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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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At least 181 records · Page 10
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Using phase boundary mapping to resolve discrepancies in the Mg 2 Si–Mg 2 Sn miscibility gap

Mg 2 Si–Mg 2 Sn compositions within the Mg–Si–Sn materials system have potential as inexpensive, efficient thermoelectrics. These compositions lie specifically along the pseudobinary line with compositions of Mg 2 Si 1-x Sn x . The alloying and possible nanostructuring within the miscibility gap could further increase the thermoelectric figure of merit (zT) for these materials. However, the solubility limits of the miscibility gap differ greatly in the literature. Such a discrepancy could be a result of differing Mg-compositions due to excess magnesium added during sample annealing. To define these limits better and explain the change in proposed solubility limits based on magnesium content, the three-phase regions on either side of the pseudobinary phase region are phase boundary mapped and defect energy calculations are performed. This study presents a new understanding of the Mg–Si–Sn ternary phase diagram around the pseudobinary phase region. The solubility limits on either side of the pseudobinary should be essentially identical between the Mg-rich and Mg-poor three-phase regions unless the system temperature is brought above about 565 °C, at which eutectic liquid Mg 0.9 Sn 0.1 forms. This creates a second Mg-rich three-phase region which intersects the pseudobinary with a lower Sn solubility. Thus, samples prepared along the pseudobinary line are not well-defined thermodynamically when excess magnesium is added. Excess Mg can push the system into a new three phase region with Mg 2 Si 1-x Sn x composition different from that of the true miscibility gap. This understanding presents new guidelines for evaluating the miscibility gap and assists strategies for microstructure engineering and thermoelectric material processing.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Novel PtNi single-atom–nanocluster (SA–NC) ensembles promote Tafel kinetics and ampere-class AEM hydrogen evolution

The development of efficient, durable, and low-PGM electrocatalysts for the hydrogen evolution reaction (HER) in alkaline media is critical for next-generation electrolysis technologies. We report a facile two-step synthesis of highly dispersed PtNi and PtNi-nitride nanoclusters (NCs) (~2.3 nm) with ultralow Pt content (0.5 at.%) anchored on N-doped Vulcan carbon. Structural and compositional characterization via XAS, XPS, HAADF-STEM, HRTEM, and EDS mapping established key structure–activity relationships across varying Pt/Ni ratios and pyrolysis temperatures. The Pt0.5Ni0.5/C-750 catalyst, an ensemble of PtNi M-N-C type single-atom (SA) moieties with neighboring PtNi nanoclusters (NC), exhibited superior HER performance in alkaline media, achieving overpotentials of 30, 115, and 210 mV at 10, 100, and 500 mA cm−2, respectively. Despite at a lower Pt content, this novel SA–NC ensemble outperformed commercial Pt/C by ~36%. A standardized literature comparison with contemporary Pt- and Ru-doped analogues reveals the as-prepared Pt0.5Ni0.5/C-750 to sit at the apex of Tafel-limited kinetics and low overpotential at 100 mA cm−2. Tafel-limited Tafel slopes in both alkaline and acidic regimes confirm favorable proton recombination kinetics. Mass activities at 200 mV reached 13.8 and 18.84 A mgPt−1 in alkaline and acidic media, respectively. However, excessive nitridation (e.g., at 650 °C) adversely altered Pt electronic structure and HER kinetics. While Ni enhanced alkaline HER, acidic HER favored Ni-free analogues. Pt0.5Ni0.5/C-750 also demonstrated robust temperature responsiveness and 300-h operational stability at high current densities (0.5–1.0 A cm−2) in MEA tests. This work presents a scalable strategy for designing thermally responsive, durable, and compositionally tunable NC catalysts with neighboring SA moieties for alkaline electrolysis.

AEMWE↗
DOE OSTI2020

Effective Opacity of the Intergalactic Medium from Galaxy Spectra Analysis

We measure the effective opacity (τ{sub eff}) of the intergalactic medium from the composite spectra of 281 Lyman-break galaxies in the redshift range 2 ≲ z ≲ 3. Our spectra are taken from the COSMOS Lyα Mapping And Tomographic Observations survey derived from the Low Resolution Imaging Spectrometer on the W.M. Keck I telescope. We generate composite spectra in two redshift intervals and fit them with spectral energy distribution (SED) models composed of simple stellar populations. Extrapolating these SED models into the Lyα forest, we measure the effective Lyα opacity (τ{sub eff}) in the 2.02 ≤ z ≤ 2.44 range. At z = 2.22, we estimate τ{sub eff} =0.159±0.001 from a power-law fit to the data. These measurements are consistent with estimates from quasar analyses at z < 2.5 indicating that the systematic errors associated with normalizing quasar continua are not substantial. We provide a Gaussian processes model of our results and previous τ{sub eff} measurements that describes the steep redshift evolution in τ{sub eff} from z = 1.5–4.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Impact of Backing Plate and Thermal Boundary Conditions for High-Speed Friction Stir Welding of 25-mm Thick Aluminum Alloy 7175-T79

Here this study demonstrates high-speed (150 mm/min) single pass friction stir butt joining of 25 mm thick aluminum alloy 7175-T79. To understand the impact of quenching and cooling rate on process responses, joint strength, and grain size distributions across the weld thickness, we performed a series of friction stir welding (FSW) trials in air and with a trailing water spray using steel and composite backing plates (BP). Welds made in air and trailing water spray exhibit significantly different hardness distributions across the nugget, heat affected zone (HAZ), and HAZ minimum hardness as evidenced by detailed microhardness mapping. The effect of trailing water spray (TWS) on joint efficiency overshadows the influence of BP composition due to the vastly different contribution to quenching. TWS also resulted in a multifaceted effect on FSW such as lowering processing temperature, increasing X and Z forces while lowering Y force, and narrow heat affecting zone. Finally, digital image correlation (DIC)-based fracture mode analysis and grain size measurements correlate with the hardness distribution.

42 ENGINEERING↗
DOE OSTI2024

Active learning of ternary alloy structures and energies

Abstract Machine learning models with uncertainty quantification have recently emerged as attractive tools to accelerate the navigation of catalyst design spaces in a data-efficient manner. Here, we combine active learning with a dropout graph convolutional network (dGCN) as a surrogate model to explore the complex materials space of high-entropy alloys (HEAs). We train the dGCN on the formation energies of disordered binary alloy structures in the Pd-Pt-Sn ternary alloy system and improve predictions on ternary structures by performing reduced optimization of the formation free energy, the target property that determines HEA stability, over ensembles of ternary structures constructed based on two coordinate systems: (a) a physics-informed ternary composition space, and (b) data-driven coordinates discovered by the Diffusion Maps manifold learning scheme. Both reduced optimization techniques improve predictions of the formation free energy in the ternary alloy space with a significantly reduced number of DFT calculations compared to a high-fidelity model. The physics-based scheme converges to the target property in a manner akin to a depth-first strategy, whereas the data-driven scheme appears more akin to a breadth-first approach. Both sampling schemes, coupled with our acquisition function, successfully exploit a database of DFT-calculated binary alloy structures and energies, augmented with a relatively small number of ternary alloy calculations, to identify stable ternary HEA compositions and structures. This generalized framework can be extended to incorporate more complex bulk and surface structural motifs, and the results demonstrate that significant dimensionality reduction is possible in thermodynamic sampling problems when suitable active learning schemes are employed.

Chemistry↗
DOE OSTI2025

Development of chemometric models to classify solid-state U materials by micro-Raman spectroscopy

Discerning uranium (U) particles found in environmental sampling is of interest for monitoring the peaceful use of nuclear material. In this study, a soft independent modeling of class analogy (SIMCA) library was successfully developed for the classification of a four-class system consisting of α-U 3 O 8 , UO 2 , UO 2 (NO 3 ) 2 ·6H 2 O (UNH), and UO 2 O 2 ·4H 2 O (studtite) by Raman spectroscopy in the presence of matrix particulates and additional outliers. Spectral variability between numerous particles of each type revealed appreciable differences as a function of particle size with respect to hydration state and potential oxide phase within each class. Interclass variability was accounted for using both unsupervised and supervised chemometric models. The supervised SIMCA model displayed reasonable sensitivity for each U class and a high degree of specificity by returning whether a spectrum belonged to one class or not. This work demonstrates how Raman spectral features and chemometrics can be used to distinguish U materials from one another and from matrix materials such as flint clay. Combining the outlined chemometric approach with Raman mapping sequences could provide a rapid, nondestructive technique to characterize the chemical composition of a diverse collection of U compounds amid background samples for environmental sampling, nuclear forensics, and industrial applications.

Actinide↗
DOE OSTI2024

Impacts of Curing-Induced Phase Segregation in Silicon Nanoparticle-Based Electrodes

We report the investigation of silicon nanoparticle composite anodes for Li-ion batteries, using a combination of two nm-scale atomic force microscopy-based techniques: scanning spreading resistance microscopy for electrical conduction mapping and contact resonance and force volume for elastic modulus mapping, along with scanning electron microscopy-based energy dispersion spectroscopy, nanoindentation, and electrochemical analysis. Thermally curing the composite anode—made of polyethylene oxide-treated Si nanoparticles, carbon black, and polyimide binder—reportedly improves the anode electrochemical performance significantly. This work demonstrates phase segregation resulting from thermal curing, where alternating bands of carbon and silicon active material are observed. This electrode morphology is retained after extensive cycling, where the electrical conduction of the carbon-rich bands remains relatively unchanged, but the mechanical modulus of the bands decreases distinctly. These electrical and mechanical factors may contribute to performance improvement, with carbon bands serving as a mechanical buffer for Si deformation and providing electrical conduction pathways. This work motivates future efforts to engineer similar morphologies for mitigating capacity loss in silicon electrodes.

25 ENERGY STORAGE↗
DOE OSTI2020

Implementation of Talbot–Lau x-ray deflectometry in the pulsed power environment using a copper X-pinch backlighter

A Talbot–Lau x-ray deflectometer can map electron density gradients in high energy density plasmas, as well as provide information about plasma elemental composition through single-image x-ray refraction and attenuation measurements. A new adaptation to a pulsed power environment used backlighting from copper X-pinches, allowing for electron density mapping of a low-Z object. Even though the X-pinch backlighter is not properly optimized for emitting x-rays in terms of source size and photon fluence, Moire fringe patterns with contrast up to 14% and fringe shift due to refraction on a beryllium object are obtained. Due to the proximity of the deflectometer with the X-pinch (~6 cm), it is shown that a protective filter is required to avoid damage in the closest (i.e., source) grating due to both plasma debris and mechanical shock. Regarding grating survival, these did not show any damage due to the intense magnetic field or heating induced by plasma radiation. Electron density on beryllium was measured with a difference lower than 16%. The areal electron density mapping of the sample was limited by source size characteristics, in similarity to transmission radiography. These results show the potential of plasma electron density as well as material mapping through Talbot–Lau x-ray deflectometry in a pulsed power environment.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2021

Corrosion of rebar in concrete. Part III: Artificial Neural Network analysis of chloride threshold data

Active corrosion of carbon steel in reinforced concrete occurs when the chloride ion concentration exceeds the chloride threshold (CT). CT is affected by many physical and chemical parameters including primary variables (pH, corrosion potential, breakdown potential, and temperature) and secondary variables (cemenet composition, concreete porosity, and water/cement ratio). A Kohonen-self organized map (KSOM) and regression artificial neural network (ANN) coupled methodology was developed to find the missing values of independent variables in the sparse database and for quantitatively evaluating the effects of these variables on CT values that are expressed in %TotalCl/cem (or %FreeCl/cem), and [Cl - ]/[OH - ].

36 MATERIALS SCIENCE↗
DOE OSTI2022

Remote Sensing of Tundra Ecosystems Using High Spectral Resolution Reflectance: Opportunities and Challenges

Abstract Observing the environment in the vast regions of Earth through remote sensing platforms provides the tools to measure ecological dynamics. The Arctic tundra biome, one of the largest inaccessible terrestrial biomes on Earth, requires remote sensing across multiple spatial and temporal scales, from towers to satellites, particularly those equipped for imaging spectroscopy (IS). We describe a rationale for using IS derived from advances in our understanding of Arctic tundra vegetation communities and their interaction with the environment. To best leverage ongoing and forthcoming IS resources, including National Aeronautics and Space Administration’s Surface Biology and Geology mission, we identify a series of opportunities and challenges based on intrinsic spectral dimensionality analysis and a review of current data and literature that illustrates the unique attributes of the Arctic tundra biome. These opportunities and challenges include thematic vegetation mapping, complicated by low‐stature plants and very fine‐scale surface composition heterogeneity; development of scalable algorithms for retrieval of canopy and leaf traits; nuanced variation in vegetation growth and composition that complicates detection of long‐term trends; and rapid phenological changes across brief growing seasons that may go undetected due to low revisit frequency or be obscured by snow cover and clouds. We recommend improvements to future field campaigns and satellite missions, advocating for research that combines multi‐scale spectroscopy, from lab studies to satellites that enable frequent and continuous long‐term monitoring, to inform statistical and biophysical approaches to model vegetation dynamics.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Cholesterol modulates membrane elasticity via unified biophysical laws

Cholesterol and lipid unsaturation underlie a balance of opposing forces that features prominently in adaptive cell responses to diet and environmental cues. These competing factors have resulted in contradictory observations of membrane elasticity across different measurement scales, requiring chemical specificity to explain incompatible structural and elastic effects. Here, we demonstrate that – unlike macroscopic observations – lipid membranes exhibit a unified elastic behavior in the mesoscopic regime between molecular and macroscopic dimensions. Using nuclear spin techniques and computational analysis, we find that mesoscopic bending moduli follow a universal dependence on the lipid packing density regardless of cholesterol content, lipid unsaturation, or temperature. Our observations reveal that compositional complexity can be explained by simple biophysical laws that directly map membrane elasticity to molecular packing associated with biological function, curvature transformations, and protein interactions. The obtained scaling laws closely align with theoretical predictions based on conformational chain entropy and elastic stress fields. These findings provide unique insights into the membrane design rules optimized by nature and unlock predictive capabilities for guiding the functional performance of lipid-based materials in synthetic biology and real-world applications.

Kumarage, Teshani [Virginia Polytechnic Inst. and ↗
DOE OSTI2021

Crystalline solutions of the Kohn-Sham equations in the fractional quantum Hall regime

A Kohn-Sham density functional approach has recently been developed for the fractional quantum Hall effect, which maps the strongly interacting electrons into a system of weakly interacting composite fermions subject to an exchange correlation potential as well as a density dependent gauge field that mimics the "flux quanta" bound to composite fermions. To get a feel for the role of various terms, we study the behavior of the self-consistent solution as a function of the strength of the exchange correlation potential, which is varied through an ad hoc multiplicative factor. Here, we find that a crystal phase is stabilized when the exchange correlation interaction is sufficiently strong relative to the composite-fermion cyclotron energy. Various properties of this crystal are examined.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2024

Accelerating Thermochemical Equilibrium Calculations for Nuclear Reactor Applications

Thermochemical properties play a key role in modeling and simulation of several key phenomena in nuclear reactors. There has been an increasing interest in incorporating CALPHAD-based formulations in multiphysics simulations including for Molten Salt Reactors where knowledge of phase evolution of the salt and the chemical potentials of various elements are of utmost importance in source term analyses and redox control. However, the size of such simulations is often limited by the high computational cost of full thermodynamic equilibrium calculations. This work discusses the current efforts aimed at accelerating thermochemical equilibrium calculations for multiphysics simulations performed using the open-source finite element / finite volume code Multiphysics Object Oriented Simulation Environment (MOOSE) [1]. While several methods have been proposed for accelerating phase equilibrium calculations [2], most focus on relatively small systems and often rely on a- priori knowledge of the state-space of the system. Nuclear materials, however, are often multi-component systems owing to the evolution of composition under irradiation and an approach based on a-priori mapping of phase diagram is often not enough. This work is aimed at demonstrating an on-the-fly surrogate modeling framework that uses active learning to reduce the number of full equilibrium calculations that must be performed. By combining with efficient coupling approaches, the surrogate framework helps in reducing the computational cost of thermodynamic equilibrium informed multiphysics simulations of nuclear materials. The performance is benchmarked against full coupling with the thermochemistry library Thermochimica [3]. This work uses a machine learning based approach for constructing surrogate models to predict the stable phases in a multicomponent system. The surrogates were constructed using neural networks and Gaussian process classification. In this work, we compare the relative performance of the two methods. We also demonstrate the use of caching previous calculations by interpolating the values from nearest neighbors. References [1] Lindsay, A.D., et al. "2.0 – MOOSE: Enabling massively parallel multiphysics simulation", SoftwareX, 20 (2022): 101202. [2] Roos, W.A. and Zietsman J.H. "Accelerating complex chemical equilibrium calculations – A Review", Calphad, 77 (2022): 102380. [3] Piro, M.H.A., et al. "The thermochemistry library Thermochimica", Computational Materials Science, 67 (2013): 266-272.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Characterization and repair of core gap manufacturing defects for wind turbine blades

Various wind turbine blade components, such as shear webs and skins, commonly use fiber-reinforced composite sandwich structures with a core material like balsa or foam. During manufacturing, core gap defects may result from the misalignment of adjacent foam or balsa core sheets in the blade mold. It is important to understand the influence that core gaps have on the structural integrity of wind turbine blades and how to mitigate their influence. This research characterized the effects of core gap defects at the manufacturing and mechanical levels for both epoxy and next-generation thermoplastic composites. Common repair methods were assessed and compared. Multiple defect sizes were compared using temperature data gathered with thermocouples embedded during manufacturing to core gap defect characteristics obtained using image-mapping techniques, optical microscopy, and mechanical characterization by long beam flexure. Results showed that peak exothermic temperatures during curing were closely related to core gap size. The long beam flexure tests determined that transverse core gaps under pure bending loads can have a substantial effect on the ultimate facesheet strength of both epoxy and thermoplastic composite sandwich structures (up to 25% strength reduction), although the size of the defect itself had less of an influence on the magnitude of the strength reduction. The supporting image-mapping techniques indicated that the distortion of the composite facesheets by the core gaps contributed to the premature failures. The repair methods used in this study did very little to improve the ultimate strength of the sandwich panels that previously had core gap defects. The repair of the thermoplastic panel resulted in a further loss in ultimate facesheet strength. This research demonstrated that there is a vital need for the development of a compatible thermoplastic polymer repair resin system and appropriate resin specific repair procedures for the next generation of recyclable thermoplastic wind blades.

17 WIND ENERGY↗
DOE OSTI2024

Identifying Disadvantaged Communities in the United States: An Energy-Oriented Mapping Tool that Aggregates Environmental and Socioeconomic Burdens

This paper defines a policy-relevant nationwide composite index to identify communities disproportionately impacted by environmental, energy, and climate injustices in the United States. We review existing vulnerability indicators and indices to assess the tradeoffs of different design parameters, including variable selection, geographic unit, dimensionality reduction, weighting, and aggregation methods. From this methodological review, we create the first nationwide, census tract-level index of cumulative burden that includes energy-relevant indicators alongside climate, social, environmental, and economic indicators, and is flexible to the inclusion of additional data sources. We provide a summary of the sources of inputs used to develop a definition for "disadvantaged communities" that can be used to prioritize energy investments. We discuss use-cases for this index including the implementation of the Justice40 Initiative, which calls for 40% of certain federal clean energy benefits to flow to disadvantaged communities in the United States. We use our results to examine historic allocations of federal energy investments and show that communities that we identify as disadvantaged received about 37% fewer funds per capita than non-disadvantaged communities.

cumulative burden↗
DOE OSTI2023

Mapping the structural trends in zinc aluminosilicate glasses

The structure of zinc aluminosilicate glasses with the composition (ZnO) x (Al 2 O 3 ) y (SiO 2 ) 1-x-y , where 0 ≤ x < 1, 0 ≤ y < 1, and x + y < 1, was investigated over a wide composition range by combining neutron and high-energy x-ray diffraction with 27 Al magic angle spinning nuclear magnetic resonance spectroscopy. The results were interpreted using an analytical model for the composition-dependent structure in which the zinc ions do not act as network formers. Four-coordinated aluminum atoms were found to be in the majority for all the investigated glasses, with five-coordinated aluminum atoms as the main minority species. Mean Al–O bond distances of 1.764(5) and 1.855(5) Å were obtained for the four- and five-coordinated aluminum atoms, respectively. The coordination environment of zinc was not observed to be invariant. Instead, it is dependent on whether zinc plays a predominantly network-modifying or charge-compensating role and, therefore, varies systematically with the glass composition. The Zn–O coordination number and bond distance were found to be 4.36(9) and 2.00(1) Å, respectively, for the network-modifying role vs 5.96(10) and 2.08(1) Å, respectively, for the charge-compensating role. The more open coordination environment of the charge-compensator is related to an enhanced probability of zinc finding bridging oxygen atoms as nearest-neighbors, reflecting a change in the connectivity of the glass network comprising four-coordinated silicon and aluminum atoms as the alumina content is increased.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Scanning Probe Microscope to Map Thermal and Thermoelectric Properties of Combinatorial Materials

The combinatorial methodology offers a high-throughput process for developing radiation-resistant sensor materials for nuclear energy applications through the synthesis and screening of large numbers of compositions and processing conditions in a single process. The combinatorial materials fabrication coupled with an automated screening process enables us to identify the optimal materials composition and processing conditions that yield both desired properties and required irradiation resistance in the most efficient and economic manner. The report focuses on the development of scanning probe technique to map with high resolution the thermal conductivity, Seebeck coefficient and electrical conductivity of combinatory materials with gradient compositions, which has been a key challenge in combinatory materials science. The above three properties play important roles in many sensor materials for nuclear energy applications.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2020

NGEE Arctic Plant Traits: Plant Community Composition, Kougarok Road Mile Marker 64, Seward Peninsula, Alaska, 2016

This dataset reports the characteristics of the dominant vegetation communities at the NGEE Arctic Kougarok field site, Kougarok Road Mile Marker 64 on the Seward Peninsula. Selected plots were surveyed from 18-23 July 2016. Environmental data (e.g., elevation, slope, soil moisture regime, disturbance type and degree, mean canopy height, etc.) were also recorded for each plot. Elevation measurements were updated 2020-08-18. All plant species (vascular plants, bryophytes and lichens) were recorded along with their percent cover in the plots as determined visually by the lead author. Plots were chosen subjectively in areas of homogeneous and representative vegetation and varied in size from 1-25 m2 depending on canopy structure and height. The site is an east-facing hillslope with vegetation that varies from the summit to the toeslope. The 30 total plots were selected as five replicate plots sampled from each of the six identified habitat types: 1) non-acidic mountain complex at the hillcrest, 2) dwarf-shrub lichen tundra on the shoulder, 3) alder shrublands predominantly on an elevational band across the backslope, 4) willow-birch tundra on the upper backslope, 5) tussock tundra on the footslope in inter-water tracks, and 6) tussock tundra mixed with willow-birch tundra or alder savanna in poor developed water tracks on the footslope. This dataset is comprised of two comma-separated (*.csv) files containing species and environmental data for the plant community composition plots. It also contains seven zipped folders of plot photographs, one map showing plot locations (*.pdf), one blank datasheet with the key to codes used in the field (*.pdf), select photos organized in a (*.pdf), and one User file (*.pdf). The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research. The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska. Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

54 ENVIRONMENTAL SCIENCES↗