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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
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Synthesis and Computational and Experimental Investigations of a para -Nicotinic Acid-Bridged Dirhenium(I) Dimer Complex
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Bridging the Gap in Carbon Free Iron Making: How Hydrogen Affects the Reduction of Iron Ore between 900 and 1590 °C
Hydrogen-based reduction of iron ore for iron and steel production has emerged as a promising alternative to coal and natural gas. Unlike other hydrogen-based iron ore reduction studies, this research focuses on a wide temperature range across 900–1590 °C, encompassing reduction in solid, mixed, and liquid (slag) phases. For a 20 min exposure to hydrogen, the reduction degree increased monotonically from ∼35% at 900 °C to >90% at 1550 °C, except between 1100 °C and 1400 °C, where it stagnated ∼60%. This experimental work challenges the widely accepted notion that higher temperatures enhance the reduction process. Instead, it reveals an overlooked kinetic bottleneck, suggesting complex thermodynamic and mass transfer limitations influenced by phase transformations, diffusion barriers, and microstructural changes. Density functional theory-based molecular dynamics simulations indicate that oxygen diffusivity in BCC iron is 3.88 × 10 –5 cm 2 /s which is ∼5–10 times higher than that in FCC iron. This study reports that this stagnant reduction degree in the mixed solid–liquid phase is due to competition of multiple mechanisms, such as surface- and bulk-diffusion, pore collapse mechanisms, and crystallographic transitions.
Tertiary Hierarchical Complexity in Assemblies of Sulfur-Bridged Metal Chiral Clusters
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Electronic Structures and Reactivity Profiles of Aryl Nitrenoid-Bridged Dicopper Complexes
Abstract not provided
Bridging the Flux Gap: Sap Flow Measurements Reveal Species-Specific Patterns of Water Use in a Tallgrass Prairie
Woody encroachment, the process through which woody plants (trees and shrubs) replace grasses and wildflowers in grassland ecosystems, is occurring globally. Because woody plants use water differently (e.g., rates, soil depths of uptake, and time periods of activity) than grasses, woody encroachment could potentially change the water cycle in grasslands. In order to understand how grassland water use might change with ongoing woody encroachment, we measured continuous rates of water loss in common plant species in a North American grassland. Furthermore, we found that the main encroaching shrub in this grassland used more water over time than grasses or wildflowers. Additionally, this shrub and grasses used a large amount of water at night compared to wildflowers. Finally, we found that different weather conditions had different impacts on how water was used by woody plants, grasses, and wildflowers. We suggest that woody encroachment might reduce the amount of water stored in grasslands, which could alter the composition of grassland plants in the future.
Using a Deep Neural Network and Transfer Learning to Bridge Scales for Seismic Phase Picking
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Scale–Bridging in Three–Dimensional Fracture Networks: Characterizing the Effects of Variable Fracture Apertures on Network–Scale Flow Channelization
Here, we incorporate observations of real fracture aperture variability observed in laboratory experiments into an ensemble of three-dimensional discrete fracture network (DFN) simulations to characterize how variations of this micro-scale feature can influence flow and transport behavior at the network scale. A shear fracture is created within a Marcellus shale sample, and the fracture aperture is measured using a triaxial direct-shear device coupled with real-time X-ray imaging at in-situ stress conditions. We construct an ensemble of fracture networks based on natural fractures in Marcellus shale and project regions of the experimental aperture field onto each fracture in the networks. Our calculations demonstrate that the degree of flow channelization, a network-scale flow field structure, is dramatically increased by local changes in the aperture field that in turn affects flow and transport properties.
Important ice processes are missed by the Community Earth System Model (CESM) in Southern Ocean mixed–phase clouds: Bridging SOCRATES observations to model developments
Global climate models (GCMs) are challenged by difficulties in simulating cloud phase and cloud radiative effect over the Southern Ocean (SO). Some of the new generation GCMs predict too much liquid and too little ice in mixed-phase clouds. This misrepresentation of cloud phase in GCMs results in weaker negative cloud feedback over the SO and a higher climate sensitivity. Based on a model comparison with observational data obtained during the Southern Ocean Cloud Radiation and Aerosol Transport Experimental Study (SOCRATES), this study addresses a key uncertainty in the Community Earth System Model version 2 (CESM2) related to cloud phase, namely ice formation in pristine remote SO clouds. It is found that sea spray organic aerosols (SSOA) are the most important type of ice nucleating particles (INPs) over the SO with concentrations one order of magnitude higher than those of dust INPs based on measurements and CESM2 simulations. Secondary ice production (SIP) which includes riming splintering, rain droplet shattering, and ice-ice collisional fragmentation as implemented in CESM2 is the dominant ice production process in moderately cold clouds with cloud temperatures greater than –20°C. SIP enhances the in-cloud ice number concentrations (Ni) by 1-3 orders of magnitude and predicts more mixed-phase (with percentage occurrence increased from 15% to 21%), in better agreement with the observations. Finally, this study highlights the importance of accurately representing cloud phase over the pristine remote SO by considering the ice nucleation of SSOA and SIP processes, which are currently missing in most GCM cloud microphysics parameterizations.
Bridging 20 Years of Soil Organic Matter Frameworks: Empirical Support, Model Representation, and Next Steps
Abstract In the past few decades, there has been an evolution in our understanding of soil organic matter (SOM) dynamics from one of inherent biochemical recalcitrance to one deriving from plant‐microbe‐mineral interactions. This shift in understanding has been driven, in part, by influential conceptual frameworks which put forth hypotheses about SOM dynamics. Here, we summarize several focal conceptual frameworks and derive from them six controls related to SOM formation, (de)stabilization, and loss. These include: (a) physical inaccessibility; (b) organo‐mineral and ‐metal stabilization; (c) biodegradability of plant inputs; (d) abiotic environmental factors; (e) biochemical reactivity and diversity; and (f) microbial physiology and morphology. We then review the empirical evidence for these controls, their model representation, and outstanding knowledge gaps. We find relatively strong empirical support and model representation of abiotic environmental factors but disparities between data and models for biochemical reactivity and diversity, organo‐mineral and ‐metal stabilization, and biodegradability of plant inputs, particularly with respect to SOM destabilization for the latter two controls. More empirical research on physical inaccessibility and microbial physiology and morphology is needed to deepen our understanding of these critical SOM controls and improve their model representation. The SOM controls are highly interactive and also present some inconsistencies which may be reconciled by considering methodological limitations or temporal and spatial variation. Future conceptual frameworks must simultaneously refine our understanding of these six SOM controls at various spatial and temporal scales and within a hierarchical structure, while incorporating emerging insights. This will advance our ability to accurately predict SOM dynamics.
Bridging Hydrological Ensemble Simulation and Learning Using Deep Neural Operators
Ensemble-based simulation and learning (ESnL) has long been used in hydrology for parameter inference, but computational demands of process-based ESnL can be quite high. To address this issue, we propose a deep neural operator learning approach. Neural operators are generic machine learning algorithms that can learn functional mappings between infinite-dimensional spaces, providing a highly flexible tool for scientific machine learning. Our approach is built upon DeepONet, a specific deep neural operator, and is designed to address several common problems in hydrology, namely, model parameter estimation, prediction at ungaged locations, and uncertainty quantification. Here we demonstrate the effectiveness of our DeepONet-based workflow using an existing large model ensemble created for an eastern U.S. watershed that is instrumented with 10 streamflow gages. Results suggest DeepONet achieves high efficiency in learning an ML surrogate model from the model ensemble, with the modified Kling-Gupta Efficiency exceeding 0.9 on holdout test sets. Parameter inference, carried out using the trained DeepONet surrogate model and genetic algorithm, also yields robust results. Additionally, we formulate and train a separate DeepONet model for physics-informed, seq-to-seq streamflow forecasting, which further reduces biases in the pre-trained DeepONet surrogate model. While this study focuses primarily on a single watershed, our approach is general and may be extended to enable learning from model ensembles across multiple basins or models. Thus, this research represents a significant contribution to the application of hybrid machine learning in hydrology.
Visual‐Analytics Bridge Complexity and Accessibility for Robust Urban Water Planning
Urban water resources planning is complicated by unprecedented uncertainty in supply and demand. Real‐world planning often simplifies the full range of uncertainty faced by a system into a limited set of deterministic scenarios to enhance accessibility for decision‐makers and the public. However, overlooking uncertainty can expose the system to failures. On the other end of the spectrum, academically developed tools for scenario analysis rigorously quantify the combined effects of multiple sources of uncertainty, but the practical application of these models is limited by the challenges of information visualization and communication of results. In short, municipal water supply planners lack access to planning frameworks that effectively integrate a rigorous treatment of uncertainty with accessible, user‐friendly visual and interactive tools to enhance user accessibility. In this work, we fill this gap by proposing Visual‐Robust Decision Making, and demonstrate an application for the city of Santa Barbara (SB), CA. Santa Barbara faces multiple uncertainties from pending state and federal regulations to changing hydrology and water demand. The city seeks to increase its water portfolio robustness by expanding its seawater desalination plant, but must decide how much capacity to add. We introduce computational tools that assess uncertainty across nine uncertain drivers identified with the help of water planners in SB. To allow public participation in the desalination expansion decision, we develop interactive visual‐analytics to aid decision‐makers and stakeholders in navigating complex scenario analysis outcomes. Our results quantify the tradeoffs between increased capacity and system robustness and aim to enhance participation and uncertainty characterization of urban water planning efforts.
Remotely Sensed High‐Resolution Soil Moisture and Evapotranspiration: Bridging the Gap Between Science and Society
This paper reviews the current state of high‐resolution remotely sensed soil moisture (SM) and evapotranspiration (ET) products and modeling, and the coupling relationship between SM and ET. SM downscaling approaches for satellite passive microwave products leverage advances in artificial intelligence and high‐resolution remote sensing using visible, near‐infrared, thermal‐infrared, and synthetic aperture radar sensors. Remotely sensed ET continues to advance in spatiotemporal resolutions from MODIS to ECOSTRESS to Hydrosat and beyond. These advances enable a new understanding of bio‐geo‐physical controls and coupled feedback mechanisms between SM and ET reflecting the land cover and land use at field scale (3–30 m, daily). Still, the state‐of‐the‐science products have their challenges and limitations, which we detail across data, retrieval algorithms, and applications. We describe the roles of these data in advancing 10 application areas: drought assessment, food security, precision agriculture, soil salinization, wildfire modeling, dust monitoring, flood forecasting, urban water, energy, and ecosystem management, ecohydrology, and biodiversity conservation. We discuss that future scientific advancement should focus on developing open‐access, high‐resolution (3–30 m), sub‐daily SM and ET products, enabling the evaluation of hydrological processes at finer scales and revolutionizing the societal applications in data‐limited regions of the world, especially the Global South for socio‐economic development.
Radiosonde‐to‐Space (R2S) Atmospheric Specifications: Bridging Observations and Models for Infrasound Propagation
Manuscript for submission to GRL. Must also pass HQ Interagency Review before submission to journal.
A combinatory ferroelectric compound bridging simple ABO 3 and A-site-ordered quadruple perovskite
The simple ABO 3 and A-site-ordered AA' 3 B 4 O 12 perovskites represent two types of classical perovskite functional materials. There are well-known simple perovskites with ferroelectric properties, while there is still no report of ferroelectricity due to symmetry breaking transition in A-site-ordered quadruple perovskites. Here we report the high pressure synthesis of an A-site-ordered perovskite PbHg 3 Ti 4 O 12 , the only known quadruple perovskite that transforms from high-temperature centrosymmetric paraelectric phase to low-temperature non-centrosymmetric ferroelectric phase. The coordination chemistry of Hg 2+ is changed from square planar as in typical A-site-ordered quadruple perovskite to a rare stereo type with 8 ligands in PbHg 3 Ti 4 O 12 . Thus PbHg 3 Ti 4 O 12 appears to be a combinatory link from simple ABO 3 perovskites to A-site-ordered AA' 3 Ti 4 O 12 perovskites, sharing both displacive ferroelectricity with former and structure coordination with latter. This is the only example so far showing ferroelectricity due to symmetry breaking phase transition in AA' 3 B 4 O 12 -type A-site-ordered perovskites, and opens a direction to search for ferroelectric materials.