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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 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
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↗
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↗