Pennsylvania State Univ University Park

NSF Awards · FY 2024 · 2024-09

The rapid evolution of quantum computing in recent years has led to significant advances, revolutionizing various disciplines, including the energy sector. Quantum computing has emerged as a key player in addressing the increasing complexity of the power system and adapting to the changing needs and goals within the grid. The goal of this project is to establish a quantum-energy training program that addresses real-world challenges in power systems using quantum techniques and offers tailored cross-disciplinary training. This will contribute to creating a stronger, more resilient, and reliable energy system. Additionally, this project will contribute to the NSF mission of advancing STEM by systematically training the quantum-energy workforce through designing tailored training materials. This initiative focuses on enabling trainees to develop quantum algorithms that can effectively address intricate challenges in power systems, particularly by leveraging computational advantages in the Noisy Intermediate-Scale Quantum regime. These capabilities are pivotal for overcoming sustainability and clean energy transition challenges that are currently unattainable through classical computational methods. Specifically, three primary objectives will be realized. First, create a project-based training program that synthesizes interdisciplinary knowledge from power engineering and quantum computing, cultivating trainees proficient in both fields. Second, offer trainees hands-on experience and practical application in power system-oriented quantum computing, coupled with personalized feedback, to enhance the widespread adoption of quantum computing. Third, create a seamless integration between problem-solving, training, and career advancement. Overall, this project will train a quantum-ready workforce in power engineering, preparing them to meet future challenges and opportunities in this cross-disciplinary field. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-09

Silicon-rich continental crust is unique to Earth and is critical for habitability, but the processes that drive the long-term stability of this crust are unclear. The most enduring tracts of continental crust have resided at the Earth’s surface for billions of years and are characterized by enrichment of uranium (U), thorium (Th) and potassium (K)—the heat-producing elements—in the upper crust. This research project will address the question: what controls the mobility of the heat-producing elements through continental crust? The researchers will measure concentrations of U and Th in rocks and minerals across two temperature profiles in exhumed sections of middle and lower continental crust. Combined with constraints on the pressure-temperature-time evolution of these rocks, they will discriminate between competing mechanisms for the mobilization of the heat-producing elements during melting of the continental crust. The research will catalyze international collaboration between scientists in the US and Switzerland, foster the training of a graduate student, and engage undergraduates in academic research. Characterizing how the heat producing elements are mobilized in continental crust is fundamental to understanding crustal evolution, the temperature and mechanical structure of crust, Earth’s heat budget and chemical differentiation of the planet. Using a suite of complementary techniques, the researchers will test five hypotheses—Equilibrium and Disequilibrium Melting, Mineral Shielding, Melt Buffering and Rejuvenation—for the distribution of the main heat producing elements, U and Th, across two well-characterized temperature profiles: contact aureoles of the Mafic Complex, Ivrea Zone, Italy and the Big Jim plutonic complex, Washington, USA. In-field Gamma Ray Spectrometer measurements will provide bulk-rock U and Th concentrations at a sampling density inaccessible to conventional geochemical techniques. Metamorphic zircon and monazite U/Th-Pb dates + trace-element abundances obtained by laser-ablation split-stream petrochronology will allow assessment of the timescale over which accessory mineral dissolution occurred. Petrologic constraints will be derived from P-T pseudosections, optimal thermobarometry, and trace-element thermometry. In conjunction, the latter two techniques will be used to reconstruct the peak metamorphic conditions and the timing and quantity of melt removal. The complete dataset will allow rigorous testing of the five hypotheses. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-09

Scientific information often takes over a decade before it is used in decision making. While some universities and funders have begun to focus study priorities on the needs of communities, there has been little attention to how academic institutions translate existing knowledge for public policymakers. Scholars report the greatest hindrances to their public engagement activities include limited time, resources, training, or technical support. Scholars who study research impact are calling for system change that would routinize knowledge mobilization and create infrastructure for research synthesis and outreach. This study produces insights about systemic barriers and facilitators to developing infrastructure within academic and funding systems that would improve the societal benefit of scientific knowledge. The study focuses on eliciting problem definitions and recommendations from those who have decision-making power in academies including leaders of: (1) public universities (e.g., vice/chancellors, vice/provosts, vice/presidents of research, or deans) and (2) research-funding philanthropies (e.g., vice/presidents or members of the board of directors). The research is action-oriented and produces recommendations on the development and resourcing of research translation and policy engagement infrastructure by gleaning decisionmakers' perspectives relative to institutional missions and roles. Findings inform institutional change initiatives; therefore, a report of recommendations derived from interviews will be shared on a public website and disseminated in partnership with university and philanthropic consortia. This research examines the perceptions of decision-makers in public universities and research-funding philanthropies regarding the structural or institutional barriers and facilitators of translating research evidence for policymakers. To offer a more complete view of the evidence ecosystem, the study includes both university and research-funding leadership. A qualitative grounded theory methodology develops robust theoretical frameworks that allow investigators to understand structural barriers and facilitators for disseminating research for public policymakers. The study explores implications for developing university infrastructure for research impact. The team investigates how public university decision-makers describe barriers and opportunities in achieving public benefit through research translation and policy engagement and how philanthropic leaders understand their role in resourcing infrastructure for research impact and policy engagement. The data analysis involves iterative and constant comparative process, leading to the development of a theoretical framework, grounded in data from participants, and guidance for research leaders and policy makers. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-09

A more diverse STEM workforce is needed to support innovation and creativity in the field. However, historically underrepresented students and low-income students are disproportionately less likely to earn undergraduate STEM degrees as compared to overrepresented students. A primary contributor to this achievement gap in graduation rates could be that historically underrepresented students are more likely to lack a sense of belonging in a STEM major. In alignment with the first goal of this solicitation, which is to conduct research in the Professional Formation of Engineers (PFE), the proposed work will evaluate the effectiveness of five teaching strategies on promoting cognitive belonging and engagement in an upper-level architectural engineering course. The teaching strategies include those that provided high structure, e.g., providing students with regular opportunities for learning the materials, and those that create an inclusive environment, e.g., in-class collaborative learning activities. These techniques will be incorporated into two offerings of the same course and evaluated using surveys, in-class observations, and interviews. If these strategies are found to be effective and eventually become widely adopted, more students will feel “I belong in engineering”, which will strengthen the engineering workforce long-term. In support of the second goal of this solicitation, which is to increase the number of researchers in this field, a mentoring and professional development plan will be implemented to develop the skills of the PI, Dr. Michelle Vigeant-Haas at Penn State University, in the engineering education research field and to grow her network. The plan includes working closely with her mentor, Dr. Karen High at Clemson University, an expert in the field, as well as a five-member advisory board composed of members from four other U.S. institutions. In addition, the PI will take relevant courses, conduct workshops, and share the results through presentations and journal publications. Belonging uncertainty contributes to the significantly lower average STEM graduation rates for historically underrepresented students. The research aim of this project is to understand how the use of high-structure and inclusive teaching strategies may impact cognitive belonging, and behavioral and social engagement in an upper-level architectural engineering class. Five instructional strategies will be incorporated into this course and be examined in the context of these three factors. A multi-method approach will be used to collect qualitative and quantitative data and a design-based method will be used to revise the strategies and measurement instruments across two course offerings. The theoretical foundation of this project consists of Vygotsky’s social constructivist theory of learning and a student engagement framework. This project will address the existing gap between these theories by exploring the impact of the proposed teaching strategies on belonging and engagement in a 400-level engineering course. The proposed work will be a significant contribution to engineering education research (EER) given most work relevant to these theories has been done in introductory math and science courses. The outcomes of this work could potentially lead to more students feeling that they belong in engineering, which will diversify the engineering workforce. The mentoring and professional development aim of this project is for the PI, Dr. Michelle Vigeant-Haas at Penn State, to develop EER skills and an EER network under the close guidance of her accomplished mentor, Dr. Karen High at Clemson University, as well as a five-member advisory board. The PI’s professional development plan includes a mentored EER experience, personal development plans, educational methods courses, and broad dissemination at both institutions, American Society for Engineering Education (ASEE) conferences, and in appropriate journals. This work will also contribute to the expansion of the number of faculty conducting EER and create opportunities for multi-institutional EER collaborations. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-09

Thunderstorms produce many disruptive, impactful hazards, including heavy rainfall, strong winds, large hail, and tornadoes. Accurate prediction of thunderstorm hazards is limited by our ability to predict exactly where storms will initially form. Current state-of-the-art storm-permitting numerical models perform best in predicting storm evolution only after storms have formed. Newer AI models can learn space and time patterns across different scales but require deeper physical validation to understand what patterns they are learning and how they translate into improved predictions. To address these challenges, this project will develop AI models to predict storm evolution. The project is expected to enable significant breakthroughs in storm initiation understanding through advancing explainable AI techniques and sophisticated uncertainty quantification methods. The project would also generate a convection initiation benchmark dataset and train students to conduct cross-disciplinary collaborative research. This project will advance geosciences and AI impact through collaborative development of a prognostic, diagnostic, and generative deep learning nowcasting tool for convection initiation, quantifying the predictability and uncertainty of predictions and working to unify uncertainty quantification paradigms from ensemble NWP and DA with those from statistical and evidential ML. Explainable AI methods adapted to link realistic perturbations in physical processes to changes in predictions can improve physical understanding of CI through exploration of specific scientific hypotheses. New uncertainty quantification approaches can generate well-tuned ensembles of ML predictions, providing insights to the practical predictability of CI. Synergy with data assimilation can also be attained by using AI for the ensemble forecast model and background error covariance, to anticipate and initiate convection in models, and to aid process studies. In particular, this research will: (1) design and optimize a state-of-the-art combined prognostic, diagnostic, and generative deep learning convection nowcasting system using artificial intelligence techniques, high resolution satellite and radar data, and surface weather fields; (2) assess the predictability and quantify the uncertainty associated with the AI system predictions, and compare to traditional ensemble NWP; and (3) discover the physical and dynamical mechanisms that control the predictability of convection through physically-informed explainable AI. The project is expected to enable significant breakthroughs in research questions surrounding convection initiation through advancing explainable AI techniques and sophisticated uncertainty quantification methods to overcome challenges associated with convection initiation predictability and explainability. The project will enable insights of the fundamental dynamical and physical processes behind convection initiation from a data-driven perspective, and how these physical processes affect predictive skill. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-09

Galaxies come in wide variety of shapes, from great star-forming spirals like our own Milky Way galaxy to the red-and-dead elliptical-type systems. For some time, astronomers have known that this diversity is largely driven by environment, with galaxies in dense regions evolving differently from those in low-density voids. To understand this diversity we must look ~10 billion years into the past and survey large swaths of the universe at “cosmic noon”, when the universe was only ~1/4 its present size, star-formation and black hole activity peaked, and the great cosmic structures we see today were just beginning to form. By characterizing the galaxies' 3D environments, the team will quantify the relationships between a galaxy’s physical properties, such as its elemental composition, gas and dust content, stellar mass, and star-formation rate, with its environment. The team will also participate in a series of local education and outreach activities in Pennsylvania and Indiana, with a particular focus on engaging students in grades 4-12 to spark their interest in science. This program is aimed at elucidating how galaxies, quasars, and gas trace the cosmic structures of the 2 < z < 4 universe, and likewise, how dense environments within the large-scale structure influence their evolution. To do this, the program will combine the data products of three large surveys: imaging from the One-hundred-square-Degrees In Narrowbands (ODIN) survey, wide-field integral-field spectroscopy of the Hobby-Eberly Telescope Dark Energy eXperiment and targeted spectroscopy from the Dark Energy Spectroscopic Instrument of up to several hundred thousand Lya-emitting galaxies at cosmic noon, residing in a wide range of large-scale environments. This unprecedented dataset will (i) confirm ODIN-detected cosmic structures and reconstruct the 3D shape of tens of massive protoclusters; (ii) identify rare astrophysical sources such as Ly-alpha blobs and active galactic nuclei (including quasars), in order to investigate their locations within the large-scale structure; (iii) infer the mean physical properties of galaxies as a function of large-scale environment based on their photometric and spectroscopic measurements; and (iv) study how intergalactic gas and galaxies trace the underlying matter distribution and each other. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-09

Flooding is rapidly becoming one of the most widely experienced, deadliest, and costly natural disasters threatening our economy, well-being, and security. While considerable effort has gone into improving flood forecasting models and mapping flood inundation hazards, mountainous settings pose unique challenges. Conditions that generate floods in mountain settings can be difficult to predict and model. Flood hazards in mountain settings are often characterized by erosional hazards that cascade through steep terrain and narrow stream and river corridors, with significant impacts on property, infrastructure, lives, and riverine ecosystems. To develop and employ actionable solutions to address the threat of mountain flooding, a deeper understanding is needed regarding the limits of existing flood forecasting services in complex mountain terrain, the needs of local communities experiencing catastrophic flooding, and the opportunities that nature-based solutions (NBS) afford for improving flood resiliency. Nature-based solutions (NBS) offer low-cost and strategic pathways to flood resilience by employing the services provided by intact forests, floodplains, wetlands, and river corridors as an alternative to engineered solutions to flood mitigation. This planning grant brings together Earth systems scientists, conservation organizations, government officials and planners, and other academic partners to consider the flood resiliency needs of communities, drawing upon examples in the Appalachian Mountains. The project objectives are to (1) assess community-based needs for improved flood hazard prediction, (2) explore the potential of new data sets and data driven modeling approaches to improve flood risk mapping, and (3) develop a pathway for the acceleration of science-based and community-engaged resiliency solutions. The objectives will be achieved through a series of knowledge-sharing webinars, field visits, participatory mapping exercises, and a grant-writing workshop. The overarching goal is to develop capacity for the integration of flood risk prediction science and NBS deployment that is responsive to community needs and builds resilience for highly vulnerable, rural communities in mountain regions. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-09

The extratropical storm track is responsible for the poleward transport of momentum, heat, and moisture across the midlatitudes. Storm track eddies and fronts are responsible for much of the high impact weather in the midlatitudes including inland flooding, strong wind, and coastal inundation events that threaten both life and property in these regions, as well as result in significant economic losses. Despite the significance of the extratropical storm track to the climate system, it remains poorly represented by current climate models. In particular, while the magnitude of the biases has improved over time, the spatial pattern of the biases remains, suggesting an ongoing gap in our of understanding of the storm track and its variability. This collaborative work aims to bridge this gap in knowledge by first demonstrating a link between tropical deep convection, planetary scale stationary wave activity, variations in the zonal mean equator to pole temperature gradient, and extratropical storm track activity. The study will also work to identify the physical mechanisms acting to establish these links and explore the extent to which these processes are represented in climate models and subseasonal to seasonal (S2S) prediction models. The study will train two PhD students in meteorology, one at each of the principal investigators home institutions, preparing the next generation of the STEM workforce. The principal investigators will additionally expand an existing partnership between Penn State and a local area rural high school to two high schools in New York with a predominance of economically disadvantaged students. These students will participate in a hybrid workshop in which the students will share their experiences with the learning materials and listen to guest speakers share career advice and opportunities. The main hypothesis to be tested by this study is that tropical heating is linked to extratropical storm track activity through modulation of planetary scale stationary wave activity. The study will also consider the impacts of the resulting storm track variability back onto the planetary stationary waves. Using gridded data sets, local and global mechanisms will be explored that may play a role in linking tropical heating to extratropical storm track activity. The nature of the tropical heating most effective at changing mid-latitude stationary wave amplitude in a way that most influences zonal mean available potential energy and storm track activity will also be explored, as will other mechanisms for exciting stationary waves that go on to impact storm track activity and their relative importance over the tropical heating mechanism. Causal relationships between stationary waves and storm track activity, latent heating and storm track activity and latent heating and stationary waves in observations will be established using lead-lag single value decomposition (SVD) analysis. Idealized modeling studies will also be carried out using a dry global circulation model to understand the causal relationships of the observed fields. The study will then explore how well CMIP6 and subseasonal to seasonal predictive models capture the links between tropical heating, planetary stationary waves, zonal mean available potential energy and extratropical storm track activity identified in the observations. The study will be carried out by the two graduate students supported by the project under the supervision of the principal investigators. These students will benefit not only from the research experience offered by the project, but also from the mentee experience offered through the outreach activity with the high school students in Pennsylvania and New York. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-09

This project studies 6-million-year-old paleontological sites to retrieve fossil, paleoenvironmental, and geological data relevant for understanding human origins. The age of the fossil sites coincides with the emergence of the human lineage, and the paleontological assemblages provide novel information regarding the ecological contexts and evolutionary histories of related fauna. The project supports field work, analytical analyses, graduate and undergraduate student training opportunities, and public science outreach. Paleontological sites sampling the critical period when human ancestors first evolved (6 million years ago) are exceptionally rare. The investigators conduct intensive (i) paleontological recovery, (ii) paleoenvironmental reconstruction through multiple proxies, and (iii) refinement and expansion of the regional geological framework in a key region for understanding hominin origins. Local paleoclimate, vegetation structure, and herbivore community ecology are investigated using phytolith analyses and stable isotope analyses of paleosol carbonates, leaf waxes, and fossil mammalian tooth enamel. Geologic work includes tephra analysis, stratigraphic measurements, and high-resolution mapping to constrain the age and environmental context and enable local correlations and linkages to other paleontological sites. The multi-proxy approach used in this project provides robust dietary niche and environmental reconstructions to better contextualize the fossil assemblages during the emergence of the human lineage. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-09

Coastal margins along the Atlantic seaboard are experiencing significant changes due to sea level rise, causing the intrusion of saltwater into freshwater systems. This inundation and groundwater salinization has led to the emergence of “ghost forests,” characterized by stressed and dead trees, which are altering the ecology and hydrology of coastal ecosystems. This research will study the impact of sea level rise on the health of coastal forests through changes in hydrological processes, with a particular emphasis on stemflow (precipitation intercepted by trees and channeled down the trunks to the soil). Stemflow plays a crucial role in transporting nutrients and organic matter to the forest floor, as well as soil moisture recharge in near-trunk soils. The research will provide new insights into coastal forest resilience and inform strategies for mitigating the effects of sea level rise. Further, the project will develop publicly available training modules to disseminate knowledge about the flow and dynamics of water in coastal forests and provide interdisciplinary training to undergraduate and graduate students. The project will investigate the impacts of sea level rise on stemflow dynamics and associated hydrological and biogeochemical processes in coastal forests along a transect from healthy to ghost forests. Field measurements, laboratory analyses, and statistical modeling approaches will be used to understand stemflow's role in the ecohydrological responses of coastal ecosystems to changing environmental conditions. Results will inform efforts to enhance ecosystem resilience and adaptation to sea level rise by elucidating how coastal forests respond to stressors such as soil salinity, vegetation changes, and hydrological dynamics. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-09

This project will fund research that strives to enable swimming robots with novel capabilities customized to a specified range of objectives and environments. Fish, shaped by hundreds of millions of years of evolution, display a diversity of body structures and neural circuits in response to ecological pressures. This project will build upon modular representations of these evolutionary solutions to implement a robot design process emulating natural selection. The project envisions design features grouped into the following three categories: (i) external flow sensing, decision-making, and power management, (ii) body and fin actuation, shape and internal state sensing, and buoyancy control, and (iii) body and fin shape and compliance control. Automated printing and packaging will allow rapid prototyping of candidate robots from this design space. Each robot will undergo physical tank trials using reinforcement learning to develop control policies for a set of characteristic movements, including speed and acceleration of turning, forward, backward, and sideways motion, and energy efficiency during sustained forward motion, which will then be evaluated by physical flow testing subject to an anticipated range of operating conditions. Candidates will compete against each other to accomplish movement-based tasks in relevant flow conditions, with high-scoring designs selected as the starting point for the next round of testing, and low-scoring designs eliminated from further consideration. After multiple such rounds, the winning configurations will be equipped with fluid flow sensors, gyros, and accelerometers, and will learn decision-making and feedback strategies for choosing and blending individual motion primitives to effectively achieve higher-level guidance and navigation objectives. This work will accelerate the application of intelligent underwater robots to address national needs and grand challenges, including search and rescue, disaster recovery, pollution and ecological monitoring, and infrastructure inspection. Associated outreach and STEM education efforts include developing a plug-and-play robot kit and a science class at the Harvard Museum of Natural History. This research will create modular robotic swimmers capable of artificial evolution, to enable novel swimming capabilities such as stable swimming in turbulent flows, navigation towards wakes of underwater objects, performing stable rheotaxis, and dynamic energy savings via real-time adjustment of robot body and caudal fin stiffness and shape. The project will first modularize fish-inspired robotics to create a Modular, Mutational, Morphing Underwater Robot (M3UBot) design space. Next, asynchronous evolution will be performed directly in the physical M3UBot design space for evolving body morphologies and learning motor control programs for modular swimming behaviors (e.g., rapid turning, acceleration, steering, forward or backward swimming). The large-scale robot evolution in physical space and the “plug-and-play” robot assembly will be enabled by innovating 3D-Printing and Electronic packaging (3DPE) for rapid design and automatic fabrication of M3UBot modules. Finally, selected prototypes from the evolved M3UBot population will be equipped with hydrodynamic pressure sensors; they will then undergo reinforcement learning for feedback control and decision-making that combines modular behaviors to navigate in challenging hydrodynamic conditions. Together, this project will transform the fundamentals and applications of underwater robotics, culminating in next-generation intelligent robotic swimmers capable of hydrodynamic perception, active shape morphing and stiffness tuning, and versatile motor skills in challenging hydrodynamic 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 · 2024-09

Nontechnical description Quantum confined structures such as quantum wells and quantum dots (QDs) are a key element in a majority of modern electronic and opto-electronic devices ranging from lasers to high-speed photodetectors, and more recently in quantum information sciences where quantum dots form the basis for spin-qubits or quantum light sources. While III-V and II-VI semiconductors have been researched extensively over the years and offer promise to applications, their widespread utility is limited by challenges associated with light extraction from the material and ability to integrate with a silicon platform. The emergence of two-dimensional (2D) materials has revolutionized the conception and design of electronic heterostructures from that of buried interfaces within lattice matched III-V multi-layer structures to atomically thin van der Waals stacks with arbitrary control over stacking. The project takes this concept further to develop 2D analogues of QD structures via the fabrication of compositionally modulated dots with deep-subwavelength (< 20 nm) dimensions embedded within atomically thin monolayer transition metal dichalcogenide sheets that can be easily integrated into device structures. The research investigates controlled synthesis of the 2D QD structures with varying composition; atomic-scale analysis of structure, chemistry, and defects; and exploration of their electronic and nanophotonic properties. The project forms the thesis research of two Ph.D. students who are co-advised by the principal investigators (PIs). Undergraduate students from the PIs and partner institutions participate in the research during the academic year or through summer research programs. Graduate and undergraduate students are exposed to a rich collaborative research environment through interactions and internships with researchers at government lab facilities. Technical description The development of bright, tunable, easy to scale and integrate quantum light courses stands as a paramount objective for applications ranging from quantum information processing to quantum sensing and metrology. Quantum dots (QDs) and defect emitters are particularly promising candidates for scalable quantum systems since they are based on a semiconductor platform which leverages existing infrastructure. Quantum emitters based on 2D transition metal dichalcogenides (TMDs) are of particular interest due to their ultra-thin nature and van der Waals bonding, which enables high light extraction efficiency and hetero-integration via layer stacking. Approaches pursued thus far to achieve quantum emission from 2D TMDs include controlled defect/impurity introduction, strained nanostructured surfaces and twisted bilayers. This project focuses on the development of a new class of 2D quantum emitters based on in-plane 2D TMD quantum dots embedded within wafer-scale continuous monolayer sheets. The research focuses on two dot/matrix combinations: MoSe2/WSe2 and MoS2/WS2 (Type II band alignment) and MoSe2/WS2 and ReS2/MoS2 (possible Type I alignment). The work encompasses studies of TMD epitaxy on single crystal substrates focused on tuning the size, shape, density and uniformity of dots and the dot/matrix interface providing insights into the fundamental mechanisms of TMD nucleation, lateral growth and heterointerface structure. Comprehensive exploration of the electronic and optical properties of the samples enables new insights into exciton confinement and charge transfer in in-plane heterostructures. A combination of scanning probe based near-field electronic (surface potential and conductance mapping) and optical techniques (Raman and photoluminescence (PL)) are used in conjunction with far-field spectroscopy (reflectance, ellipsometry and PL) and gated measurements to determine the nature of band alignment and exciton confinement in these heterostructures. The project provides fundamental insights into quantum confinement in in-plane TMD heterostructures and lays the groundwork for future development of TMD QDs monolayers for quantum light emission. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-09

Water plays a crucial role in supporting life on Earth, not only in rivers and lakes, but also within the soil and atmosphere. The way water moves and is stored can affect everything from plant growth to water quality. When it rains, water doesn’t just sit on top of the land; it gets absorbed into the soil to support plants and animals, seeps down to become groundwater, evaporates back into the air, or runs off into streams and oceans. The way water is distributed on the planet is changing due to human activities and global warming, yet it is not fully understood how such changes impact water stored in the ground, plant growth, or how water availability regulates where and how much carbon is stored belowground. To solve this puzzle, this project will measure the movement and amount of water and carbon in a Kansas grassland where the climate is getting wetter, and the land cover is changing where shrubs are encroaching and replacing grasses. The project will train undergraduate and graduate students, as well as a postdoctoral researcher, to collect water, soil, and plant data and bring these data together to understand how the water and carbon cycle are changing. This project will use the Konza Prairie (Kansas, USA) and its long-term research platform of watershed-scale manipulations (e.g., fire and grazing) in a mesic grassland to answer the question: How does concurrent climate and land cover disturbance alter below-ground water and carbon cycle processes, and their interactions? Using an interdisciplinary approach that incorporates new data collection, data harvesting, and numerical modeling the project team will: 1) quantify the degree to which woody encroachment alters soil macropore abundance, preferential flow occurrence, and vertical water fluxes; 2) measure the impact of woody encroachment on groundwater residence times and the fractional sources of water (e.g., interflow, shallow groundwater, deep groundwater) that support stream flow; 3) quantify changes in carbon subsurface processes (e.g., respiration rates, decomposition, weathering) and fluxes; and 4) model the impact of synchronous changes in climate (i.e., precipitation and evapotranspiration) on the capacity for carbon sequestration as woody encroachment progresses in grasslands. By addressing the overarching question in grasslands with intermittent streams underlain by carbonate bedrock, the research will provide transferable knowledge on the ecohydrologic function in systems particularly vulnerable to climate and land cover disturbance. This project is co-funded by the Division of Earth Sciences Hydrologic Sciences program and the Division of Environmental Biology Ecosystem Science program. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-09

The impacts of anthropogenic climate change are becoming increasingly apparent with sea-level rise and increases in the number of extreme weather-related events, such as floods, wildfires, droughts and storms. An interdisciplinary approach to solving the complex interconnections of the atmosphere, oceans, and land is needed. Urban systems also play an important role, as approximately 80% of the U.S. population live in cities, and cities are especially prone to negative effects of climate change, such as heat stress, air pollution, and flooding. Disadvantaged communities are more at risk to effects of climate change because of infrastructure inequities. The Climate, Air Quality, and Urban Systems Research Experience for Undergraduates program at Penn State will provide participants with internship projects on research topics that will address climate resiliency and environmental justice, train the students in the pursuit of the scientific method, and inspire them to pursue research as a career. Students will choose to work with one or more leading climate science researchers who have designed a clear and concise research project with achievable goals. The program mentors are world-renowned researchers in the following areas related to climate science: impacts (wildfires, forests, health, and water quality), measurement systems (in-situ greenhouse gasses, energy balance, and remote sensing), air quality, urban systems, hydrological modeling, oceanography, building energy solutions, data assimilation, cloud processes, radar meteorology and climate communication. The program is a complete, summer-long immersion in the research process complemented with weekly activities to enhance students' professional development through workshops and seminars on science communication, graduate school, and career opportunities. Students will report their research in the form of a peer-reviewed journal article, a conference-style poster, and a short (2-3 minute) oral presentation. Students will be encouraged to present research at national meetings and produce peer-reviewed publications. The program will accommodate 15 students each year. The program will provide students with sufficient knowledge so that they not only contribute to solving societally relevant scientific problems but also to the improvement of scientific literacy within academia and throughout our educational systems and society at large. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-09

Most flowering plants, including many key food and cash crops such as cocoa and coffee, rely on animal pollinators for reproduction. Disruptions to these plant-pollinator relationships due to climate and landscape changes are expected to have severe negative consequences for plant reproduction and ecosystem functioning. This is especially true in tropical regions, where a higher proportion of plants depend on animal pollination compared to temperate ecosystems. Such disruptions in tropical pollination systems also pose a threat to food security and global economies, given the significant number of crops imported from these regions. However, tropical plant-pollinator interactions remain understudied. This project provides research training and mentorship to 15 U.S. students over three years in Colombia, one of the most biologically and agriculturally diverse countries in the world. Colombia’s highly variable topography and diversity of climates support an astonishing diversity of native plants, as well as tropical and temperate crops, making it a natural laboratory. The project offers students a unique opportunity to integrate multiple fields of research, from behavior to ecophysiology, and to apply cutting-edge techniques to address pressing scientific and societal challenges in conservation and agriculture. Plant-pollinator interactions are increasingly affected by climate and landscape changes through various mechanisms. Yet these interactions remain understudied, posing a significant risk to efforts for biodiversity conservation and ecosystem services. This IRES project aims to bridge this knowledge gap by engaging the next generation of U.S. scientists in investigating fundamental questions about the drivers of changes and disruptions in plant-pollinator interactions in rapidly transforming and understudied tropical ecosystems. The project offers training in research and science communication to 15 U.S. students from underrepresented groups (five per year) giving them a unique research experience in an international setting. Guided by a transdisciplinary and multicultural team of researchers, students participate in collaborative research during an eight-week program in Colombia, a megadiverse tropical country. The project addresses predictions regarding phenotypic plasticity, the interactive effects of environmental stressors on organisms’ thermal tolerance, and the roles of temperature and humidity in shaping plant-pollinator interactions, among other topics. This project provides students with a unique opportunity to integrate observational, comparative, and experimental methods across various scales of biological organization while building a robust network of national and international collaborators. The results of this project will enhance our understanding of ecology, physiology, and the conservation of plant-pollinator interactions, with practical applications for sustainable agriculture. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-09

NON-TECHNICAL SUMMARY Separating different kinds of molecules in a liquid or vapor stream lies at the heart of most industrial chemical processes. Traditional separations which boil and condense liquids to separate them are energy-intensive. Polymer membranes are increasingly used to perform these separations more efficiently. Membranes also find increasing applications in producing drinkable water from brackish or sea water, as well as in fuel cells and chemical batteries, which will play an important role in an energy future powered by wind and solar. Polymer membranes separate molecules by size and by affinity. Small pores allow small molecules to pass but block large molecules; membranes decorated with charges can encourage or discourage the passage of charged or polar molecules. Evidently, there are tradeoffs in membrane design: bigger pores allow molecules to pass easily, but degrade the ability of the membrane to discriminate between different molecules. Membrane design has proceeded slowly, by trial and error. An atomic-scale view of membrane structure, and how it affects the entry and passage of different molecules, would enable better designs. Experimental probes of membranes on the atomic scale are vital but limited. An alternative approach is to use molecular simulations, in which movies are made of the molecules in a small region of a membrane. Simulations can in principle show how molecules pass through a membrane, and allow us to measure the affinity of the membrane for different species. But for these movies to accurately represent real molecular motion, multiple challenges must be met: 1) realistic molecular arrangements of membranes must be constructed; 2) forces between atoms must be realistic, particularly for strongly interacting charged species; 3) simulations must cover enough time that molecules explore the membrane; 4) special techniques must be developed to “encourage” molecules normally repelled by the membrane to enter, so that rejection efficiency can be measured; 5) simulations in which one species is pulled through the membrane must be performed to measure the resistance experienced by molecules as they move. This project aims to meet all these challenges, and thereby enable simulations to assess the performance of membranes of different structures and compositions, which will help design better membranes for myriad applications in a sustainable future. The principal investigator (PI) for this project emphasizes broader impacts in undergraduate and graduate education, including: 1) extensive online simulation tutorials; 2) a newly developed course in writing and presenting for scientists and engineers; and 3) a recently written book based on the course, which is unique in its teaching of writing and presenting together, in the broader context of the scientific enterprise. TECHNICAL SUMMARY Aqueous membranes for reverse osmosis, ion exchange membranes for chemical flow batteries, and nanofiltration membranes for lithium recovery all face the common design challenge of readily transporting some species while strongly rejecting others. This inevitably involves tradeoffs: bigger pores improve transport but decrease selectivity, and stronger species binding promotes selectivity but impedes transport. Transport depends on the pore space geometry, membrane flexibility, and species binding to membrane moieties. Ion selectivity can be manipulated in several ways, including 1) high concentrations of bound ions that attract counterions and repel like-charge ions; 2) narrow pores too small for ions to be well solvated; and 3) favorable interactions with bound polar species. Because membrane permeability and ion selectivity both depend on Angstrom-scale structure and kinetics, atomistic simulations have the potential to advance understanding and aid design of aqueous membranes for reverse osmosis, chemical flow batteries, and ion recovery. This project develops and exploits new approaches to unlock this potential, by addressing key challenges in membrane simulations: 1. validated ion potentials, so that mobile ions stick properly to bound ions; 2. fast atomistic simulations, to thoroughly equilibrate membrane structures; 3. transfer free energies, which quantify how well a membrane excludes ions; 4. full transport measurements, to predict all fluxes in response to any gradients. Atomistic simulations are well suited to explore transport and selectivity, providing unique insight to complement experimental results, if the key challenges listed above are met. Simulations can also help the membrane science community to revise and refine conflicting traditional models of transport (free-volume mediated diffusion, versus percolative flow) and selectivity (Donnan exclusion, versus size exclusion), which have persisted for decades. Polymer membranes are essential elements for sustainable technologies, including reverse osmosis to supply fresh water, chemical flow batteries to store wind and solar energy, and nanofiltration to recover lithium for electric vehicles. Membrane-based separations are much more energy efficient than traditional alternatives based on phase transitions. The PI’s research program emphasizes broader impacts in university education, including: 1) an extensive set of simulation tutorials online, to which new techniques developed under this project will be added; 2) a recently developed 3-credit course in writing and presenting for scientists and engineers; and 3) a recently book based on my course, which is unique in its teaching of writing and presenting together, in the broader context of the scientific enterprise. STATEMENT OF MERIT REVIEW This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-09

The use of environmental DNA (eDNA, or genetic material shed by organisms) to measure biodiversity is a revolutionary approach that transforms the ability of biologists to observe biodiversity on Earth. In freshwater environments, eDNA in just a liter of water can indicate what fish, insects, and bacteria are present. Despite the rapid advances and adoption of this approach, very little is known about how long eDNA lasts and how fast it disintegrates in nature. Understanding the fate of eDNA in streams and rivers presents a major challenge for interpreting an eDNA “hit”. This NSF award, known as the "DISTANCE" project, will address this knowledge gap by studying the environmental factors that promote or inhibit eDNA movement and degradation in U.S. streams, especially those that are part of the National Ecological Observatory Network (NEON). Infrastructure of the NSF-funded Emerge training program, which broadens undergraduate and graduate student participation in freshwater science, will be expanded as part of the DISTANCE project. Opportunities for student and postdoctoral training will be integrated into the research studies. The term “eDNA spiraling” has been used to describe the fate of eDNA as it flows downstream, where it can be degraded by microbes, deposited in streambed sediments, resuspended from the streambed, and transported further downstream. Hypotheses will be tested that relate water chemistry, microbial communities, and hydrogeomorphology to the three major processes driving eDNA fate: degradation, deposition, and transport. NEON infrastructure will be leveraged by conducting eDNA spiraling experiments at NEON stream sites. Replicated eDNA spiraling experiments will be conducted in two NEON streams and one Critical Zone Observatory site to determine how the type of eDNA (i.e., originating species) and eDNA particle size distribution (determined through sequential filtering) influence eDNA spiraling metrics. Fish and macroinvertebrate biodiversity assessments will be paired at NEON sites with eDNA metabarcoding to investigate whether eDNA spiraling metrics can predict the congruence of community data generated by eDNA metabarcoding compared to traditional methods. DISTANCE has three broader impacts. First, infrastructure of the NSF-funded Emerge program, which broadens participation in freshwater science, will be expanded. Emerge trains undergraduate, graduate, and early career scientists from underrepresented groups in data analysis and visualization (using R software) and in collaborative science. Training in data analysis and visualization for Emerge alumni will be expanded by offering in-person workshops on “Introduction to bioinformatics of eDNA and DNA metabarcoding data.” Workshops will follow The Carpentries pedagogy and be made open access for other Data Carpentries instructors to teach. Second, we will extend NEON infrastructure by generating new, open-access eDNA datasets for NEON sites. Third, this work will provide training experiences for undergraduate students, graduate students, and one postdoc funded by the project, giving them opportunities to practice teaching and mentorship themselves, as implemented in a hierarchical mentoring plan. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-09

Acoustic wavefront shaping is a technique for precisely controlling the sound field in space. This research project's novelties lie in investigating an approach to overcoming the diffraction limit in acoustic wavefront shaping using nonlinear acoustic effects, which can significantly advance acoustic imaging, therapeutic ultrasound, multizone sound field reproduction, and active noise control. The project's impacts are expected to create a paradigm shift in the vibrant research area of acoustic wavefront shaping, spurring more technological innovation and scientific knowledge in nonlinear acoustics, which possesses characteristics that transcend the limitations imposed by linear acoustics. This project aims to provide a deeper understanding and broader applications of nonlinear acoustic phenomena. This project is also expected to support research-integrated education and benefit society and national security through technological advancements. Acoustic wavefront shaping forms the fundamental basis for numerous applications. Three particularly noteworthy examples of novel approaches in acoustic wavefront shaping include acoustic vortex beams, holograms, and self-bending beams. This research project aims to investigate integrating nonlinear acoustics with advanced wavefront shaping to surpass the diffraction limit. Specifically, the project involves generating nonlinear vortex beams to explore their potential in long-range communication, achieving high-resolution acoustic holograms, and developing nonlinear self-bending beams to create a remote whispering beam. The project will start from the extended second-order nonlinear acoustic wave equation and establish an accurate and efficient numerical model based on the finite-difference time-domain method. The numerical model will be utilized to investigate fundamental physics pertaining to nonlinear acoustic wave propagation. The team will then experimentally demonstrate significantly enhanced acoustic wavefront shaping by leveraging nonlinear acoustic effects, focusing on the three aforementioned applications. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-09

Place-based innovation—the policy interest in developing the local endowments, institutions, and interactions required of dynamic innovation ecosystems—places new demands on data that federal collections were never designed to satisfy. To date, local measures of innovation incidence have relied on patent data that is available at the county level. However, patents are a weak innovation indicator as not all innovations are patentable; firms may prefer other means of intellectual property protection even for patentable inventions; and distinctions between product, process, and business practice innovation are usually unavailable. Innovation data collected in the Annual Business Survey (ABS) address all these weaknesses but are too sparse to provide accurate estimates of innovation incidence for all but the largest metropolitan areas. This project will investigate the feasibility of using the much larger Economic Census (EC) that contains no innovation data to substantially increase the number of firms in a small area to produce more accurate innovation rate estimates. This is done by predicting innovation behavior of firms in the EC from variables that are also included in the ABS, using a technique called small area estimation. This method “borrows strength” from a much larger general dataset (EC) to enhance the predictive power of a smaller, more detailed dataset (ABS). It is regularly used to produce local estimates of phenomena of policy interest that would be prohibitively expensive to collect, such as disease incidence or childhood poverty rates. This project is the first time these techniques have been applied to innovation data. The goal of this project is to generate the Small Area Innovation Rate Estimation (SAIRE). Preliminary analysis using the ABS has found that commonly used control variables such as industry sector, firm size category, or state where the firm is located are predictive of innovation behavior and would be an improvement over naïve local area estimates. The project will investigate possible increases in efficiency by replacing the fixed effects used in the preliminary analysis with random effects in a generative Bayesian multilevel model. In addition to expected increases in efficiency from aspatial pooling provided by a random effects specification, estimation of innovation phenomena may be improved by modeling spatial dependence across proximate small areas. More precise innovation rate estimates may be possible by adding other firm or local characteristics into the predictive model such as cloud computing or local human capital endowments. The two major methodological challenges presented by the research are 1) incorporating complex sample design in the small area estimation as the probability of selection and innovation may be dependent on the same variables such as firm size; and 2) assessing the extent to which firm-level variables in ABS are predictive of establishment-level innovation in EC for multi-unit firms. Accurate meso-level measures of SAIRE would inform the targeting and evaluation of place-based innovation initiatives such as the Regional Innovation Engines program as well as addressing questions such as the role of innovation in reallocation growth that cannot be analyzed using current microdata. 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

Transport (moving something from one position to another) is central to describing many important phenomena in the physical sciences. In physics, there remain open challenges to understanding how physical quantities like charge, heat, and even information evolve and undergo transport in systems of many interacting particles, especially when quantum effects are taken into account. Recently, new approaches to the study of transport in quantum systems have emerged based on the concepts of "synthetic dimensions" and "synthetic lattices," in which our normal picture of transport in real space is abstracted to the transport of population in a space spanned by the internal states of small quantum systems such as individual atoms or molecules. For example, population can "move" between the electronic states of an atom (like hydrogen) through the absorption of light from an incident laser field. In this way, a collection of atoms, which is well-understood and highly controllable with lasers or other electromagnetic fields, can be used to "simulate" a more complex condensed matter system, and lead to advances in our understanding of the complex system. The experimental effort in the current project will extend this type of approach to the study of transport to a new regime of strong inter-particle interactions. The team will conduct experiments based on samples of atoms that can be individually controlled and detected at the microscopic level. Microwave electromagnetic fields will be used to precisely control the transport of population between states of the atoms, allowing for new kinds of explorations into transport phenomena. Additionally, the team will lead an effort to broaden the scope and impact of undergraduate research opportunities, with a primary emphasis on increasing the participation of members from underrepresented groups. This effort will focus on building a new, undergraduate student-led research project on networks of mechanical oscillators that are coupled by "synthetic," or engineered and indirect, forces. This effort will incorporate undergraduates from diverse backgrounds in cutting-edge research related to new kinds of transport phenomena, and will use these human-scale experiments to generate visualization video content that will be utilized for outreach and instruction. This project builds on previous work designing synthetic lattices in neutral atoms and photons, extending these ideas to a new platform for the exploration of many-body transport phenomena based on the internal degrees of freedom of ultracold Rydberg atoms. By considering the problem of quantum transport taking place in an internal state space (driven by coherent microwave transitions and strong dipole-dipole interactions) rather than real space, this approach leverages the ability to manipulate internal degrees of freedom with spectroscopic control. This spectroscopic control allows for the precise engineering of synthetic lattices with nontrivial band topology, kinetic frustration, and tunable disorder. Resonant dipole-dipole interactions between Rydberg atoms will lead to new phenomena with relevance to the interplay of topology and strong interactions, to the study of relaxation and thermalization in isolated interacting disordered systems, and perhaps to the emergence of entirely new forms of many-body phenomena. The research team will explore the ability to engineer novel synthetic lattice models in the internal state space of Rydberg atoms, and will explore how resonant dipole-dipole interactions lead to new many-body phenomena in these synthetic lattices. An additional effort related to broadening the scope and impact of undergraduate research will lead a project to create topological lattice models based on synthetically-coupled oscillator networks. This research effort will enable new functionality of engineered mechanical networks, including new capabilities for designing non-reciprocity, artificial gauge fields, disorder, and strong nonlinearities. The undergraduate led research team will use these newly developed methods to study transport phenomena in new classes of mechanical oscillator networks. 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 investigates how and why restoration programs impact social and environmental systems in forest-grassland ecosystems. While restoration is purported to address interlinked crises of environmental degradation, biodiversity loss, and climate change, little is known about what restoration programs achieve as outcomes and what tradeoffs exist between global and local sustainability goals and needs. The research hypotheses that effective restoration involves significant tradeoffs between ecological and social goals and is the result of the interplay of other forces, including participatory governance, land rights, and technical capacity. The research employs multiple methods, including remote sensing, field-based ecological measurements of biodiversity and carbon storage, household surveys, interviews, and focus group discussions to address globally important critical knowledge gaps in restoration science. By generating empirical evidence and insights about restoration governance processes and outcomes, the research contributes to advance understanding and application of restoration as a nature-based solution. While restoring degraded ecosystems as a nature-based solution is a high policy priority to address interlinked crises of environmental degradation, biodiversity loss, and climate change while achieving social benefits, little empirical evidence exists on the socio-environmental outcomes of restoration and their drivers. Also, tradeoffs between social and ecological goals and local and global priorities are not well understood. This research project seeks to fill these knowledge gaps by investigating how and why restoration programs transform landscapes and livelihoods in forest-grassland ecosystems, which face distinct social-ecological tradeoffs due to the complex interactions of grassland and woodland covers and their relationships with anthropogenic and natural fire and herbivory. The project investigates how restoration changes ecological and social conditions, identifies what drives the ecological and social changes induced by restoration, and develops cost-effective indicators and tools to advance systematic assessment of socio-environmental benefits/tradeoffs of restoration interventions. The research adopts a land change system perspective and integrates theories of landscape ecology, governance systems, and impact evaluation to examine the multi-scalar socio-environmental system of dryland forest-grassland mosaics’ restoration. Beyond its scientific merits, the research aims to reinforce the science-policy-society interface in restoration efforts, and contribute to graduate/undergraduate education as well as career development of postgraduate scholars and junior faculty. 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

Despite its societal benefits, generative AI also raises many societal and legal concerns. For instance, it may be abused to generate harmful content and boost disinformation campaigns. Watermark-based detection of AI-generated content is a key technology to address these societal and legal concerns. Several companies--such as Google, OpenAI, Stability AI, and Microsoft--have deployed such techniques to watermark their AI-generated content. Despite the growing deployment and adoption of watermarking techniques, there remains an education gap among various stakeholders--such as students, government policymakers, the current workforce, and the imminent generation of workers--regarding the theoretical foundations, technical implementations, and practical applications of these watermark techniques. The objective of this project is to bridge this education gap in watermarking AI-generated content. This project will develop a systematic lab-based curriculum that offers a diverse range of laboratory exercises for hands-on experience and practical skills acquisition of watermarking AI-generated content. The project will also develop and release an Open-Watermark-Platform with integrated labs to enable hands-on learning on watermarking. The lab-based curriculum and other education materials developed in this project will train the next generation of workforce to meet the urgent needs of watermarking AI-generated content and enable continual learning for the current workforce and policymakers. This project is supported by the Secure and Trustworthy Cyberspace (SaTC) program, which funds proposals that address cybersecurity and privacy, and in this case, cybersecurity education. The SaTC program aligns with the Federal Cybersecurity Research and Development Strategic Plan and the National Privacy Research Strategy to protect and preserve the growing social and economic benefits of cyber systems while ensuring security and privacy. 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 PI and his group will perform a series of experiments with gases of atoms confined in one dimension (1D). 1D gases are the rare many-body quantum system that can be accurately modeled theoretically, even when they are taken out of equilibrium. Their out-of-equilibrium behavior can also be precisely measured experimentally. For two decades, parallel progress in 1D gas experiments and theory have increased our understanding of many-body quantum systems, especially out of equilibrium. Such understanding is increasingly important for quantum computing, quantum simulation and quantum sensing. These new experiments will expand the reach of 1D gas models by controllably relaxing the aspect of 1D gases that has made them so amenable to theoretical study, a feature called “integrability”. The 1D gases will be taken from nearly integrable to non-integrable, along a path that retains the experimental precision and explanatory clarity that has characterized previous 1D gas studies. Success will bring us closer to a universal understanding of many-body quantum dynamics, deepening our understanding of nature and likely impacting emerging quantum technologies. Work on these experiments will teach graduate students cutting edge technologies and physical theories and prepare them for future work across the gamut of quantum science and technologies. The PI and his group will make a series of measurements of 1D Bose gases, which consist of ultra-cold 87Rb atoms trapped in a 2D optical lattice, which forms a 2D array of tubes for the atoms. Specifically, they will superimpose an additional 1D lattice onto the 1D gases, creating a 1D lattice-gas. The additional 1D lattice turns the system from nearly integrable to non-integrable. The group will measure the 1D lattice-gas analog of well-studied quantities of the 1D gas, both in an out of equilibrium. These quantities include momentum distributions and distributions of rapidities, which are the momenta of the quasiparticles that account for interparticle interactions. The lattice-gas versions of these quantities are well-defined operationally in much of the non-integrable regime. The goal is to use them as the basis for modeling dynamics. The overarching goal is to create as universal a framework as possible for the study of out-of-equilibrium many-body quantum systems. Understanding the dynamics of closed quantum many-body systems is important for validating quantum simulations, especially with atoms in optical lattices. The understanding gleaned could also be relevant to other closed or nearly closed interacting quantum systems, including quantum computers and quantum sensors, especially those that involve squeezing or other particle-particle interactions. The training in experimental physics obtained by undergraduates and graduate students working on this experiment is comprehensive; it is good preparation for many different types of experimental work. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-08

In an increasingly global and diverse society, engineering programs are called to produce engineers at all levels who have intercultural competency (also known as global competencies), representing the ability to work with stakeholders across the world and from a variety of cultural backgrounds. These competencies will only become more important, highlighted by the United Nations Sustainable Development Goals, the European Union’s OCED calls to global action to fight global challenges, and the National Academies’ Grand Challenges for Engineering, which innately require global collaboration. Ph.D.- and Master’s educated engineers are thought-leaders who will be at the forefront of developing the technologies that will lead to water sustainability, sustainable energy, and climate solutions, which are inherently global problems. However, intercultural competency research rarely extends to engineering graduate student populations. Current statistics indicate over 50% of engineering graduate students in the United States are international, yet very little intercultural competency training, education, or research is conducted for graduate students. Future Ph.D.-holders, regardless of occupational trajectory or citizenship status, must be equipped to be thought leaders to tackle global challenges like climate change in an increasingly global engineering economy. To meet this need, the purpose of this project is to investigate how graduate engineering students develop intercultural competencies “in the wild” in authentic academic research laboratory environments. Given that over 58% of engineering doctoral students across U.S. institutions are international, the research laboratory becomes a place that, if harnessed, could facilitate intercultural competency development for both U.S. and international students as future thought-leaders. This project is well-aligned with the NSF Research in the Formation of Engineers program in that it focuses on the development of critical competencies for the next generation workforce. Informed by Deardorff’s process model of intercultural competence and theories of graduate socialization, this project will answer the following research questions: What are the current levels of intercultural competency in graduate engineering students and faculty research supervisors at R1 institutions in the United States? What factors augment or inhibit the development of intercultural competencies in engineering graduate students in research lab contexts? How do graduate engineering students develop intercultural competencies in research laboratories over time? To answer these questions, researchers at Penn State and University of Nebraska-Lincoln will collaborate on a two-phase multiple methods project comprising a nationwide benchmarking phase to provide contextual details on the climate impacting graduate student development of intercultural competencies from both the faculty and student perspectives and follow-up deep interview and longitudinal mixed methods phase to understand the development of intercultural competencies over time. Findings from this research will transform both the graduate engineering education research subdiscipline and the global engineering education subdiscipline, which rarely interact. This study will offer the inaugural understanding of how intercultural competencies are fostered or limited as a function of graduate engineering research groups over time. The combination of qualitative and quantitative data will add substantial value to the development of models and future theories for how intercultural competency development may occur “in the wild” as a function of routine laboratory environments. Insights will be translated through the broader impacts activities to hundreds of our own institutions’ departments annually through graduate colloquium series and the development of graduate intercultural competency self-audit toolkits developed as part of this grant. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-08

A detailed understanding of white dwarf stars is important for a range of astrophysical questions, from post-main sequence exoplanet habitability to Type Ia supernova cosmology. Successful inference in these areas is limited by our knowledge of white dwarf properties. White dwarf masses and effective temperatures are most commonly estimated with the so-called spectroscopic technique. However, there is significant evidence that these results suffer systematic errors from incomplete physics of the stellar atmosphere models, as well as from the methods used to fit the observed absorption line profiles. The study of the vibrational modes of pulsating white dwarfs provides a way to sensitively map their interior structures. A research collaboration between the City University of New York Queens College and Penn State Scranton will apply a new “seismic technique” to bring seismic and astrometric data together to provide accurate white dwarf masses and effective temperatures. Undergraduate students will contribute to various aspects of the proposed work as part of the AstroCom NYC program, which fosters the careers of students from disadvantaged backgrounds with research and career mentoring. Classroom labs will be published and incorporated into the curriculum of the astronomy stream of the Freshman Research Initiative (FRI) program at the University of Texas at Austin. FRI provides hands-on learning opportunities to first-year undergraduate students from underrepresented backgrounds who wish to pursue STEM careers. The need for independent methods to determine the global parameters of white dwarf stars motivates the development of a new “seismic technique” for characterizing pulsating white dwarfs. This technique combines precision measurements of white dwarf pulsation periods with Gaia parallaxes in a probability estimate of mass and effective temperature (Teff). The measured quantities of mean pulsation period spacing and absolute G-band magnitude each permit solutions that follow monotonic trends in the mass-Teff plane, but in opposing directions. Analyzed together, these values reveal a unique solution. Focusing on the interface between data and models, the seismic technique will achieve accurate measurements due to its relative simplicity compared to the line broadening physics of the common “spectroscopic technique.” Reliable uncertainties will be obtained by marginalizing over a range of potential white dwarf interior structures and interstellar extinction values. 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.