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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 469 records · Page 26
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Lithium metal stripping mechanisms revealed through electrochemical liquid cell electron microscopy

An understanding of lithium stripping is as important as that of lithium plating to achieve significant advances in using lithium metal anodes for high-energy rechargeable batteries. However, there have been limited studies on lithium stripping compared to lithium plating. Here we report the lithium stripping mechanisms revealed through in-situ electrochemical liquid cell transmission electron microscopy (TEM). We directly observe and compare the stripping behavior of the in-situ grown lithium dendrites and lithium nanograins covered by a lithium fluoride-rich solid-electrolyte interphase (SEI). Furthermore, we find the sporadic lithium stripping behavior and three important modes that can describe the stripping of individual lithium deposits, regardless of their morphology: (i) symmetric stripping, (ii) surface-preferred asymmetric stripping, and (iii) interface-preferred asymmetric stripping. In addition, SEI chemical mapping with high spatial resolution shows a remarkable SEI loss at the end of the lithium metal stripping, which illustrates the importance of SEI protection in the subsequent cycles.

25 ENERGY STORAGE↗
DOE OSTI2026

Integrating very-high-resolution imagery, Sentinel-2 time-series data, and machine learning to map shrub fractional abundance across arid and semi-arid ecosystems in China

Shrub fractional abundance (SFA), the proportion of shrub cover per unit area, serves as a critical indicator of environmental aridity and ecosystem health in arid and semi-arid regions, particularly across the Mongolian steppe. However, large-scale SFA mapping in Mongolian steppe ecosystems remains challenging due to the small crown size of shrubs, their sparse distribution, and spectral overlap with coexisting low vegetation (e.g., grasses and herbs), which hinders accurate detection using coarser-resolution satellite data or traditional field surveys. To address these challenges, we developed a two-step approach that integrates very-high-resolution (VHR) imagery, time-series Sentinel-2 data, and deep learning techniques. First, we generated high-accuracy benchmark maps of individual shrub crowns from 0.5 m VHR imagery by combining manual segmentation with a hybrid deep learning framework (Dino V2 and convolutional neural networks). Second, we used these shrub crown maps as training data to build an XGBoost model for predicting SFA from 20 m Sentinel-2 time-series data, leveraging phenological information to improve estimation. We validated our approach across 70 sites (1km 2 each) in the Inner Mongolia Autonomous Region, which is representative of Mongolian steppe ecosystems. From VHR imagery, we mapped 1.31 million shrub crowns with an accuracy of R 2 = 0.92. Scaling up with Sentinel-2 data yielded regional SFA maps with an R 2 = 0.60. Further SHAP (SHapley Additive exPlanations) analysis on the developed XGBoost model revealed that phenological metrics (particularly observations in early-May, mid-July, and late-September), which distinguish shrub phenology from that of other land cover types (e.g., grasses and bare soil), were the most influential predictors of SFA. Finally, our regional SFA maps uncovered unimodal relationships between shrub distribution and climate variables, peaking at mean annual minimum temperatures near 0 °C and annual precipitation around 200 mm. Collectively, these findings demonstrate how the integration of multi-source remote sensing and machine learning can overcome historical limitations in SFA mapping, enabling accurate, spatially continuous assessments across vast Inner-Mongolian steppe ecosystems. Our framework has the potential to be applied to other steppe ecosystems and dryland ecosystems across the Mongolian steppe and beyond, offering a foundation for improved monitoring and ecological impact assessments in the face of global climate changes.

Arid and semi-arid landscapes↗
DOE OSTI2021

A vortex sheet based analytical model of the curled wake behind yawed wind turbines

Motivated by the need for compact descriptions of the evolution of non-classical wakes behind yawed wind turbines, we develop an analytical model to predict the shape of curled wakes. Interest in such modelling arises due to the potential of wake steering as a strategy for mitigating power reduction and unsteady loading of downstream turbines in wind farms. We first estimate the distribution of the shed vorticity at the wake edge due to both yaw offset and rotating blades. By considering the wake edge as an ideally thin vortex sheet, we describe its evolution in time moving with the flow. Vortex sheet equations are solved using a power series expansion method, and an approximate solution for the wake shape is obtained. The vortex sheet time evolution is then mapped into a spatial evolution by using a convection velocity. Apart from the wake shape, the lateral deflection of the wake including ground effects is modelled. Our results show that there exists a universal solution for the shape of curled wakes if suitable dimensionless variables are employed. For the case of turbulent boundary layer inflow, the decay of vortex sheet circulation due to turbulent diffusion is included. Finally, we modify the Gaussian wake model by incorporating the predicted shape and deflection of the curled wake, so that we can calculate the wake profiles behind yawed turbines. Model predictions are validated against large-eddy simulations and laboratory experiments for turbines with various operating conditions.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2026

Calibration-free analysis of Li isotope ratios using laser ablation and laser absorption spectroscopy

We introduce a rapid, calibration-free, all-optical method for high-precision lithium isotope ratio measurements in solid materials using laser ablation combined with tunable laser absorption spectroscopy. A new asynchronous acquisition method is used to acquire time-resolved, high-resolution spectra of the 6Li and 7Li D1 and D2 transitions near 671 nm. Isotope ratios and atomic column densities are extracted from measured spectra via a physics-based fitting model including hyperfine structure. Under 1 Torr air, spectra recorded = 0.75 ms after plasma onset exhibit narrow linewidths corresponding to Doppler temperatures = 400 K, enabling resolution of the isotope peaks with high signal-to-noise ratios. Analysis of LiAlO2 samples with varying 6Li:7Li ratios demonstrates isotopic precisions of 0.6–1.7% for spectra acquired in 30 s. Isotope ratios determined from the spectral fits show accuracy within –0.3% to –1.7% of reference ICP-MS measurements without requiring calibration to external standards. By eliminating sample preparation and enabling spatially resolved isotopic mapping, this method offers a rapid analysis approach to lithium isotope determination in solid materials relevant to nuclear energy, safeguards, and geochemistry.

Phillips, Mark C.↗
DOE OSTI2020

Photophysics and Electronic Structure of Lateral Graphene/MoS 2 and Metal/MoS 2 Junctions

Integration of semiconducting transition metal dichalcogenides (TMDs) into functional optoelectronic circuitries requires an understanding of the charge transfer across the interface between the TMD and the contacting material. In this work, we use spatially resolved photocurrent microscopy to demonstrate electronic uniformity at the epitaxial graphene/molybdenum disulfide (EG/MoS 2 ) interface. A 10× larger photocurrent is extracted at the EG/MoS 2 interface when compared to the metal (Ti/Au)/MoS 2 interface. This is supported by semi-local density functional theory (DFT), which predicts the Schottky barrier at the EG/MoS 2 interface to be ~2× lower than that at Ti/MoS 2 . We provide a direct visualization of a 2D material Schottky barrier through combination of angle-resolved photoemission spectroscopy with spatial resolution selected to be ~300 nm (nano-ARPES) and DFT calculations. A bending of ~500 meV over a length scale of ~2-3 μm in the valence band maximum of MoS 2 is observed via nano-ARPES. We explicate a correlation between experimental demonstration and theoretical predictions of barriers at graphene/TMD interfaces. Spatially resolved photocurrent mapping allows for directly visualizing the uniformity of built-in electric fields at heterostructure interfaces, providing a guide for microscopic engineering of charge transport across heterointerfaces. This simple probe-based technique also speaks directly to the 2D synthesis community to elucidate electronic uniformity at domain boundaries alongside morphological uniformity over large areas.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Estimating Submicron Aerosol Mixing State at the Global Scale With Machine Learning and Earth System Modeling

Abstract This study integrates machine learning and particle‐resolved aerosol simulations to develop emulators that predict submicron aerosol mixing state indices from the Earth system model (ESM) simulations. The emulators predict aerosol mixing state using only quantities that are predicted by the ESM, including bulk aerosol species concentrations, which do not by themselves carry mixing state information. We used PartMC‐MOSAIC as the particle‐resolved model and NCAR's CESM as the ESM. We trained emulators for three different mixing state indices for submicron aerosol in terms of chemical species abundance ( χ a ), the mixing of optically absorbing and nonabsorbing species ( χ o ), and the mixing of hygroscopic and nonhygroscopic species ( χ h ). Our global mixing state maps show considerable spatial and seasonal variability unique to each mixing state index. Seasonal averages varied spatially between 13% and 94% for χ a , between 38% and 94% for χ o , and between 20% and 87% for χ h with global annual averages of 67%, 68%, and 56%, respectively. High values in one index can be consistent with low values in another index depending on the grouping of species and their relative abundance, meaning that each mixing state index captures different aspects of the population mixing state. Although a direct validation with observational data has not been possible yet, our results are consistent with mixing state index values derived from ambient observations. This work is a prototypical example of using machine learning emulators to add information to ESM simulations.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Forward Modeling of 3-D Ion Properties in Jupiter’s Magnetosphere Using Juno/JADE-I Data

The Jovian Auroral Distributions Experiment Ion sensor (JADE-I) on NASA’s Juno mission provides in-situ measurements of ions from 0.1 to 46.2 keV/q inside Jupiter’s magnetosphere. JADE-I is used to study the plasma with two types of datasets from the same measurement: Time-of-flight (TOF) and SPECIES. The TOF dataset provides mass-per-charge measurements with a range of 1–64 amu/q but oversamples particles over 6π steradian viewing per spacecraft spin and has little directional information. On the other hand, the SPECIES dataset can provide a good measurement of the flow direction but does not provide mass-per-charge information due to the telemetry limit. In this study, we developed a 2-step forward modeling method that combines the advantages and avoids the disadvantages of TOF and SPECIES data to derive the 3-D properties of heavy ions. Assuming that the ion velocity distribution can be described with the kappa distribution, we first perform the forward model fit of the TOF data to calculate the relative abundance of heavy ion species. Then we fix the relative abundance and perform the second forward model fit on the SPECIES data. Here, using this method, we obtain the densities of different heavy ions, the shared temperature and kappa value, and the 3-D flow velocity vector. Some data examples of the equatorial plasma disk before Perijove 24 are included to demonstrate the method. Plasma properties can then be mapped to explore spatial and temporal variabilities in Jupiter’s magnetosphere.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Correlating Nanoscale Secondary Ion Mass Spectrometry and Atom Probe Tomography Analysis of Uranium Enrichment in Metallic Nuclear Fuel

Distribution and enrichment of 235U within nuclear fuels are essential to material performance and reactor safety. With the development of metallic U fuels, understanding 235U homogeneity has become increasingly important, however, it is difficult to map with high spatial, and mass resolution to analyze both matrix and secondary phase precipitates. Here, we analyze 235U enrichment in matrix and carbide phases in LEU-Mo via two chemical imaging modalities: nanoscale secondary ion mass spectrometry (nanoSIMS) and atom probe tomography (APT). NanoSIMS and APT provided consistent results, with no statistically significant difference between measured and nominal enrichment (19.75 % 235U).

U-10Mo, atom probe tomography, nanoscale secondary↗
DOE OSTI2021

Instrument for spatially resolved, temperature-dependent electrochemical impedance spectroscopy of thin films under locally controlled atmosphere

We demonstrate an instrument for spatially resolved measurements (mapping) of electrochemical impedance under various temperatures and gas environments. Automated measurements are controlled by a custom LabVIEW program, which manages probe motion, sample motion, temperature ramps, and potentiostat functions. Sample and probe positioning is provided by stepper motors. Dry or hydrated atmospheres (air or nitrogen) are available. The configurable heater reaches temperatures up to 500 °C, although the temperature at the sample surface is moderated by the gas flow rate. The local gas environment is controlled by directing flow toward the sample via a glass enclosure that surrounds the gold wire probe. Software and hardware selection and design are discussed. Reproducibility and accuracy are quantified on a Ba(Zr,Y)O 3-δ proton-conducting electrolyte thin film synthesized by pulsed laser deposition. The mapping feature of the instrument is demonstrated on a compositionally graded array of electrocatalytically active Ba(Co,Fe,Zr,Y)O 3-δ thin film microelectrodes. The resulting data indicate that this method proficiently maps property trends in these materials, thus demonstrating the reliability and usefulness of this method for investigating electrochemically active thin films.

47 OTHER INSTRUMENTATION↗
DOE OSTI2022

Structured illumination with thermal imaging (SI-TI): A dynamically reconfigurable metrology for parallelized thermal transport characterization

The recent push for the “materials by design” paradigm requires synergistic integration of scalable computation, synthesis, and characterization. Among these, techniques for efficient measurement of thermal transport can be a bottleneck limiting the experimental database size, especially for diverse materials with a range of roughness, porosity, and anisotropy. Traditional contact thermal measurements have challenges with throughput and the lack of spatially resolvable property mapping, while non-contact pump-probe laser methods generally need mirror smooth sample surfaces and also require serial raster scanning to achieve property mapping. Here, we present structured illumination with thermal imaging (SI-TI), a new thermal characterization tool based on parallelized all-optical heating and thermometry. Experiments on representative dense and porous bulk materials as well as a 3D printed thermoelectric thick film (~50 μm) demonstrate that SI-TI (1) enables paralleled measurement of multiple regions and samples without raster scanning; (2) can dynamically adjust the heating pattern purely in software, to optimize the measurement sensitivity in different directions for anisotropic materials; and (3) can tolerate rough (~3 μm) and scratched sample surfaces. Here, this work highlights a new avenue in adaptivity and throughput for thermal characterization of diverse materials.

42 ENGINEERING↗
DOE OSTI2025

Rippled shock propagation in a laser-driven target at multimegabar pressures

The evolution of non-uniform shocks produced by modulated laser irradiation or surface perturbations is relevant to studies of inertial confinement fusion and material properties at high-energy-density conditions. We present results from an experiment conducted at the OMEGA EP laser facility, where a 300 GPa shock was driven into a fused silica sample with pre-fabricated single-mode surface modulations. Using time-resolved optical velocimetry, we captured the continuous evolution of rippled shock motion, enabling a comprehensive mapping of the spatial amplitude history from formation to phase reversal in a single experiment. Initially, the ablation-driven shock inherits a fraction of the surface modulation amplitude from the sample, which subsequently grows before decaying, ultimately leading to the flattening of the rippled shock and a phase reversal. We find that two-dimensional inviscid hydrodynamic simulation of the experiment is able to qualitatively capture many aspects of the rippled shock evolution but over-predicts the initial amplitude growth. This experimental platform, capable of accommodating varying ripple wavelengths, lays the groundwork for a potential viscometry method at extreme pressures, where viscous effects manifest as differences in shock flattening times between rippled shocks of two distinct wavelengths propagating through the sample.

36 MATERIALS SCIENCE↗
DOE OSTI2025

GIScience in the era of Artificial Intelligence: a research agenda towards Autonomous GIS

The advent of generative AI exemplified by large language models (LLMs) opens new ways to represent and compute geographic information and transcends the process of geographic knowledge production, driving geographic information systems (GIS) towards autonomous GIS. Leveraging LLMs as the decision core, autonomous GIS can independently generate and execute geoprocessing workflows to perform spatial analysis. In this vision paper, we further elaborate on the concept of autonomous GIS and present a conceptual framework that defines its five autonomous goals, five levels of autonomy, five core functions, and three operational scales. We demonstrate how autonomous GIS could perform geospatial data retrieval, spatial analysis, and map making with four proof-of-concept GIS agents. We conclude by identifying critical challenges and future research directions, including fine-tuning and self-growing decision-cores, autonomous modelling, and examining the societal and practical implications of autonomous GIS. By establishing the groundwork for a paradigm shift in GIScience, this paper envisions a future where GIS moves beyond traditional workflows to autonomously reason, derive, innovate, and advance geospatial solutions to pressing global challenges. Meanwhile, we emphasize that as we design and deploy increasingly intelligent geospatial systems, we carry a responsibility to ensure they are developed in socially responsible ways, serve the public good, and support the continued value of human geographic insight in an AI-augmented future.

Autonomous GI↗
DOE OSTI2022

(3 + 1)-dimensional Schwinger pair production with quantum computers

Real-time quantum simulation of quantum field theory in (3+1)D requires large quantum computing resources. With a few-qubit quantum computer, we develop a novel algorithm and experimentally study the Schwinger effect, the electron-positron pair production in a strong electric field, in (3+1)D. The resource reduction is achieved by treating the electric field as a background field, working in Fourier space transverse to the electric field direction, and considering parity symmetry, such that we successfully map the three spatial dimension problems into one spatial dimension problems. In this work, we observe that the rate of pair production of electrons and positrons is consistent with the theoretical predication of the Schwinger effect. Our work paves the way towards exploring quantum simulation of quantum field theory beyond one spatial dimension.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2023

Progressive and instantaneous nature of lithium nucleation discovered by dynamic and operando imaging

The understanding of lithium (Li) nucleation and growth is important to design better electrodes for high-performance batteries. However, the study of Li nucleation process is still limited because of the lack of imaging tools that can provide information of the entire dynamic process. We developed and used an operando reflection interference microscope (RIM) that enables real-time imaging and tracking the Li nucleation dynamics at a single nanoparticle level. This dynamic and operando imaging platform provides us with critical capabilities to continuously monitor and study the Li nucleation process. We find that the formation of initial Li nuclei is not at the exact same time point, and Li nucleation process shows the properties of both progressive and instantaneous nucleation. In addition, the RIM allows us to track the individual Li nucleus’s growth and extract spatially resolved overpotential map. The nonuniform overpotential map indicates that the localized electrochemical environments substantially influence the Li nucleation.

25 ENERGY STORAGE↗
DOE OSTI2025

Architecture-Aware Models of AI Engines for High-Performance Matrix Matrix Multiplication

The AI Engine (AIE) architecture, available in systems from mobile SoCs to server-class FPGAs, aims to efficiently execute AI/ML tasks through a two-dimensional array of compute tiles. Previous work on AIEs has explored different approaches to mapping computation across spatial arrays, but the compute kernel running on each tile has not been the focus. Additionally, the AIE-ML architecture introduces memory tiles and omits programmable logic, requiring new approaches to staging and moving data throughout the array. In this work we update analytical models developed for CPUs to produce the design of high performance kernels while introducing new model considerations such as memory structure, throughput, and latency as required by the AIE hardware. We evaluate our models by developing AIE-ML kernels for matrix multiplication in low-precision data types showing performance up to 95% of compute peak for the kernel when data resides in local memory and above 90% of compute peak when data resides in main memory.

Binder, Elliott D. [Carnegie Mellon University, Pi↗
DOE OSTI2022

Operando Synchrotron Studies of Inhomogeneity during Anode-Free Plating of Li Metal in Pouch Cell Batteries

Operando synchrotron X-ray diffraction (XRD) studies have not previously been used to directly characterize Li metal in standard batteries due to the extremely weak scattering from Li atoms. In this work, it is demonstrated the stripping and plating of Li metal can be effectively quantified during battery cycling in appropriately designed synchrotron XRD experiments that utilize an anode-free battery configuration in which a Li-containing cathode material of LiNi 0.6 Mn 0.2 Co 0.2 O 2 (NMC622) is paired with a bare anode current collector consisting of either Cu metal (Cu/NMC) or Mo metal (Mo/NMC). In this configuration, it is possible to probe local variations in the deposition and stripping of Li metal with sufficient spatial sensitivity to map the inhomogeneity in pouch cells and to follow these processes with sufficient time resolution to track state-of-charge-dependent variations in the rate of Li usage at a single point. For the Cu/NMC and Mo/NMC batteries, it was observed that the initial plating of Li occurred in a very homogeneous manner but that severe macroscopic inhomogeneity arose on a mm-scale during the subsequent stripping of Li, contrasting with the conventional wisdom that the greatest challenges in Li metal batteries are associated with Li deposition.

25 ENERGY STORAGE↗
DOE OSTI2025

The Interactive Hydropower Generation Map

This interactive map shows the spatial distribution and temporal trends of net generation for hydropower facilities in the U.S., as included in the Existing Hydropower Assets (EHA) Net Generation Plant Database.

Schmidt, Erik [Oak Ridge National Laboratory (ORNL↗