Arizona State University

NSF Awards · FY 2024 · 2024-08

A strong foundation in computer science (CS) will define success in the workforce of tomorrow. A key step towards increasing meaningful participation and learning in CS education for all learners is equipping their teachers with effective instructional strategies. This project will develop and study approaches to prepare 4th and 5th grade general and special education teachers to teach CS to a broad range of learners, including those with disabilities, through professional development. This project will investigate the impact of this professional development on teachers' instructional practices, as well as the learning, ability beliefs, and CS attitudes of elementary students with and without disabilities. This project includes cycles of learning and teaching with two partner school districts in Arizona. During these cycles, teacher teams will first learn about effective CS instructional practices and then implement these practices within classrooms. Teachers will engage in collaborative planning, lesson feedback cycles, and technical support during teaching. The study is guided by both development and impact research questions. Development questions include: How do 4th/5th grade and special and general education teachers adapt this professional development to their instructional practices to increase the participation of all learners, including students with disabilities in CS education? Impact questions include: How does participation impact teachers' competence in teaching CS to all elementary students, including those with disabilities? This project will contribute to the empirical literature on effective online, sustained professional learning to elementary and special education teachers and expand opportunities for all students in CS education. The Discovery Research preK-12 program (DRK-12) seeks to significantly enhance the learning and teaching of science, technology, engineering and mathematics (STEM) by preK-12 students and teachers, through research and development of innovative resources, models and tools. Projects in the DRK-12 program build on fundamental research in STEM education and prior research and development efforts that provide theoretical and empirical justification for proposed projects.  This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-08

With support from the Improving Undergraduate STEM Education: Hispanic-Serving Institutions (HSI Program), this Track 2 project aims to determine if virtual field experiences (VFE) can enhance the ability of students to learn science skills and encourage them to pursue a science career. This work is critical because, although field learning is one of the highest-impact teaching practices, the capacity for instructors to provide field learning is limited due to cost and time. These limitations are particularly strong at community colleges and colleges that lack suitable field sites nearby. Moreover, conventional field learning may exclude students whose family or work obligations do not allow them to join a field trip outside of normal class time. Field locations also may not allow students with disabilities to fully participate. Existing research into VFEs shows that they can be as effective as in-person field experiences at improving content knowledge, but it is not known whether VFEs are similarly effective in training fundamental science skills, such as observation, or in positively impacting students' attitudes, such as their sense of science identity. This project will compare learning and attitudinal outcomes of introductory biology and ecology students following the use of virtual or in-person field experiences. The diversity of the three HSIs studied and the project's collaboration with the Virtual Field Research Coordination Network ensure the results will be generalizable. When completed, this project will offer guidance on how VFEs can bring field learning to more students, enhancing instruction in an equitable manner. Virtual field experiences are an established part of science education, with demonstrated positive impacts on content learning and student satisfaction. They also have practical advantages over in-person field learning, such as lower costs and greater accessibility. Yet important questions remain about whether they are as effective as in-person field learning in building students' scientific skills and inspiring non-cognitive outcomes like scientific identity. First, by constructing a virtual field experience that closely parallels an in-person one, the project will employ a quasi-experimental design to compare cognitive and non-cognitive changes pre- to post-experience between students in virtual and in-person conditions. The second experiment will use a pre-/post-experience design, without a comparison group, but will study outcomes from a virtual field experience that brings students to multiple sites across different ecosystems. Outcome measures in both experiments will include a combination of previously validated measures and content and science skills assessments that are specific to the project. By expanding field learning access, this project will directly benefit the students at the institutions studied. This project is funded by the HSI Program, which aims to enhance undergraduate STEM education, broaden participation in STEM, and build capacity at HSIs. 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 school counselors play an integral role in supporting student trajectories toward science, technology, engineering, and mathematics (STEM) careers. Many professional learning experiences for counselors have not focused specifically on developing awareness of a broad array of STEM careers and the corresponding high school activities and coursework that can establish student trajectories toward these careers. This project addresses this gap in practice by developing year-long professional learning experiences focused on engineering-related careers, with and for high school counselors. The professional learning experiences will highlight numerous characteristics of engineering and underscore strengths that all students can offer to engineering fields to address future workforce needs. Research will explore whether and how the professional learning experiences influence the participant dispositions and practices of engineering-related counseling. The project will broaden participation in engineering through a research-based approach to professional learning for high school counselors, such that they can effectively encourage more students to consider and pursue trajectories and careers in engineering-related fields. In this exploratory project, three cohorts of high school counselors from rural, urban, and suburban schools will participate in a year-long professional learning experience focused on advancing engineering counseling practices. Research will explore whether and how the counselors develop efficacy and dispositions of engineering career counseling, and put into practice.. The research team will conduct analyses and triangulation of the following data sources: transcripts from interviews and focus groups with the counselors; artifacts collected from the professional learning experience; and pre- and post-surveys. The educational materials associated with the professional learning experience will be disseminated widely via professional networks of school counselors, while the empirical results will be shared through various counseling and educational research venues. The project contributes to the capacity-building of school counselors who play an important role in building interests in engineering careers. Long-term broader impacts include - strengthening and expanding our nation's engineering workforce. The Discovery Research preK-12 program (DRK-12) is an applied research program that seeks to significantly enhance the learning and teaching of science, technology, engineering, and mathematics (STEM) by preK-12 students and teachers. Projects in the DRK-12 program build on fundamental research in STEM education and prior research and development efforts that provide theoretical and empirical justification for funded projects. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-08

This project focuses on several problems in commutative algebra, the branch of mathematics that explores properties of polynomial equations, which are fundamental for modeling diverse phenomena in science and engineering. As a result, commutative algebra has strong connections with biology, computer science, physics, and other quantitative fields. When equations involve multiple variables, their comprehensive study can become intractable. A powerful strategy in such cases involves decomposing polynomials into smaller pieces and using information from these components to derive general properties, a theme known as multigraded commutative algebra. Another significant approach concerns understanding the asymptotic behavior of sequences of sets of equations known as filtrations. This project will advance these research directions by addressing key questions within the field. Furthermore, this project will have a broader impact on the postdoctoral, graduate, and undergraduate student population through mentoring initiatives and the organization of seminars, conferences, and workshops. The project will advance the understanding of Hilbert series through a detailed investigation of multidegree support and K-polynomials of multiprojective schemes. This research will explore connections between the topology of schemes and the combinatorial aspects of K-polynomials, with direct implications for Schubert geometry, toric geometry, and multiparameter persistent homology. Additionally, the project will employ Presburger and Ehrhart methods to analyze the quasi-polynomial behavior of homological functors applied to multigraded modules. Divisorial filtrations, which are defined via valuations, exhibit intricate geometric properties and include significant examples such as symbolic powers and integral closure powers of ideals. The project will study the growth rate of the number of generators of these filtrations. Furthermore, the project will investigate whether divisorial filtrations are F-split, potentially indicating mild F-singularities in their blowup algebras and low complexities in the growth of homological functors. 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 social-ecological relationships within Pacific Northwest high-elevation ecological zones, emphasizing long-term cultural burning practiced by indigenous communities. The primary objectives of the project are to conduct archaeological surveys of subalpine ecosystems; interpret processes of high-elevation subsistence during the early Holocene and subsequent changes in burning practices; and investigate how subsistence land management influenced historical fire regimes and long-term ecosystem stability/change. This study employs an interdisciplinary approach that interweaves archaeology, paleoecology, participatory mapping, and computational modeling to evaluate how human-environment interactions shape high-elevation landscapes. This research develops partnerships with federal land managing agencies and indigenous partners in the co-interpretation of research results. Historically, fire has been used as a part of a set of tools to manage ecosystems but relatively little is known about the influence of historical practices of burning in high-elevation ecosystems. This project evaluates the spatial distribution and chronology of high elevation land-use, documents long-term trends in climate and human influence on fire and vegetation, and records material evidence for cultural burning practices to rejuvenate or maintain important plant species. This research develops a novel paleo-fire reconstruction and introduces new methods of soil charcoal analysis to make more substantial connections between past human land-use, cultural fire, and vegetation change. Datasets resulting from this project are evaluated using a computational “virtual laboratory” to examine dynamics among climate and cultural burning that may not be interpretable from the archaeological and fire history data alone. Results from this project advance the understanding of social-environmental change and stability in high-elevation ecosystems to enhance cooperative management strategies (e.g., co-stewardship, co-management), maintain cultural ecosystem services, reduce potential future wildfire severity, and buffer against ecosystem loss in these unique landscapes. 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 will support the development and operations of the OpenTopography (OT) cyberinfrastructure platform to provide seamless web-based access to topographic data from technologies such as lidar, along with processing tools and education and training resources. With the most comprehensive collections of topographic data on the internet, OT enables fundamental geoscience discoveries and innovative applications with societal value addressing hazards, resilience, and education. Open-access topographic data offer great potential to increase resiliency in communities that lack resources to mitigate natural hazards. OT’s education, community engagement, workforce development, and governance are designed to ensure broad and active participation, ensuring a strong U.S. science workforce. OT will advance science and education for the broader academic community and provide a comprehensive platform for FAIR (Findable, Accessible, Interoperable, Reusable) topography data management, publication, and citation. The OT platform will support new artificial intelligence and machine learning use cases as well as additional capabilities for facilitating trustworthy and reproducible science. OT will enable new topographic data processing capabilities over larger spatial areas and across broader time spans, enabling wide use of OT across Earth science domains and educational levels. OT will expand knowledge of topography data to new communities and support workforce development via a multi-tiered plan including collaboration with community college faculty. Education and outreach activities include webinars, educational videos on topography, in-person short courses at national meetings, and entry-level undergraduate curriculum developed in collaboration with community college faculty. This award by the Geoinformatics Program in the Division of Earth Sciences is jointly supported by the Directorate for Computer and Information Science and Engineering. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-08

Natural resource sustainability remains a key challenge exacerbated by climate uncertainty and continued resource exploitation for meeting development goals. Sustainable management of these resources necessitates coordinated efforts among stakeholders who interact closely with the resource. As the world is increasingly becoming urbanized, this shift impacts the way communities operate. Institutions, the formal and informal rules governing human behavior, are critical for communities to manage shared resources. When communities transition from rural to urban, the transition period coincides with uncertainty about which rules and regulations are current. This transitional uncertainty may impact how people interact and engage in collective action to manage their shared resources. While the role of rural and urban institutions in determining collective action for resource governance is well-researched, much less is understood about how institutional transitions resulting from urbanization affect collective action. Understanding these dynamics is essential for developing strategies to manage natural resources sustainably amid urban growth and development pressures. This project aims to understand how urban transitions influence collective decision making for shared groundwater governance – a key resource for meeting much of the world’s drinking water and irrigation needs. By employing semi-structured interviews with key resource users, document analysis and experimental surveys, the project investigates the relationship between urban transitions and propensity for collective action at the level of the community and the individual resource user. First, the project investigates institutional changes prompted by urbanization that aid or hinder shared management of groundwater. Second, the study empirically tests resource users’ reliance on collective action for coping with resource uncertainties as mediated by rural-urban institutional transitions. In doing so, this research advances understanding of the potential for collective action to address resource dilemmas in the context of rural-urban transition. 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 aims to address the dynamic modeling and system stability challenges presented by the massive integration of distributed energy resources (DERs) in carbon-neutral power systems. Examples include rooftop solar panels, electric vehicles, and retail-scale batteries. The project will bring transformative change the field of power system dynamic modeling through the development of a new reduced-order dynamic modeling paradigm for accurately representing deep-DER-penetrated distribution power systems in the transient stability assessments of large-scale electric power systems. The intellectual merits of the project include leveraging theoretical tools in dynamic systems, nonlinear system identification, and convex machine learning to learn the white-box nonlinear governing equations for enhancing the existing oversimplified dynamic models for the DER-penetrated distribution power systems. The broader impacts of the project include significantly reducing the society’s electricity outage risks and accelerating decarbonization by enabling power system practitioners toward accurately modeling the dynamic behavior and assessing the stability of DER-penetrated distribution power systems, as well as contributing to the workforce development by a multifaceted outreach, mentoring, education, and knowledge dissemination plan that covers high-school students through graduate students and industry practitioners. The goal of this project will be achieved by developing physics-based and machine learning tools to create robust reduced-order distribution grid dynamic models with massive DERs, advanced controls, and high system uncertainty. The research objectives include: 1) identifying and modeling critical nonlinear dynamic structures that are missing from existing oversimplified dynamic load models; 2) creating reduced-order topology-preserving steady-state distribution grid models for accurately representing the impacts of feeder-level steady-state voltage and load variations on the overall distribution grid dynamics; 3) incorporating advanced machine learning controls, enhancing model robustness against uncertainties, and optimal parameter identification for the white-box reduced-order topology-preserving dynamic distribution grid models. Such tools will enable power system practitioners toward systematically and rigorously identifying and mitigating the dynamic and algebraic nonlinear structural deficiencies in existing oversimplified dynamic load models and learning the white-box nonlinear differential algebraic equations via physically interpretable and theoretically rigorous machine 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 · 2024-08

Part 1: NON-TECHNICAL DESCRIPTION The need for improved energy storage materials for rechargeable batteries cannot be overstated. This is particularly true for sodium-ion batteries, which are an emergent technology following up on lithium-ion batteries. Owing to the higher natural abundance of sodium in comparison to lithium, especially in the U.S., it is conceivable that sodium-ion batteries can replace lithium-ion batteries for mid-to-large scale applications in the near future. However, identifying suitable materials with higher charge storage capacities and cycle life for sodium-ion batteries remains a major challenge, which could be overcome with more fundamental research aimed at understanding how the structure of the material affects ion migration and how the structure of the material is affected by repeated electrochemical cycling. Through this collaborative project, supported by the Solid State and Materials Chemistry program in the Division of Materials Research at NSF, researchers at Arizona State University and the University of Delaware jointly identify structural features of open framework materials, based on the elements silicon, germanium and tin, which can promote fast, sodium-ion diffusion. Of particular interest are a class of compounds known as clathrates, which exhibit cage-like structures that can host a variety of metal guest atoms, including lithium and sodium. The team also develops new approaches to synthesize such materials. Thereby, the gathered new knowledge helps establish connections between the structural aspects to the physical, electrochemical, and materials chemistry properties, which can lead to new materials for improved battery technologies. The fundamental science gained from these studies could also have far reaching impacts in other fields where these materials have potential applications, such as superconductors, thermoelectrics, optoelectronics, magnets, and photovoltaics. Additionally, this collaboration between two universities and three different departments (materials science, chemistry, and physics) engages students in multidisciplinary research. Outreach and educational activities also provide students with interdisciplinary training and immerse them into areas outside their immediate field of expertise. Part 2: TECHNICAL DESCRIPTION This collaborative project, supported by the Solid State and Materials Chemistry program in the Division of Materials Research at NSF, identifies structural features that lead to fast ion diffusion and aims to obtain better understanding of electrochemically driven phase transformations in Li-Tetrel (Tt) and Na-Tt systems, particularly for clathrates and other open framework structures. The specific objectives of the research are to: (1) Understand the structural subtleties for Tt (Tt = Si, Ge, Sn) clathrate and related materials that promote high ionic mobility; (2) Understand ionic transport within this phases; (3) Re-evaluate phase equilibria within the Na-Tt systems using novel synthetic strategies and isostructural model compounds; and (4) Use electrochemistry to inform solid-state synthesis and vice versa, to enable new synthetic approaches for energy-related bulk materials. Through a concerted approach combining the synthetic, structural and electrochemical characterization, and theoretical expertise of the PIs, this work furthers understanding the electrochemical behavior, leading to new insights on structural features that result in fast diffusion pathways, low ion migration barriers, and phase stability. Novel synthetic approaches combining high temperature coulometric titration and low temperature flux methods are used to trap kinetic/metastable phases and controllably synthesize high quality single-crystalline materials. Isostructural compounds containing key Li local environments are employed as model compounds to understand the ion (de)insertion processes in Li-Tt and Na-Tt binary (and ternary/quaternary) compounds. By means of a unique feedback loop connecting electrochemistry and synthesis, information about phases formed during electrochemical lithiation/sodiation is used to design novel precursors for synthesis, and solid-state reactions using chemical oxidation are adapted to develop electrochemical synthesis methods with finer control over composition. Synchrotron X-ray studies are used to characterize the local and crystalline structures and phase evolution during electrochemical reaction and/or synthesis. In all cases, density functional theory calculations support experimental findings and guide materials design, particularly by identifying formation energies and ionic transport mechanisms. 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 strong nuclear force, responsible for binding protons and neutrons tightly together in the atomic nucleus, has long been understood at its core to be explained by the interactions of particles called "quarks" that form compounds called "hadrons". Until 2003, all hadrons appeared as compounds of a quark and an antiparticle quark (mesons) or three-quark compounds (baryons, like protons and neutrons). Since then, scores of "exotic" 4-quark or 5-quark hadrons have been discovered, but no simple picture has yet been developed that describes how they are assembled. This project continues the development of a promising universal model of exotic hadrons, the dynamical diquark model (DDM), which has successfully described a number of exotic hadrons as compounds in which the components themselves are bound two-quark subunits (diquarks). Recent work on this model has been to combine the diquark compounds with effects caused by the presence of two-meson components with which they can mix (the DDM diabatic extension). In this project the PI and his students will develop diabatic techniques for precise predictions of measurable properties for the full spectrum of exotic hadrons, including predictions of yet-unseen exotics. This project addresses three major scientific lines of inquiry and one of intense public interest: First, the project will build on prior work to describe the exotics sector using the diabatic dynamical diquark mode (DDM)l. Second, the PI will mentor a PhD student on including effects from two-meson states mixing that depend upon those states' spin quantum numbers, thus creating the most advanced model of exotics to date. Third, the PI will apply the diabatic DDM to study open-heavy-flavor exotics such as the recently observed doubly-charmed T_cc meson that contains two charm -- and no anti-charm -- quarks. In addition, this project will help address a critical shortage of high-school physics teachers in Arizona by leveraging the establishment of Arizona State University's first-in-the-nation online Bachelor of Science in Physics degree program. Under the PI's guidance an established Arizona high-school science teacher will have the opportunity to gains the skills necessary for physics teaching certification in Arizona. 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

Water is crucial for life on Earth, and understanding how it cycles and is stored within the Earth is key to understanding its habitability. The Earth's interior is one of the least understood areas in this regard. Some minerals in the Earth's deep layers can hold water within their atomic structures. Although these minerals contain only a small amount of water, the vast size of the Earth's interior means it could potentially hold much more water than all of the oceans on the surface combined. However, detecting and measuring this water is difficult because the water is present in such small quantities and the samples from deep within the Earth or synthesized in labs are very small. Researchers at Arizona State University are developing new methods using a technique called vibrational electron energy loss spectroscopy (vibEELS) with advanced electron microscopes to measure water in these minerals down to the nanometer scale. This will also allow them to analyze the structure of minerals in detail, a task that was very challenging with existing methods. They are working on techniques to measure both the amount of water and the structure and chemical composition of these water-containing minerals simultaneously. This research will set up new analytical methods at Arizona State University and Brookhaven National Laboratory for use by Earth scientists widely. Researchers will also develop and share open-source software for analyzing these measurements. The research team will host a workshop to share these new techniques to a wider group of Earth and environmental scientists. The project will also provide research opportunities for undergraduate and graduate students at Arizona State University. The project aims to establish protocols for vibrational electron energy loss spectroscopy (vibEELS) using the Nion ultra-STEM. The primary objectives are to: (1) quantify H2O in minerals and quenched melts at the nanometer scale; (2) conduct structural analyses of minerals through single-crystal vibrational spectroscopy; and (3) integrate vibEELS with electron diffraction, energy-dispersive spectrometry, electron energy loss spectroscopy, and imaging for comprehensive mineral analyses. Despite hydrogen loss during Earth's formation, significant amounts remain in the interior, likely equivalent to multiple oceans. This hydrogen is crucial in the geological water cycle, influencing magma and rock properties. Accurate methods are needed to study hydrogen’s atomic-scale integration and quantify its storage in minerals and melts. Existing techniques like SIMS, NanoSIMS, Raman, and infrared spectroscopy have limitations in spatial resolution and structural information. The Nion ultra-STEM, with its energy resolution, enables vibEELS to measure electron energy loss from lattice vibrations with excellent nanometer spatial resolution, suitable for multi-phase samples which is common for Earth science research. Preliminary vibEELS measurements on hydrous ringwoodite, hydrated CaTiO3 perovskite, and stishovite have shown promising results. VibEELS can detect both OH and H2 bands. Integrating vibEELS with electron diffraction allows for noble analyses of vibrational mode intensity and crystallographic orientation. This project aims to harness vibEELS to offer new insights into hydrogen’s role in Earth’s interior. The work plan is designed to maximize the research's broader impact by: (1) establishing vibEELS workflows at ASU’s Eyring Materials Center and Brookhaven National Laboratory; (2) developing open-source software for vibEELS analysis; and (3) hosting a vibEELS workshop at the Fall American Geophysical Union meeting in the second year. By introducing innovative analytical techniques and providing open access to these tools, the project aims to benefit the Earth science community significantly. 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 examines demographically diverse young adults and how they develop social and political identities, as well as how resources and opportunities for youth political engagement have changed during election seasons. The project will contribute to our understanding of the development of political attitudes, behaviors, and identities during this period of rapid life transitions that characterize young adulthood. This project will further our understanding of how demographic groups are integrated into U.S. society and will also identify ways to promote political participation among young adults. This multi-method study will consist of four waves of data collection. First, in September 2024, we anticipate collecting online survey responses from about 500 young adults ages 23 to 34 who participated in our 2020 survey, and 1,500 new participants ages 18 to 22 and 1,000 new participants ages 23 to 34. Second, we will recruit 40 original study participants and 40 new participants who will be first time voters in 2024 to participate in a photovoice exercise. Third, in October 2024, we expect to re-interview approximately 150 young adults ages 23 to 34 who participated in the original study, and 80 new participants ages 18 to 22 using purposive sampling to capture heterogeneity in experiences and outlooks by racial/ethnic and gender identity, political affiliation and voting intentions, and area of residence. Fourth, in the weeks immediately following the 2024 presidential election, we will administer a second online survey to all pre-election survey respondents. Data analysis will include statistical analysis of our survey data using regression analysis to estimate how sociodemographic and other background characteristics and contexts of political socialization are associated with political identities and forms of political engagement. Interviews and photovoice data will be analyzed using a qualitative data analysis software that supports multiple researchers' continuous data inputting and analysis. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-08

This project assesses information sharing rules and practices in four International Environmental Agreements (IEAs) that govern global biodiversity conservation and sustainable use. Information sharing is critical to building trust and cooperative action among diverse actors and IEA member states in order to reach jointly agreed-upon global conservation objectives. In practice, however, IEA member countries follow a range of strategies for the selective sharing of information in order to promote different economic, social, or political objectives. At the same time, national funding agencies, non-governmental conservation organizations, and businesses are investing heavily in emerging technologies for monitoring and sharing data about global biodiversity conditions. Nonetheless, little is known about the current design and effectiveness of IEA information sharing platforms, or how IEA parties interact with global scientific data infrastructures in the context of meeting treaty obligations. In response, the project will advance knowledge of the complex landscape of global information sharing in conservation by examining the formal IEA information sharing rules and how they are mediated and operationalized through digital infrastructures by a variety of actors, including IEA Secretariats, government representatives, researchers, and conservation organizations. It will also map the data and decision-making linkages and gaps within and across the IEA information sharing platforms. Project findings will provide a systematic and holistic understanding of information sharing’s role in environmental governance and inform improvement and innovation in biodiversity resource management. The project uses a mixed-method approach to analyze the degree to which conservation-related data are exchanged on IEA platforms, how the level of exchange differs within and across IEA regimes, and how information sharing has been codified formally and in practice. This is accomplished by (i) using a standardized syntax called the Institutional Grammar to parse formal rules governing information sharing practices into core components and identify their rule type configurations by function (e.g., monitoring); (ii) examining the IEA platforms’ technical architectures and contents through a combination of IT staff interviews, data analytics, and database structure review; and (iii) interviewing key international and national decisionmakers to gain insights on information sharing perceptions and practices. The qualitative and quantitative data gained in steps (i) to (iii) will inform a Structural Equation Model designed to identify factors salient to the variation in actors’ information sharing propensities. IEA platforms offer a major opportunity to investigate long-standing assumptions about the importance of information sharing to effective resource governance. This project taps into that potential to investigate how the social and technical designs of IEA data infrastructures influence trust and transparency. Project results will include descriptive analyses of similarities and differences in the configuration of formal IEA information sharing rules, insights into IEA platform design, interconnectivity, and management of shared information, and the propensity of actors to engage with the rules and platform infrastructure and effectively share information. These results are also significant for recognizing and addressing equity and justice issues, e.g. for marginalized peoples and lower income nations. 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 increasing frequency and intensity of extreme weather events, such as floods and wildfires, are challenging the ability of communities and individuals to recover from adverse events. Insurance is one of the means to minimize the impact of climate change. Insurance policies, however are becoming less accessible in high-risk areas, impacting households’ ability to deal with losses related to natural hazards. This raises questions on how to manage risk, who should bear the cost of increasing climate-related damages, and what solutions can be adopted to reduce vulnerability to extreme weather events. This project investigates these aspects, by exploring motivations behind risk-sharing (i.e. the willingness of individuals to pool together risk to avoid financial losses) and the relationship between risk-sharing decisions and risk-reduction investments. The research insights can inform the development of climate insurance rules and support vulnerable communities exposed to risk. This project focuses on individuals’ decision-making processes, looking at the interplay between social preferences, risk reduction and collective action. Through pre-registered online behavioral experiments with a nationally representative sample, the project investigates risk-sharing decisions, their determinants, and the impact of risk-sharing choices on risk-reduction investments. First, the project identifies the impact of perceptions of fairness and unequal opportunities on risk-sharing decisions. Second, it explores the relationship between insurance choices and risk reduction measures, examining individual and collective forms of risk reduction (i.e. mitigation). This project extends advancements made in the field of social preferences to risk-sharing contexts, providing additional evidence about the motivation of risk-sharing, and provides causal evidence on the relationship between insurance choices and risk-reduction investments. 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

Concerns over energy security and global warming have driven a recent dramatic increase in installed wind capacity. Moreover, the need for optimal wind conditions and limited availability of suitable land has resulted in the aggregation of turbines into high-density wind farms. In such farms, aerodynamic coupling between turbines strongly influences the efficiency of the turbines. Because the coupling between turbines is mediated by wind, however, there is significant and variable delay in the interaction between turbines–the upstream turbines affect downstream turbines with a delay determined by velocity of the wind exiting the upstream turbines. Thus, our ability to efficiently control wind farms is fundamentally limited by our ability to control large-scale networks with uncertain, time-varying and even state-dependent delay. The project will develop methods to design and analyze controllers for such systems with networked dynamics and delays. This will enable a host of benefits for wind energy including improved power capture, reduced loading, and active power control for grid services. The work will also enable the design of safe and efficient networked controllers in other domains including fleets of autonomous vehicles and swarms of uninhabited aerial vehicles. The goal of the project is to develop new theory and algorithms which allow for robust analysis and control of nonlinear systems with uncertain and variable delay. To do this, we combine the Integral Quadratic Constraint (IQC) framework for robust analysis and control Integral with the Partial Integral Equation (PIE) framework for optimal control of fixed-delay linear systems. Unlike previous work which considered the entire delay to be a source of uncertainty, we partition the delay into nominal and uncertain/variable parts. We then use PIE’s for the known/fixed-delay linear part of the system and IQC’s for the uncertain/nonlinear part. This approach reduces the uncertainty in the system and thereby increases the accuracy/performance of the resulting analysis/controllers. The technical approach of the project is divided into 4 Technical Challenges. (T1) First, we consider uncertain/nonlinear dynamics and known, fixed delay and formulate a convex stability test. (T2) Next, we model static uncertainty in delay using a nominal PIE subsystem and an unstructured parametric uncertainty acting on infinite-dimensional channels. We generalize the PIE and IQC frameworks to handle infinite-dimensional channels including a parameterization of dynamic PIE multipliers. (T3) Third, we consider time-varying and state-dependent delay. These cases are modeled by a nominal PIE coupled with either linear parameter-varying uncertainty or a nonlinearity. (T4) Finally, for robust control design, we use an alternation between a synthesis step and an IQC analysis step. The results are applied to models of wind farm control. 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 field of nucleic acid bionanotechnology aims to manufacture nanoscale-sized devices and structures, with applications ranging from creating new biomaterials to diagnostics and therapeutic devices. One of the key factors to help the nucleic acid nanotechnology field to unlock its full potential and realize more complex designs at the nanoscale is the need of sophisticated modeling tools available to experimental laboratories. Manufacturing of modern-day machinery, such as cars, planes, and processors, relies on computer design and simulation testing (such as flow of air around airplane) as an essential part of the design process, using computer-aided design software. The oxDNA ecosystems comprises simulation models and design tools aimed at computer-based testing and validation of nucleic acid nanostructures that can aid experimentalists in testing their structures as well as in designing complex behavior that would be otherwise impossible to achieve without efficient modeling tools. The oxDNA software has now reached a maturity, and this project transitions it toward an open-source ecosystem, where academic researchers as well as the biotechnology industry contribute towards its development and together identify new functionalities that need to be added to push the frontier of the complexity of devices that can be manufactured at nanoscale. Together, these efforts unlock development and realization of the next generation of bionanotechnology devices. The oxDNA software is a set of tools, coarse-grained models, and associated web-based services to model DNA and RNA nanodevices, as well as DNA/RNA-protein hybrid nanostructures. To efficiently model the system sizes and timescales associated with assembly and operation of these devices, it coarse-grains the representation of nucleic acids while retaining the main properties of the molecules that are relevant to the nanostructure operation. The associated ecosystem of tools helps design these nanostructures, provides webservers to run the simulations and libraries to evaluate the simulation results, and redesigns the nanostructures to perform desired function. All tools are developed under GNU Public License and are freely accessible along with their source code. This project deploys new simulation servers, improves the usability of the GUI to make the tools more accessible to a broader user community, creates a network of developers by organizing in-person as well as online training workshops, and establishes the oxDNA ecosystem as an online resource for crowd-sourcing real-world design challenges for practical applications of DNA and RNA nanotechnology. The ecosystem has the potential for transformative impact on the nucleic acid nanotechnology field and associated biotech industry by enabling researchers to perform a large fraction of design in silico to create novel complex architectures. 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

Doctorates in engineering higher education are critical for the country’s competitiveness and broadening participation in at specialized levels of engineering decisions. Yet, the concept of "support" for graduate education across institutions remains largely unclear. High engineering doctorate graduation rates indicate a degree of institutional success in effectively supporting their graduate students. However, it remains unclear how institutions promote the success of doctoral students beyond simply degree completion. To better understand the role of institutions on Ph.D. success, understanding and enhancing the concept of support for graduate students beyond enrollment—is crucial. This CAREER project will lay the foundation for institutions to effectively support students to lead research and innovation through their graduate engineering programs. Specifically, the integrated education and research plan explores how engineering institutions’ approaches to support impact graduate students in engineering. This project addresses the research question How do institutions support engineering doctoral student socialization? To do so, this project will employ a multi-case study methodology using institutions as the unit of analysis to longitudinally study the experiences of engineering doctoral students at two different types of engineering institutions: the University of Puerto Rico at Mayagüez, a research-emerging engineering institution, and Arizona State University, an R1. This project will leverage trusted site coordinators in each institution’s College of Engineering to recruit students in the first year of their engineering Ph.D. and follow them over a four-year period via monthly prompts to understand their Ph.D. experiences. This project will also involve graduate education stakeholders at these institutions via periodic interviews to understand the institutional factors that impact doctoral students’ experiences at their institutions. The collected data will be analyzed alongside institutional data and documents relating to the support efforts at each university to contextualize the participants’ perspectives. By triangulating these data sources, this work will uncover the connection between doctoral student experiences as a function of the structures for “support.” The methods will be guided via two frameworks: (1) Garcia, Núñez, and Sansone’s framework for support and (2) the Graduate Student Socialization framework. Combining these two frameworks will address two vantage points in the analyses—one from the students’ development in institutions and a second from the institutions’ impact on students. The education plan focuses on doctoral education leaders from both institutions. Using a community of practice model, graduate associate deans, program leads, and staff will learn how institutions can support doctoral students in engineering through collaborative workshops showcasing research findings. Outcomes include a list characterizing high-impact practices articulating insights across both institutions and an established network of practice-sharing. Finally, this project will develop a scoresheet to be used as a rubric by graduate education leaders at other institutions to evaluate institutional structures for support and their impact on doctoral students. As a result, this work will help identify and disseminate best practices that other institutions can implement to better support doctoral 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 · 2024-08

Experts predict that water scarcity may cause future conflict. In response, scientists and technologists have developed new methods for maximizing the use of water as a resource, as well as for extracting resources found in water. This project examines such efforts, particularly technologies to make wastewater usable through membrane-based filtration technologies and retrofittable fermenters. It asks to what extent these new water extraction technologies reproduce existing social and environmental issues around water usage, or alternatively provide the basis for more equitable resource distribution systems in the twenty-first century. The project will support training graduate and undergraduate students in an interdisciplinary approach to conducting scientific data collection and analysis. It includes interactive exhibits for presentation at industry conferences and county fairs that help expert and everyday expert groups understand how they anticipate that technologies for retooling water resource extraction might contribute to addressing water and resource insecurity. The project will develop conceptual tools for understanding resource extraction in a future characterized by scarcity and conflict. It investigates how devices used for water resource extraction are being retooled in the early 21st century in response to anticipated challenges shaped by water quantity, water quality, and climate-related water risks. To do this, the research team will use historical, ethnographic, and arts-informed empirical research methods to examine how scientists, decision makers, and other stakeholders anticipate using already-existing membrane and wastewater fermentation technologies (devices) to extend and retool historical conceptions of where water exists, what constitutes a usable form of water/resource, and how water can be used. By studying these devices at new frontiers of water extraction, this project aims to develop the concept of retooling to help scholars describe the zone in between scholarship on maintenance and innovation; and to generate insights into broader shifts in resource extraction and innovation decisions. 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

Artificial intelligence (AI) systems such as hospital robots and disaster-recovery support systems need to plan reliably and efficiently to accomplish complex user-desired tasks such as cleaning up a room or creating an effective resource-management strategy. This amounts to models for reasoning over millions of decision points while taking contingencies into account. Current approaches typically use hierarchical world models, which commands actions from a high-level central source to lesser agents, for making this process feasible. However, creating such models requires extensive hand-engineering by domain experts who have prior knowledge of expected tasks – thus undermining the utility and applicability of autonomous systems. This project will develop a new class of algorithms for enabling AI systems to autonomously learn hierarchical world models and high-level actions. For instance, using the approaches to be developed in this project, a disaster-recovery support system would be able to distill its past experience into relational world models with high-level actions (e.g., ``use truck 17 to deliver trauma medication to survivors at site 3'' and ``deploy search and rescue personnel to site 2'') and to reason over them rather than over what to do at each millisecond. The resulting auto-generated abstraction hierarchy will enable AI systems to compute safe and reliable plans for user-desired objectives in stochastic settings. These objectives will be achieved through two broad classes of algorithms for learning abstract world models. Both approaches will address problems involving long-horizons and sparse rewards in stochastic settings where test problems are significantly different from training problems. Bottom-up abstractions will focus on learning abstractions based on unannotated behavior in training scenarios. This thrust will develop algorithms for learning to predict critical regions (sets of salient states) in test problems, and then for inventing high-level actions as moves to and from critical regions. Top-down abstractions will start with an uninformed, coarse abstract representation of the state space and selectively increase the resolution of representation to better explain the ongoing experience. Both paradigms will yield well-defined, interpretable world-models that can be used for scalable and reliable planning in real-world settings. Benchmarks, test scenarios, algorithms and software developed in the project will be made broadly available to the research community in open-source format. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-07

The broader impact of this Partnerships for Innovation - Technology Translation (PFI-TT) project addresses safety concerns caused by tire failures. The project may achieve significant economic benefits by reducing financial losses associated with tire blowouts. According to U.S. National Transportation Safety Board (NTSB) and National Highway Traffic Safety Administration (NHTSA) reports, each year about 33,000 tire-related passenger vehicle crashes occur, resulting in about 19,000 injuries. In 2020, over 600 fatalities occurred as a result of tire-related passenger vehicle crashes. The National Safety Council (NSC) estimates the average economic cost per non-fatal disabling injury to be around $98,000, and the average cost per death to be around $1.66 million. Enabled by this technology, a potential 20% tire blowout accident mitigation will save about $600 million in direct cost annually, in addition to saving lives, protecting families, and reducing car insurance costs. This PFI project will create a technology to mitigate or avoid vehicle accidents and collisions after a tire blowout. A high-fidelity and real-time implementable tire blowout model will be developed to predict vehicle dynamics and motions after tire blowout. Based on the developed model, the characteristics of tire blowout and the impacts on vehicle motions will be evaluated online and utilized in shared control design. The design will be commercialized as a new advanced driver assistance systems (ADAS) function that does not rely on drivers’ experience or expertise to handle tire blowout accidents. The developed model and control algorithms will be verified and validated in a full-size vehicle under various operation conditions. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-07

This award provides support to U.S. researchers participating in a project competitively selected by a 55-country initiative on global change research through the Belmont Forum. The Belmont Forum is a consortium of research funding organizations focused on support for transdisciplinary approaches to global environmental change challenges and opportunities. It aims to accelerate delivery of the international research most urgently needed to remove critical barriers to sustainability by aligning and mobilizing international resources. Each partner country provides funding for their researchers within a consortium to alleviate the need for funds to cross international borders. This approach facilitates effective leveraging of national resources to support excellent research on topics of global relevance best tackled through a multinational approach, recognizing that global challenges need global solutions. This award provides support for the U.S. researchers to cooperate in consortia that consist of partners from at least three of the participating countries. The teams will develop transdisciplinary and convergent research approaches on cultural heritage and climate change, foster collaboration among the research community across several regions, and contribute to knowledge advances at the global level. The project focuses on integrating local and traditional knowledge (LTK), social science and natural science to determine how past practices can deliver innovative local solutions to environmental change in coastal regions, and how these integrated approaches can help people (re)discover more sustainable ways of living in their rapidly changing coastal environments. Understanding the management and adaptation methods used by these communities provides important insights into diverse strategies for sustaining coastal ecology, local livelihoods and food security today. LTK is accumulated and transmitted through stories by resident communities in place over many years and generations. Stories reflect lessons on resilience, sustainability and adaptation. Gathering stories and using place-based understandings is a well-recognized methodology that can deepen understanding of how coastal communities draw upon their cultural heritage. The project focuses on three northern coastal regions: Kodiak Island in Alaska, Dublin Bay in Ireland and southwest Wales. All three areas have relied heavily on their shorelines for centuries. They have linguistic and cultural preservation in common and provide contrasting examples of rural and metropolitan living. Moreover, similar vulnerabilities and challenges affect these areas as a result of a changing climate. The project involves collaboration with a range of community-led initiatives to understand how stories are being used to develop low-tech, bottom-up, nature-based strategies and solutions for local coastal management. The project explores how the knowledge embedded in these stories is being adapted in different localities to reinvent cultural heritage-led sustainable innovation solutions. It highlights the importance of intergenerational knowledge and how looking to the past can help create a legacy for future generations. 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

TECHNICAL AND NON-TECHNICAL SUMMARY: This award provides partial funding to the 2025 ACS Polymer Chemistry Division National Graduate Research Conference (NGRPC), which will be focused on "Polymer Sustainability: Diverse Strategies for Addressing Global Challenges". The conference will include participants across the areas of polymer science and engineering with expertise including chemical synthesis, composites, rheology, 3D printing, and self-assembly. A broad selection of forward-looking topics will be discussed in focused sessions on circular plastic economy, quantitative sustainability, bioinspired macromolecular design, green-chemistry approaches to macromolecular synthesis, additive manufacturing and sustainability, stimuli-responsive polymers, and systems modeling. The conference is poised to provide a multidisciplinary educational platform for students and expand the scope of their research, establish meaningful collaborations and mentorships, and offer an opportunity to highlight students' research especially as it interfaces with the broad and growing needs for sustainability. . 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

Land surface characteristics of urban cities have long been known to affect rainfall amount, intensity, and timing. There is also growing appreciation of the role of a warming Earth as a driver of extreme precipitation amplification. This project will study the combined role of urban expansion and large-scale climate change on precipitation enhancement near urban areas through advanced numerical modeling. The results of the work will be disseminated to local stakeholders in Texas and Arizona to help inform decisions about infrastructure development, land planning, and resilience. The project will include training and educational opportunities for up to 8 early career scientists and students. The project will test the following hypothesis: ‘Large scale climate change will be the dominant factor responsible for increased extreme summertime rainfall; urban expansion will amplify impacts that will intensify existing events’. The project seeks to advance fundamental knowledge characterizing the processes impacting the spatio-temporal evolution of extreme summer rainfall over two rapidly expanding metropolitan areas across the US Sun Belt: Austin (TX) and Phoenix (AZ). The project work will: (1) conduct a set of Weather Research and Forecasting (WRF) simulations comprising of a suite of historically representative extreme wet summer seasons; (2) conduct a set of WRF simulations with identical configuration as the historical experiments, comprising of a suite of projected extreme wet summer seasons that account for urban expansion and greenhouse gas emissions, separately, and in tandem, using two contrasting dynamical downscaling methods (dynamical, and the pseudo-global warming approach); (3) conduct a process-based examination of the key physical drivers responsible for projected extreme summer season rainfall changes relative to the historical baseline. The high-resolution output in time (hourly) and space (2km grid spacing for the innermost domain) will permit improved process-based understanding of the mechanisms driving simulated extreme rainfall changes. 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 proposed project aims to serve the national interest by improving science literacy for all undergraduate students. The project intends to convene a workshop of thought leaders across a variety of scientific disciplines and educational contexts to develop a model for science literacy that addresses contemporary challenges such as climate change, emerging infectious diseases, and science misinformation and disinformation. The idea for the workshop is grounded in a science literacy framework published in 1990 entitled “The Liberal Art of Science”. Workshop participants will gather in Washington DC to develop an updated framework entitled “The Liberating Art of Science”. Planned sessions will examine the current ecosystem of science instruction in the liberal arts context and the reality of trying to teach science in an era of vocal distrust of science. Topics will include defining activities to best prepare students for engaging with science throughout their lives and identifying exemplars that can illustrate how scientific literacy can be achieved in the classroom. The NSF IUSE: EDU Program supports research and development projects to improve the effectiveness of STEM education for all 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 · 2024-07

Aquaculture is the fastest-growing animal food production sector globally and already accounts for more than half of the fish consumed worldwide. Land-based aquaculture production has increased substantially in recent decades and is expected to expand faster than marine-based production. Recent work suggests that the shift from rice to aquaculture in coastal areas is not motivated simply by profit or policy drivers. Instead, the more frequent large storm surges and salinity related to increasing environmental variability may be catalyzing the shift. There are also indications that the shift to aquaculture is not benign for either the environment or the people. The dynamic relationships between aquaculture land transformations, environmental drivers, and social-environmental consequences, however, have received relatively little empirical attention. This doctoral dissertation research project aims to explain the historical geographical patterns of aquaculture land transitions, the motivations driving these transitions at a household level, and the consequences of these transitions on the people, the environment, and places. The project investigates whether shifts to aquaculture are an adaptative response and whether this human intervention is detrimental for marginalized groups, especially women and landless laborers. An additional objective is to ascertain the extent to which these practices are potentially accelerating sea-level rise outcomes in fragile coastal areas. The project also facilitates the training of an early-career researcher to conduct interdisciplinary research. To understand the patterns, drivers, and consequences of aquaculture land transformation, the research design uses a mixed-methods approach. First, remote sensing methods and causal inferential analysis are employed to understand the relationship between past experiences of storm surges, historical changes in coastal salinity, and temporal land changes to aquaculture. Second, the researchers employ a novel, large-scale social survey to understand the specific hierarchy of factors driving household-level land change decisions. Finally, to understand the consequences of these transitions on people and places, the targeted survey on aquaculture impacts and focus group discussions, particularly with marginalized social groups, are undertaken. The project leverages a natural experiment to collect and study soil and groundwater quality changes that can be attributed to aquaculture practice. In turn, this research shows the extent to which aquaculture practices and management plans can help to balance tradeoffs between social and environmental outcomes. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.