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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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Microstructural Constraints on the Formation of Diamond in Ureilites, the Case from the Kenna Meteorite

The origin of diamonds in ureilitic meteorites, a major family of ultramafic achondrites, has long been debated, with various formation mechanisms inferred, including: 1) shock conversion of preexisting graphite due to a hypervelocity impact on the ureilite parent body; 2) vapor deposition during condensation of the solar nebula; or 3) static transformation in the mantle of a large planetesimal (>1000 km in diameter). The Kenna ureilite is a 10.9 kg stone that was found in Kansas in 1972 and is dominantly composed of olivine and pigeonite (68% and 22% modal abundance, respectively), together with minor metal and carbon-rich regions. Within the carbon-rich domains researchers have identified microdiamonds up to ~10 µm in size. Due to the proximity of the extremely hard microdiamonds with soft graphite and moderately hard silicates, achieving an appropriately flat surface for electron backscatter diffraction (EBSD) analysis has proved challenging. We have, therefore, employed a Hitachi ArBlade 5000 broad beam Ar ion milling system to prepare a sample with an adequate surface uniformity. The ion milling dramatically improved indexing of the EBSD patterns and facilitated high-resolution microstructural analysis of the carbon-rich domains. The carbon-bearing regions occur as tabular domains between polygonal olivine and pigeonite, and are composed of aggregates of micrometer-scale diamond with intermediate graphite, oxides and metal grains. Within the microdiamonds, {111} twins are abundant, many of which form ragged lamellae consistent with mechanical twinning. The microdiamond assemblages appear to pseudomorph tabular graphite habits, which together with the abundant deformation twins, supports a shock transformation mechanism for microdiamond formation within the Kenna meteorite. To compliment the EBSD microstructural analyses, coordinated laser Raman, focused ion beam/transmission electron microscopy imaging and diffraction data, and NanoSIMS N and C tomographic isotopic imaging will be collected. These data will provide further constraints on the formation of diamonds within ureilites.

Timmons M Erickson↗
DOE OSTI2026

Data for Magnetic Anisotropy due to Localized Structural Defects in Strained Yttrium Iron Garnet Thin Films

This dataset contains electron microscopy, magnetic, and X-ray diffraction characterization data for YIG/YSGG thin films associated with the manuscript "Magnetic anisotropy due to localized structural defects in strained yttrium iron garnet thin films". Characterizations include: scanning electron nano diffraction (SEND, or 4D-STEM), dark field transmission electron microscopy (DF-TEM), energy dispersive X-ray spectroscopy (EDS), ferrimagnetic resonance (FMR), superconducting quantum interference device (SQUID), scanning transmission electron microscopy (STEM), X-ray diffraction (XRD).

Electron microscopy↗
DOE OSTI2023

Multi-Scale Characterization of Porosity and Cracks in Silicon Carbide Cladding after Transient Reactor Test Facility Irradiation

Silicon carbide (SiC) ceramic matrix composite (CMC) cladding is currently being pursued as one of the leading candidates for accident-tolerant fuel (ATF) cladding for light water reactor applications. The morphology of fabrication defects, including the size and shape of voids, is one of the key challenges that impacts cladding performance and guarantees reactor safety. Therefore, quantification of defects’ size, location, distribution, and leak paths is critical to determining SiC CMC in-core performance. This research aims to provide quantitative insight into the defect’s distribution under multi-scale characterization at different length scales before and after different Transient Reactor Test Facility (TREAT) irradiation tests. A non-destructive multi-scale evaluation of irradiated SiC will help to assess critical microstructural defects from production and/or experimental testing to better understand and predict overall cladding performance. X-ray computed tomography (XCT), a non-destructive, data-rich characterization technique, is combined with lower length scale electronic microscopic characterization, which provides microscale morphology and structural characterization. This paper discusses a fully automatic workflow to detect and analyze SiC-SiC defects using image processing techniques on 3D X-ray images. Following the XCT data analysis, advanced characterizations from focused ion beam (FIB) and transmission electron microscopy (TEM) were conducted to verify the findings from the XCT data, especially quantitative results from local nano-scale TEM 3D tomography data, which were utilized to complement the 3D XCT results. In this work, three SiC samples (two irradiated and one unirradiated) provided by General Atomics are investigated. The irradiated samples were irradiated in a way that was expected to induce cracking, and indeed, the automated workflow developed in this work was able to successfully identify and characterize the defects formation in the irradiated samples while detecting no observed cracking in the unirradiated sample. These results demonstrate the value of automated XCT tools to better understand the damage and damage propagation in SiC-SiC structures for nuclear applications.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2026

Unsupervised Segmentation and Clustering Workflow for Efficient Processing of 4D-STEM and 5D-STEM Data

Four-dimensional scanning transmission electron microscopy (4D-STEM) enables mapping of diffraction information with nanometer-scale spatial resolution, offering detailed insight into local structure, orientation, and strain. However, as data dimensionality and sampling density increase, particularly for in situ scanning diffraction experiments (5D-STEM), robust segmentation of structurally consistent behavior across sequential measurements becomes essential for efficient and physically meaningful analysis. Here, we introduce a clustering framework that identifies crystallographically distinct domains from 4D-STEM datasets. By using local diffraction-pattern similarity as a metric, the method extracts closed contours delineating spatially contiguous regions. This approach produces cluster-averaged diffraction patterns that improve signal quality while reducing data volume by orders of magnitude, enabling rapid and accurate orientation, phase, and strain mapping. We demonstrate the applicability of this approach to in situ liquid-cell 4D-STEM data of gold nanoparticle growth. Our method provides a scalable and generalizable route for spatially coherent segmentation, data compression, and quantitative structure–strain mapping across diverse 4D-STEM modalities. The full analysis code and example workflows are publicly available to support reproducibility and reuse.

4D-STEM↗
DOE OSTI2022

RCSB Protein Data bank: Tools for visualizing and understanding biological macromolecules in 3D

Abstract Now in its 52nd year of continuous operations, the Protein Data Bank (PDB) is the premiere open‐access global archive housing three‐dimensional (3D) biomolecular structure data. It is jointly managed by the Worldwide Protein Data Bank (wwPDB) partnership. The Research Collaboratory for Structural Bioinformatics Protein Data Bank (RCSB PDB) is funded by the National Science Foundation, National Institutes of Health, and US Department of Energy and serves as the US data center for the wwPDB. RCSB PDB is also responsible for the security of PDB data in its role as wwPDB‐designated Archive Keeper. Every year, RCSB PDB serves tens of thousands of depositors of 3D macromolecular structure data (coming from macromolecular crystallography, nuclear magnetic resonance spectroscopy, electron microscopy, and micro‐electron diffraction). The RCSB PDB research‐focused web portal ( RCSB.org ) makes PDB data available at no charge and without usage restrictions to many millions of PDB data consumers around the world. The RCSB PDB training, outreach, and education web portal ( PDB101.RCSB.org ) serves nearly 700 K educators, students, and members of the public worldwide. This invited Tools Issue contribution describes how RCSB PDB (i) is organized; (ii) works with wwPDB partners to process new depositions; (iii) serves as the wwPDB‐designated Archive Keeper; (iv) enables exploration and 3D visualization of PDB data via RCSB.org ; and (v) supports training, outreach, and education via PDB101.RCSB.org . New tools and features at RCSB.org are presented using examples drawn from high‐resolution structural studies of proteins relevant to treatment of human cancers by targeting immune checkpoints.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

RCSB Protein Data Bank: powerful new tools for exploring 3D structures of biological macromolecules for basic and applied research and education in fundamental biology, biomedicine, biotechnology, bioengineering and energy sciences

Abstract The Research Collaboratory for Structural Bioinformatics Protein Data Bank (RCSB PDB), the US data center for the global PDB archive and a founding member of the Worldwide Protein Data Bank partnership, serves tens of thousands of data depositors in the Americas and Oceania and makes 3D macromolecular structure data available at no charge and without restrictions to millions of RCSB.org users around the world, including >660 000 educators, students and members of the curious public using PDB101.RCSB.org. PDB data depositors include structural biologists using macromolecular crystallography, nuclear magnetic resonance spectroscopy, 3D electron microscopy and micro-electron diffraction. PDB data consumers accessing our web portals include researchers, educators and students studying fundamental biology, biomedicine, biotechnology, bioengineering and energy sciences. During the past 2 years, the research-focused RCSB PDB web portal (RCSB.org) has undergone a complete redesign, enabling improved searching with full Boolean operator logic and more facile access to PDB data integrated with >40 external biodata resources. New features and resources are described in detail using examples that showcase recently released structures of SARS-CoV-2 proteins and host cell proteins relevant to understanding and addressing the COVID-19 global pandemic.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Nearly hyperuniform, nonhyperuniform, and antihyperuniform density fluctuations in two-dimensional transition metal dichalcogenides with defects

Hyperuniform many-body systems in d-dimensional Euclidean space R d are characterized by completely suppressed (normalized) infinite-wavelength density fluctuations, and appear to be endowed with novel exotic physical properties. Recently, hyperuniform systems of disordered varieties have been observed in the context of various atomic-scale two-dimensional (2D) materials. Here, we analyze the effects of localized defects on the density fluctuations across length scales and on the hyperuniformity property of experimental samples of two-dimensional transition metal dichalcogenides. In particular, we extract atomic coordinates from time series annular dark field-scanning transmission electron microscopy (ADF-STEM) imaging data of 2D tungsten chalcogenides with the 2H structure (Te-doped 2H-WSe 2 ) showing continuous development and evolution of electron-beam induced defects, and construct the corresponding chemical-bonding informed coordination networks between the atoms. We then compute a variety of pair statistics and bond-orientational statistics to characterize the samples. At low defect concentrations, the corresponding materials are nearly hyperuniform, characterized by significantly suppressed scattering at the zero wave-number limit (omitting forward/ballistic scattering). As more defects are introduced, the (approximate) hyperuniformity of the materials is gradually destroyed, and the system becomes non-hyperuniform even when the material still contains a significant amount of crystalline regions. At high defect concentrations, the structures become antihyperuniform with diverging (normalized) large-scale density fluctuations, mimicking those typically observed at the thermal critical points associated with phase transitions. Overall, the defected materials possess varying degrees of orientation order, and there is apparently no intermediate hexatic phase emerging. To understand the observed nearly hyperuniform density fluctuations in the slightly defected materials, we construct a minimalist structural model and demonstrate that the experimental samples can be essentially viewed as perturbed honeycomb crystals with small correlated displacements and double chalcogen vacancies. Moreover, the small correlated displacements alone can significantly degrade hyperuniformity of the perfect honeycomb structure. Therefore, even a small amount of vacancies, when coupled with correlated displacements, can completely destroy hyperuniformity of the system.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2024

A robust synthetic data generation framework for machine learning in high-resolution transmission electron microscopy (HRTEM)

Machine learning techniques are attractive options for developing highly-accurate analysis tools for nanomaterials characterization, including high-resolution transmission electron microscopy (HRTEM). However, successfully implementing such machine learning tools can be difficult due to the challenges in procuring sufficiently large, high-quality training datasets from experiments. In this work, we introduce Construction Zone, a Python package for rapid generation of complex nanoscale atomic structures which enables fast, systematic sampling of realistic nanomaterial structures and can be used as a random structure generator for large, diverse synthetic datasets. Using Construction Zone, we develop an end-to-end machine learning workflow for training neural network models to analyze experimental atomic resolution HRTEM images on the task of nanoparticle image segmentation purely with simulated databases. Further, we study the data curation process to understand how various aspects of the curated simulated data—including simulation fidelity, the distribution of atomic structures, and the distribution of imaging conditions—affect model performance across three benchmark experimental HRTEM image datasets. Using our workflow, we are able to achieve state-of-the-art segmentation performance on these experimental benchmarks and, further, we discuss robust strategies for consistently achieving high performance with machine learning in experimental settings using purely synthetic data. Construction Zone and its documentation are available at https://github.com/lerandc/construction_zone.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Announcing the launch of Protein Data Bank China as an Associate Member of the Worldwide Protein Data Bank Partnership

The Protein Data Bank (PDB) is the single global archive of atomic-level, three-dimensional structures of biological macromolecules experimentally determined by macromolecular crystallography, nuclear magnetic resonance spectroscopy or three-dimensional cryo-electron microscopy. The PDB is growing continuously, with a recent rapid increase in new structure depositions from Asia. In 2022, the Worldwide Protein Data Bank (wwPDB; https://www.wwpdb.org/) partners welcomed Protein Data Bank China (PDBc; https://www.pdbc.org.cn) to the organization as an Associate Member. PDBc is based in the National Facility for Protein Science in Shanghai which is associated with the Shanghai Advanced Research Institute of Chinese Academy of Sciences, the Shanghai Institute for Advanced Immunochemical Studies and the iHuman Institute of ShanghaiTech University. This letter describes the history of the wwPDB, recently established mechanisms for adding new wwPDB data centers and the processes developed to bring PDBc into the partnership.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

In-situ ETEM study of plasma-facing tungsten nanofuzz oxidation at atmospheric pressure: Microstructure evolution and substrate-free oxidation kinetics

To enable sustainable carbon-free fusion energy, managing reactor structural material degradation during normal operation as well as accident scenarios is vital. Tungsten (W) plasma-facing materials (PFMs) are susceptible to aggressive high-temperature oxidation during air-ingress fusion reactor accidents, yet there's a lack of oxidation kinetic data for irradiated tungsten. Here, in this study, we utilize atmospheric environmental transmission electron microscopy (ETEM) to present the first kinetic data for substrate-free W nanofuzz oxidation at 400 ºC and 500 ºC in 1 bar dry air. Comparison with pristine bulk W during the early parabolic stage suggests an irradiationdecelerated oxidation for W nanofuzz. Our time-resolved in-situ characterization reveals a durable amorphous surface oxide, likely promoted by high-flux He + irradiation-induced surface defects, serving as an effective passivating layer that impedes nanofuzz oxidation onset. This surface oxide layer also interfaces well with newly formed orthorhombic WO 3 , facilitated by stress relief through He bubble shrinkage, providing lasting passivating protection throughout the nanofuzz parabolic oxidation. This new finding challenges conventional notions of irradiation's negative impact on metal oxidation, and calls for advanced characterization to enhance our understanding of fusion energy materials degradation, informed by further accident modeling.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Physicochemical properties of the Portland cement-based mortar exposed to deep seafloor conditions at a depth of 1680 m

Achieving breakthroughs in marine technologies requires the development of infrastructures submerged in deep-sea environments, whose physicochemical effects on cement-based materials considerably differ from those of shallow seas. However, very few studies focused on the cement-based materials subjected to deep-sea conditions. This work investigates the changes in the compressive strength and phase composition of the cement mortar kept on the seafloor with a depth of 1680 m for 608 d. The mortar specimens salvaged from the seafloor exhibited severe visible damages, including softened mashy structures and significantly decreased compressive strengths. The obtained X-ray diffraction, scanning electron microscopy, and nuclear magnetic resonance data revealed that the dissolution of portlandite, decalcification of calcium (alumino) silicate hydrate, and formation of brucite, magnesium (alumino) silicate hydrate, a hydrotalcite-like phase, thaumasite, and ettringite likely contributed to the disintegration of mortar, which could be further accelerated by the low temperature of the deep-sea environment.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Deep learning for electron and scanning probe microscopy: From materials design to atomic fabrication

Machine learning and artificial intelligence (ML/AI) are rapidly becoming an indispensable part of physics research, with applications ranging from theory and materials prediction to high-throughput data analysis. In parallel, the recent successes in applying ML/AI methods for autonomous systems from robotics through self-driving cars to organic and inorganic synthesis are generating enthusiasm for the potential of these techniques to enable automated and autonomous experiment in imaging. Here, we discuss recent progress in application of machine learning methods in scanning transmission electron microscopy and scanning probe microscopy, from applications such as data compression and exploratory data analysis to physics learning to atomic fabrication.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Automated Energy-Dispersive X-ray Spectroscopy Analysis for Multi-Modal Few-Shot Learning

Scanning transmission electron microscopy (STEM) is a powerful tool that allows for the atomic-scale analysis of a materials’ structure, chemistry, and defect domains (Akers et al. 2021). The current generation of microscopes generate vast amounts of data, surpassing the limits of effective manual analysis traditionally performed by domain experts (Spurgeon et al. 2021). While recent strides in machine learning have significantly enhanced the processing of large and intricate datasets acquired through electron microscopy, the prevalent use of proprietary software packages for initial data collection poses a challenge. In many cases, these software packages act as a ‘black box’, constraining user functionality and hindering the output of data in a format that is conducive to seamless integration into machine learning models. This work addresses these challenges by adapting HyperSpy, an open-source Python library, for the analysis and quantification of raw energy dispersive spectroscopy (EDS) data acquired through STEM. The modified HyperSpy code successfully facilitates user-defined segmentation of the data, enabling the integration of atomic %, weight %, and raw EDS spectra for each segmented region into an existing few-shot machine learning model. While initial results reveal discrepancies in quantified atomic and weight percentages when compared to proprietary software, ongoing efforts aim to rectify this issue by refining the fit of the HyperSpy model to the EDS spectra. Overall, this research underscores the potential of open-source tools like HyperSpy to enhance the accessibility of analytical tools, fostering a transparent and user-friendly environment for seamlessly incorporating electron microscopy data into machine learning models.

36 MATERIALS SCIENCE↗
NASA NTRS1995

The Fluid Processing Apparatus: from Flight Hardware to Electron Micrographs

Since the early years of space biology, a major drawback in spaceflight plant experiments has been the inability to fix specimens in microgravity, relying instead on fixation after return to Earth. As there, it is of a growing interest to look at the effect of microgravity on the structure and the developmental polarity of root graviperceptive cells, or columella cells, and so, it is important to use flight hardware which allows specimen fixation in space therefore avoiding the confounding effects of rapid readaptation to gravity after landing. As part of the Bioserve Space Technologies, a Center for the Commercial Development of Space (CCDS), we now have experiment flight opportunities through the Commercial Generic Bioprocessing Apparatus (CGBA) payload. In this study the Fluid Processing Apparatus (FPA) was used to grow seedlings for a limited period of time prior to fixation of the tissue in a microgravity environment. Upon return to Earth, the samples were processed for electron microscopy. This report describes the microscopic data obtained from the two space flights (STS-54 and STS-60). In both cases, the electron micrographs of the columella cells revealed well preserved cell structure, well defined microtubules, and the presence of calcium precipitates formed by a antimonate precipitation method.

Hilaire, Emmanuel↗
DOE OSTI2025

Effect of growth dynamics on the structural, photophysical and pseudocapacitance properties of famatinite copper antimony sulphide colloidal nanostructures (including nanosheets)

Facile phase selective synthesis of copper antimony sulphide (CAS) nanostructures is important because of their tunable photoconductive and electrochemical properties. In this study, off-stoichiometric famatinite phase CAS (fCAS) quasi-spherical and quasi-hexagonal colloidal nanostructures (including nanosheets) of sizes, 2.4–18.0 nm were grown under variable conditions of temperature (60–200 °C), time and oleylamine capping ligand concentration using copper(II) acetylacetonate and antimony(III) diethyldithiocarbamate precursors. Data from powder X-ray diffraction, Raman spectroscopy and high-resolution scanning/transmission electron microscopy confirm the tetragonal structure of the famatinite phase. X-ray photoelectron spectroscopy, transmission electron microscopy and scanning electron microscopy-energy dispersive X-ray spectroscopy data suggest a correlation of particle size, morphology and composition of the off-stoichiometric fCAS nanostructures with growth temperature and time, and oleylamine concentration. The off-stoichiometric Cu 3-a Sb 1+b S 4±c (a, b, c – mole fractions) nanostructures being severely copper-deficient and antimony-rich, exhibit shallow-lying acceptor copper vacancy states, deep-lying donor states of antimony interstitials, sulphur vacancies and antimony-copper antisites and shallow-lying acceptor surface trapping states. Further, these electronic states are likely implicated in tunable UV-visible absorption and bandgaps between 2.3 and 2.8 eV, and broad visible-NIR photoluminescence with fast recombination of radiative lifetimes between 0.2 and 6.2 ns, confirmed from absorption, steady-state and time-resolved photoluminescence spectroscopies. Additionally, cyclic voltammetry and electrochemical impedance spectroscopy confirm that electrodes of the fCAS nanostructures display slightly variable pseudocapacitance of charge-storage primarily via possible sodium ion intercalation with a high specific capacitance of ~84 F g -1 obtained at a scan rate of 5 mV s -1 . Overall, these results show the influence of composition, in particular point defects, phase quality and morphology on the optical and pseudocapacitance properties of fCAS nanostructures, suitable as solar absorbers or electrodes for energy storage devices.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Seeing is Believing: Autonomous Microscopy and the Data Revolution in Materials Science

This presentation explores the transformative potential of autonomous electron microscopy and artificial intelligence (AI) in accelerating materials science discovery, particularly for energy applications and materials operating in extreme environments. We discuss pioneering self-driving laboratories at NREL designed to intelligently probe material synthesis and degradation across multiple scales, aiming to rapidly bridge the gap between atomic-level understanding and the development of high-performance, reliable materials. Utilizing advanced machine learning techniques, such as few-shot learning and multimodal analysis integrating imaging and spectroscopy, we demonstrate methods to extract actionable descriptors for material behavior, quantify complex microstructural evolution, and statistically link synthesis parameters to defect populations. This AI-driven approach promises to accelerate the creation of predictive materials tailored for specific missions, enabling faster development cycles and enhanced material assurance.

36 MATERIALS SCIENCE↗