using potential energy landscape analysis to determine how different MROs impose different barriers for STZ activation. 5) As well as integrating the activation energy information and other STZ properties into mesoscale simulations to investigate how heterogeneous distributions of local structures and STZ activation energies influence softening behavior and shear localization during deformation. This research is examining MGs that have undergone various thermomechanical treatments to understand how ageing and rejuvenation affect local structures and softening behaviors. The results are providing detailed insights into the complex interplay between atomic-scale structures and macroscopic mechanical properties in MGs, contributing to the development of MGs with improved ductility by mitigating autocatalysis and promoting dispersed shear band activities. This project is jointly funded by the Division of Materials Research’s Metals and Metallic Nanostructures (MMN) and Ceramics (CER) Programs. 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 academic-industry collaborative project aims to develop new methods to search for and discover mineral deposits enriched in copper and nickel. Copper is essential to electricity generation, distribution, and storage. It is used in rechargeable batteries, electric motors, electrical wiring and connectors, charging stations and infrastructures needed to connect renewable energy to the main electrical grid. Nickel is used in a wide range of industries including manufacture of stainless and heat-resistant steels and non-ferrous alloys used in specialized industrial, aerospace, and military applications. Nickel is also used as a cathode material in lithium-ion batteries in electric vehicles and local and grid-scale energy storage because it increases the energy density of batteries. This research team is made of university professors and students along with mining industry professionals. They will collaborate throughout the project to ensure that the research deliverables have measurable impacts on the mining industry. The industry scientists add substantial practical knowledge about critical mineral deposits and ground and surface hydrogeological systems that are vital for the success of the project. Students at the University of Michigan and Juniata College will do field work in Minnesota and Michigan. They will learn to log drill core, make observations of hand samples, understand surface and groundwater flow, measure the compositions of ore samples, and how to effectively communicate research results. Industry and academia will benefit from each other, while providing invaluable applied experience for students. Researchers will test the hypothesis that the ratio of the abundances of copper-65 to copper-63 in ground and surface waters proximal to the Tamarack copper-nickel-cobalt-sulfide ore deposit will reveal a systematic change of 2 to 4 per mil with proximity to the sulfide ore body and 'downstream' waters, thus providing a vector toward sulfide mineralization. Copper, nickel and cobalt are considered critical materials for energy (U.S. Department of Energy) and nickel and cobalt are considered critical minerals (U.S. Geological Survey) vital for downstream manufacture of battery electric vehicles, photovoltaic solar panels, wind turbines, grid-scale battery energy storage and the generation and distribution of electricity. 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

Solar flares, as the most intense eruptions of solar radiation, can cause enhanced ionization in the upper atmosphere on a global scale. This enhancement can impact aviation, maritime, and military communication systems and global navigation systems. However, our understanding of the flare-to-flare variations in the upper atmosphere is still illusive and thus prohibits forecasting capabilities. The results of this project will significantly deepen our understanding of flare-to-flare variations in the upper atmosphere and improve our predictability of the global upper atmosphere during solar flares. As Solar Cycle 25 is approaching its maximum accompanied by more frequent and intense solar flares, it is timely to carry out the research that will investigate the solar flares’ impact on the upper atmosphere in detail. This project will be valuable in mitigating the impact of solar flares on aviation, maritime, and military communication systems and global navigation systems, which are critical for both everyday life and national security. The overarching science goal is to investigate the evolutions of the global ionospheric total electron contents (TEC) during solar flares and to improve our ability to predict the responses of the global TEC to solar flares. Specific science questions that this project aims to address include: 1. What is the temporal evolution of the global TEC during solar flares? 2. How do the different phases of solar flares (e.g., coronal dimming and EUV late phase) impact the responses of the global TEC? 3. How can we predict the response of global TEC to solar flares using machine learning (ML) models? To address these SQs, the research team plans to utilize TEC data from the worldwide GNSS receivers, perform detailed analysis with an aim to investigate its responses to solar flares. The solar spectral irradiance during such events will be from empirical models (e.g., FISM2) or observations (e.g., SDO EVE). The investigation will use state-of-the-art physics-based numerical models (e.g., GITM) to investigate the responses of the TEC during different phases of solar flares, especially the EUV late phase and coronal dimming. The project plans to develop ML models to predict the global TEC responses to solar flares. To carry out the tasks, a team including experts in ground-based observations, numerical modeling, and machine learning would be involved in the research. The outcome of this project can help mitigate the impact of solar flares on our technological society. The project will support an early career researcher and promote integration of research and education. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-08

Oceans and lakes cover over 70% of the Earth's surface, and they are being recognized as the next research frontier for addressing the global challenges in climate change and clean energy. With the blue economy poised to double its value to $3 trillion by 2030, there's a critical need for a skilled workforce capable of harnessing ocean renewable energy technologies and developing the blue economy. Although the theoretical capacity of offshore wind energy is 18 times today’s global electricity demand and the power density of ocean waves is over 10 times that of solar power, marine energy harvesting, including waves, currents and offshore wind energy, has not achieved widespread commercial acceptance. This project seeks to address this gap by organizing an educational workshop to identify the critical technical and professional skills and innovative experiential learning methodologies for the intellectual advancement of marine energy technologies Technically, the workshop will convene academic researchers, industry representatives, government agencies, policy makers, and interested students to discuss the latest advancements in ocean renewable energy, evaluate its impact, and identify essential skills for the blue workforce. Through keynote talks, panel discussions, and breakout sessions, participants will explore topics such as marine energy research convergence integrating engineering with physics, economics, environmental, and social sciences, experiential learning with cross-cutting partnerships among academia, industry, government, national labs, and non-government organizations, multi-faceted professional skill training including communication, leadership, ethics, teamwork, entrepreneurship and community engagement. This project aims to outline actionable strategies for workforce development, curriculum innovation, interdisciplinary training, community engagement, and DEI, ultimately contributing to the growth of ocean-based renewable energy technologies and the blue economy. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-08

This award funds a research project that will study the effects of measurement error regarding race and ethnicity in criminal justice data, its implications for research, and how it might be used to advance understanding of racial disparities in the U.S. judicial system. Researchers using administrative data collected by the Bureau of Justice Statistics assume the quality of data collected for operational purposes is of high quality for research use. In contrast with many data collections by the government where race is self-reported, racial and ethnic information in the justice system is often populated by justice agency personnel, reflecting their perceptions of race and ethnicity. As a result, measurement error is likely to exist in statistical reporting on the demographics of individuals that come into contact with the justice system, as well as in research examining the extent and implications of racial disparities. Such measurement errors could have significant implications for individuals who come into contact with the judicial system in the U.S. This research will not only attempt to correct these errors but also study the effects of these errors on the judicial system. The results of this research will help policy makers develop better policies to reduce racial disparities in the U.S. criminal justice system and help to establish the U.S. a global leader in the provision of a fair and efficient judicial system. This award will support a research project that will study why criminal justice data may misclassify the race/ethnicity of people who come in contact with the system and the consequences of such misclassification for such individuals. The PIs will address three inter-related issues: (i) measure the extent of discordance between survey and administratively recorded racial and ethnic information on justice involved individuals, (ii) document what factors increase the rate of mismeasurement in the justice population, and (iii) investigate how misclassification impacts individual trajectories. The PIs will do this by leveraging an individual-level linkage between agency-recorded race and ethnicity contained in data from the Criminal Justice Administrative Records System (CJARS) and a wealth of self-reported micro-level race and ethnicity data from various surveys and administrative data sets held by the U.S. Census Bureau. Using these data resources, the PIs will measure the degree of racial or ethnic mismeasurement in the criminal justice system, pinpoint the stage(s) of the justice system where this mismeasurement occurs, explore local and temporal correlates with mismeasurement error variation, and evaluate the causal impact that mismeasurement has on individuals whose race/ethnicity was inaccurately recorded on their justice outcomes and life trajectories. The results of this research will help policy makers develop policies to reduce racial disparities in the U.S. criminal justice system and help to establish the U.S. a global leader in the provision of a fair and efficient judicial system. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-08

This award aims to support research that intends to develop a novel approach to metamaterials engineering and manufacturing for emerging optoelectronics. Using a combination of state-of-the-art manufacturing tools, focused-ion-beam-assisted molecular-beam epitaxy (FiMBE) is pioneered, enabling high purity non-precious metals to be patterned and integrated with high quality semiconductors. The new knowledge generated is expected to revolutionize nanomanufacturing, opening a new frontier for light emission and enabling the development of emerging optoelectronics, including energy-efficient image sensing, catalysis, and light harvesting, which boosts the US economy. The project involves a unique multi-disciplinary approach, with interactions between students, faculty, and scientists from several departments, universities, laboratories, and countries. The principal investigator continues a legacy of community building and broadening participation by providing opportunities for high school students and teachers to join research cohorts and participate in local, regional, and national science fairs, leading to transformational and lasting benefits to the student-body of the school and the greater community at large. The project augments the semiconductor industry and responds to the Chips and Science Act. The award is also supported by the NNI Special Program Initiative. This project seeks to support research that intends to develop a combined computational-experimental toolkit for nanomanufacturing, which consists of kinetic Monte Carlo simulation-guided integration of nanoparticle (NP) arrays into epitaxial growth processes, followed by electromagnetic simulation-driven metamaterials design and discovery. The ultra-clean molecular-beam epitaxy (MBE) environment enables systematic investigations of elemental (gallium) and multi-metal (gallium-indium-bismuth) nanoparticle arrays. For both nanoparticle array formation and overgrowth, substrate chemistry/orientation, effusion cell temperature, and substrate temperature ramping and cooling rates are varied. Real-time probes, including reflection high-energy electron diffraction (RHEED), multi-beam optical stress sensor (MOSS), and spectroscopic ellipsometry (SE) are utilized to monitor array formation and overgrowth. A machine learning approach involving convolutional neural networks is used to classify RHEED patterns, accelerating the identification of array periodicities and optimization of overgrown layer crystallinity. The macroscopic and microscopic optical responses are measured using ex-situ spectroscopic ellipsometry and photoluminescence spectroscopy, in combination with high-resolution scanning transmission electron microscopy (STEM) and high-energy resolution electron-energy loss spectra (EELS). Finally, electromagnetic (EM) simulations are used to design structures with multiple alternating layers of NP arrays and high-crystallinity semiconductors or insulators for emerging optoelectronics. Expected outcomes of the work include new insights into nanoscale growth kinetics to enable seamless integration of metallic nanoparticle arrays with single-crystal semiconductors and insulators. Additional outcomes include novel strategies for tuning absorption, scattering, and emission of incident electromagnetic waves, as well as controlled propagation of surface plasmons along the arrays. 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 well-being of engineering students is critical to understand given the mental health crisis that is accelerating on college campuses across the country. Engineering is known for its culture of hardship which negatively impacts the well-being of its students. Engineering students must navigate that culture of hardship as they examine and determine what types of careers they will pursue in the future. Given these connections, this project explores how engineers think about their well-being and careers in tandem. Identifying connections between the two, and how they change, will help researchers and practitioners support the well-being and career development of engineering students, leading to a thriving STEM discipline on and off college campuses. Engineering students who are better able to thrive are more likely to continue into thoughtful and impactful engineering careers that are apt to positively impact the country's competitiveness. This primarily qualitative longitudinal project uses interviews and concept maps to explore how students perceive connections between their well-being and future goals as engineers, and how these conceptualizations grow and change over time. A thematic approach aids the analysis. The work will also leverage a novel machine learning method, network analysis, to identify quantifiable changes in engineering students' concept maps. Given this project's novel contributions to nascent well-being research in engineering education, the project findings will lay important groundwork for future studies on student well-being. Researchers and practitioners can use the work to improve the concurrent well-being and career development processes that engineers are hypothesized to engage in as they pursue their degrees. This work supports broader salutogenic discussions surrounding human development and thriving which will lead to greater contributions to the economic and social well-being of people in the United States. This project is supported by NSF's EDU Core Research (ECR) program. The ECR program emphasizes fundamental STEM education research that generates foundational knowledge in the field. Investments are made in critical areas that are essential, broad and enduring: STEM learning and STEM learning environments, broadening participation in STEM, and STEM workforce development. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-08

The Earth’s surface is made of a series of tectonic plates which, over geologic time, move across the surface. Where plates move towards each other, one sinks into the Earth’s mantle, in the process known as subduction. As the rock sinks, the rock experiences increasing pressure, and the minerals, including the mineral olivine, Mg2SiO4, adopt new, denser crystals structures. These crystal structure changes result in volume reductions which have been postulated to contribute to deep earthquakes in subducting plates. Unfortunately, past experiments have not been able to fully explain the large number of deep-focus subduction zone earthquakes occurring between 475-660 km in depth. The hypothesis of this project is that the large shear stresses or grain-size reductions likely encountered during subduction deepen the Mg2SiO4 crystal structure transformations to the conditions found at the depth the earthquakes occur. To evaluate this hypothesis, this project will carefully measure the crystal structures of nano-grains of Mg2SiO4 thin films under stress as a function of pressure and temperature in diamond anvil cells. If successful, this work will boost our understanding of deep-Earth processes and help launch a new field of Thin Film Mineral Physics where the novel composition, grain size, and deviatoric stress control possible in thin film samples can be used to study a variety of natural or synthetic materials in extreme environments. This project will enable expanding outreach efforts to programs focused on STEM across age levels, from elementary and middle school, to university students, and to grandparents. The mantle discontinuities at ~410, ~520, and ~660 km critically impact deep-Earth structure and dynamics. These discontinuities have been attributed to phase transformations between olivine, wadsleyite, ringwoodite, and the bridgmanite + periclase assemblage. Recently, this team detected phase transformation in thin films at high-pressure. In these experiments, the thin film Mg2SiO4 1) forsterite-to-wadsleyite and 2) (akimotoite + periclase)-to-(bridgmanite + periclase) phase transformations occurred at pressure and temperature conditions similar to those reported for bulk Mg2SiO4. In contrast, the thin film wadsleyite-to-ringwoodite transformation occurred ~500 K higher at 18 GPa (~2.5 GPa lower at 1900 K) than it does in bulk Mg2SiO4. This suggests that the either small grain sizes or large deviatoric stresses possible in thin films (and postulated to exist within subducting slabs) may impact the wadsleyite-to-ringwoodite transformation within subduction zones. Hence, the objective of this work is to 1) determine if the previously observed thin film Mg2SiO4 phase boundary shifts can be reproduced in anhydrous Mg2SiO4 thin films, 2) carefully map out the 0.1 - 30 GPa and 300 - 2300 K phase boundaries of anhydrous Mg2SiO4 thin films, and 3) establish the fabrication and testing protocols necessary to identify the mechanisms responsible for any observed phase boundary shifts. To achieve these aims, the project will produce anhydrous Mg2SiO4 thin films via Pulsed Laser Deposition, 2) optimize thin film sample-loading procedures into a Diamond Anvil Cell and use synchrotron-based X-Ray Diffraction or Raman spectroscopy to construct the world’s first high-pressure phase diagram of a thin-film sample. In addition to elucidating how grain size and deviatoric stress may impact Mg2SiO4 phase transformations, this work will highlight how thin films, capable of supporting static tensile or compressive deviatoric stresses up to ~10 GPa, can be loaded into laser-heated Diamond Anvil Cells to apply large, well-controlled, and complex deviatoric stress states on optically-accessible samples. The proposed work will have a broad impact by 1) allowing PI Nicholas to produce a new Michigan State University (MSU) demonstration station for 4-8th grade students on “Rocks and Minerals”, 2) allowing PI Nicholas, PI Li, and PI Chen to incorporate interdisciplinary Materials Science and Geophysics teaching strategies and content into their courses, and 3) exciting broader society about science via the development of a new “Amazing Crystals” course for the Grandparent University Summer Camp run by Michigan State University each June. 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 resurgence of deep neural networks has led to revolutionary success across almost all areas of engineering and science. Despite recent endeavors, current theoretical understandings of deep networks remain fragmented and only pertain to idealized and over-simplified network models. There is a significant lack of a systemic and unified approach for designing and explaining deep networks. Therefore, the underlying principles behind the success of deep learning still largely remain a mystery, which hinders its further development and adoption to broader applications. Nevertheless, the blessings of dimensionality imply that real-world data often reside in low-dimensional structures, and ample empirical evidence implies that there is a strong connection between deep learning and low-dimensional modeling. This connection implicitly appears in many different forms, in terms of learned representations, network architectures, and optimization strategies. However, these connections are far from being elucidated nor are they fully exploited. Based on the theory of data compression and optimal coding for learning from low-dimensional structures, this project aims to bridge the gap between the theory and practice of deep learning by developing a principled and unified mathematical framework. To develop this framework requires two steps. First, this project will design white-box deep networks by unrolled optimization schemes for maximizing the information gain of the resulting representation, which can be measured precisely by the coding rates of the representation. Second, the project will guarantee correctness through rigorous mathematical analysis of the optimization objective for learned representations. Third, this project will ensure consistency of the learned representations through a self-correcting closed-loop transcription framework that integrates encoding and decoding into a complete autonomous learning system. This new framework naturally unifies representation learning for all purposes: discriminative, generative, and auto-encoding, and is generalizable to all settings: supervised, unsupervised, self-supervised, and continuous learning. 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 Advanced by Interdisciplinary Science and Engineering (RAISE) award is made in response to Dear Colleague Letter 23-109, as part of the NSF-wide Clean Energy Technology initiative. Clean energy technologies provide opportunities to intentionally consider social justice impacts in ways that have not been addressed with legacy energy systems. While clean energy technologies target reducing greenhouse gas emissions that drive climate change and should theoretically benefit marginalized and underserved communities, research indicates that clean energy technologies can follow the same path of social injustice unless intentional change is made in the way technologies are developed and deployed. We hypothesize that the application of social justice theory to energy systems–energy justice–can inform the development of clean energy technologies, increasing the distribution of benefits and limiting the negative impacts of clean energy technologies. The proposed research moves beyond applying energy justice as an evaluative lens on energy systems and instead uses the concept as a design lens to shape engineering research and development questions. The approach uniquely uses social science theory to inform the creation of engineering knowledge for just and sustainable futures, and the use of multiple-capitals accounting makes visible and values energy justice in the context of specific business models. This approach can generate qualitative and quantitative insights such as how energy justice increases the productive capacity and dynamic efficiency of clean energy businesses and the socioecological systems in which they are embedded. This work will enable energy justice to be applied more expansively, reliably, and systematically by integrating energy justice effects into the design of clean energy technologies, business models, and policies and processes that guide renewable energy research and development. The approach of the proposed research is to (1) uniquely develop and apply the framework of energy justice to shape the research, development and design of a clean energy system, specifically focusing on a case study of hydrothermal liquefaction of waste streams; and (2) articulate the social, economic, and environmental value generated by application of the energy justice design framework. The project is highly interdisciplinary and synergistically leverages theoretical frameworks and approaches of energy justice, chemical processing, and community capitals. The use of a targeted chemical process—hydrothermal liquefaction to create usable chemicals and feedstocks from waste streams—allows specific demonstration of the approach and the associated results. Both the process and the outcomes are important deliverables from this project. 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 Creating Inclusive Scientific Societies through Policies and Practices (CRISSPP) project brings three universities, University of Michigan, University of Connecticut, and University of North Texas into a partnership to develop, implement, and assess a set of evidence-based guidelines and practices for scientific organizations (beginning with Psychology) to promote inclusion and minimize systemic exclusion. The research literature indicates that academic exclusion includes social, informational, and epistemic exclusion, and professional societies can play a central role in members’ academic careers, facilitating the dissemination of their scholarship and providing opportunities to establish prominence within the field. The guidelines and practices will help professional societies create and sustain positive disciplinary environments that lead to success for all faculty. The project will empower organizations to shape individual members’ experiences of inclusion/exclusion and the organization’s climate in four critical areas: governance, awards, conferences, and publications. The CRISSPP guidelines and practices will (1) conduct climate surveys and audits, (2) construct interventions (to include transparency audits, toolkits, including guidelines and rubrics as appropriate, commitment to optimal processes, pathway development (for governance), educational workshops and, (in the case of conferences), a brief daily online climate assessment tool), and (3) assess the overall impact of these interventions on the organizations and on members’ sense of belonging. The guidelines, practices, and lessons learned will initially be shared within Division 9 of the American Psychological Association and up to nine additional partner organizations in psychology, reaching over 20,000 members. This partnership will be evaluated internally and externally, formatively and summatively, to improve the guidelines and practices for other organizations and identify implementation issues that may need to be addressed. The NSF ADVANCE program is designed to foster gender equity through a focus on the identification and elimination of organizational barriers that impede the full participation and advancement of diverse faculty in academic institutions.  Organizational barriers that inhibit equity may exist in policies, processes, practices, and the organizational culture and climate.  ADVANCE "Partnership" awards provide support for the adaptation and adoption of evidence-based strategies to academic, non-profit institutions of higher education and non-academic, non-profit organizations. 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

Renewable electricity sources provide many benefits to society, including ensuring sustainability, enhancing energy independence, and reducing pollution. Effective scale-up of renewable electricity sources, such as wind and solar, to meet the growing societal demand requires developing low-cost energy storage technologies. Redox flow batteries are a promising technology for large-scale and long-duration storage. However, the energy storage capacity per volume of a battery is limited by the chemical properties of the substances responsible for energy storage and transfer. This project will investigate the chemical substances used to store energy, their various molecular forms, their ability to convert between these molecular forms, and how increasing their quantity can reduce energy storage costs and integrate renewable electricity into the grid. The project will provide research and educational opportunities for graduate, and undergraduate researchers. As part of the program, the investigators at the University of Kansas and University of Michigan will give demonstrations involving energy conversion and storage at elementary and middle schools and public venues, such as the Kansas's School of Engineering’s annual exposition. This project aims to advance the understanding of supersaturated, or especially highly concentrated electrolytes, for high energy density flow batteries. The objective is to understand the structure and activity of vanadium and iron metal ions in concentrated and supersaturated metal ion aqueous solutions through three tasks. Task 1 will identify the structure of different vanadium complexes in supersaturated solutions through combined experimental and computational spectroscopy (e.g., UV-Vis and X-ray absorption) of vanadium ions, and the role of supporting electrolyte and preparation conditions on their structure. This task aims to improve the understanding of metal ion structure at concentrated and supersaturated conditions where the local structures are currently unknown. Task 2 is focused on identifying structure-activity relations. In this task electrochemical activity will be measured by determining which ionic structures from Task 1 are capable of electrochemical reactions and evaluating different electrolytes for their performance in redox flow batteries. Task 3 will determine the conversion of electrochemically inactive species to electrochemically active species and identify methods that facilitate this interconversion. The findings of this project will be used to understand ion chemical and electrochemical kinetics and evaluate the feasibility of supersaturated electrolytes in high-energy-density energy storage systems. The gained understanding of the molecular structure and functionality will be used to identify electrolyte compositions and preparation methods that result in supersaturated electrolytes with longer stability and higher chemical and electrochemical activities than what is currently known. The project will create future opportunities to study supersaturated and extreme structures in condensed phases and to explore the feasibility of supersaturated electrolytes in high-energy-density energy storage systems. 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

In applied machine learning and statistics, it is common practice to search through many different models before estimating a best model: one that is simple to explain, while still providing good predictive performance. Over the past decade, several methods have emerged which first estimate a model from a range of choices, and then fit the estimated model to extract useful trends and predict future outcomes. However, predictive accuracy, on its own, has limited explanatory value and point estimators with high uncertainties may lead to poor replicability down the line. Yet, most such models, supervised or unsupervised, lack uncertainties for the related estimators. This project introduces a new class of perturbation methods to quantify uncertainties in machine learning models, with various applications in regression, classification, and dimension reduction. The research plans have three main goals. The first goal is to develop methods that can be used with different types of data and are not limited to specific models. The second goal is to ensure that the methods can be scaled and applied to decentralized datasets on multiple machines. The last goal is to create versatile methods that can be used with different estimation techniques. An overarching goal is to allow researchers to apply these techniques to various data types and forms, without being constrained by unrealistic assumptions or limited methods for model estimation. The project's educational and outreach plans are closely tied to its research plans. The project will help the PI conduct summer training programs with K-12 outreach, develop new curricula, and broaden participation of underrepresented groups in the field. This project aims to develop methods for attaching uncertainties to the outputs of model estimation methods. The research agenda is structured into three main aims. In the first aim, the project will introduce distribution-free methods that can quantify uncertainties in a flexible class of semiparametric models. In the second aim, the project will develop distributed methods that use decentralized data from a cluster of nodes to quantify uncertainties in an estimated model. These methods will need only basic, aggregated statistics from each node and will be accompanied by communication-efficient algorithms. In the third aim, the project will focus on developing perturbation methods as a versatile approach for uncertainty quantification that can be used in a wide range of model estimation algorithms, both supervised and unsupervised. To achieve these aims, the project will use perturbation to exploit a link between the geometric properties of estimators and their underlying probability, and will employ an integrated approach using mathematical statistics, probability theory and optimization. Throughout the project and after its completion, the methodology and open-source software will be applied to improve biomedical decision-making and replication in health studies. 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 social and economic cost of crime in the U.S. is very high. Individuals, families, and neighborhoods pay these costs in different ways, through the direct cost of crime and indirect costs imposed on families and neighborhoods. This CAREER award funds several distinct projects that provide better data-based measures of specific impacts of crime and involvement with the criminal justice system. The projects all use newly available data from the Criminal Justice Administrative Records System (CJARS), a nationally integrated data repository that combines criminal justice data from 42 states and the federal government over a long time span. The research provides new insights into how increasing crime rates affect family stability and the health and welfare of children. It also considers how the number and rates of criminal conviction have changed over time and across locations. The project also considers how increased crime increases the costs of government programs in unexpected ways. This award funds research that leverages recently linked criminal justice data from CJARS to study how involvement in the justice system shapes the lives of individuals, families, and their communities in the U.S. The research seeks to answer the following questions: (i) how has the risk of CJS (criminal justice system) involvement changed over recent decades and by local geography? (ii) does CJS involvement impact household instability for minor children? (iii) what are the effects of the rise in mass incarceration for retirement in the coming decades? (iv) does CJS involvement affect returns on investment for small business loans for entrepreneurs? and (v) how accurate is demographic data collected for operational purposes by criminal justice agencies and what are the implications of poor data quality? A common theme in the research is the integration of national administrative and survey data paired with natural experiments to exploit exogenous policy variation. The project will also develop training workshops to expand the number of researchers and policy makers using CJARS to study the causes and consequences of crime and the justice system in the U.S. The results of this research will provide a better understanding of how the criminal justice system affects the lives of ordinary people. 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 rapid developments of data-intensive applications such as Artificial Intelligence have created significant strains in energy consumption and computing resources. These challenges can be efficiently addressed through computing hardware and algorithm innovations. On the hardware side, the performance of the system is increasingly limited by data movement costs, and new bio-inspired computing architectures can more efficiently process data at significantly lower energy consumption. On the algorithm side, bio-inspired algorithms can process information with much smaller model sizes, leading to further energy and throughput gains. The research will broadly impact neuromorphic computing, AI, and brain-machine interfaces. There are also plans to focus on commercialization of the research results and subsequent tech transfer. Integrating this research with education, the investigator will collaborate with local high schools with large minority student population at annual summer camps hosted by the university, train undergraduate and graduate students, and develop new course modules. This project aims to develop highly efficient bio-inspired edge computing systems that can natively process information at all stacks of the system, based directly on internal device and network dynamics. By leveraging the internal ionic/electronic/thermal dynamic processes in emerging devices, networks and systems can autonomously process spatiotemporal data with high performance, energy efficiency and reliability. Two types of devices, short-term memory memristor and 2nd-order memristor, will be developed and used to directly process temporal inputs and achieve self-learning, respectively. Combined with new network architectures such as reservoir nodes and columnar networks with lateral and feedback connections, the proposed Reservoir Node Networks and Neuromorphic Retina Networks will be able to directly process asynchronous inputs from neuromorphic sensors such as event-based cameras or touch sensors, extract spatiotemporal features at different temporal and spatial scales, and perform object detection and other decisions with unparalleled energy efficiency, latency and robustness. These device and architecture developments will in turn stimulate developments of new bio-inspired algorithms, and enable applications including efficient on-sensor processing, brain-machine interfaces and autonomous systems. 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 advances understanding of the glycoprotein hormone stanniocalcin, which is involved in regulating calcium concentrations in vertebrates. Calcium is an essential ion and plays key roles in a wide range of biological processes. The levels of calcium ion in the blood are tightly regulated by several hormones. The first stanniocalcin (Stc1) was discovered in fish in the 1960s, and recent advances in genomics have revealed that all vertebrates, including humans, have multiple stanniocalcin genes. Zebrafish, for instance, have 4 distinct stanniocalcin genes (stc1a, stc1b, stc2a, and stc2b). Genetic deletion of zebrafish Stc1a results in kidney stone formation, cardiac and body swelling, and premature death, but it is unclear how Stc1a loss leads to these defects. Moreover, the expression, regulation, and functions of Stc2a, Stc1b, and Stc2b are essentially unexplored. This project will determine whether impaired renal function in Stc1a-deficient animals leads to the accumulation of osmotic water and progressive development of cardiac edema and body swelling in zebrafish. The Stc1a receptor will be identified to gain an understanding of how Stc1a works at the molecular level. The expression, regulation and physiological functions of Stc1b, Stc2a, and Stc2b in gill and brain will also be investigated. The results should reveal novel insights on the physiological functions of stanniocalcin isoforms and their underlying mechanisms of action, and will fill a major gap in the field of comparative endocrinology. A mechanistic understanding of the role of stanniocalcins in brain, kidney, and gills should contribute to the development of new applications in the aquaculture industry. The research will incorporate training of undergraduate students and a post-doctoral fellow, contributing to workforce development. The researchers will develop a hands-on research activity for a K-12 summer camp in collaboration with the University of Michigan Museum of Natural History, using the zebrafish mutant lines developed in the project to teach the participants about hormones and calcium homeostasis. Stanniocalcin 1 (STC/Stc 1) was discovered in bony fish as a hypocalcemic hormone over half a century ago. Recent studies suggest that all vertebrates, including humans, have multiple STC/stc genes. In zebrafish, which have 4 stanniocalcin genes (stc1a, stc1b, stc2a, and stc2b), loss of Stc1a results in kidney stone formation, cardiac and body edema, and premature death. Mechanistically, Stc1a regulates ionocyte proliferation and calcium uptake by suppressing local insulin-like growth factor (IGF) signaling. However, the molecular identity of STC1/Stc1 receptor(s) is currently unknown in zebrafish or any other vertebrate. Furthermore, the expression, regulation, and functions of stc2a, stc1b, and stc2b are largely unexplored. This project tests the hypothesis that Stc1a, mediated by its binding to the cell surface-tethered metalloproteinase Papp-aa and/or the multi-ligand endocytosis receptor Megalin, regulate ionocyte proliferation, calcium uptake, and kidney function, while the other three stanniocalcins function locally in the brain and gills via context-dependent mechanisms. Specifically, the research will determine (1) whether Stc1a plays dual roles in ionocyte proliferation and calcium uptake by regulating local IGF signaling and epithelial calcium channel Trpv6 expression; (2) whether loss of Stc1a impairs renal function, leading to osmoregulation imbalance and body edema; (3) the molecular identity of Stc1a receptor(s); (4) the roles of Stc1b and Stc2a in brain growth and function; and (5) the possible role of Stc2a/2b in mediating hypoxia-induced growth reduction. The research will make conceptual contributions to the field of endocrinology. The project includes research training at multiple levels, including a Research Experiences for Undergraduates component. 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 award will increase access and engagement in STEM at the NSF ZEUS Laser User Facility at the University of Michigan. The ZEUS Education and Outreach team will partner with the University of Michigan Women in Science and Engineering (WISE) program and the Physics Lecture Demonstration Laboratory on this project. The team will design a hands-on learning space and interactive exhibits that will transform the visitor experience to ZEUS and create an inclusive and welcoming environment. This will expand and enhance the accessibility of the NSF ZEUS facility and its research for visitors. The team will also design comprehensive and collaborative educational programming, including online tools, that will allow students and educators to access the science and capabilities of ZEUS. This will allow to engage a broader audience of learners, especially with individuals and groups where visiting the facility is a barrier to access. Finally, the team will design assessment tools and evaluation metrics that focus on understanding the unique impacts of the programming for different learners and provide clear guidance on how to improve and expand programs going forward. The NSF Zettawatt-Equivalent Ultrashort pulse laser System or ZEUS is a 3 Petawatt (3 x 1015 W) laser user facility at the University of Michigan funded by the US National Science Foundation. NSF ZEUS will be able to operate in multiple modes: a single 3 PW beamline, dual beam setup with 2.5 PW and 500 TW, or up to 300 TW at higher rep rate (5 Hz in “burst mode'”). Additionally, there will be a long-pulse shock driver that can be used with one of the other beams. As a user facility, NSF ZEUS will have a broad impact on the plasma physics, high-field science, and high energy density physics communities. The ZEUS user community is primarily composed of scientists, postdoctoral fellows, graduate students, and an occasional undergraduate student in a supporting role. The present award will enable the ZEUS Education and Outreach team to design a sustainable outreach and education program that allows for more meaningful engagement for undergraduate students and more opportunity to introduce students in middle school and high school to the science of the NSF ZEUS facility. This work will broaden the impact of the NSF ZEUS facility by engaging more, and more demographically diverse student population in STEM via making more people aware of the uniqueness of the facility, and by teach students and teachers about the amazing science that can be accessed with NSF ZEUS. 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

Free and accessible open source software tools for simulation and scientific computation enhance the transparency, reproducibility, and quality of research outcomes. In physics, the software suite: Applications and Libraries for Physics Simulations (ALPS), provides an open source software ecosystem for applications in condensed matter, quantum computing, quantum information, and related fields. This project will support the scientific community by providing a maintainable and sustainable open source infrastructure for ALPS, along with community building efforts. As part of this project, software will be re-licensed with permissible open source licenses; support infrastructure such as source code management and bug tracking, web pages, and documentation will be improved; and testing infrastructure will be established. This work on software infrastructure will allow ALPS to continue to provide software for cutting edge research on classical and quantum systems. 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.