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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 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Fast X-ray Nanotomography with Sub-10 nm Resolution as a Powerful Imaging Tool for Nanotechnology and Energy Storage Applications

In the last decade, transmission X-ray microscopes (TXMs) have come into operation in most of the synchrotrons worldwide. They have proven to be outstanding tools for non-invasive ex and in situ 3D characterization of materials at the nanoscale across varying range of scientific applications. However, their spatial resolution has not improved in many years, while newly developed functional materials and microdevices with enhanced performances exhibit nanostructures always finer. Here, optomechanical breakthroughs leading to fast 3D tomographic acquisitions (85 min) with sub-10 nm spatial resolution, narrowing the gap between X-ray and electron microscopy, are reported. These new achievements are first validated with 3D characterizations of nanolithography objects corresponding to ultrahigh-aspect-ratio hard X-ray zone plates. Then, this powerful technique is used to investigate the morphology and conformality of nanometer-thick film electrodes synthesized by atomic layer deposition and magnetron sputtering deposition methods on 3D silicon scaffolds for electrochemical energy storage applications.

36 MATERIALS SCIENCE↗
DOE OSTI2023

3D Carbon Coating Enabled High‐capacity and Stable Micro‐sized Silicon Suboxide‐graphite Blended Anodes for Practical Lithium‐ion Batteries

Abstract Silicon oxide (SiO x ) is a promising anode candidate of lithium‐ion batteries (LIBs) owing to its extremely high specific capacity. However, the low initial Coulombic efficiency (ICE) and rapid capacity degradation of SiO x , triggered by the enormous volume variation upon repeated (de)lithiation, gravely hinder its practical use. Herein, two mass‐produced micro‐sized SiO x @C composites with obviously different morphologies for commercial LIBs are reported. Particularly, the SiO x ‐graphite blended anode (SiO x @3D‐G‐Gr) based on SiO x wrapped by three‐dimensional (3D) carbon layers (SiO x @3D‐G) exhibits a capacity of 519 mAh g −1 , an ICE of 90.0 % and a capacity retention of 83.4 % at 0.2 C over 100 cycles. which is far exceeding its counterpart SiO x @C‐H‐Gr (65.7 %). The obtained impressive properties of SiO x @3D‐G originate from the critical contribution of 3D carbon layers, which serves as the effective stress buffer and protective layer as well as the strong networks for electron/Li + transport. Accordingly, the full‐cell based on SiO x @3D‐G‐Gr anode and commercial LiCoO 2 cathode delivers a capacity of 803 mAh and an excellent capacity retention of 95.6 % (616 mAh, 96.6 % for graphite, respectively) at 1 C over 100 cycles with a stabilized CE of nearly 100 %. The micro‐sized SiO x @3D‐G showing a promising prospect in the commercial‐grade anodes in LIBs.

Electrochemistry↗
DOE OSTI2021

X-ray microscopy enables multiscale high-resolution 3D imaging of plant cells, tissues, and organs

Capturing complete internal anatomies of plant organs and tissues within their relevant morphological context remains a key challenge in plant science. While plant growth and development are inherently multiscale, conventional light, fluorescence, and electron microscopy platforms are typically limited to imaging of plant microstructure from small flat samples that lack a direct spatial context to, and represent only a small portion of, the relevant plant macrostructures. We demonstrate technical advances with a lab-based X-ray microscope (XRM) that bridge the imaging gap by providing multiscale high-resolution three-dimensional (3D) volumes of intact plant samples from the cell to the whole plant level. Serial imaging of a single sample is shown to provide sub-micron 3D volumes co-registered with lower magnification scans for explicit contextual reference. High-quality 3D volume data from our enhanced methods facilitate sophisticated and effective computational segmentation. Advances in sample preparation make multimodal correlative imaging workflows possible, where a single resin-embedded plant sample is scanned via XRM to generate a 3D cell-level map, and then used to identify and zoom in on sub-cellular regions of interest for high-resolution scanning electron microscopy. In total, we present the methodologies for use of XRM in the multiscale and multimodal analysis of 3D plant features using numerous economically and scientifically important plant systems.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Synchrotron-based diffraction-enhanced imaging and diffraction-enhanced imaging combined with CT X-ray imaging systems to image seeds at 30 keV

Utilized the upgraded Synchrotron-based non-destructive Diffraction-enhanced imaging and Diffraction-enhanced imaging coupled with CT X-ray imaging systems to image the chickpea seeds, to enhance the contrast in plant root architecture, visibility of fine structures of root architecture growth and some aspects of physiology at acceptable level. DEI-CT images were acquired with 30 keV synchrotron X-rays. A series of DEI-CT slices were assembled together, to form a 3D data set. DEI-CT images explored more structural information and morphology. Noticed detailed anatomical, physiological observations, and contrast mechanisms. Furthermore, with these systems, some of the complex plant traits, root morphology, growth of laterals and subsequent laterals can be visualized directly.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Automated segmentation of soft X-ray tomography: Native cellular structure with submicron resolution at high-throughput for whole-cell quantitative imaging in yeast

Soft X-ray tomography (SXT) is an invaluable tool for quantitatively analyzing cellular structures at suboptical isotropic resolution. However, it has traditionally depended on manual segmentation, limiting its scalability for large datasets. Here, we leverage a deep learning-based autosegmentation pipeline to segment and label cellular structures in hundreds of cells across three Saccharomyces cerevisiae strains. This task-based pipeline uses manual iterative refinement to improve segmentation accuracy for key structures, including the cell body, nucleus, vacuole, and lipid droplets, enabling high-throughput and precise phenotypic analysis. Using this approach, we quantitatively compared the three-dimensional (3D) whole-cell morphometric characteristics of wild-type, VPH1-GFP, and vac14 strains, uncovering detailed strain-specific cell and organelle size and shape variations. We show the utility of SXT data for precise 3D curvature analysis of entire organelles and cells and detection of fine morphological features using surface meshes. Our approach facilitates comparative analyses with high spatial precision and statistical throughput, uncovering subtle morphological features at the single-cell and population level. This workflow significantly enhances our ability to characterize cell anatomy and supports scalable studies on the mesoscale, with applications in investigating cellular architecture, organelle biology, and genetic research across diverse biological contexts.

Chen, Jianhua [Lawrence Berkeley National Laborato↗
DOE OSTI2026

Simulation to a Newborn Supernova Remnant from a Low-mass Iron Core Star

Supernova remnant observations show a high degree of asymmetry, mixing, and inhomogeneity. These asymmetries are seeded during the early seconds of the explosion and are further enhanced and modified as the shock and ejecta move through the stellar progenitor and into the circumstellar medium. We present simulations of a 9.6 M⊙ zero-metallicity progenitor initialized after shock revival and evolved for several years when the ejecta is in the circumstellar medium. A suite of 1D and 2D simulations examines the effects of neutron-star wind and radioactive decay heating. In 1D, decay heating forms a low-density bubble that suppresses the reverse shock. While in 2D, the heating is localized to metal-rich pockets, inflating them and compressing the surrounding material into dense shells. In 3D, the neutron-star wind and decay heating modify the plume morphology, producing more large-scale structures. The extended plume morphology leads to an asymmetrical shock breakout. After breakout, the leading plumes cannot keep up with the shock front, resulting in deceleration and fragmentation by the reverse shock while retaining the large-scale asymmetry. The projected ejecta morphology and velocities are strongly viewing angle dependent. The relatively uniform metal-rich distribution does not resemble the strongly inhomogeneous ejecta structure of Cas A. The 160-isotope decay network shows that 24.4% of the radioactive heating comes from decay chains other than the canonical 56Ni chain. The low explosion energy, low 56Ni yield, and Ni/Fe ratio greater than unity suggest an observational signature similar to an electron capture supernova.

Neopane, Sudarshan [University of Tennessee (UT)]↗
DOE OSTI2020

Three-dimensional imaging of mitochondrial cristae complexity using cryo-soft X-ray tomography

Mitochondria are dynamic organelles that change morphology to adapt to cellular energetic demands under both physiological and stress conditions. Cardiomyopathies and neuronal disorders are associated with structure-related dysfunction in mitochondria, but three-dimensional characterizations of the organelles are still lacking. In this study, we combined high-resolution imaging and 3D electron density information provided by cryo-soft X-ray tomography to characterize mitochondria cristae morphology isolated from murine. Using the linear attenuation coefficient, the mitochondria were identified (0.247 ± 0.04 µm -1 ) presenting average dimensions of 0.90 ± 0.20 µm in length and 0.63 ± 0.12 µm in width. The internal mitochondria structure was successfully identified by reaching up the limit of spatial resolution of 35 nm. The internal mitochondrial membranes invagination (cristae) complexity was calculated by the mitochondrial complexity index (MCI) providing quantitative and morphological information of mitochondria larger than 0.90 mm in length. The segmentation to visualize the cristae invaginations into the mitochondrial matrix was possible in mitochondria with MCI ≥ 7. Altogether, we demonstrated that the MCI is a valuable quantitative morphological parameter to evaluate cristae modelling and can be applied to compare healthy and disease state associated to mitochondria morphology.

3-D reconstruction↗
DOE OSTI2022

Linker Redox Mediated Control of Morphology and Properties in Semiconducting Iron‐Semiquinoid Coordination Polymers**

Abstract The emergence of conductive 2D and less commonly 3D coordination polymers (CPs) and metal–organic frameworks (MOFs) promises novel applications in many fields. However, the synthetic parameters for these electronically complex materials are not thoroughly understood. Here we report a new 3D semiconducting CP Fe 5 (C 6 O 6 ) 3 , which is a fusion of 2D Fe‐semiquinoid materials and 3D cubic Fe x (C 6 O 6 ) y materials, by using a different initial redox‐state of the C 6 O 6 linker. The material displays high electrical conductivity (0.02 S cm −1 ), broad electronic transitions, promising thermoelectric behavior ( S 2 σ=7.0×10 −9 W m −1 K −2 ), and strong antiferromagnetic interactions at room temperature. This material illustrates how controlling the oxidation states of redox‐active components in conducting CPs/MOFs can be a “pre‐synthetic” strategy to carefully tune material topologies and properties in contrast to more commonly encountered post‐synthetic modifications.

Wang, Lei↗
DOE OSTI2023

Engineering Supramolecular Hybrid Architectures with Directional Organofluorine Bonds

Understanding how chemical modifications alter the atomic‐scale organization of materials is of fundamental importance in materials engineering and the target of considerable efforts in computational prediction. Incorporating covalent and noncovalent interactions in designing crystals while “piggybacking” on the driving force of molecular self‐assembly has augmented efforts to understand the emergence of complex structures using directed synthesis. In this work, microcrystalline powders of the silver 2‐, 3‐, and 4‐fluorobenzenethiolates are prepared and their structures are resolved by small‐molecule serial femtosecond X‐ray crystallography. These three compounds enable the emergence and role of supramolecular synthons in the crystal structures of 3D metal‐organic chalcogenolates to be examined. The unique divergence in their optoelectronic, morphological, and structural behaviors is assessed. The extent of CHF interactions and their influence on the structure and the observed trends in the thermal stability of the crystals are quantified through theoretical calculations and thermogravimetric analysis.

Kotei, Patience A.↗
DOE OSTI2021

Self-Assembled BaTiO 3 –Au x Ag 1– x Low-Loss Hybrid Plasmonic Metamaterials with an Ordered “Nano-Domino-like” Microstructure

Metallic plasmonic hybrid nanostructures have attracted enormous research interest due to the combined physical properties coming from different material components and the broad range of applications in nanophotonic and electronic devices. However, the high loss and narrow range of property tunability of the metallic hybrid materials have limited their practical applications. In this study, a metallic alloy-based self-assembled plasmonic hybrid nanostructure, i.e., a BaTiO 3 –Au x Ag 1– x (BTO) vertically aligned nanocomposite, has been integrated by a templated growth method for low-loss plasmonic systems. Comprehensive microstructural characterizations including high-resolution scanning transmission electron microscopy (HRSTEM), energy-dispersive X-ray spectroscopy (EDS), and three-dimensional (3D) electron tomography demonstrate the formation of an ordered “nano-domino-like” morphology with Au 0.4 Ag 0.6 nanopillars as cylindrical cores and BTO as square shells. By comparing with the BTO–Au hybrid thin film, the BTO–Au 0.4 Ag 0.6 alloyed film exhibits much broader plasmon resonance, hyperbolic dispersion, low-loss, and thermally robust features in the UV–vis–NIR wavelength region. This study provides a feasible platform for a complex alloyed plasmonic hybrid material design with low-loss and highly tunable optical properties toward all-optical integrated devices.

36 MATERIALS SCIENCE↗
DOE OSTI2021

TomoEncoders: 3D Autoencoders for feature extraction in X-ray tomography

Real-time steering of time-resolved or in-situ X-ray tomography requires capturing changes in morphological descriptors in a sample (e.g., porosity, particle size, and crack width) during continuous data acquisition. Image segmentation (2D or3D) followed by quantitative measurement is the conventional method for tracking changes in these descriptors with respect to a previous time-step or a 3D search in a volume. However, image segmentation is expensive. As a faster and unsupervised alternative, a feature-extraction approach using a convolutional autoencoders was developed, where the latent space of the encoder responds to relative changes in morphology with-out prior knowledge of the morphological descriptors.

TEKAWADE, ANIKET↗
DOE OSTI2022

High‐Resolution Electron Tomography of Ultrathin Boerdijk–Coxeter–Bernal Nanowire Enabled by Superthin Metal Surface Coating

Abstract The rapid advancement of transmission electron microscopy has resulted in revolutions in a variety of fields, including physics, chemistry, and materials science. With single‐atom resolution, 3D information of each atom in nanoparticles is revealed, while 4D electron tomography is shown to capture the atomic structural kinetics in metal nanoparticles after phase transformation. Quantitative measurements of physical and chemical properties such as chemical coordination, defects, dislocation, and local strain have been made. However, due to the incompatibility of high dose rate with other ultrathin morphologies, such as nanowires, atomic electron tomography has been primarily limited to quasi‐spherical nanoparticles. Herein, the 3D atomic structure of a complex core–shell nanowire composed of an ultrathin Boerdijk–Coxeter–Bernal (BCB) core nanowire and a noble metal thin layer shell deposited on the BCB nanowire surface is discovered. Furthermore, it is demonstrated that a new superthin noble metal layer deposition on an ultrathin BCB nanowire could mitigate electron beam damage using an in situ transmission electron microscope and atomic resolution electron tomography. The colloidal coating method developed for electron tomography can be broadly applied to protect the ultrathin nanomaterials from electron beam damage, benefiting both the advanced material characterizations and enabling fundamental in situ mechanistic studies.

Song, Xiaohui↗
DOE OSTI2025

Nanotomography for Quantitative 3D Particle Reconstruction

Particulates are ubiquitous across fuel cycle operations and carry critical information about particle formation, processing, and potential proliferation-related activities. Traditional analytical techniques, including micro-Raman spectroscopy and standard electron microscopy, are often limited in spatial resolution or dimensionality, particularly when used to examine metallic or submicron-scale features. Understanding particle morphology, phase distribution, and internal porosity is essential for constraining formation conditions, thermodynamic environments, and material transport behavior. In this report, we demonstrate the application of plasma focused ion beam nanotomography to reconstruct micron-scale particulates at nanoscale resolution. Using high-resolution backscattered electron imaging and Avizo software, we obtained 3D reconstructions that enabled quantitative analysis of particle morphology, phase composition, and internal voids. Representative examples include a Ta particle with a large central void and a composite particle with embedded tetrahedral crystalline structures. These reconstructions reveal structural and compositional details that are inaccessible through conventional 2D imaging. The results demonstrate that nanotomography provides both qualitative and quantitative insights into particle formation and behavior. Using nanotomography, porosity and phase distributions can be quantified to inform models of particle density, transport, and solidification conditions. Beyond technical insights, the workflow developed here establishes a transferable capability for analyzing heterogeneous particles and has potential applications in bulk materials studies via x-ray computed tomography or other volumetric imaging modalities. Ongoing efforts are focused on optimizing the workflow to process multiple particles simultaneously, increasing throughput and statistical robustness. Overall, this work illustrates the power of nanotomography as a tool for connecting particulate morphology to formation mechanisms, composition, and transport, thereby strengthening analytical capabilities for nuclear forensics, fuel cycle analysis, and related scientific investigations.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2021

Effect of particle characteristics on the evolution of particle size, particle morphology, and fabric of sands loaded under uniaxial compression

This work presents the results and analyses of uniaxial compression experiments performed on three silica sands. The sands have comparable particle-size distributions, but their particles differ in morphology and strength. Cylindrical samples of the three sands were compressed in a loading device placed inside an X-ray microscope (XRM) and scanned at multiple stress levels during uniaxial compression. 3D tomography data of the samples obtained from the XRM at different stress levels were then analyzed to obtain the distributions of particle size, particle morphology, and interparticle contact normals within the sample. Results indicate that: (1) the compressibility of the sands loaded under uniaxial compression is closely tied to particle morphology and strength and (2) the anisotropy in the orientations of interparticle contact normals generally increases with axial stress; however, this increase is limited by the occurrence of particle crushing in the sample.

3D X-ray computed tomography↗
DOE OSTI2021

Algorithm-Driven Advances for Scientific CT Instruments: From model-based to deep learning-based approaches

Multiscale 3D characterization is widely used by materials scientists to further their understanding of the relationships between microscopic structure and macroscopic function. Scientific computed tomography (SCT) instruments are one of the most popular choices for 3D nondestructive characterization of materials at length scales ranging from the angstrom scale to the micron scale. These instruments typically have a source of radiation (such as electrons, X-rays, or neutrons) that interacts with the sample to be studied and a detector assembly to capture the result of this interaction (see Figure 1 ). A collection of such high-resolution measurements is made by reorienting the sample, which is mounted on a specially designed stage/holder after which reconstruction algorithms are used to produce the final 3D volume of interest. The specific choice of which instrument to use depends on the desired resolution and properties of the materials being imaged. Additionally, the end goal of SCT scans includes determining the morphology, chemical composition, or dynamic behavior of materials when subjected to external stimuli. In summary, SCT instruments are powerful tools that enable 3D characterization across multiple length scales and play a critical role in furthering the understanding of the structure–function relationships of different materials.

42 ENGINEERING↗
DOE OSTI2026

Modeling the contributions to acoustic nonlinearity from complex dislocation networks using 3D dislocation dynamics

Nonlinear ultrasonic parameters are highly sensitive to microstructural features that affect macroscale material behavior, providing a nondestructive means to characterize their evolution. Although dislocations are known to be a strong source of acoustic nonlinearity, establishing quantitative links between the acoustic nonlinearity parameter (β), measured via Second Harmonic Generation, and dislocation morphology—such as dislocation length and density—remains an open challenge. This work advances the numerical modeling of dislocation–β relationships using 3D dislocation dynamics (DD) simulations in two approaches: a “static” method computing strain and stress fields from dislocation configurations in the absence of external loading, and a “quasi-static” method to estimate β from the curvature of dislocation lines under applied load. First, the static method is combined with finite element analysis to investigate a recent assertion that heterogeneous initial strain fields can induce higher harmonic generation in a linear elastic medium; the present results do not corroborate this outcome. Then, the quasi-static method is applied to multiple-dislocation scenarios through parametric studies, revealing behaviors not predicted by analytical models, such as the competing interactions of edge and screw dislocations and the significant influence of applied stress on β. Finally, the simulations are used to model SHG experimental results and validate the hypothesis that β can decrease during plastic deformation, despite increasing dislocation density. As the DD code used here is open-source, it provides a practical platform for future investigation into microstructure–β relationships important to the interpretation of SHG results.

Materials science↗
DOE OSTI2022

A deep learning approach for semantic segmentation of unbalanced data in electron tomography of catalytic materials

In computed TEM tomography, image segmentation represents one of the most basic tasks with implications not only for 3D volume visualization, but more importantly for quantitative 3D analysis. In case of large and complex 3D data sets, segmentation can be an extremely difficult and laborious task, and thus has been one of the biggest hurdles for comprehensive 3D analysis. Heterogeneous catalysts have complex surface and bulk structures, and often sparse distribution of catalytic particles with relatively poor intrinsic contrast, which possess a unique challenge for image segmentation, including the current state-of-the-art deep learning methods. To tackle this problem, we apply a deep learning-based approach for the multi-class semantic segmentation of a γ-Alumina/Pt catalytic material in a class imbalance situation. Specifically, we used the weighted focal loss as a loss function and attached it to the U-Net’s fully convolutional network architecture. We assessed the accuracy of our results using Dice similarity coefficient (DSC), recall, precision, and Hausdorff distance (HD) metrics on the overlap between the ground-truth and predicted segmentations. Our adopted U-Net model with the weighted focal loss function achieved an average DSC score of 0.96 ± 0.003 in the γ-Alumina support material and 0.84 ± 0.03 in the Pt NPs segmentation tasks. We report an average boundary-overlap error of less than 2 nm at the 90th percentile of HD for γ-Alumina and Pt NPs segmentations. The complex surface morphology of γ-Alumina and its relation to the Pt NPs were visualized in 3D by the deep learning-assisted automatic segmentation of a large data set of high-angle annular dark-field (HAADF) scanning transmission electron microscopy (STEM) tomography reconstructions.

36 MATERIALS SCIENCE↗