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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 91 records · Page 5
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

High-Energy X-ray Diffraction Microscopy for Nuclear Forensics (FY23 Project Report)

Morphological information on nuclear material has been identified using visible light and scanning electron microscopy. These identify qualitative differences in particle morphology. Three-dimensional imaging of materials through alternating scanning electron microscopy imaging and focused ion beam milling has also been used. Unfortunately, these techniques are time- and labor-intensive, with significant sample preparation required and lengthy analysis times. Further, the resulting 3D images are qualitative, require manual identification, and do not capture statistically-representative populations. High energy X-ray 3D imaging using a direct-beam or diffracted-beam (High-Energy Diffraction Microscopy) have been developed at the Advanced Photon Source and can produce quantitative information on grains (phase, location, etc.) and pores (size distribution, sphericity) in a material. These techniques require only minutes to characterize a sample volume and are non-destructive, thus suitable for a wide range of existing samples and for confirmatory analyses to be carried out using conventional microscopy techniques.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Inactivation of Fluorescent Lipid Bilayers by Irradiation With 300 keV Electrons Using Liquid Cell Transmission Electron Microscopy

Liquid cell transmission electron microscopy allows for imaging of samples in a fully hydrated state at high resolution and has the potential for visualizing static or dynamic biological structures. However, the ionizing nature of the electron beam makes it difficult to discern real physiological dynamics from radiation induced artifacts within liquid cell samples. Electron flux thresholds for achieving high resolution structures from biological samples frozen in ice have been described extensively by the cryo-electron microscopy field, while electron flux thresholds which do not result in a functional change for biological samples within the hydrated environment of a transmission electron microscope liquid cell is less clear. Establishing these functional thresholds for biologically relevant samples is important for accurate interpretation of results from liquid cell experiments. Here we demonstrate the electron damage threshold of fluorescently tagged lipid bilayers by quantifying the change in fluorescence before and after electron exposure. We observe the reduction of fluorescent signal in bilayers by 25% after only 0.0005 e − /Å 2 and a reduction of over 90% after 0.01 e − /Å 2 . These results indicate that the loss of function occurs at irradiation thresholds far below a typical single high resolution (scanning) transmission electron microscopy image and orders of magnitude below fluxes used for preserving structural features with cryo-electron microscopy.

Moser, Trevor↗
NASA NTRS2003

The Fluids Integrated Rack and Light Microscopy Module Integrated Capabilities

The Fluids Integrated Rack (FIR), a facility class payload, and the Light Microscopy Module (LMM), a subrack payload, are scheduled to be launched in 2005. The LMM integrated into the FIR will provide a unique platform for conducting fluids and biological experiments on ISS. The FIR is a modular, multi-user scientific research facility that will fly in the U.S. laboratory module, Destiny, of the International Space Station (ISS). The first payload in the FIR will be the Light Microscopy Module (LMM). The LMM is planned as a remotely controllable, automated, on-orbit microscope subrack facility, allowing flexible scheduling and control of fluids and biology experiments within the FIR. Key diagnostic capabilities for meeting science requirements include video microscopy to observe microscopic phenomena and dynamic interactions, interferometry to make thin film measurements with nanometer resolution, laser tweezers for particle manipulation, confocal microscopy to provide enhanced three-dimensional visualization of structures, and spectrophotometry to measure photonic properties of materials. The LMM also provides experiment sample containment for frangibles and fluids. This paper will provide a description of the current FIR and LMM designs, planned capabilities and key features. In addition a brief description of the initial five experiments planned for LMM/FIR will be provided.

Motil, Susan M.↗
DOE OSTI2020

Fast Scanning Probe Microscopy via Machine Learning: Non-Rectangular Scans with Compressed Sensing and Gaussian Process Optimization

Fast scanning probe microscopy enabled via machine learning allows for a broad range of nanoscale, temporally resolved physics to be uncovered. However, such examples for functional imaging are few in number. Here, using piezoresponse force microscopy (PFM) as a model application, a factor of 5.8 reduction in data collection using a combination of sparse spiral scanning with compressive sensing and Gaussian process regression reconstruction is demonstrated. It is found that even extremely sparse spiral scans offer strong reconstructions with less than 6% error for Gaussian process regression reconstructions. Further, the error associated with each reconstructive technique per reconstruction iteration is analyzed, finding the error is similar past ≈15 iterations, while at initial iterations Gaussian process regression outperforms compressive sensing. Finally, this study highlights the capabilities of reconstruction techniques when applied to sparse data, particularly sparse spiral PFM scans, with broad applications in scanning probe and electron microscopies.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Bayesian Active Learning for Scanning Probe Microscopy: From Gaussian Processes to Hypothesis Learning

Recent progress in machine learning methods and the emerging availability of programmable interfaces for scanning probe microscopes (SPMs) have propelled automated and autonomous microscopies to the forefront of attention of the scientific community. However, enabling automated microscopy requires the development of task-specific machine learning methods, understanding the interplay between physics discovery and machine learning, and fully defined discovery workflows. This, in turn, requires balancing the physical intuition and prior knowledge of the domain scientist with rewards that define experimental goals and machine learning algorithms that can translate these to specific experimental protocols. Here, we discuss the basic principles of Bayesian active learning and illustrate its applications for SPM. We progress from the Gaussian process as a simple data-driven method and Bayesian inference for physical models as an extension of physics-based functional fits to more complex deep kernel learning methods, structured Gaussian processes, and hypothesis learning. These frameworks allow for the use of prior data, the discovery of specific functionalities as encoded in spectral data, and exploration of physical laws manifesting during the experiment. Here, the discussed framework can be universally applied to all techniques combining imaging and spectroscopy, SPM methods, nanoindentation, electron microscopy and spectroscopy, and chemical imaging methods and can be particularly impactful for destructive or irreversible measurements.

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

Mind the gap: Bridging the divide between AI aspirations and the reality of autonomous microscopy

What does materials science look like in the “Age of Artificial Intelligence?” Each material’s domain—synthesis, characterization, and modeling—has a different answer to this question, motivated by unique challenges and constraints. This work focuses on the tremendous potential of autonomous characterization within electron microscopy. We present our recent advancements in developing domain-aware, multimodal models for microscopy analysis capable of describing complex atomic systems. We then address the critical gap between the theoretical promise of autonomous microscopy and its current practical limitations, showcasing recent successes while highlighting the necessary developments to achieve robust, real-world autonomy.

2D materials↗
NASA NTRS1998

Atomic force microscopy imaging of fragments from the Martian meteorite ALH84001

A combination of scanning electron microscopy (SEM) and environmental scanning electron microscopy (ESEM) techniques, as well as atomic force microscopy (AFM) methods has been used to study fragments of the Martian meteorite ALH84001. Images of the same areas on the meteorite were obtained prior to and following gold/palladium coating by mapping the surface of the fragment using ESEM coupled with energy-dispersive X-ray analysis. Viewing of the fragments demonstrated the presence of structures, previously described as nanofossils by McKay et al. (Search for past life on Mars--possible relic biogenic activity in martian meteorite ALH84001. Science, 1996, pp. 924-930) of NASA who used SEM imaging of gold-coated meteorite samples. Careful imaging of the fragments revealed that the observed structures were not an artefact introduced by the coating procedure.

Carbonates/analysis↗
DOE OSTI2025

Multiscale Characterization of Additive Manufacturing Components with Computed Tomography, 3D X-ray Microscopy, and Deep Learning

Additive manufacturing (AM) facilitates the creation of complex-geometry parts, driving advancements in lightweight aerospace components, high-efficiency engine cooling channels, and customized medical implants. However, ensuring the quality and reliability of AM parts remains challenging due to internal defects, surface irregularities, porosity, and residual trapped powder, which are often inaccessible to traditional inspection methods. Recent developments in X-ray computed tomography (XCT) and 3D X-ray microscopy (XRM), particularly systems equipped with resolution-at-a-distance (RaaD™) capabilities, enable high-resolution, non-destructive evaluation of AM components across multiple scales, from sub-micrometer to macroscopic levels. This paper explores modern XCT and XRM techniques for multiscale characterization of AM parts, focusing on their ability to detect and analyze defects such as porosity, cracks, inclusions, and surface roughness, while offering insights into defect formation mechanisms, material properties, and process-induced variations. The integration of deep learning (DL) frameworks, including Simurgh, DeepRecon, and DeepScout, enhances XCT/XRM workflows by reducing scan times, improving resolution recovery, and enabling accurate defect detection even with limited projection data. These DL-based methods overcome limitations of traditional reconstruction techniques, enabling faster, more reliable characterization of dense materials like Inconel 718 and novel alloys such as AlCe. Applications include process parameter optimization, high-throughput quality control, and multistage AM process evaluation, with DL-enhanced workflows accelerating analysis times from weeks to days. Correlative imaging approaches further validate XCT and XRM data against scanning electron microscopy (SEM) images of physically sectioned samples, confirming the accuracy of DL-based reconstructions and enabling comprehensive defect analysis. While challenges remain in generalizing DL models to diverse materials and imaging conditions, improvements in resolution, noise reduction, and defect detection highlight the transformative potential of these methods. This multiscale and correlative approach enables precise identification and correlation of microstructural features with the overall performance of AM components. By integrating advanced XCT, XRM, and DL techniques, this paper demonstrates a significant leap forward in AM characterization, offering valuable insights into the relationships between processing parameters, microstructure, and part performance, and driving innovations that enhance the quality and reliability of AM products for demanding industrial applications.

Additive manufacturing↗
DOE OSTI2020

Imaging Carrier Dynamics and Transport in Hybrid Perovskites with Transient Absorption Microscopy

In this review, the recent progress in using transient absorption microscopy to image charge transport and dynamics in semiconducting hybrid organic–inorganic perovskites is discussed. The basic principles, instrumentation, and resolution of transient absorption microscopy are outlined. With temporal resolution as high as 10 fs, sub-diffraction-limit spatial resolution, and excited-state structural resolution, these experiments have provided crucial details on charge transport mechanisms that have been previously obscured in conventional ultrafast spectroscopy measurements. Morphology-dependent mapping unveils spatial heterogeneity in carrier recombination and cooling dynamics. By spatially separating the pump and probe beams, carrier transport across grain boundaries has been directly visualized. Further, femtosecond temporal resolution allows for the examination of nonequilibrium transport directly, revealing extraordinarily long-range hot carrier migration. The application of transient absorption microscopy is not limited to hybrid perovskites but can also be useful for other polycrystalline materials in which morphology plays an important role in carrier transport.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Electron Microscopy Approaches to Unraveling the Structure of Amorphous Materials

Determining atomic structures in crystalline materials—where atoms are arranged in rigid, periodic lattices—has been highly successful using probes such as electrons, X-rays, and neutrons. In contrast, amorphous materials, despite their ubiquity and technological importance, remain far more challenging to characterize with comparable accuracy and precision. This review highlights existing, emerging, and potential (scanning) transmission electron microscopy ((S)TEM) techniques for probing short- and medium-range order in amorphous materials. Approaches ranging from high-resolution (S)TEM imaging and selected electron diffraction pattern to four-dimensional STEM (4D-STEM) based pair distribution function, fluctuation electron microscopy, tomography, ptychography, and spectroscopic methods are discussed, emphasizing their ability to provide complementary insights across multiple length scales—from sub-angstrom local environments to nanometer-scale correlations. Here, we further explore the promise of multimodal and correlative strategies, as well as the growing role of machine learning and physics-informed AI in enabling real-time, quantitative interpretation of complex structural signatures. Together, these advances point toward a future where electron microscopy not only reveals the hidden order in amorphous systems but also establishes robust structure–property relationships, paving the way for materials innovation in disordered matter.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
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

Multi defect detection and analysis of electron microscopy images with deep learning

Electron microscopy is widely used to explore defects in crystal structures, but human detecting of defects is often time-consuming, error-prone, and unreliable, and is not scalable to large numbers of images or real-time analysis. In this work, we discuss the application of machine learning approaches to find the location and geometry of different defect clusters in irradiated steels. We show that a deep learning based Faster R-CNN analysis system has a performance comparable to human analysis with relatively small training data sets. Furthermore, this study proves the promising ability to apply deep learning to assist the development of automated microscopy data analysis even when multiple features are present and paves the way for fast, scalable, and reliable analysis systems for massive amounts of modern electron microscopy data.

36 MATERIALS SCIENCE↗
DOE OSTI2026

NanoPSD: A software for automatic detection of Nano-Particle Shape Distribution in electron microscopy images

Accurate quantification of the size and morphology of nanoparticles from electron microscopy (EM) images is essential to understand growth mechanisms, surface reactivity, and functional behavior in nanoscale materials. Manual analysis remains slow, subjective, and difficult to reproduce in large datasets. We introduce NanoPSD (Nano-Particle Shape Distribution), an open-source and fully automated framework for quantitative particle detection and morphology analysis from EM images. NanoPSD integrates adaptive contrast enhancement, polarity-agnostic scale-bar detection, Optical Character Recognition (OCR)-based calibration, and classical segmentation via Otsu thresholding with morphological refinement. Particle contours are used to extract geometric descriptors, including equivalent circular diameter, aspect ratio, circularity, and solidity, enabling automated classification into spherical, rod-like, and aggregate morphologies. The framework supports both single-image and batch processing, generating publication-quality visualizations, LaTeX-ready tables, and structured comma-separated values (CSV) datasets. As a demonstration, we applied NanoPSD to plasma-synthesized nanoparticle samples diagnosed via transmission electron microscopy (TEM). The code produced statistically robust size and morphology distributions spanning a few to tens of nanometers with minimal user supervision. The pipeline demonstrates high reproducibility and scalability, processing large image collections with consistent calibration and output formatting. Its modular design enables seamless integration of future deep-learning-based segmentation models, providing a pathway toward intelligent, data-driven electron microscopy analysis.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Electron microscopy data on irradiation effects in glassy carbon, nuclear graphite, pyrolytic carbon, and carbon fibers

Glassy carbon, a monoatomic allotrope of carbon, is a candidate material for components in fission nuclear power systems due to its radiation tolerance. This article presents comprehensive electron microscopy data revealing the effects of neutron and electron irradiation on glassy carbon. For comparison, additional data are provided for pyrolytic graphite and carbon fibers, materials that exhibit similar structural behavior under irradiation. In situ electron irradiation experiments further illustrate the real-time microstructural evolution of glassy carbon during exposure. The dataset is organized into five parts: (1) transmission electron microscopy (TEM) micrographs of as-received and neutron-irradiated glassy carbon; (2) TEM micrographs of neutron-irradiated graphite; (3) TEM micrographs of unirradiated and irradiated carbon–carbon composites; (4) TEM micrographs of pyrolytic carbon specimens in both conditions; (5) scanning transmission electron microscopy (STEM) micrographs of as-received and neutron-irradiated glassy carbon and (6) in situ electron irradiation data of a glassy carbon particle. These datasets provide valuable insights into radiation-induced structural changes in carbon-based materials relevant to nuclear applications.

36 MATERIALS SCIENCE↗
DOE OSTI2022

A comparison of carbon impurities in pre- and post-melt uranium Part 2: Scanning/Transmission electron microscopy analysis

We report the speciation and morphology of U carbide inclusions in pre- and post-melt U metal have been explored using high-resolution analytical electron microscopy (AEM). This report presents Part 2 of our study, in which aberration (C s ) corrected transmission and scanning transmission electron microscopy (S)TEM was used to elementally and crystallographically characterize C-containing impurities and defect features in U samples for comparison with results obtained by scanning electron microscopy (SEM) in Part 1, previously published by this journal. Elemental mapping and unit cell matching of inclusions by (S)TEM are consistent with phases observed by SEM, and new dislocations can be observed associating with some inclusion morphologies and not others.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Advanced magnetic X-ray spectro-microscopies to characterize mesoscopic magnetic materials

Here, this article provides a brief overview of advanced magnetic X-ray spectro-microscopies that are widely used in characterizing mesoscopic magnetic materials. Common to those techniques are various X-ray magnetic dichroism effects that are used as magnetic contrast mechanism. A particular interest is to use those techniques to image the underlying microscopic spin structures in magnetic materials with high spatial resolution and to ultimately resolve their full 3D mesoscale characteristics with elemental sensitivity, as well as their ultrafast dynamics upon excitations with field and current pulses. Recent research examples using various magnetic X-ray spectro-microscopies are presented to showcase their specific features with a focus on imaging novel topological spin textures, such as vortices, skyrmions and Hopfions, which are considered as potential building blocks towards low power, high-speed and high-density magnetic devices that could transform information technologies and potentially be used in biomedical applications. Future developments with magnetic X-ray spectro-microscopies harness the full coherence of next generation X-ray sources and could open the path towards single shot imaging with spatial and temporal resolutions down to fundamental magnetic length and time scales.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Identification and demonstration of roGFP2 as an environmental sensor for cryogenic correlative light and electron microscopy

Cryogenic correlative light and electron microscopy (cryo-CLEM) seeks to leverage orthogonal information present in two powerful imaging modalities. While recent advances in cryogenic electron microscopy (cryo-EM) allow for the visualization and identification of structures within cells at the nanometer scale, information regarding the cellular environment, such as pH, membrane potential, ionic strength, etc., which influences the observed structures remains absent. Fluorescence microscopy can potentially be used to reveal this information when specific labels, known as fluorescent biosensors, are used, but there has been minimal use of such biosensors in cryo-CLEM to date. Here we demonstrate the applicability of one such biosensor, the fluorescent protein roGFP2, for cryo-CLEM experiments. At room temperature, the ratio of roGFP2 emission brightness when excited at 425 nm or 488 nm is known to report on the local redox potential. When samples containing roGFP2 are rapidly cooled to 77 K in a manner compatible with cryo-EM, the ratio of excitation peaks remains a faithful indicator of the redox potential at the time of freezing. Using purified protein in different oxidizing/reducing environments, we generate a calibration curve which can be used to analyze in situ measurements. As a proof-of-principle demonstration, we investigate the oxidation/reduction state within vitrified Caulobacter crescentus cells. The polar organizing protein Z (PopZ) localizes to the polar regions of C. crescentus where it is known to form a distinct microdomain. Finally, by expressing an inducible roGFP2-PopZ fusion we visualize individual microdomains in the context of their redox environment.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

A Three-Dimensional Reconstruction Algorithm for Scanning Transmission Electron Microscopy Data from a Single Sample Orientation

Abstract Increasing interest in three-dimensional nanostructures adds impetus to electron microscopy techniques capable of imaging at or below the nanoscale in three dimensions. We present a reconstruction algorithm that takes as input a focal series of four-dimensional scanning transmission electron microscopy (4D-STEM) data. We apply the approach to a lead iridate, PbIrO, and yttrium-stabilized zirconia, YZrO, heterostructure from data acquired with the specimen in a single plan-view orientation, with the epitaxial layers stacked along the beam direction. We demonstrate that Pb–Ir atomic columns are visible in the uppermost layers of the reconstructed volume. We compare this approach to the alternative techniques of depth sectioning using differential phase contrast scanning transmission electron microscopy (DPC-STEM) and multislice ptychographic reconstruction.

47 OTHER INSTRUMENTATION↗