Grants

NSF

Rivers and deltas of the world have been extensively engineered for centuries. Past engineering projects have, collectively, enabled the prosperous economies of deltaic regions that are enjoyed today, but have also created conditions that limit the regions in the future. In many places, including along the United States Gulf Coast, planners and governments are implementing new projects that aim to restore coastal changes, enhance economic and environmental support, and mitigate risk to human lives and livelihoods. However, there is not a robust understanding of how past delta management has influenced human decisions and outcomes. This project uses novel numerical and computational modeling approaches to study how human engineering decisions cascade through space and time over centuries of landscape change to create system conditions that limit or enhance the portfolio of management decisions available at future times. This work develops tools with coastal planners to determine the portfolio of projects that maximize coastal survival. Delta engineering projects induce geomorphic change across space and time scales that impacts human lives and society. This project integrates cascading human decision making into landscape evolution models with three modeling approaches that inform one another and have complementary strengths: agent-based modeling, dynamical system modeling, and participatory modeling. Project research focuses on two testable hypotheses towards the tools and quantitative understanding of cascading decision making that are needed for delta planning: (1) local-scale engineering interventions can lead to less geomorphologically stable delta landscapes, when compared to a few larger system-scale interventions, and (2) human decisions can push the coupled system across tipping points, to both system benefit and detriment. Finally, research and development across all modeling approaches in this project is guided by community engagement. 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

This project investigates the economic, social, and demographic processes that allowed the success of some cities while others in the immediate area declined. Processes that operate in ancient cities are not fundamentally different from those operating in modern cities. Therefore, findings have the potential to impact urban planning and issues affecting contemporary cities. Scholarship on urbanism suggests a number of mechanisms that boost the growth and sustainability of cities, including the creation of neighborhoods, the development of marketplaces, and enhanced efficiencies that come from increasing scale and higher settlement density. Archaeology provides valuable insights about the growth and success of cities because the actions of parts of the population whose lives are often not recorded in written histories are nevertheless preserved in the material record. Findings are disseminated to improve the public’s understanding of science and the scientific method. This archaeological site provides a unique opportunity to address several major questions regarding urbanism and economics. Settlement scaling theory proposes that increasing density transforms cities into social reactors that enhance productivity. Thus, household economic indicators coupled with demography may also illuminate urban success. If urban growth rates are fast enough to indicate migration into the city, the degree to which newcomers assimilated or maintained distinctiveness impacts urban viability. The project uses lidar, a form of aerial laser scanning that sees through dense tropical vegetation and reveals thousands of ancient houses, to create the first systematic and detailed map of the residential areas of the city. Excavations in a representative sample of households provide the first robust understanding of how regional and domestic economies intersected. 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

Solar flares are transient, yet dramatic energy release events in the Sun’s atmosphere that, together with the closely associated phenomenon of coronal mass ejections (CMEs), form a fundamental part of geo-effective space weather. Since their first discovery in 1859, substantial efforts have been made in understanding the energetics of flares. However there remains outstanding questions concerning the ways in which energy is released and transported in flares. We propose a comprehensive project targeted at advancing our understanding of flare energy deposition into the solar chromosphere. This project will focus on spectral lines of orthohelium, which have been shown to exhibit the unusual characteristic of initially dimming in the earliest stages of energy deposition into the lower atmosphere, before then brightening. These dimmings have been shown to be related to the properties of non-thermal particles present in flares. Using a combination of exceptionally high spatial resolution imaging spectroscopy of the He I 10830 Å and D3 5876 Å lines (via the 1.6m BBSO/GST) and state-of-the-art radiation hydrodynamic modelling (RADYN and RH codes) we will: 1) Take new observations using GST and perform analysis of the spatial and spectral characteristics of flare emission and enhanced absorption of the He I 10830 Å and D3 5876 Å, both individually and in relation to each other; and 2) Determine if our models are consistent with those observations, in terms of contrast level, the temporal behavior and spectral properties, and hence infer the physical conditions that produce the helium spectra. The team comprises both ground-based and space-based observational experts, and modelling experts, allowing us to complement the BBSO/GST observations with IRIS and SDO data and forward modelling. 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

Emissions from diesel engines negatively impact human health. Small diesel particulates (< 2.5 µm) have been linked to premature cardiovascular and respiratory deaths in metropolitan areas, as well as lung cancer. This project will investigate a new approach for electrostatic precipitation (ESP) technologies to reduce the emission of diesel particulates. The team will explore nanosecond high-voltage pulses to enhance ESP, also known as Plasma-enhanced Electrostatic Precipitation (PE-ESP). If successful, the new electrostatic precipitators will have a much smaller footprint. The new technology will open up new applications, such as in ships and trucks. By enabling cleaner transportation and shipping, the proposed work directly addresses urgent local and national air quality concerns, supports public health, and advances strategic efforts to meet stricter emissions standards and policy targets. This project will explore the application of nanosecond high-voltage pulse discharges as a novel approach in the context of electrostatic precipitation. The team’s preliminary results show that these nanosecond high-voltage pulses provide significant enhancement over conventional electrostatic precipitators (ESPs). However, the fundamental mechanism(s) underlying this enhancement are poorly understood. The fundamental understanding gained by this study will provide useful information about how to overcome current limitations and further improve PE-ESP. The studies include 1) investigating a reverse polarity two-terminal PE-ESP; 2) performing time-domain ESP simulations; and 3) evaluating a novel three-terminal PE-ESP configuration. The work is interdisciplinary, involving high voltage electronics, electrostatics, and fluid-dynamics, as well as combustion and aerosol science. The project will broaden its impact by expanding a workshop for high school science teachers, targeting schools in central Los Angeles and near the port area, to raise awareness about air pollution and engage students in scientific research. Undergraduate students will gain hands-on research experience to build confidence and interest in STEM careers. Additionally, a new course module on plasma-driven pollution remediation will integrate research findings directly into interdisciplinary curriculum development. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF

This goal of this project is to turn two greenhouse gases — carbon dioxide (CO₂) and methane (CH₄) — into methanol, which is a useful chemical and a cleaner fuel. The process uses a plasma, which is a special state of matter made from superheated particles, with clean energy such as solar power. Instead of capturing and storing CO₂, this project takes it from the air near places where methane is released and turns it into methanol. This approach could reduce costs, make use of the U.S.'s natural gas supplies, and improve national security. It could also boost the economy by creating new markets for these gases. The project will train college students in science and technology and include hands-on activities to interest younger students in science and engineering. While conventional thermal dry methane reforming (DMR) transforms CO2 and CH4 into CO and H2, the project team has demonstrated that the use of perovskites and a non-thermal plasma route promotes the direct production of methanol. Successful completion of this project will advance the understanding of the plasma-catalyst interactions at the nanoscale responsible for the oxygenate yield. Another intellectual contribution will be the development of plasma-activated/plasma-enhancing perovskites and dual-function metal-perovskites for CO2 capture and conversion, which can be applied across a wide range of approaches. Additionally, a comparative study of the thermal vs. plasma route will provide insights into the isolated effects of the fields and plasma-charged particles. The objectives for this project are to fundamentally understand: (1) the interactions between plasma-originated species and perovskites under different plasma scenarios and their implications for reactivity, (2) the mechanistic impact of plasma properties and composition and individual species during CO2 conversion, (3) the material response (charging, polarization, electric field) to plasma and surface properties of perovskites that enable the CO2 catalytic conversion to methanol, (4) the link between surface properties and plasma conditions and the boundaries between thermal and plasma effects, and (5) the link between material composition and CO2 binding and reactivity. 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

This goal of this project is to turn two greenhouse gases — carbon dioxide (CO₂) and methane (CH₄) — into methanol, which is a useful chemical and a cleaner fuel. The process uses a plasma, which is a special state of matter made from superheated particles, with clean energy such as solar power. Instead of capturing and storing CO₂, this project takes it from the air near places where methane is released and turns it into methanol. This approach could reduce costs, make use of the U.S.'s natural gas supplies, and improve national security. It could also boost the economy by creating new markets for these gases. The project will train college students in science and technology and include hands-on activities to interest younger students in science and engineering. While conventional thermal dry methane reforming (DMR) transforms CO2 and CH4 into CO and H2, the project team has demonstrated that the use of perovskites and a non-thermal plasma route promotes the direct production of methanol. Successful completion of this project will advance the understanding of the plasma-catalyst interactions at the nanoscale responsible for the oxygenate yield. Another intellectual contribution will be the development of plasma-activated/plasma-enhancing perovskites and dual-function metal-perovskites for CO2 capture and conversion, which can be applied across a wide range of approaches. Additionally, a comparative study of the thermal vs. plasma route will provide insights into the isolated effects of the fields and plasma-charged particles. The objectives for this project are to fundamentally understand: (1) the interactions between plasma-originated species and perovskites under different plasma scenarios and their implications for reactivity, (2) the mechanistic impact of plasma properties and composition and individual species during CO2 conversion, (3) the material response (charging, polarization, electric field) to plasma and surface properties of perovskites that enable the CO2 catalytic conversion to methanol, (4) the link between surface properties and plasma conditions and the boundaries between thermal and plasma effects, and (5) the link between material composition and CO2 binding and reactivity. 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

Laura Motta of the Woods Hole Oceanographic Institution and Jochen Autschbach of the University at Buffalo are supported by an award from the Chemical Theory, Models, and Computational Methods program in the Division of Chemistry to develop and implement a theoretical method to explore new types of spin-forbidden chemical reactions. The team will implement quantum-theoretical calculations of chemical reactions that would not occur without a magnetic interaction between the spins of the electrons and the spin of the nucleus of a heavy element in these molecules. This magnetic interaction is called hyperfine coupling (HFC). The resulting theoretical method developments will be used to investigate the magnetic isotope effect (MIE) in reactions of small molecules containing mercury. Phenomena such as the “magnetic compass” in migratory birds may also be facilitated by such processes, and therefore the research has the potential to solve long-standing scientific mysteries. A greater understanding of spin-forbidden reactions and whether they can be facilitated by HFC will have broad-ranging impacts across various scientific disciplines, from chemistry to environmental sciences. The proposed research will be integrated exclusively into open-source quantum chemistry software. The methodologies will also be integrated into STEM education through participation in the REU program, outreach lectures, and involvement of a graduate student and a postdoctoral scholar in the research. The team will develop electronic-structure and non-adiabatic molecular dynamics (NAMD) methodology to simulate spin-forbidden reactions driven by HFC. Existing quantum dynamics methods lack a proper spin-independent (scalar effects, SR) and spin-dependent (spin-orbit coupling, SOC) relativistic treatment, preventing the simultaneous evaluation of SOC and HFC interactions, thus limiting the assessment of intersystem crossing in radical pair intermediates. The limitations of quantum dynamics in explaining spin-forbidden reactions are highlighted by the observable photochemical mercury MIE. The objectives are: (1) Expand the current implementation of Exact 2-component (X2C) relativistic HFC in OpenMolcas at the multireference level to include multiple excited states with different multiplicities; (2) Develop and implement the calculation of the matrix elements of the total HFC; (3) Incorporate HFC in the simulation of nonadiabatic molecular dynamics over long time scales using the SHARC program as interfaced with OpenMolcas. The outcomes of this work will directly impact our understanding of photochemically derived isotope effects observed in natural samples and thus contribute to a comprehensive understanding of important environmental processes. This project will foster the scientific development of summer students, graduate students, and postdoctoral researchers in the field of computational photochemistry. These researchers will receive extensive training in the application of advanced computational methods. 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

With support from the Chemical Structure and Dynamics (CSD) program in the Division of Chemistry, Professors Ji and Fang of Oregon State University in collaboration with Professor Greaney of the University of California, Riverside will explore the roles of local solvation structures in expanding the stability of water-based electrolytes for energy storage technologies. While previous efforts to improve the water stability against electrolysis have focused on changes to the O–H bond strength, the team will pursue a new approach by considering the impact of changing the hydroxide and proton solvation energies. The team will use a combination of femtosecond stimulated Raman spectroscopy (FSRS) and ab initio molecular dynamics (AIMD) simulations to study electrolytes of varying salt concentrations, cations and anions, and pH values to support their hypothesis that the electrochemical window of aqueous solutions can be expanded by discouraging their solvation of hydroxides and protons, the byproducts of hydrogen evolution reaction (HER) and oxygen evolution reaction (OER), respectively. Their studies will elucidate key factors in chemical environment that dictate HER and OER onset potentials in aqueous electrolytes. Besides three graduate students who will directly perform the project tasks, the three PI’s labs will engage in outreach activities via online videos and in-person chemistry summer camps. This collaborative research project by Ji and Fang Labs at Oregon State University and Greaney Lab at UC Riverside will systematically investigate how solvation environments in aqueous electrolytes affect water’s HER and OER as well as the electrolytes’ resulting electrochemical stability window (ESW). The team will reveal how the local chemical environment with various cations and anions at low temperatures affects the electrolytic properties of aqueous solutions, and identify a richer set of Raman spectrum descriptors and predictors for chemical environments in concentrated aqueous electrolytes via PIs’ complementary expertise in electrochemistry, FSRS, and atomistic modeling. The direct observation of water’s H–O–H bending mode and use of a photoacid will provide deep insights into water’s local environment, bridging kinetics on ultrafast timescales to thermodynamics at equilibrium. The broader impacts of this work involve the development of a powerful experimental and theoretical platform to rationally design aqueous electrolytes with a significantly expanded or suppressed ESW for safe grid-scale storage batteries and green hydrogen production. The project, with cross-disciplinary knowledge and in the context of battery technology, will effectively engage STEM learners with combined science and engineering mindsets and natural curiosity about water in a myriad of 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

Stratocumulus clouds are ubiquitous over large areas of Earth's oceans. Despite their relatively consistent structure compared to other cloud structures, like thunderstorm complexes, there is significant small-scale spatial variability in stratocumulus clouds that impacts further cloud and precipitation development. This project will use an emerging modeling technique and existing cloud observations to identify the sources of spatial variability at the cloud microphysics level. The research team will then identify the best methods to simulate these sources of variability. The broader societal impact of the project would be to improve modeling of clouds which affect Earth’s radiation balance. There is also a substantial educational aspect to the project, enhancing the training of the next generation of atmospheric scientists. The primary objectives of this project are to understand the microphysical sources of spatial variability in low-level stratocumulus clouds, how this spatial variability influences the temporal evolution of the mean cloud and precipitation processes, and how the design of microphysics parameterizations influences the ability to simulate the observed spatial structure of these cloud and precipitation fields. The research team will run Large Eddy Simulations (LES) of drizzling stratocumulus clouds using Cloud Model 1 (CM1) based on observed cases with Lagrangian microphysics. Lagrangian particle-based methods employ particles that are representative of a multiplicity of millions to billions of identical hydrometeors, which get around problems introduced in traditional bulk and bin microphysics schemes. The project will focus on four primary potential sources of microphysical variability resulting in rain: condensation, stochastic collisions, turbulence-enhanced collision kernels, and giant aerosol particles. The results of the simulations and comparison with observations will then be used to train the Bayesian Observationally-constrained Statistical-physical Scheme (BOSS), with the introduction of a stochastic component. Simulations will be run in a purely non-stochastic mode and a fully stochastic mode to address the hypothesis that the non-stochastic BOSS will not be able to simulate the spatial variability whereas the stochastic BOSS will. Further simulations will address the importance of the identified microphysical variability as well as whether BOSS can be run in a single-category mode with increased predictability. 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

This award funds a research project examining the factors influencing exchange rates, with a focus on international asset prices and capital flows. Understanding the drivers of exchange rates is a central objective of international macroeconomics and finance. Exchange rates are key prices affecting the relative cost of imports and exports, as well as the returns to investing in assets abroad. Yet exchange rates have long puzzled economists: they are inconsistent with benchmark models in economics and are very difficult to predict. This project makes progress on these puzzles by exploring the relationship between exchange rates and other international asset prices, such as bonds and stocks in different countries, as well as macroeconomic quantities, such as capital flows across borders. This research helps market participants use exchange rates to better understand macroeconomic conditions in the global economy. It also helps decision-makers better understand how monetary and fiscal policies affect exchange rates and thus the broader economy. The research findings could inspire innovative international policy frameworks, improve the well-being of households, businesses, and investors, and reinforce global market activity. This award project explores exchange rates through the lens of general equilibrium models with international capital market frictions. These capital market frictions are reflected in a central role for global arbitrageurs to intermediate capital flows across borders. Such models thus allow the researchers to accommodate classic forces that have been thought to drive exchange rates, such as the supply or demand for goods, as well as drivers that have been proposed in more recent research, such as changes in the intermediation capacity of global arbitrageurs. The project includes research to decompose the drivers of the dollar exchange rate versus other advanced economies through the lens of such a model. The co-movements of exchange rates with bond yields, spreads in financial markets, and capital flows suggest a nuanced variance decomposition of exchange rates: at high frequencies, shocks to intermediation capacity play an important role in driving exchange rates, but at low frequencies and overall, demand shocks play the most important role. The project includes further research to understand the sources of demand shocks driving variation in these prices and capital flows. The researchers study the co-movements of exchange rates, bonds, and stocks to differentiate between potential sources of demand shocks. The project also includes research to understand how heterogeneous exposures to demand or intermediation shocks can explain differences in exchange rate behavior across countries. The researchers study how patterns in trade linkages and in net foreign assets give rise to heterogeneous effects of these shocks. The project finally includes research on the propagation of macroeconomic policies through exchange rates. The researchers study whether state-dependence in the effects of foreign exchange rate intervention can account for mixed evidence on its effectiveness in the data. This research could help researchers and decision-makers gain deeper insights into exchange rates and their relationship with international asset prices, capital flows, and other macroeconomic factors, ultimately improving the well-being of the U.S. population and strengthening the country's leadership in international markets. 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

This award funds a research project examining the factors influencing exchange rates, with a focus on international asset prices and capital flows. Understanding the drivers of exchange rates is a central objective of international macroeconomics and finance. Exchange rates are key prices affecting the relative cost of imports and exports, as well as the returns to investing in assets abroad. Yet exchange rates have long puzzled economists: they are inconsistent with benchmark models in economics and are very difficult to predict. This project makes progress on these puzzles by exploring the relationship between exchange rates and other international asset prices, such as bonds and stocks in different countries, as well as macroeconomic quantities, such as capital flows across borders. This research helps market participants use exchange rates to better understand macroeconomic conditions in the global economy. It also helps decision-makers better understand how monetary and fiscal policies affect exchange rates and thus the broader economy. The research findings could inspire innovative international policy frameworks, improve the well-being of U.S. households, businesses, and investors, and reinforce the U.S.'s leadership in global markets. This award project explores exchange rates through the lens of general equilibrium models with international capital market frictions. These capital market frictions are reflected in a central role for global arbitrageurs to intermediate capital flows across borders. Such models thus allow the researchers to accommodate classic forces that have been thought to drive exchange rates, such as the supply or demand for goods, as well as drivers that have been proposed in more recent research, such as changes in the intermediation capacity of global arbitrageurs. The project includes research to decompose the drivers of the dollar exchange rate versus other advanced economies through the lens of such a model. The co-movements of exchange rates with bond yields, spreads in financial markets, and capital flows suggest a nuanced variance decomposition of exchange rates: at high frequencies, shocks to intermediation capacity play an important role in driving exchange rates, but at low frequencies and overall, demand shocks play the most important role. The project includes further research to understand the sources of demand shocks driving variation in these prices and capital flows. The researchers study the co-movements of exchange rates, bonds, and stocks to differentiate between potential sources of demand shocks. The project also includes research to understand how heterogeneous exposures to demand or intermediation shocks can explain differences in exchange rate behavior across countries. The researchers study how patterns in trade linkages and in net foreign assets give rise to heterogeneous effects of these shocks. The project finally includes research on the propagation of macroeconomic policies through exchange rates. The researchers study whether state-dependence in the effects of foreign exchange rate intervention can account for mixed evidence on its effectiveness in the data. This research could help researchers and decision-makers gain deeper insights into exchange rates and their relationship with international asset prices, capital flows, and other macroeconomic factors, ultimately improving the well-being of the U.S. population and strengthening the country's leadership in international markets. 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

Coastal ecosystems are among the most valuable and vulnerable places on earth. These ecosystems support numerous keystone organisms, including habitat-forming species, like seagrass, that serve as the foundation for community structure and function, while also providing critical ecosystem services to people. Unfortunately, seagrasses are increasingly impacted by human activities and have experienced significant degradation and loss over the last century. Further, changes in habitat suitability is causing seagrass species to shift their geographic distributions, resulting in losses of some species and gains of other species in many coastal regions. Along the United States Atlantic Coast, the temperate seagrass, Zostera marina (eelgrass), has declined considerably in recent decades at the southern edge of its range, while the subtropical seagrass, Halodule wrightii (shoal grass), has increased in abundance at the northern edge of its range (North Carolina), where the two seagrass species overlap. This project aims to assess where and how H. wrightii could expand into higher latitudes of the western Atlantic coast, potentially replacing Z. marina as the dominant habitat-forming seagrass. Further, the results of this research will provide critical information and approaches for determining how seagrasses and their associated ecosystem services may shift as habitats become more or less suitable. The outcomes of this project will be used and incorporated into coastal ecosystem management plans, and seagrass monitoring, mapping, and restoration efforts in the United States. Predicting responses of habitat-forming, foundation species is key for ensuring the long-term maintenance of ecosystem structure, functions, and services. Distinguishing eco-evolutionary characteristics of edge-of-range populations that could facilitate or inhibit range expansion can improve predictions for how species may adapt or migrate as habitats become more or less suitable. The overarching objective of this proposal is to determine how shifts in habitat suitability (i.e. temperature, light availability, and biotic interactions) will affect productivity, eco-physiology, gene expression, genetic structure, and colonization potential of a subtropical seagrass at its leading northern range. This project aims to identify ecological, physiological, and genetic mechanisms that may facilitate the expansion of an understudied foundation seagrass species, H. wrightii, into higher latitudes of the western Atlantic coast. The Project Team will examine and evaluate population structure, phenotypic plasticity, and genetic differentiation among H. wrightii populations at range edges versus interiors (stress vs. comfort zones). Manipulative mesocosm and field experiments will be used to evaluate H. wrightii acclimation potential to biotic and abiotic stressors, as well as the effects of a temperate seagrass species, Z. marina, on H. wrightii productivity and gene expression. This project will expand understanding of the abiotic and biotic mechanisms that underlie organismal resilience by examining variation in colonization potential and genetic differentiation of a habitat-forming species at its northern range limit. 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

Coastal ecosystems are among the most valuable and vulnerable places on earth. These ecosystems support numerous keystone organisms, including habitat-forming species, like seagrass, that serve as the foundation for community structure and function, while also providing critical ecosystem services to people. Unfortunately, seagrasses are increasingly impacted by human activities and have experienced significant degradation and loss over the last century. Further, changes in habitat suitability is causing seagrass species to shift their geographic distributions, resulting in losses of some species and gains of other species in many coastal regions. Along the United States Atlantic Coast, the temperate seagrass, Zostera marina (eelgrass), has declined considerably in recent decades at the southern edge of its range, while the subtropical seagrass, Halodule wrightii (shoal grass), has increased in abundance at the northern edge of its range (North Carolina), where the two seagrass species overlap. This project aims to assess where and how H. wrightii could expand into higher latitudes of the western Atlantic coast, potentially replacing Z. marina as the dominant habitat-forming seagrass. Further, the results of this research will provide critical information and approaches for determining how seagrasses and their associated ecosystem services may shift as habitats become more or less suitable. The outcomes of this project will be used and incorporated into coastal ecosystem management plans, and seagrass monitoring, mapping, and restoration efforts in the United States. Predicting responses of habitat-forming, foundation species is key for ensuring the long-term maintenance of ecosystem structure, functions, and services. Distinguishing eco-evolutionary characteristics of edge-of-range populations that could facilitate or inhibit range expansion can improve predictions for how species may adapt or migrate as habitats become more or less suitable. The overarching objective of this proposal is to determine how shifts in habitat suitability (i.e. temperature, light availability, and biotic interactions) will affect productivity, eco-physiology, gene expression, genetic structure, and colonization potential of a subtropical seagrass at its leading northern range. This project aims to identify ecological, physiological, and genetic mechanisms that may facilitate the expansion of an understudied foundation seagrass species, H. wrightii, into higher latitudes of the western Atlantic coast. The Project Team will examine and evaluate population structure, phenotypic plasticity, and genetic differentiation among H. wrightii populations at range edges versus interiors (stress vs. comfort zones). Manipulative mesocosm and field experiments will be used to evaluate H. wrightii acclimation potential to biotic and abiotic stressors, as well as the effects of a temperate seagrass species, Z. marina, on H. wrightii productivity and gene expression. This project will expand understanding of the abiotic and biotic mechanisms that underlie organismal resilience by examining variation in colonization potential and genetic differentiation of a habitat-forming species at its northern range limit. 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

Stars form out of clouds of gas found in galaxies. Most of this gas is in the form of cold atoms of hydrogen, but before stars can form, this gas must undergo several chemical and physical processes that are poorly understood. The investigators leading this proposal will analyze data from both the NSF-funded Green Bank Telescope (GBT) and the Atacama Large Millimeter Array (ALMA), which can be used to measure the amounts of hydrogen gas in a broad sample of galaxies. They will use this to test how the depletion timescales of these gas reservoirs depend on other measured galaxy properties, placing important constraints on the growth of galaxies. At the same time, they will train undergraduate researchers in the techniques of radio astronomy. This program seeks to understand the differences in atomic hydrogen (HI) depletion times using the SDSS-IV’s Mapping Nearby Galaxies at Apache Point Observatory (MaNGA) and HI-MaNGA (21cm follow-up for MaNGA) galaxy surveys to search for evidence of systematic variations due to (1) ionization/heating sources, (2) fractions of diffuse versus dense HI, and (3) internal motions (e.g., velocity dispersion and bulk non-rotational flows), all of which may alter the efficiency with which HI is processed. The investigators will add new data from the GBT and ALMA to quantify the molecular hydrogen (H2) fraction for a subset of galaxies to probe whether long HI depletion times are driven by inefficient H2 formation from HI or inefficient formation of stars out of H2. In parallel with these efforts, the investigator will use the GBT to fully complete the HI-MaNGA survey, improving its legacy value for the astronomical community. 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

Stars form out of clouds of gas found in galaxies. Most of this gas is in the form of cold atoms of hydrogen, but before stars can form, this gas must undergo several chemical and physical processes that are poorly understood. The investigators leading this proposal will analyze data from both the NSF-funded Green Bank Telescope (GBT) and the Atacama Large Millimeter Array (ALMA), which can be used to measure the amounts of hydrogen gas in a broad sample of galaxies. They will use this to test how the depletion timescales of these gas reservoirs depend on other measured galaxy properties, placing important constraints on the growth of galaxies. At the same time, they will train undergraduate researchers in the techniques of radio astronomy. This program seeks to understand the differences in atomic hydrogen (HI) depletion times using the SDSS-IV’s Mapping Nearby Galaxies at Apache Point Observatory (MaNGA) and HI-MaNGA (21cm follow-up for MaNGA) galaxy surveys to search for evidence of systematic variations due to (1) ionization/heating sources, (2) fractions of diffuse versus dense HI, and (3) internal motions (e.g., velocity dispersion and bulk non-rotational flows), all of which may alter the efficiency with which HI is processed. The investigators will add new data from the GBT and ALMA to quantify the molecular hydrogen (H2) fraction for a subset of galaxies to probe whether long HI depletion times are driven by inefficient H2 formation from HI or inefficient formation of stars out of H2. In parallel with these efforts, the investigator will use the GBT to fully complete the HI-MaNGA survey, improving its legacy value for the astronomical community. 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

Critical minerals are essential for products such as electronics, motors, and batteries. The demand for critical minerals could be met in part by recovering them from waste and process streams. However, current recovery processes are expensive, and valuable materials in waste streams often are unrecovered. This project will develop polymeric membranes for use in low-cost filtration-type processes. These membranes will improve the recovery of target ions of critical minerals from aqueous streams. Most polymer membranes are manufactured by methods that result in pores and voids of varying sizes. This project will develop experimental methods to precisely control and measure the sizes of pores and voids in membranes. Using computations and experiments, the research will show that suitable pore sizes can improve recovery of the target ions. This project will improve national security and improve economic competitiveness in critical minerals. Additional benefits include the training and mentoring of teachers and community-college students on modern separation technologies. Polymeric membranes have emerged as versatile, low-cost, and scalable materials systems for a wide variety of gas and liquid separation applications. Such membranes are generally prepared using free radical polymerization, which produce membranes with a heterogeneous pore and network structure. The influence of network heterogeneity on ion transport in hydrated membranes is poorly-understood. This project will pursue a combination of experiments and computer simulations to systematically tune and characterize network heterogeneity in polymer networks and understand its impact on ion transport and separations. Experimental work will implement methods for controlling and quantifying membrane heterogeneity, and measuring the impact of heterogeneity on ion transport and selectivity. Molecular simulations will capture the effects of network heterogeneity and ion-specific interactions. The resulting insights on the role of water-filled voids on ion diffusion and sorption will guide the experimental work. The knowledge generated in this project will immediately impact the field of membrane separations. Unraveling the interplay between membrane heterogeneities and ligand-ion interactions on ion selectivity will impact a variety of contemporary applications of extraction, recycling and reuse of critical minerals. The project includes training of K-12 science teachers on activities relating to membrane separations, development of a hands-on activity to be used in K-12 community outreach events, and training undergraduate researchers from Houston and Austin area community colleges. 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

Critical minerals are essential for products such as electronics, motors, and batteries. The demand for critical minerals could be met in part by recovering them from waste and process streams. However, current recovery processes are expensive, and valuable materials in waste streams often are unrecovered. This project will develop polymeric membranes for use in low-cost filtration-type processes. These membranes will improve the recovery of target ions of critical minerals from aqueous streams. Most polymer membranes are manufactured by methods that result in pores and voids of varying sizes. This project will develop experimental methods to precisely control and measure the sizes of pores and voids in membranes. Using computations and experiments, the research will show that suitable pore sizes can improve recovery of the target ions. This project will improve national security and improve economic competitiveness in critical minerals. Additional benefits include the training and mentoring of teachers and community-college students on modern separation technologies. Polymeric membranes have emerged as versatile, low-cost, and scalable materials systems for a wide variety of gas and liquid separation applications. Such membranes are generally prepared using free radical polymerization, which produce membranes with a heterogeneous pore and network structure. The influence of network heterogeneity on ion transport in hydrated membranes is poorly-understood. This project will pursue a combination of experiments and computer simulations to systematically tune and characterize network heterogeneity in polymer networks and understand its impact on ion transport and separations. Experimental work will implement methods for controlling and quantifying membrane heterogeneity, and measuring the impact of heterogeneity on ion transport and selectivity. Molecular simulations will capture the effects of network heterogeneity and ion-specific interactions. The resulting insights on the role of water-filled voids on ion diffusion and sorption will guide the experimental work. The knowledge generated in this project will immediately impact the field of membrane separations. Unraveling the interplay between membrane heterogeneities and ligand-ion interactions on ion selectivity will impact a variety of contemporary applications of extraction, recycling and reuse of critical minerals. The project includes training of K-12 science teachers on activities relating to membrane separations, development of a hands-on activity to be used in K-12 community outreach events, and training undergraduate researchers from Houston and Austin area community colleges. 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

This award funds a research project that combines extremely rich and detailed data with economic theory to study how the burden of market failures is shared in the economy. Market failures prevent resources, such as labor and capital, from being used in the places where they are most valuable. While it is known that market failures are a feature of all economies and contribute to income differences across countries, the question of which types of households are affected most by these market failures is much less researched. This project makes progress on this gap by developing tools to connect and analyze multiple datasets that provide an extensive set of links in the economy between households and firms, between both households and firms and the government, and between the firms themselves. Understanding which groups benefit and which groups lose out from policies that address such market failures is vital to the design of approaches that maximize the welfare of any nation. The research outcomes could be essential for researchers and decisionmakers in shaping optimal U.S. policies and potentially enhancing the wellbeing of households and businesses. This award funds a research project that develops tools to estimate distortions—markups, markdowns, and taxes that prevents resources being allocated to their best uses—at a highly disaggregated level and trace them to individuals through arbitrary trade, employment, and financial networks. This methodology is applied to administrative data, where the research team can map out the flow of goods and money for the entire economy by linking firm-to-firm networks with firm-to-consumer, firm-to-lender, and firm-to-employee networks alongside ownership registries. These methods reveal how the burden of distortions is shared among households belonging to different regions, demographic groups, skill groups, and income levels. In addition, they enable answers to various questions, such as which distortions do the most to compress and expand the distribution of living standards; what trade-offs are observed in approaches designed to improve the impacts of distortions; and to what degree overlapping distortions necessitate our wide-ranging analysis as opposed to focusing on one specific distortion or sector at a time. The theory and methods are developed in a way that they can be applied to any country with similar data. This project advances knowledge in several ways. First, it operationalizes recent theoretical work on general equilibrium models of heterogeneous agents in distorted economic environments. Second, by assembling a complete empirical mapping of economic relationships between agents in an economy, it measures the distributional impact of the main distortions that are present in an economy (those on labor, capital, output, and intermediate inputs) throughout all sectors. This is relevant because studying the impact of reducing distortions in one specific market is influenced by distortions in other markets. Thus, to fully assess the trade-offs of reducing distortions, one needs to go beyond specific distortions and specific sectors. The results have the potential to significantly reshape how economists perceive the implications of market distortions and the policies responding to them. By addressing who bears the costs and who benefits from market distortions, a key theme for decisionmakers, the findings of this research could lead to optimal policies related to market failures and enhance the welfare of the U.S. population. 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

This award funds a research project that combines extremely rich and detailed data with economic theory to study how the burden of market failures is shared in the economy. Market failures prevent resources, such as labor and capital, from being used in the places where they are most valuable. While it is known that market failures are a feature of all economies and contribute to income differences across countries, the question of which types of households are affected most by these market failures is much less researched. This project makes progress on this gap by developing tools to connect and analyze multiple datasets that provide an extensive set of links in the economy between households and firms, between both households and firms and the government, and between the firms themselves. Understanding which groups benefit and which groups lose out from policies that address such market failures is vital to the design of approaches that maximize the welfare of any nation. The research outcomes could be essential for researchers and decisionmakers in shaping optimal U.S. policies and potentially enhancing the wellbeing of households and businesses. This award funds a research project that develops tools to estimate distortions—markups, markdowns, and taxes that prevents resources being allocated to their best uses—at a highly disaggregated level and trace them to individuals through arbitrary trade, employment, and financial networks. This methodology is applied to administrative data, where the research team can map out the flow of goods and money for the entire economy by linking firm-to-firm networks with firm-to-consumer, firm-to-lender, and firm-to-employee networks alongside ownership registries. These methods reveal how the burden of distortions is shared among households belonging to different regions, demographic groups, skill groups, and income levels. In addition, they enable answers to various questions, such as which distortions do the most to compress and expand the distribution of living standards; what trade-offs are observed in approaches designed to improve the impacts of distortions; and to what degree overlapping distortions necessitate our wide-ranging analysis as opposed to focusing on one specific distortion or sector at a time. The theory and methods are developed in a way that they can be applied to any country with similar data. This project advances knowledge in several ways. First, it operationalizes recent theoretical work on general equilibrium models of heterogeneous agents in distorted economic environments. Second, by assembling a complete empirical mapping of economic relationships between agents in an economy, it measures the distributional impact of the main distortions that are present in an economy (those on labor, capital, output, and intermediate inputs) throughout all sectors. This is relevant because studying the impact of reducing distortions in one specific market is influenced by distortions in other markets. Thus, to fully assess the trade-offs of reducing distortions, one needs to go beyond specific distortions and specific sectors. The results have the potential to significantly reshape how economists perceive the implications of market distortions and the policies responding to them. By addressing who bears the costs and who benefits from market distortions, a key theme for decisionmakers, the findings of this research could lead to optimal policies related to market failures and enhance the welfare of the U.S. population. 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

Red feather coloration in the House Finch and other songbirds serves as an honest signal of individual condition. This finding is both well documented and quite remarkable. It is far from obvious why the hue of feathers reveals the individual quality or what prevents low-quality birds from cheating this system. A series of recent breakthrough studies brings us to the doorstep of understanding honest signaling via red feather coloration. We now know key enzymes and transporters responsible for the production and accumulation of red plumage pigments. In the proposed research, a team of scientists with expertise spanning animal physiology, cell biology, and genomics will use both whole animals and cell culture to study the molecular and biochemical mechanisms of coloration. The investigators will examine and compare these processes in House Finches in good or poor condition to deduce the specific mechanisms that promote or inhibit the production and accumulation of red pigments and hence, control plumage coloration. Understanding the biochemical and cellular rules that govern carotenoid coloration in songbirds is important not just for a better understanding of this central process in nature but because carotenoid pigments play a key role in cellular homeostasis in humans. A better understanding of carotenoid systems in birds will inevitably lead to a better understanding of ourselves. The investigators will share these insights and support STEM education through an ornithology summer camp program, curricula development and training workshops for public school teachers, and middle and high school student visits to investigator laboratories. The biochemical, genetic, and evolutionary processed that either prohibit or enable honest communication of information in the context of mate choice remain poorly understood. The proposed research will focus on uncovering fundamental rules of honest signaling by integrating subcellular, cellular, and whole-organism approaches. In birds including the House Finch, high-quality males produce feathers with brighter and redder carotenoid coloration than do low-quality males. Understanding how the condition of individuals affects the function of these genes and hence color expression is the focus of proposed work. The Resource Tradeoff Hypothesis proposes that stress causes loss of red coloration because carotenoids must be diverted away from ornamentation so they can function as antioxidants or immune system enhancers. Alternatively, the Shared Pathway Hypothesis proposes that oxidation of yellow dietary pigments to red ornamental pigments depends on core cellular processes, and particularly cellular respiration in mitochondria, that are sensitive to organism function and environmental conditions. These hypotheses make contrasting predictions regarding the cellular and environmental conditions that will affect ketolation and hence production of red coloration, and we will employ both whole animal and cell culture experiments to test these predictions. The proposed research holds the potential to significantly advance understanding of the ubiquitous links between stress, individual condition, and carotenoid coloration. The experiments that we propose will provide important new insights for how, at a mechanistic level, carotenoid color serves as a signal of condition and hence key insights into a fundamental rule of life—how to keep signaling honest. 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

While laser based additive manufacturing (AM) offers significant advantages for fabricating intricately shaped metallic components, high incidence of undesired microstructural features and defects remain barriers to a wider industrial adoption of this process. A few recent studies have investigated an approach, referred to as ultrasonically assisted (UA)-AM, that is based on superimposing ultrasonic vibration during laser AM for suppressing defects and refining microstructure. However, the fundamental mechanisms of the UA-AM process are not completely understood, and the current models ignore the transient nature and far-from-equilibrium conditions of the process. This project will utilize synchrotron imaging and diffraction with high space and time resolutions to observe the core mechanisms of the UA-AM process. The knowledge derived from this research can benefit AM industries and facilitate effective and innovative incorporation of UA into various manufacturing processes that involve rapid melting and solidification. The project will contribute to multidisciplinary workforce training and promote STEM education with an e-Learning module and hands-on activities for K-12 students. To observe the underlying physics of UA including the melt dynamics and microstructure evolution, this project will develop an innovative UA laser melting system that fits into the advanced synchrotron radiation facilities. UA effects on the AM process will be analyzed through in situ synchrotron imaging and diffraction analysis, followed by postmortem characterization and integrated multi-scale, multi-physics modeling. In situ imaging will target: (1) melt geometry overview (2) melt flow dynamics (3) dendritic microstructure growth and interactions. Diffraction analysis will focus on melt interior and heat affected zone to determine the phase transformation, thermal history, and stress development. Also, residual stress distribution will be measured with ex situ diffraction analysis. A computational fluid dynamics model coupling the acoustic field will be developed to determine the spatial distribution and temporal evolution of temperature, velocity and pressure fields. These physical fields will serve as input for the phase field modeling of alloy solidification, fully revealing UA effects on the thermo-mechanical-chemical fields and microstructure evolution during laser-based AM processes. 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

While laser based additive manufacturing (AM) offers significant advantages for fabricating intricately shaped metallic components, high incidence of undesired microstructural features and defects remain barriers to a wider industrial adoption of this process. A few recent studies have investigated an approach, referred to as ultrasonically assisted (UA)-AM, that is based on superimposing ultrasonic vibration during laser AM for suppressing defects and refining microstructure. However, the fundamental mechanisms of the UA-AM process are not completely understood, and the current models ignore the transient nature and far-from-equilibrium conditions of the process. This project will utilize synchrotron imaging and diffraction with high space and time resolutions to observe the core mechanisms of the UA-AM process. The knowledge derived from this research intends to benefit AM industries and facilitate effective and innovative incorporation of UA into various manufacturing processes that involve rapid melting and solidification. The project looks to contribute to multidisciplinary workforce training and promote STEM education with an e-Learning module and hands-on activities for K-12 students. To observe the underlying physics of UA including the melt dynamics and microstructure evolution, this project seeks to develop an innovative UA laser melting system that fits into the advanced synchrotron radiation facilities. UA effects on the AM process will be analyzed through in situ synchrotron imaging and diffraction analysis, followed by postmortem characterization and integrated multi-scale, multi-physics modeling. In situ imaging will target: (1) melt geometry overview (2) melt flow dynamics (3) dendritic microstructure growth and interactions. Diffraction analysis will focus on melt interior and heat affected zone to determine the phase transformation, thermal history, and stress development. Also, residual stress distribution will be measured with ex situ diffraction analysis. A computational fluid dynamics model coupling the acoustic field will be developed to determine the spatial distribution and temporal evolution of temperature, velocity and pressure fields. These physical fields will serve as input for the phase field modeling of alloy solidification, looking to fully reveal UA effects on the thermo-mechanical-chemical fields and microstructure evolution during laser-based AM processes. 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

This project will investigate the cycling of the nutrient element nitrogen in Lake Erie. This study will be the first to comprehensively study the nitrogen cycle throughout the year, including in winter when the lake is covered with ice. Recent work in other lakes suggest that during winter, under-ice processes are important in determining the forms of nitrogen available to phytoplankton in spring and summer. A healthy spring bloom of phytoplankton called diatoms, in turn, is important to the lake food web and in mitigating against harmful algal blooms that can affect water quality. However, very little is known about winter nitrogen cycling in any of the Great Lakes, including Lake Erie, arguably one of the most important resources in the country in terms of drinking water supply, recreation, and fisheries. The project will support undergraduate students at both the University of Michigan and Bowling Green State University, and a graduate student at BGSU. The team will create a curriculum module on the Great Lakes and water quality for high school students. The goal of this work is to understand how winter nitrogen cycling impacts nutrient balance and phytoplankton community structure in large, temperate waterbodies, using Lake Erie as a case study. The researchers will generate the first quantitative measurements of nitrification, nitrogen uptake, and ammonia regeneration during winter in Lake Erie, to assess the effects of ice phenology on nitrification rates and how changes in nitrogen availability and speciation may affect seasonal phytoplankton communities. By combining nitrogen cycling rate measurements with community composition data, they will also investigate how different nitrogen substrates may favor certain phytoplankton groups (e.g., diatoms, dinoflagellates, cyanobacteria) across seasons. Over three years the team will 1) Measure ice onset, duration, and thickness throughout winter sampling; 2) track water column nitrogen substrate pools (nitrate, ammonia, organic nitrogen) and rates of uptake/transformation; 3) examine seasonal phytoplankton abundance and gene expression; and 4) conduct stable isotope probing experiments to measure cell-specific nitrogen incorporation in winter phytoplankton communities. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF

Modern scientific challenges—from predicting complex fluid flows to modeling plasma behavior in fusion reactors—demand computationally efficient, trustworthy surrogates that can rival traditional numerical solvers while harnessing the power of artificial intelligence. Scientific machine learning (SciML), identified as a core technology for AI, offers immense potential for surrogate modeling in both data‑rich and data‑scarce situations; of particular interest is the field of operator learning. However, current operator learning frameworks lack unified theoretical foundations, robustness guarantees, and scalable training methods, which limit their adoption in high-stakes applications. The Unified Neural Operator (UNO) considered in this project will fill this gap by embedding all operator learning techniques into a unifying framework, marrying the mathematical rigor of traditional methods with the expressivity of modern AI. By delivering certifiable, interpretable AI‑driven surrogates, UNO advances Presidential priorities in artificial intelligence and nuclear energy—supporting both next‑generation AI capabilities and efficient modeling of magnetohydrodynamic systems critical for fusion energy—while fulfilling NSF’s mission “to promote the progress of science; to advance the national health, prosperity, and welfare; and to secure the national defense” Within SciML, operator learning has shown tremendous potential as a powerful tool for creating surrogate models, leading to a bevy of deep machine learning (ML)-based operator learning techniques known as “neural operators”. However, poorly-understood robustness characteristics, lack of explainability and interpretability, and the sheer variety of such approaches make it challenging for practitioners to choose the appropriate methods for different tasks, especially in the context of scientific applications. This project tackles these urgent challenges through the inception of a new computational framework: the Unified Neural Operator (UNO). UNO distills neural operators down into three essential components: an input encoder, a set of basis functions for the output space, and a projection operator. The work will (1) provide a mathematical formalism that both encompasses existing neural operators and allows us to generate novel architectures that target specific tasks and problems; (2) provide algorithms for scalable and adaptive training and inference, allowing UNO to adapt to local solution features and to tackle high-dimensional data efficiently in data-rich regimes; (3) provide a robust theoretical foundation in the form of universal approximation theorems, error estimates, and a guiding theoretical framework for robust sampling and adaptivity. The UNO framework also allows for automatic and natural uncertainty quantification capabilities of existing and new neural operators. In data-poor situations, the UNO framework preserves accuracy by analytically preserving physics, thereby making it well-suited to both in situ and ex situ surrogate modeling in scientific applications. The challenging applications targeted by this project include turbulent, multiscale, and multiphysics fluid flow problems. 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.