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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

An Automated Scanning Transmission Electron Microscope Guided by Sparse Data Analytics

Abstract Artificial intelligence (AI) promises to reshape scientific inquiry and enable breakthrough discoveries in areas such as energy storage, quantum computing, and biomedicine. Scanning transmission electron microscopy (STEM), a cornerstone of the study of chemical and materials systems, stands to benefit greatly from AI-driven automation. However, present barriers to low-level instrument control, as well as generalizable and interpretable feature detection, make truly automated microscopy impractical. Here, we discuss the design of a closed-loop instrument control platform guided by emerging sparse data analytics. We hypothesize that a centralized controller, informed by machine learning combining limited a priori knowledge and task-based discrimination, could drive on-the-fly experimental decision-making. This platform may unlock practical, automated analysis of a variety of material features, enabling new high-throughput and statistical studies.

47 OTHER INSTRUMENTATION↗
DOE OSTI2021

In Situ Transmission Electron Microscopy: Signal processing challenges and examples

Transmission electron microscopy (TEM) is a powerful tool for imaging material structure and characterizing material chemistry. Recent advances in data collection technology for TEM have enabled high-volume and high-resolution data collection at a microsecond frame rate. Here, taking advantage of these advances in data collection rates requires the development and application of data processing tools, including image analysis, feature extraction, and streaming data processing techniques. In this article, we highlight a few areas in materials science that have benefited from combining signal processing and statistical analysis with data collection capabilities in TEM and present a future outlook on opportunities of integrating signal processing with automated TEM data analysis.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Nanoscale trace-element zoning in pyrite framboids and implications for paleoproxy applications

Pyrite framboids (spherical masses of nanoscale pyrite) are among the earliest textures of pyrite to form in sediments. It has been proposed that their trace-element (TE) contents can be used to track the TE composition of the water column in which they formed. However, it is not clear how these TEs are associated with the framboidal pyrite grains. For instance, it is important to know whether they are incorporated uniformly or are enriched in different regions of the framboid. Here, we used high-resolution scanning transmission electron microscopy to identify chemical zoning within pyrite framboids. We found that initial, nanoscale pyrite euhedral crystals, which make up the volumetric majority of the framboids, are covered/infilled by later pyrite that templates on the earlier pyrite. Further, this later pyrite is enriched in TEs, suggesting that many TEs are incorporated in pyrite relatively late (during early diagenesis; not in the water column). This observation suggests that although chemical analyses of pyrite framboids may provide ocean-water chemistry trends through time, the details are complex. Specifically, the TEs found in pyrite may be linked to adsorption onto organic matter, detrital material, and authigenic minerals such as Fe- and Mn-oxide phases followed by desorption in the sediments or release via dissolution and incorporation into pyrite as overgrowths on the initial nanoscale euhedral crystals that make up framboids. While the use of pyrite chemistry to understand past ocean conditions remains promising, and even diagenetic additions may not preclude the utility of pyrite for reconstructing ancient ocean conditions, care must be taken in interpretations because the end concentration may be influenced by diagenesis.

58 GEOSCIENCES↗
DOE OSTI2023

Resolution of Virtual Depth Sectioning from Four-Dimensional Scanning Transmission Electron Microscopy

Abstract One approach to three-dimensional structure determination using the wealth of scattering data in four-dimensional (4D) scanning transmission electron microscopy (STEM) is the parallax method proposed by Ophus et al. (2019. Advanced phase reconstruction methods enabled by 4D scanning transmission electron microscopy, Microsc Microanal25, 10–11), which determines the scattering matrix and uses it to synthesize a virtual depth-sectioning reconstruction of the sample structure. Drawing on an equivalence with a hypothetical confocal imaging mode, we derive contrast transfer and point spread functions for this parallax method applied to weakly scattering objects, showing them identical to earlier depth-sectioning STEM modes when only bright field signal is used, but that improved depth resolution is possible if dark field signal can be used. Through a simulation-based study of doped Si, we show that this depth resolution is preserved for thicker samples, explore the impact of shot noise on the parallax reconstructions, discuss challenges to making use of dark field signal, and identify cases where the interpretation of the parallax reconstruction breaks down.

3D imaging↗
DOE OSTI2024

High‐speed 4‐dimensional scanning transmission electron microscopy using compressive sensing techniques

Abstract Here we show that compressive sensing allows 4‐dimensional (4‐D) STEM data to be obtained and accurately reconstructed with both high‐speed and reduced electron fluence. The methodology needed to achieve these results compared to conventional 4‐D approaches requires only that a random subset of probe locations is acquired from the typical regular scanning grid, which immediately generates both higher speed and the lower fluence experimentally. We also consider downsampling of the detector, showing that oversampling is inherent within convergent beam electron diffraction (CBED) patterns and that detector downsampling does not reduce precision but allows faster experimental data acquisition. Analysis of an experimental atomic resolution yttrium silicide dataset shows that it is possible to recover over 25 dB peak signal‐to‐noise ratio in the recovered phase using 0.3% of the total data. Lay abstract : Four‐dimensional scanning transmission electron microscopy (4‐D STEM) is a powerful technique for characterizing complex nanoscale structures. In this method, a convergent beam electron diffraction pattern (CBED) is acquired at each probe location during the scan of the sample. This means that a 2‐dimensional signal is acquired at each 2‐D probe location, equating to a 4‐D dataset. Despite the recent development of fast direct electron detectors, some capable of 100kHz frame rates, the limiting factor for 4‐D STEM is acquisition times in the majority of cases, where cameras will typically operate on the order of 2kHz. This means that a raster scan containing 256^2 probe locations can take on the order of 30s, approximately 100‐1000 times longer than a conventional STEM imaging technique using monolithic radial detectors. As a result, 4‐D STEM acquisitions can be subject to adverse effects such as drift, beam damage, and sample contamination. Recent advances in computational imaging techniques for STEM have allowed for faster acquisition speeds by way of acquiring only a random subset of probe locations from the field of view. By doing this, the acquisition time is significantly reduced, in some cases by a factor of 10‐100 times. The acquired data is then processed to fill‐in or inpaint the missing data, taking advantage of the inherently low‐complex signals which can be linearly combined to recover the information. In this work, similar methods are demonstrated for the acquisition of 4‐D STEM data, where only a random subset of CBED patterns are acquired over the raster scan. We simulate the compressive sensing acquisition method for 4‐D STEM and present our findings for a variety of analysis techniques such as ptychography and differential phase contrast. Our results show that acquisition times can be significantly reduced on the order of 100‐300 times, therefore improving existing frame rates, as well as further reducing the electron fluence beyond just using a faster camera.

Robinson, Alex W.↗
NASA NTRS1992

X-ray scattering measurements from thin foil X-ray mirrors

Thin foil X-ray mirrors are to be used as the reflecting elements in the telescopes of the X-ray satellites Spectrum-X-Gamma (SRG) and ASTRO-D. High resolution X-ray scattering measurements from the Au coated and dip-lacquered Al foils are presented. These were obtained from SRG mirrors positioned in a test quadrant of the telescope structure and from ASTRO-D foils held in a simple fixture. The X-ray data is compared with laser data and other surface structure data such as STM, atomic force microscopy (AFM), TEM, and electron micrography. The data obtained at Cu K-alpha(1), (8.05 keV) from all the mirrors produced on Al foils shows a scatter which limits the obtainable half-power width to above 1.5 arcmin. Mirrors based on electroformed Ni foils, however, show local regions with a factor of 4 better performance, and they are being developed for future applications.

Christensen, F. E.↗
DOE OSTI2021

Understanding TRISO Coated Particle Neutron Irradiation Behavior:Evolution of Advanced Micro Analysis and Electron Microscopy Approaches

Objectives and motivation• Provide data for fuel qualification in support of reactor licensing• Establish a domestic commercial TRISO fuel fabrication capability Approach • Focus is on developing and testing UCO TRISO fuel –Develop fuel fabrication and QC measurement methods, first at lab scale and then at industrial scale –Perform irradiation testingover a range of conditions (burnup, temperature, fast neutron fluence) –Perform post-irradiation examination and safety testingto demonstrate and understand performance during irradiation and during accident conditions –Develop fuel performance modelsto better predict fuel behavior –Perform fission product transport experiments to improve understanding and refine models of fission product transport

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2024

Automated Grain Boundary (GB) Segmentation and Microstructural Analysis in 347H Stainless Steel Using Deep Learning and Multimodal Microscopy

Austenitic 347H stainless steel offers superior mechanical properties and corrosion resistance required for extreme operating conditions such as high temperature. The change in microstructure due to composition and process variations is expected to impact material properties. Identifying microstructural features such as grain boundaries thus becomes an important task in the process-microstructure-properties loop. Applying convolutional neural network (CNN)-based deep learning models is a powerful technique to detect features from material micrographs in an automated manner. In contrast to microstructural classification, supervised CNN models for segmentation tasks require pixel-wise annotation labels. However, manual labeling of the images for the segmentation task poses a major bottleneck for generating training data and labels in a reliable and reproducible way within a reasonable timeframe. Microstructural characterization especially needs to be expedited for faster material discovery by changing alloy compositions. Here, in this study, we attempt to overcome such limitations by utilizing multimodal microscopy to generate labels directly instead of manual labeling. We combine scanning electron microscopy images of 347H stainless steel as training data and electron backscatter diffraction micrographs as pixel-wise labels for grain boundary detection as a semantic segmentation task. The viability of our method is evaluated by considering a set of deep CNN architectures. We demonstrate that despite producing instrumentation drift during data collection between two modes of microscopy, this method performs comparably to similar segmentation tasks that used manual labeling. Additionally, we find that naïve pixel-wise segmentation results in small gaps and missing boundaries in the predicted grain boundary map. By incorporating topological information during model training, the connectivity of the grain boundary network and segmentation performance is improved. Finally, our approach is validated by accurate computation on downstream tasks of predicting the underlying grain morphology distributions which are the ultimate quantities of interest for microstructural characterization.

36 MATERIALS SCIENCE↗
DOE OSTI2021

RCSB Protein Data Bank: Celebrating 50 years of the PDB with new tools for understanding and visualizing biological macromolecules in 3D

We report the Research Collaboratory for Structural Bioinformatics Protein Data Bank (RCSB PDB), funded by the US National Science Foundation, National Institutes of Health, and Department of Energy, has served structural biologists and Protein Data Bank (PDB) data consumers worldwide since 1999. RCSB PDB, a founding member of the Worldwide Protein Data Bank (wwPDB) partnership, is the US data center for the global PDB archive housing biomolecular structure data. RCSB PDB is also responsible for the security of PDB data, as the wwPDB-designated Archive Keeper. Annually, RCSB PDB serves tens of thousands of three-dimensional (3D) macromolecular structure data depositors (using macromolecular crystallography, nuclear magnetic resonance spectroscopy, electron microscopy, and micro-electron diffraction) from all inhabited continents. RCSB PDB makes PDB data available from its research-focused RCSB.org web portal at no charge and without usage restrictions to millions of PDB data consumers working in every nation and territory worldwide. In addition, RCSB PDB operates an outreach and education PDB101.RCSB.org web portal that was used by more than 800,000 educators, students, and members of the public during calendar year 2020. This invited Tools Issue contribution describes (i) how the archive is growing and evolving as new experimental methods generate ever larger and more complex biomolecular structures; (ii) the importance of data standards and data remediation in effective management of the archive and facile integration with more than 50 external data resources; and (iii) new tools and features for 3D structure analysis and visualization made available during the past year via the RCSB.org web portal.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Reward Driven Workflows for Unsupervised Explainable Analysis of Phases and Ferroic Variants From Atomically Resolved Imaging Data

Rapid progress in aberration corrected electron microscopy necessitates development of robust methods for the identification of phases, ferroic variants, and other pertinent aspects of materials structure from imaging data. While unsupervised methods for clustering and classification are widely used for these tasks, their performance can be sensitive to hyperparameter selection in the analysis workflow. In this study, the effects of descriptors and hyperparameters are explored on the capability of unsupervised ML methods to distill local structural information, exemplified by the discovery of polarization and lattice distortion in Sm − dopped BiFeO 3 (BFO) thin films. It is demonstrated that a reward-driven approach can be used to optimize these key hyperparameters across the full workflow, where rewards are designed to reflect domain wall continuity and straightness, ensuring that the analysis aligns with the material's physical behavior. This approach allows the discovery of local descriptors that are best aligned with the specific physical behavior, providing insight into the fundamental physics of materials. The reward driven workflow is further extended to disentangle structural factors of variation via an optimized variational autoencoder (VAE). Lastly, the importance of well-defined rewards is explored as a quantifiable measure of the success of the workflow.

Barakati, Kamyar [University of Tennessee, Knoxvil↗
DOE OSTI2025

Applications of visualization technology in the structural sciences

The structural sciences are undergoing a transformation driven by advancements in visualization technologies that aid researchers in understanding and communicating experimental data from complex molecular systems. New applications of integrative structural biological and biophysical approaches add a wide variety of complementary information from a broad range of scientific disciplines. These approaches extend structural biophysical methodologies to enable research by the incorporation of a variety of data streams and utilization of tools like molecular graphics, virtual reality, and machine learning. To redefine how structural data—particularly from cryo-electron microscopy and x-ray crystallography—are fed forward for scientific exploration and communication, the advances in tools for data visualization and interpretation have been critical. By bringing molecular systems into an interactive three-dimensional space, these novel technologies enhance research workflows, facilitate structure-based drug design, and create engaging educational experiences. Taken together, these visualization innovations are essential tools for advancing the field by making concepts more accessible and compelling.

Eng, Edward T. [New York Structural Biology Center↗
DOE OSTI2021

Understanding the Role of CdTe in Polycrystalline CdSe x Te 1– x /CdTe-Graded Bilayer Photovoltaic Devices

Grading of bandgap by alloying CdTe with selenium to form a CdSe x Te 1–x /CdTe-graded bilayer device has led to a device efficiency over 19%. A CdSe x Te 1–x absorber would increase the short-circuit current due to its lower bandgap but at the expense of open-circuit voltage. It has been demonstrated that adding a CdTe layer at the back of such a CdSe x Te 1–x film reduces the voltage deficit caused by the lower bandgap of absorber from selenium alloying while maintaining the higher short-circuit current. This leads to a photovoltaic device that draws advantage from both materials with an efficiency greater than either of them. In this report there is a detailed account using device data, ultraviolet photoelectron spectroscopy, electron microscopy, and first-principles density functional theory modeling is provided, which shows that CdTe acts as an electron reflector for CdSe x Te 1–x .

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

Stable nanovesicles formed by intrinsically planar bilayers

Quatsome nanovesicles, formed through the self-assembly of cholesterol (CHOL) and cetyltrimethylammonium bromide (CTAB) in water, have shown long-term stability in terms of size and morphology, while at the same time exhibiting high CHOL-CTAB intermolecular binding energies. We hypothesize that CHOL/CTAB quatsomes are indeed thermodynamically stable nanovesicles, and investigate the mechanism underlying their formation. A systematic study was performed to determine whether CHOL/CTAB quatsomes satisfy the experimental requisites of thermodynamically stable vesicles. Coarse-grain molecular dynamics simulations were used to investigate the molecular organization in the vesicle membrane, and the characteristics of the simulated vesicle were corroborated with experimental data obtained by cryo–electron microscopy, small- and wide-angle X-ray scattering, and multi-angle static light scattering. CHOL/CTAB quatsomes fulfill the requisites of thermodynamically stable nanovesicles, but they do not exhibit the classical membrane curvature induced by a composition asymmetry between the bilayer leaflets, like catanionic nanovesicles. Instead, CHOL/CTAB quatsomes are formed through the association of intrinsically planar bilayers in a faceted vesicle with defects, indicating that distortions in the organization and orientation of molecules can play a major role in the formation of thermodynamically stable nanovesicles.

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

Kinetically Controlled Synthesis of Rhodium Nanocrystals with Different Shapes and a Comparison Study of Their Thermal and Catalytic Properties

Here, we report the synthesis of Rh nanocrystals with different shapes by controlling the kinetics involved in the growth of preformed Rh cubic seeds. Specifically, Rh nanocrystals with cubic, cuboctahedral, and octahedral shapes can all be obtained from the same cubic seeds under suitable reduction kinetics for the precursor. The success of such a synthesis also relies on the use of a halide-free precursor to avoid oxidative etching, as well as the involvement of a sufficiently high temperature to remove Br – ions from the seeds while ensuring adequate surface diffusion. The availability of Rh nanocrystals with cubic and octahedral shapes allows for an evaluation of the facet dependences of their thermal and catalytic properties. The data from in situ electron microscopy studies indicate that the cubic and octahedral Rh nanocrystals can keep their original shapes up to 700 and 500 °C, respectively. When tested as catalysts for hydrazine decomposition, the octahedral nanocrystals exhibit almost 4-fold enhancement in terms of H 2 selectivity relative to the cubic counterpart. As for ethanol oxidation, the order is reversed, with the cubic nanocrystals being about three times more active than the octahedral sample.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Structures via Reasoning - Applying AI to Cryo Electron Microscopy to Reveal Structural Variability

There have been breakthroughs of latest cryo electron microscopy (cryo-EM) data analysis algorithms to classify cryo-EM image data. However, most of these cryo-EM reconstruction methods have focused on classifying distinctly different biomolecule structures. Here, we present our approaches of deep learning to differentiate homologous structures that are distinguishable only with inner morphological differences. We succeeded supervised classification of these subtly different homologues. However, we could not differentiate them with unsupervised methods. Here we discuss what further approaches are likely needed for successful unsupervised classification.

47 OTHER INSTRUMENTATION↗
NASA NTRS1984

Proposed structures for poorly characterized phases in C2M carbonaceous chondrite meteorites

A recent investigation of C2M carbonaceous chondrite meteorite matrices using electron microscopy and High-Resolution Transmission Electron Microscopy (HRTEM) has provided data on the structure and chemistry of Poorly Characterized Phases (PCP). It is suggested that a dominant matrix variety (10 A PCP) has a structure equivalent to iron-rich tochinilite (6Fe0.9S5/Fe, Mg//OHO2/), which consists of coherently intrastratified mackinawite and brucite sheets. In addition, it is proposed that 17 A PCP is a commensurate intergrowth of serpentine and tochinilite layers. Various forms of PCP observed in carbonaceous chondrites appear to be intergrowths of tochinilite, serpentine, and tochinilite-serpentine minerals.

Mackinnon, I. D. R.↗
NASA NTRS1994

Moessbauer mineralogy of calcined Murchison meteorite

The three Antarctic meteorites B7904, Y82162, and Y86720 are unusual because they have characteristics in common with both CI and CM groups and because they apparently underwent thermal alteration after hydrous alteration on their parent body. They are also spectrally similar (visible and near-IR) to C, G, B, and F asteroids, which may imply that the surface materials on those asteroids may have undergone thermal alteration. Based on the reflectance spectra of samples of Murchison (CM2 carbonaceous chondrite) that were thermally altered in the laboratory (cryopumped and initial 10(exp -5) atm H2), Hiroi et al. concluded that putative thermal alteration occurred at temperatures of 600 to 1000 C. Similar experiments have been done on Murchison and reported mineralogical changes based on data from transmission electron diffraction microscopy, electron diffraction, and analytical electron microscopy. We report here the Mossbauer mineralogy of the same samples of thermally-altered Murchison. Mossbauer mineralogy gives the molar distribution of Fe among its oxidation states and iron-bearing mineralogies.

Morris, Richard V.↗
NASA NTRS2003

Fast Characterization of Magnetic Impurities in Single-Wall Carbon Nanotubes

We have demonstrated that the magnetic susceptibility measurement is a non-destructive, fast and accurate method to determine the residual metal catalysts in a few microgram single-wall carbon nanotube (SWCNT) sample. We have studied magnetic impurities in raw and purified SWCNT by magnetic susceptibility measurements, transmission electron microscopy, and thermogravimetry. The data suggest that the saturation magnetic moment and the effective field, which is caused by the interparticle interactions, decreases and increases respectively with the decrease of the particle size. Methods are suggested to overcome the uncertainty associated.

Chen, Feng↗