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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
NASA NTRS2016

Transmission Electron Microscopy of Magnetite Plaquettes in Orgueil

Magnetite sometimes takes the form of a plaquette - barrel-shaped stack of magnetite disks - in carbonaceous chondrites (CC) that show evidence of aqueous alteration. The asymmetric nature of the plaquettes caused Pizzarello and Groy to propose magnetite plaquettes as a naturally asymmetric mineral that can indroduce symmetry-breaking in organic molecules. Our previous synchrotron X-ray computed microtomography (SXRCT) and electron backscatter diffraction (EBSD) analyses of the magnetite plaquettes in fifteen CCs indicate that magnetite plaquettes are composed of nearly parallel discs, and the crystallographic orientations of the discs change around a rotational axis normal to the discs surfaces. In order to further investigate the nanostructures of magnetite plaquettes, we made two focused ion beam (FIB) sections of nine magnetite plaquettes from a thin section of CI Orgueil for transmission electron microscope (TEM) analysis. The X-ray spectrum imaging shows that the magnetite discs are purely iron oxide Fe3O4 (42.9 at% Fe and 57.1 at% O), which suggest that the plaquettes are of aqueous origin as it is difficult to form pure magnetite as a nebular condensate. The selected area electron diffraction (SAED) patterns acquired across the plaquettes show that the magnetite discs are single crystals. SEM and EBSD analyses suggest that the planar surfaces of the magnetite discs belong to the {100} planes of the cubic inverse spinel structure, which are supported by our TEM observations. Kerridge et al. suggested that the epitaxial relationship between magnetite plaquette and carbonate determines the magnetite face. However, according to our TEM observation, the association of magnetite with porous networks of phyllosilicate indicates that the epitaxial relationship with carbonate is not essential to the formation of magnetite plaquettes. It was difficult to determine the preferred rotational orientation of the plaquettes due to the symmetry of the cubic structure, however, we are able to observe small but consistent rotational orientation across several discs within a plaquette.

Chan, Q. H. S.↗
DOE OSTI2021

Development of liquid cells for high resolution imaging and chemical analysis in situ with Transmission Electron Microscopy

The development of liquid cells for transmission electron microscopy (TEM) have enabled breakthroughs in the study of nanoscale structural, morphological, or chemical changes during materials growth and applications [1]. The challenges of achieving high resolution imaging and chemical analysis through liquids often arise from the liquid cells being too thick. Making ultra-thin freestanding membranes and controlling the liquid thickness are some of the key attributes to the recent liquid cell developments.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

TEM Approaches for Microstructure-Informed Prediction of Mechanical Properties in Structural Alloys

Predicting the mechanical performance of structural alloys from their evolving microstructure remains a major challenge in materials science, particularly for nuclear structural materials, where irradiation-induced defects span multiple types and length scales and interact through complex mechanisms. The dispersed barrier hardening (DBH) [1] and Friedel–Kroupa–Hirsch (FKH) [2,3] models have been widely used to evaluate the hardening contributions of individual obstacles and to estimate tensile strength from quantified microstructures; however, when multiple size-dependent obstacles coexist and evolve, predicting temperature-dependent tensile strength becomes significantly more complex, and a fully consistent hardening model is still lacking. Transmission electron microscopy (TEM) plays a central role in refining hardening models and enabling predictive assessments of tensile strength evolution by providing quantitative characterization of dislocations, irradiation-induced defects (e.g., dislocation loops and cavities), precipitates, and grain structure (Fig. 1.). These experimentally measured defect densities are incorporated into physically based hardening models with size- and shape- dependent obstacle strengths [4], using root-sum-square superposition for obstacles of comparable strength and linear superposition for dissimilar ones [5]. In addition, recent advances in TEM [6-8], including high-resolution imaging, 4D-STEM strain mapping, EDS/EELS elemental analysis, and flash-polishing-based TEM specimen preparation and extraction-replica methods (Fig. 2), further improve the accuracy of microstructural quantification. By comparison with prior studies as well as our own results, we show that when TEM-derived microstructural information is carefully integrated with physically grounded hardening models, yield strength (or irradiation-induced hardening) measured at room temperature can be predicted with good quantitative agreement across multiple alloy classes. In-situ TEM combined with high-temperature mechanical testing represents an important next step for refining hardening models by directly probing dislocation–obstacle interactions across varying irradiation doses and temperatures [9]. Because the barrier strength factor (α) depends on both temperature and obstacle size, it should not be treated as a constant fitting parameter; rather, it must be explicitly evaluated to achieve physically meaningful predictions of mechanical behaviour at operating temperatures. This presentation therefore discusses why all strengthening contributions (e.g., Peierls stress, solid-solution strengthening, voids, bubbles, dislocation loops, dislocation lines, and grain boundaries) must be considered collectively, why appropriate superposition methods are essential when obstacles possess different barrier strength factors, how hardness measurements can be meaningfully related to tensile properties, and how TEM-derived microstructural information can be systematically incorporated into hardening models. More broadly, it outlines a pathway toward microstructure-informed prediction of mechanical properties and supports the goal of establishing science-based tools for evaluating structural materials in extreme environments [10].

Lin, Yan-Ru [ORNL] (ORCID:0000000339991473)↗
DOE OSTI2024

Evaluating Cryo–TEM Reconstruction Accuracy of Self–Assembled Polymer Nanostructures

Cryogenic transmission electron microscopy (cryo–TEM) combined with single particle analysis (SPA) is an emerging imaging approach for soft materials. However, the accuracy of SPA–reconstructed nanostructures, particularly those formed by synthetic polymers, remains uncertain due to potential packing heterogeneity of the nanostructures. In this study, the combination of molecular dynamics (MD) simulations and image simulations is utilized to validate the accuracy of cryo–TEM 3D reconstructions of self–assembled polypeptoid fibril nanostructures. Using CryoSPARC software, image simulations, 2D classifications, ab initio reconstructions, and homogenous refinements are performed. By comparing the results with atomic models, the recovery of molecular details is assessed, heterogeneous structures are identified, and the influence of extraction location on the reconstructions is evaluated. In conclusion, these findings confirm the fidelity of single particle analysis in accurately resolving complex structural characteristics and heterogeneous structures, exhibiting its potential as a valuable tool for detailed structural analysis of synthetic polymers and soft materials.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Seeking regularity from irregularity: unveiling the synthesis–nanomorphology relationships of heterogeneous nanomaterials using unsupervised machine learning

Nanoscale morphology of functional materials determines their chemical and physical properties. However, despite increasing use of transmission electron microscopy (TEM) to directly image nanomorphology, it remains challenging to quantify the information embedded in TEM data sets, and to use nanomorphology to link synthesis and processing conditions to properties. We develop an automated, descriptor-free analysis workflow for TEM data that utilizes convolutional neural networks and unsupervised learning to quantify and classify nanomorphology, and thereby reveal synthesis–nanomorphology relationships in three different systems. While TEM records nanomorphology readily in two-dimensional (2D) images or three-dimensional (3D) tomograms, we advance the analysis of these images by identifying and applying a universal shape fingerprint function to characterize nanomorphology. After dimensionality reduction through principal component analysis, this function then serves as the input for morphology grouping through unsupervised learning. We demonstrate the wide applicability of our workflow to both 2D and 3D TEM data sets, and to both inorganic and organic nanomaterials, including tetrahedral gold nanoparticles mixed with irregularly shaped impurities, hybrid polymer-patched gold nanoprisms, and polyamide membranes with irregular and heterogeneous 3D crumple structures. In each of these systems, unsupervised nanomorphology grouping identifies both the diversity and the similarity of the nanomaterial across different synthesis conditions, revealing how synthetic parameters guide nanomorphology development. Our work opens possibilities for enhancing synthesis of nanomaterials through artificial intelligence and for understanding and controlling complex nanomorphology, both for 2D systems and in the far less explored case of 3D structures, such as those with embedded voids or hidden interfaces.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Imaging the kinetics of anisotropic dissolution of bimetallic core–shell nanocubes using graphene liquid cells

Chemical design of multicomponent nanocrystals requires atomic-level understanding of reaction kinetics. Here, we apply single-particle imaging coupled with atomistic simulation to study reaction pathways and rates of Pd@Au and Cu@Au core-shell nanocubes undergoing oxidative dissolution. Quantitative analysis of etching kinetics using in situ transmission electron microscopy (TEM) imaging reveals that the dissolution mechanism changes from predominantly edge-selective to layer-by-layer removal of Au atoms as the reaction progresses. Dissolution of the Au shell slows down when both metals are exposed, which we attribute to galvanic corrosion protection. Morphological transformations are determined by intrinsic anisotropy due to coordination-number-dependent atom removal rates and extrinsic anisotropy induced by the graphene window. Our work demonstrates that bimetallic coreshell nanocrystals are excellent probes for the local physicochemical conditions inside TEM liquid cells. Furthermore, single-particle TEM imaging and atomistic simulation of reaction trajectories can inform future design strategies for compositionally and architecturally sophisticated nanocrystals.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2020

Effects of microstructure on the oxidation behavior of A3 matrix‐grade graphite

Abstract The oxidation behavior of matrix‐grade graphite in air‐ or steam‐ingress accident scenarios is of great interest for high‐temperature gas reactors (HTGRs). In this study, the microstructures of two variants of matrix‐grade graphite based on the German A3‐3 and A3‐27 formulations were characterized with scanning electron microscopy (SEM), transmission electron microscopy (TEM), and Raman spectroscopy, and correlated to oxidation behavior observed through thermogravimetric analysis (TGA) and differential scanning calorimetry (DSC). Through TEM imaging and selected area electron diffraction (SAED), a higher volume fraction of partially graphitized carbon was identified in the A3‐3 type graphite than in the A3‐27 type. This structure is believed to have contributed to the accelerated oxidation exhibited by A3‐3 in the chemical reaction‐controlled oxidation regime.

Bratten, Adam↗
DOE OSTI2023

Materials characterization: Can artificial intelligence be used to address reproducibility challenges?

Material characterization techniques are widely used to characterize the physical and chemical properties of materials at the nanoscale and, thus, play central roles in material scientific discoveries. However, the large and complex datasets generated by these techniques often require significant human effort to interpret and extract meaningful physicochemical insights. Artificial intelligence (AI) techniques such as machine learning (ML) have the potential to improve the efficiency and accuracy of surface analysis by automating data analysis and interpretation. In this perspective paper, we review the current role of AI in surface analysis and discuss its future potential to accelerate discoveries in surface science, materials science, and interface science. We highlight several applications where AI has already been used to analyze surface analysis data, including the identification of crystal structures from XRD data, analysis of XPS spectra for surface composition, and the interpretation of TEM and SEM images for particle morphology and size. We also discuss the challenges and opportunities associated with the integration of AI into surface analysis workflows. These include the need for large and diverse datasets for training ML models, the importance of feature selection and representation, and the potential for ML to enable new insights and discoveries by identifying patterns and relationships in complex datasets. Most importantly, AI analyzed data must not just find the best mathematical description of the data, but it must find the most physical and chemically meaningful results. In addition, the need for reproducibility in scientific research has become increasingly important in recent years. The advancement of AI, including both conventional and the increasing popular deep learning, is showing promise in addressing those challenges by enabling the execution and verification of scientific progress. By training models on large experimental datasets and providing automated analysis and data interpretation, AI can help to ensure that scientific results are reproducible and reliable. Although integration of knowledge and AI models must be considered for the transparency and interpretability of models, the incorporation of AI into the data collection and processing workflow will significantly enhance the efficiency and accuracy of various surface analysis techniques and deepen our understanding at an accelerated pace.

Materials Science↗
NASA NTRS1994

Application of an alkylammonium method for characterization of phyllosilicates in CI chondrites

Many meteorites and interplanetary dust particles (IDP's) with primitive compositions contain significant amounts of phyllosilicates, which are generally interpreted as evidence of protoplanetary aqueous alteration at an early period in the solar system. These meteorites are chondrites of the carbonaceous and ordinary varieties. Characterization of phyllosilicates in these materials is important because of the important physico-chemical information they hold, e.g., from well characterized phyllosilicates, thermodynamic stability relations and hence the conditions of formation of phyllosilicates in the parent body of the meteorite can be predicted. Although we are at a rudimentary level of understanding of the minerals resulting from the aqueous alteration in the early solar nebula, we know that the most common phyllosilicates present in chondritic extraterrestrial materials are serpentines, smectites, chlorites, and micas. The characterization of fine grained minerals in meteorites and IDP's rely heavily on electron beam instruments, especially transmission electron microscopy (TEM). Typically, phyllosilicates are identified by a combination of high resolution imaging of basal spacings, electron diffraction analysis, and chemical analysis. Smectites can be difficult to differentiate from micas because the smectites loose their interlayer water and the interlayers collapse to the same basal spacing as mica in the high vacuum of the TEM. In high-resolution TEM (HRTEM) images, smectite basal spacings vary from 1 nm up to 1.5 nm, while micas show 1 or 2 nm basal spacings. Not only is it difficult to differentiate smectites from micas, but there is no way of identifying different classes of smectites in meteorites and IDP's. To differentiate smectites from micas and also to recognize the charge differences among smectites, an alkylammonium method can be employed because the basal spacings of alkylammonium saturated smectites expand as a function of alkylamine chain length and the layer-charge density of the 2:1 expanding phyllosilicate, and the final product is significantly more stable under electron beam examination. Such a method was tested on standard clays and several meteorite samples using four alkylammonium salts.

Golden, D. C.↗
DOE OSTI2021

Use of Transmission Electron Microscopy for Analysis of Aerosol Particles and Strategies for Imaging Fragile Particles

For over 25 years, transmission electron microscopy (TEM) has provided a method for the study of aerosol particles with sizes from below the optical diffraction limit to several microns, resolving the particles as well as smaller features. The wide use of this technique to study aerosol particles has contributed important insights about environmental aerosol particle samples and model atmospheric systems. TEM produces an image that is a 2D projection of aerosol particles that have been impacted onto grids and, through associated techniques and spectroscopies, can contribute additional information such as the determination of elemental composition, crystal structure, and 3D particle structures. Soot, mineral dust, and organic/inorganic particles have all been analyzed using TEM and spectroscopic techniques. TEM, however, has limitations that are important to understand when interpreting data including the ability of the electron beam to damage and thereby change the structure and shape of particles, especially in the case of particles composed of organic compounds and salts. In this paper, we concentrate on the breadth of studies that have used TEM as the primary analysis technique. Another focus is on common issues with TEM and cryogenic-TEM. Insights for new users on best practices for fragile particles, that is, particles that are easily susceptible to damage from the electron beam, with this technique are discussed. Tips for readers on interpreting and evaluating the quality and accuracy of TEM data in the literature are also provided and explained.

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

A versatile machine learning workflow for high-throughput analysis of supported metal catalyst particles

Accurate and efficient characterization of nanoparticles (NPs), particularly regarding particle size distribution, is essential for advancing our understanding of their structure-property relationship and facilitating their design for various applications. In this study, we introduce a novel two-stage artificial intelligence (AI)-driven workflow for NP analysis that leverages prompt engineering techniques from state-of-the-art single-stage object detection and large-scale vision transformer (ViT) architectures. This methodology is applied to transmission electron microscopy (TEM) and scanning TEM (STEM) images of heterogeneous catalysts, enabling high-resolution, high-throughput analysis of particle size distributions for supported metal catalyst NPs. The model's performance in detecting and segmenting NPs is validated across diverse heterogeneous catalyst systems, including various metals (Ru, Cu, PtCo, and Pt), supports (silica (SiO 2 ), γ-alumina (γ-Al 2 O 3 ), and carbon black), and particle diameter size distributions with mean and standard deviations ranging from 1.6 ± 0.2 nm to 9.7 ± 4.6 nm. The proposed machine learning (ML) methodology achieved an average F1 overlap score of 0.91 ± 0.01 and demonstrated the ability to disentangle overlapping NPs anchored on catalytic support materials. The segmentation accuracy is further validated using the Hausdorff distance and robust Hausdorff distance metrics, with the 90th percent of the robust Hausdorff distance showing errors within 0.4 ± 0.1 nm to 1.4 ± 0.6 nm. In conclusion, our AI-assisted NP analysis workflow demonstrates robust generalization across diverse datasets and can be readily applied to similar NP segmentation tasks without requiring costly model retraining.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Size-Resolved Shape Evolution in Inorganic Nanocrystals Captured via High-Throughput Deep Learning-Driven Statistical Characterization

Precise size and shape control in nanocrystal synthesis is essential for utilizing nanocrystals in various industrial applications, such as catalysis, sensing, and energy conversion. However, traditional ensemble measurements often overlook the subtle size and shape distributions of individual nanocrystals, hindering the establishment of robust structure–property relationships. In this study, we uncover intricate shape evolutions and growth mechanisms in Co 3 O 4 nanocrystal synthesis at a subnanometer scale, enabled by deep-learning-assisted statistical characterization. By first controlling synthetic parameters such as cobalt precursor concentration and water amount then using high resolution electron microscopy imaging to identify the geometric features of individual nanocrystals, this study provides insights into the interplay between synthesis conditions and the sizedependent shape evolution in colloidal nanocrystals. Utilizing population-wide imaging data encompassing over 441,067 nanocrystals, we analyze their characteristics and elucidate previously unobserved size-resolved shape evolution. This high-throughput statistical analysis is essential for representing the entire population accurately and enables the study of the size dependency of growth regimes in shaping nanocrystals. Our findings provide experimental quantification of the growth regime transition based on the size of the crystals, specifically (i) for faceting and (ii) from thermodynamic to kinetic, as evidenced by transitions from convex to concave polyhedral crystals. Additionally, we introduce the concept of an “onset radius,” which describes the critical size thresholds at which these transitions occur. This discovery has implications beyond achieving nanocrystals with desired morphology; it enables finely tuned correlation between geometry and material properties, advancing the field of colloidal nanocrystal synthesis and its applications.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2020

Ultra-Microtome for the Preparation of TEM Samples from Battery Cathodes

An ultra-microtome method is developed for the preparation of transmission electron microscopy (TEM) specimens from the cathodes of Li-ion battery. Results indicate that the samples prepared with the ultra-microtome method yield atomic resolution imaging from the core area of particles of hundreds to thousands of nanometers. Analysis of the TEM samples prepared with ultra-microtome indicates that no observable structural damage, such as microcracking, is introduced during the cutting process, and the cathodes cut by ultra-microtome exhibit unchanged micro-morphologies compared with the uncut ones. These results demonstrate that ultra-microtome is a promising method for the preparation of TEM specimens and revealing in-depth microstructures of fragile materials such as battery cathodes.

transmission electron microscopy, Sample Preparati↗
NASA NTRS1990

Defect Clustering and Nano-Phase Structure Characterization of Multi-Component Rare Earth Oxide Doped Zirconia-Yttria Thermal Barrier Coatings

Advanced oxide thermal barrier coatings have been developed by incorporating multi- component rare earth oxide dopants into zirconia-yttria to effectively promote the creation of the thermodynamically stable, immobile oxide defect clusters and/or nano-scale phases within the coating systems. The presence of these nano-sized defect clusters has found to significantly reduce the coating intrinsic thermal conductivity, improve sintering resistance, and maintain long-term high temperature stability. In this paper, the defect clusters and nano-structured phases, which were created by the addition of multi-component rare earth dopants to the plasma- sprayed and electron-beam physical vapor deposited thermal barrier coatings, were characterized by high-resolution transmission electron microscopy (TEM). The defect cluster size, distribution, crystallographic and compositional information were investigated using high-resolution TEM lattice imaging, selected area diffraction (SAD), and energy dispersive spectroscopy (EDS) analysis techniques. The results showed that substantial defect clusters were formed in the advanced multi-component rare earth oxide doped zirconia-yttria systems. The size of the oxide defect clusters and the cluster dopant segregation was typically ranging fiom 5 to 50 nm. These multi-component dopant induced defect clusters are an important factor for the coating long-term high temperature stability and excellent performance.

Zhu, Dongming↗
DOE OSTI2023

Using Geophysical Information to Investigate Subsurface Structure within the High-Hydraulic Conductivity Analysis Zone

Within the 200 East Area of the Central Plateau and southeastward toward the Columbia River, a high-hydraulic conductivity zone (HCZ) has been interpreted to extend through the 200-PO-1 and 200-BP-5 operable units on the Hanford Site. The HCZ is a controlling hydraulic feature that impacts groundwater flow out of the 200 East Area and the fate of eastwardly migrating plumes from the 200 West Area. The lateral extent of the HCZ is highly uncertain, and despite strong evidence for the existence of the HCZ based on water-level data and contaminant plume tracking, there is still a limited understanding of how to define its boundaries. To provide additional information on the nature and extent of the HCZ, three surface geophysical methods – electrical resistivity tomography (ERT), time-domain electromagnetics (TEM), and seismic methods – were used to collect data south of 200 East. In addition, existing data from 200 East, consisting of surface seismic data, a borehole check shot survey in 699-37-47A, and borehole stratigraphic interpretations, were used to aid interpretations of newly collected seismic data south of 200 East. This work presumed that the contrast in subsurface geophysical properties would be a first-order aid identifying a transmissive zone(s) within the HCZ analysis area by imaging contrasts and/or anomalies in geophysical properties. While seismic, ERT, and TEM methods have sensitivity to overlapping physical properties (porosity, moisture content, lithology), the resolution and physics used to acquire each of these datasets are different, and therefore the information can also be different. Figure S.1 shows the locations of the geophysical data considered in this report.

58 GEOSCIENCES↗
NASA NTRS1991

Clay minerals in primitive meteorites and interplanetary dust 2. Smectites and micas

The classification is briefly summarized of stony meteorites and cosmic dust, and the mineralogy and chemistry is described of serpentine group minerals. The occurrence of smectites and micas in extraterrestrial materials is examined. The characterization of fine grained minerals in meteorites and IDPs relies heavily on electron beam instruments, especially the transmission electron microscope (TEM). Typically, phyllosilicates are identified by a combination of high resolution imaging of basal spacings, electron diffraction, and chemical analysis. Smectites can be difficult to differentiate from micas because the smectites lose their interlayer water and the interlayer partly collapse in the high vacuum of the TEM.

Keller, L. P.↗
DOE OSTI2022

In Situ Observations of Barium Sulfate Nucleation in Nanopores

In this work, the nucleation and growth of barium sulfate in nanoporous silica was investigated using in situ small-angle X-ray scattering and X-ray pair distribution function analysis, together with ex situ transmission and scanning transmission electron microscopy (TEM and STEM) imaging. We found that crystalline barite formation in micropores is likely preceded by a nonbulk barite phase in the nanopores, indicating a possible nonclassical nucleation pathway for barium sulfate under confinement. The nucleation of barium sulfate inside the nanopores stopped at ~12% of the pores filled and was seemingly limited by the formation of crystals near the exterior of the silica particles, which likely blocked subsequent solute transport into the interior of the nanopores. The growth rate of barium sulfate was fit using the Johnson–Mehl–Avrami–Kolmogorov equation and constrained using a growth rate of barite of ~1.0 × 10 –7 mol/m 2 /s, obtained from previous studies, but is consistent with TEM and STEM observations made here. The inferred nucleation rate of barium sulfate inside nanopores is estimated to be on the order of 1.0 × 10 9 nuclei/m 2 /s, which is 2 orders of magnitude higher than previous measurements on a planar silica substrate (~1.0 × 10 7 nuclei/m 2 /s). This implies that the ability of silica nanopores to promote barium sulfate nucleation is sufficiently high as to create a potentially self-limiting condition, where the nucleation reaction is shut down prematurely because rapid growth blocks reactant transport.

36 MATERIALS SCIENCE↗