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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 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Pushing the limits: Resolving paleoseawater signatures in nanoscale fluid inclusions by atom probe tomography

New insight into the geochemistry of ancient environments can be gained through structural and chemical analyses of nanometer-scale features within minerals. Here, we present recent developments using atom probe tomography (APT) enabling direct visualization of nanoscale fluid inclusions trapped within pyrite (FeS 2 ) and thereby chemical characterization of remnant seawater. Pyrite framboids (spherical clusters of nanocrystals) were sampled from the Middle Devonian Leicester Pyrite Member (New York). Scanning transmission electron microscopy shows low density regions distributed within the pyrite consistent with nanoscale pores (<4 nm in size). APT 3D visualization and compositional mapping reveals that the nanopores are filled with water. The inclusions appear to preserve the elemental signature of the water column in which the framboids formed, specifically seawater components including Na, K, Mg, and Ca. Mg/Ca ratios within the pyrite were generally measured to be within 0.6±0.2 – consistent with calcite-dominated seawater conditions existing in the Middle Devonian. Furthermore, this study demonstrates the potential for a novel approach to reconstruct paleoenvironmental conditions from coupled elemental and structural analyses of nanoscale fluid inclusions.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Atmospheric Rivers in the Eastern and Midwestern United States Associated With Baroclinic Waves

Atmospheric rivers (ARs) significantly impact the hydrological cycle and associated extremes in western continental regions. Recent studies suggest ARs also influence water resources and extremes in continental interiors. AR detection tools indicate that AR conditions are relatively frequent in areas east of the Rocky Mountains. The origin of these ARs, whether from synoptic-scale waves or mesoscale processes, is unclear. This study uses meteorological composite maps and transects of AR conditions during the four seasons. The analysis reveals that ARs east of the Rockies are associated with long-wave, baroclinic Rossby waves. This result demonstrates that eastern North American ARs are dynamically similar to their western coastal counterparts, though mechanisms for vertical moisture flux differ between the two. These findings provide a foundation for understanding future climate change and ARs in this region and offer new methods for evaluating climate model simulations.

58 GEOSCIENCES↗
DOE OSTI2025

PAVC: The foundation for a Pan-Arctic Vegetation Cover database

Field-measured Arctic vegetation cover data is essential for creating accurate, high-quality vegetation structure and composition maps. Extrapolating field data into high-resolution cover maps provides detailed, function-specific information for use in Earth System Models, vegetation classifications, and monitoring vegetation change over time and space. However, field campaigns that collect plant cover vary substantially in scope, method, and purpose, which makes them difficult to unify across data stores, and they are often not designed to meet remote sensing needs. In this work, we synthesized and harmonized field-based fractional cover data from various data stores to create a high-quality, consistent repository schema for remote sensing-based vegetation cover mapping applications. We developed a reproducible workflow for synthesizing visual estimate and point-intercept fractional cover data. The resultant Pan-Arctic Vegetation Cover (PAVC) database contains synthesized fractional cover at both the species and plant functional type levels. The latter includes absolute foliar cover for deciduous shrubs and trees, evergreen shrubs and trees, forbs, graminoids, lichen, bryophytes, and “other” vegetation, as well as absolute cover for litter and top cover for water and bare ground.

Steckler, Morgan R. [Oak Ridge National Laboratory↗
DOE OSTI2026

Patch-Based Convolutional Neural Networks for Multiple Microstructural Features Detection in FIB-SEM Micrographs of Irradiated Nuclear Fuel

Focused ion beam scanning electron microscopy (FIB-SEM) tomography has increasingly been utilized for acquiring three-dimensional (3D) microstructure features at the sub-micron scale in irradiated nuclear materials. This technique involves sequential ion beam slicing followed by electron beam imaging and compositional mapping using energy dispersive spectroscopy (EDS). Despite its growing use, several challenges persist. These include the time-intensive nature of data collection of EDS data, difficulties in distinguishing between various microstructures, and issues with image alignment. These challenges currently limit the broader application of FIB-SEM tomography in the field. To overcome these limitations, we propose using convolutional neural networks (CNNs) to automate microstructure identification in SEM images. Our study introduces a new framework for identifying microstructures in irradiated U-10Zr (wt. %) metallic fuel with limited annotated data. The framework includes the creation of a reliable annotated dataset with paired SEM and ground truth data from EDS maps, the applications of CNNs for microstructure identification, and the validation of model performance. Specifically, we employed the Segment Anything Model (SAM) to align SEM images with corresponding EDS maps and focused ion beam (FIB) tomography SEM data. We evaluate several models, including Patch-based U-Net, Attention U-Net, and Residual U-Net, finding that patch-based U-Net exhibits superior segmentation performance and consistency. This approach reduces reliance on EDS detectors and aids in accelerating nuclear material analysis process, highlighting the potential of advanced deep learning techniques to improve microstructural understanding in nuclear material. This is the first framework to integrate SAM and Patch-based CNN models for semantic segmentation of irradiated nuclear materials, with potential applicability to other tomography datasets.

36 - MATERIALS SCIENCE↗
DOE OSTI2026

Automated scanning probe microscopy of combinatorial ferroelectric libraries: Gaussian-process-guided exploration and noise-aware experiment planning

Combinatorial materials libraries provide an efficient route for mapping composition–property relationships, but their broader impact depends on rapid, quantitative, and functionally relevant characterization. Scanning Probe Microscopy (SPM), including piezoresponse force microscopy (PFM), offers significant potential for quantitative, functionally relevant combi-library readouts. Here, we implement a fully automated SPM workflow for ferroelectric combinatorial libraries and benchmark Gaussian-process-based Bayesian optimization strategies for autonomous experiment planning. The workflow integrates automated probe motion, contact optimization, imaging, and dual amplitude resonance tracking-PFM spectroscopy, and uses scalarized spectroscopic observables to guide subsequent measurements. Stage motion, probe engagement, in-contact tuning, imaging, spectroscopy, and the choice of the next measurement location all proceed without human input. We demonstrate the approach on Sm-doped BiFeO 3 and Zn x Mg 1−x O libraries. By comparing vanilla Bayesian optimization with a measured-noise variant, we show that explicit treatment of local reproducibility can improve modeling of composition-dependent response when the measured variance is physically meaningful, but can also reduce robustness when variability is dominated by outliers or topographic artifacts. Furthermore, these results establish automated SPM as a bridge between combinatorial synthesis and quantitative functional characterization.

Liu, Yu [University of Tennessee, Knoxville, TN (U↗
DOE OSTI2025

Interfacial alloying contributions to the magnetic proximity effect in Pt/dysprosium iron garnet

The magnetic proximity effect (MPE), a magnetic response from a heavy metal layer induced by an adjacent magnetic layer, plays an important role in the behavior of heavy-metal/ferro- or ferrimagnet thin film heterostructures. However, in the case of heavy-metal/rare earth iron garnet bilayers the presence and magnitude of the MPE has been debated. We previously reported an MPE in a Pt/dysprosium iron garnet (DyIG) heterostructure where the two layers were grown without breaking vacuum, finding that the magnetic moment present in the Pt layer scales with the magnitude of the DyIG moment and varies nonmonotonically through the DyIG compensation temperature. However, the origin of the magnetic signal from the Pt layer was not identified. In contrast, Pt/DyIG grown with a vacuum break did not exhibit an MPE. Here we investigate the origin of the magnetic moment in the Pt layer using x-ray absorption spectroscopy, x-ray circular dichroism, composition mapping and simulations of ion bombardment. In conclusion, the results are consistent with the presence of an intermixed Fe-Dy-Pt interfacial alloy layer formed during the growth of the Pt on the DyIG and resulting in a measurable MPE.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2024

Correlative X-ray micro-nanotomography with scanning electron microscopy at the Advanced Light Source

Geological samples are inherently multi-scale. Understanding their bulk physical and chemical properties requires characterization down to the nano-scale. A powerful technique to study the three-dimensional microstructure is X-ray tomography, but it lacks information about the chemistry of samples. To develop a methodology for measuring the multi-scale 3D microstructure of geological samples, correlative X-ray micro- and nanotomography were performed on two rocks followed by scanning electron microscopy with energy-dispersive spectroscopy (SEM-EDS) analysis. The study was performed in five steps: (i) micro X-ray tomography was performed on rock sample cores, (ii) samples for nanotomography were prepared using laser milling, (iii) nanotomography was performed on the milled sub-samples, (iv) samples were mounted and polished for SEM analysis and (v) SEM imaging and compositional mapping was performed on micro and nanotomography samples for complimentary information. Correlative study performed on samples of serpentine and basalt revealed multiscale 3D structures involving both solid mineral phases and pore networks. Significant differences in the volume fraction of pores and mineral phases were also observed dependent on the imaging spatial resolution employed. This highlights the necessity for the application of such a multiscale approach for the characterization of complex aggregates such as rocks. Information acquired from the chemical mapping of different phases was also helpful in segmentation of phases that did not exhibit significant contrast in X-ray imaging. Adoption of the protocol used in this study can be broadly applied to 3D imaging studies being performed at the Advanced Light Source and other user facilities.

58 GEOSCIENCES↗
DOE OSTI2023

Mineralogy and petrology of fine‐grained samples recovered from the asteroid (162173) Ryugu

Abstract Samples returned from the carbonaceous asteroid (162173) Ryugu by the Hayabusa2 mission revealed that Ryugu is composed of materials consistent with CI chondrites and some types of space weathering. We report detailed mineralogy of the fine‐grained Ryugu samples allocated to our “Sand” team and report additional space weathering features found on the grains. The dominant mineralogy is composed of a fine‐grained mixture of Mg‐rich saponite and serpentine, magnetite, pyrrhotite, pentlandite, dolomite, and Fe‐bearing magnesite. These grains have mineralogy comparable to that of CI chondrites, showing severe aqueous alteration but lacking ferrihydrite and sulfate. These results are similar to previous works on large Ryugu grains. In addition to the major minerals, we also find many minerals that are rare or have not been reported among CI chondrites. Accessory minerals identified are hydroxyapatite, Mg‐Na phosphate, olivine, low‐Ca pyroxene, Mg‐Al spinel, chromite, manganochromite, eskolaite, ilmenite, cubanite, polydymite, transjordanite, schreibersite, calcite, moissanite, and poorly crystalline phyllosilicate. We also show scanning transmission electron microscope and scanning electron microscope compositional maps and images of some space‐weathered grains and severely heated and melted grains. Although our mineralogical results are consistent with that of millimeter‐sized grains, the fine‐grained fraction is best suited to investigate impact‐induced space weathering.

Geochemistry & Geophysics↗
DOE OSTI2020

Editors’ Choice—Flooded by Success: On the Role of Electrode Wettability in CO 2 Electrolyzers that Generate Liquid Products

Economic operation of carbon dioxide (CO 2 ) electrolyzers generating liquid products will likely require high reactant conversions and product concentrations, conditions anticipated to challenge existing gas diffusion electrodes (GDEs). Notably, electrode wettability will increase as lower surface tension products (e.g., formic acid, alcohols) are introduced into electrolyte streams, potentially leading to flooding. To understand the hydraulically stable operating envelopes in mixed aqueous-organic liquid domains, we connect intrinsic electrode wettability descriptors to operating parameters such as electrolyte flow rate and current. We first measure contact angles of water-organic product dilutions on polytetrafluoroethylene (PTFE) and graphite surfaces as planar analogues for GDE components. We then use material balances around the reactive gas-liquid-solid interface to calculate product mass fractions as functions of water sweep rate and current. Product composition maps visualize the extent to which changes in cell performance influence capillary pressure, a determinant of GDE saturation. Analyses suggest that formic acid mixtures pose little risk for GDE flooding across a wide range of conditions, but effluents containing <30% alcohol by mass may cause flooding. This study reveals opportunities to integrate microstructural features and oleophobic surface treatments into GDEs to repel aqueous-organic mixtures and expand the window of stable operating conditions.

25 ENERGY STORAGE↗
DOE OSTI2024

Geothermal Play Fairway Analysis of Low-Temperature Resources for Sedimentary Basin Geothermal Play Types: An Example in the Denver Basin

This project is part of a nationwide effort to highlight the advantages of incorporating low-temperature geothermal resource evaluation into the implementation of combined heat and power (CHP), and geothermal direct use (GDU) technologies (e.g., space heating and/or cooling). The initiative aims to hasten the nation's decarbonization process by exploring the potential for using low-temperature geothermal resources (< 150 Degrees Celsius) in selected sedimentary basins that have several population centers. The Play Fairway Analysis (PFA) techniques were modified from earlier studies of sedimentary basin geothermal play types (SBGPTs) that assessed the viability of low-temperature resources. The decision-making process for leveraging low-temperature geothermal resources for GDU and CHP applications is complex and considers a variety of factors, including geological, economic, and risk criteria. This study covers workflows, relevant datasets, python code, and both common and composite maps used to create low-temperature geothermal resource favorability maps for the Denver Basin, which extends across Colorado, Nebraska, and Wyoming. The replication of these methodologies in other SBGPTs can evaluate potential for low-temperature resources. The proposed geothermal PFA approach for low-temperature geothermal resources includes: (1) identifying available relevant data and grouping data sets into PFA criteria (e.g., geological, economic, and risk criteria); (2) analyzing data gaps enable future focalized exploration; (3) performing uncertainty quantification; (4) weighting relevant data; (5) developing favorability and common risk maps for low-temperature geothermal resources to identify potential locations for more focused data collection. This project will facilitate future deployment of CHP and GDU by providing data, tools, and a workflow applicable to low-temperature geothermal resources in sedimentary basins.

15 GEOTHERMAL ENERGY↗
DOE OSTI2026

Understanding Defect Activation and Kinetics in Next Generation CdTe Absorbers

This project investigated the role of defects and dopants in cadmium telluride (CdTe) solar cells to improve efficiency and long-term stability. Advanced X-ray microscopy and spectroscopy techniques were used to map composition, identify defect structures, and measure charge collection at the nanoscale. These experimental results were combined with computational modeling to understand how dopants such as copper and arsenic interact with other elements during processing. The study found that many dopants become electrically inactive due to the formation of complexes with chlorine and oxygen, limiting carrier concentration and device performance. It also showed that selenium, used to enhance efficiency, can migrate within the material during both manufacturing and operation, altering local structure and potentially affecting stability. The project developed new diagnostic and analysis tools for studying photovoltaic materials and provided a mechanistic understanding of key performance limitations. These insights support the development of more efficient, stable, and cost-effective CdTe solar technologies for large-scale energy deployment.

14 SOLAR ENERGY↗
DOE OSTI2025

HAMscope: a snapshot Hyperspectral Autofluorescence Miniscope for real-time molecular imaging

We introduce HAMscope, a compact, snapshot hyperspectral autofluorescence miniscope that enables real-time, label-free molecular imaging in a wide range of biological systems. By integrating a thin polymer diffuser into a widefield miniscope, HAMscope spectrally encodes each frame and employs a probabilistic deep learning framework to reconstruct 30-channel hyperspectral stacks (452-703 nm) or directly infer molecular composition maps from single images. A scalable multi-pass U-Net architecture with transformer-based attention and per pixel uncertainty estimation enables high spatio-spectral fidelity (mean absolute error ∼0.0048) at video rates. While initially demonstrated in plant systems, including lignin, chlorophyll, and suberin imaging in intact poplar and cork tissues, the platform is readily adaptable to other applications such as neural activity mapping, metabolic profiling, and histopathology. We show that the system generalizes to out-of-distribution tissue types and supports direct molecular mapping without the need for spectral unmixing. HAMscope establishes a general framework for compact, uncertainty-aware spectral imaging that combines minimal optics with advanced deep learning, offering broad utility for real-time biochemical imaging across neuroscience, environmental monitoring, and biomedicine.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Integrating very-high-resolution UAS data and airborne imaging spectroscopy to map the fractional composition of Arctic plant functional types in Western Alaska

Widespread changes in vegetation cover and composition are driving strong impacts on Arctic ecosystem functioning and global climate feedbacks. An accurate characterization of tundra vegetation composition is required to understand how the Arctic will respond to future climate change. However, quantifying tundra vegetation composition over large areas is challenging as commonly-used satellite observations are too coarse, spatially and spectrally, to differentiate low-lying tundra vegetation types. Recent airborne and spaceborne imaging spectroscopy platforms provide better data to characterize vegetation composition. Yet, our ability to characterize vegetation composition with imaging spectroscopy remains largely unexplored in the Arctic, particularly due to a lack of ground observations needed to train and test classification models. To address this problem, we collected very-high-resolution (VHR, ~5 cm) unoccupied aerial system (UAS) imagery at three low-Arctic tundra sites located on the Seward Peninsula, western Alaska. In this paper, we examine the feasibility of integrating imagery from the UAS and the hyperspectral Airborne Visible/Infrared Imaging Spectrometer, Next Generation (AVIRIS-NG) airborne instrument to map the fractional composition of 12 key Arctic plant functional types (PFTs). To this end, we first mapped the 12 PFTs from our VHR UAS imagery using random forest classification. We then used these UAS-derived PFT maps as ground truth to develop partial least squares regression (PLSR) models to predict the fractional cover (FCover) of each PFT from AVIRIS-NG imagery. Further, we evaluated the performance of our PLSR models using reserved UAS samples, as well as by mapping PFT FCover and dominant PFT for large tundra landscapes. Our results show that 1) Arctic PFTs can be effectively mapped using VHR UAS imagery, with overall accuracy between 86% and 92%, 2) when the UAS mapped PFTs were used to inform PLSR scaling models, the FCover of the 12 PFTs could be effectively estimated from AVIRIS-NG imagery with a mean absolute error (MAE) <0.13, and 3) our PLSR models outperformed traditional, fully constrained least-squares (FCLS) linear mixture analysis and produced high-quality, spatially contiguous PFT FCover and PFT maps that captured vegetation spatial patterns with similar accuracy to those developed from UAS imagery. The developed PLSR models have the potential to be broadly applied for quantifying vegetation composition with AVIRIS-NG images to help monitor tundra vegetation dynamics and improve process-based modeling of tundra ecosystems.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

High spatial resolution thermal conductivity mapping of SiC/SiC composites

Herein, silicon carbide (SiC) ceramic matrix composites are being investigated as a new generation of fuel cladding materials due to its higher accident tolerance compared to Zircaloy. Characterization of the thermal conductivity of SiC constituents and their interfaces and interphases in SiC/SiC composites is needed as inputs to models of cladding performance. We used time-domain thermoreflectance (TDTR) to map the thermal conductivity with a spatial resolution of 2 µm. The SiC/SiC composite is comprised of Hi-Nicalon Type S fibers in a matrix made by chemical vapor infiltration (CVI matrix). The interphase material is a pyrolytic carbon/SiC multilayer. We report thermal conductivity maps of the SiC fiber and SiC matrix at temperatures of 25 °C, 90 °C, 164 °C, and 250 °C. The fiber has a uniform and isotropic thermal conductivity of 22 W m -1 K -1 at 25 °C; the thermal conductivity of fiber is approximately independent of temperature. The matrix thermal conductivity is not uniform and varies from 50 to 120 W m -1 K -1 at 25 °C; the thermal conductivity of the matrix scales approximately with the inverse of the square root of temperature, i.e., ∝ 1/T 1/2 . The thermal conductivity of the interphase at room temperature is 6 W m -1 K -1 . The calculated spatially averaged thermal conductivity of this SiC/SiC composite at 1000°C is 29 W m -1 K -1 .

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2021

Impact Mapping for Geospatial Reasoning and Decision Making

Objective The reported study evaluated a novel approach to aiding geospatial reasoning and decision making. Background Impact mapping aims to alleviate the cognitive demands of geospatial tasks in part by externalizing data in the form of an integrated decision surface. This is achieved by aggregating data across multiple sources of information and visualizing their combined utility rather than objective measurements or individual utility. Previous research has shown that geospatial decisions improve when aided in this manner, but it remains unknown if dynamic decision making, often plagued by fatigue and anchoring bias, would benefit similarly. Method The experiment implemented a systematic manipulation of the presence of a composite impact map and the number of attributes present in a two-stage disaster relief, resource allocation task to investigate when and how impact mapping is beneficial or deleterious to decision makers. Results The presence of the composite impact map increased the utility of selected sites, increased re-planning decisions, reduced information display views, and reduced workload. Generally, the effect of the composite impact map was greater when participants were asked to evaluate more attributes. Conclusion Composite impact maps appear to improve repeated geospatial reasoning and minimize anchoring bias because they alleviate the cognitive demands otherwise necessary to interpret and maintain information from multiple attributes. Application Data visualization techniques, such as impact mapping, can improve repeated geospatial decision making in environments that include high cognitive demand.

Illingworth, David A.↗
DOE OSTI2026

A Play-Based Exploration of CO 2 Storage in the Illinois Basin: Development and Application of the Play-Based Exploration Workflow

This report documents the workflow developed and applied for a play-based exploration (PBE) of carbon capture and storage (CCS) potential in the Illinois Basin, conducted under U.S. Department of Energy Cooperative Agreement DE-FE0032366. The project was led by the Illinois State Geological Survey (ISGS) at the University of Illinois Urbana-Champaign in collaboration with Visage Energy. The goal of this workflow is to identify locations where subsurface, surface, and societal conditions align to support safe and effective geologic CO 2 storage. To achieve this, the project team developed a systematic, seven-step workflow for conducting play-based exploration: (1) defining play elements, (2) collecting data, (3) constructing a geodatabase, (4) defining suitability criteria, (5) conducting suitability mapping, (6) generating Common Risk Segment (CRS) maps, and (7) creating composite play maps. The workflow is designed to be repeatable and adaptable for CCS assessments in other regions or for alternative subsurface energy applications such as hydrogen or natural gas storage.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2025

High-resolution national mapping of natural gas composition substantially updates methane leakage impacts

Methane is emitted from oil and gas operations alongside heavier hydrocarbons and non-hydrocarbon gases, shaping emissions management decision-making, including air quality impacts. Yet, most assessments assume fixed gas composition, overlooking significant spatial and temporal variations. Here, we generate a high-resolution, data-driven map of natural gas composition across the United States, reconstructing methane, heavier hydrocarbons, and non-hydrocarbon species using spatio-temporal interpolation and oil-and-gas production patterns. Our approach is able to reduce composition prediction errors by 39% in terms of Mean Absolute Error (MAE) compared to standard techniques and reveals that methane loss rates have been underestimated by more than 50% in some regions. Beyond methane, we uncover substantial variability in co-emitted gases, exposing blind spots in current emissions inventories and emissions management frameworks. Our work enables more accurate emissions assessments, guides targeted measurement strategies, and informs emissions management decision-making. It also provides a general framework for prediction in environmental applications that integrate sparse measurements with auxiliary variables.

03 NATURAL GAS↗