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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 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Advanced techniques in automated high-resolution scanning transmission electron microscopy

Scanning transmission electron microscopy is a common tool used to study the atomic structure of materials. It is an inherently multimodal tool allowing for the simultaneous acquisition of multiple information channels. Despite its versatility, however, experimental workflows currently rely heavily on experienced human operators and can only acquire data from small regions of a sample at a time. Here, we demonstrate a flexible pipeline-based system for high-throughput acquisition of atomic-resolution structural data using an all-piezo sample stage applied to large-scale imaging of nanoparticles and multimodal data acquisition. As a result, the system is available as part of the user program of the Molecular Foundry at Lawrence Berkeley National Laboratory.

4D-STEM↗
DOE OSTI2025

A machine-learning approach to measure 3D sample properties from 2D Transmission Electron Microscopy images

Transmission Electron Microscopy (TEM) is a powerful tool for the characterization of materials at the nanoscale; however, its inherent two-dimensional (2D) nature poses significant challenges to accurately measure three-dimensional (3D) properties. We introduce a supervised machine-learning model that predicts 3D structural information, such as sample thickness and curvature, from a series of conventional 2D TEM images. The model, a U-Net convolutional neural network, is trained on a large synthetic dataset generated from dynamical diffraction simulations that model TEM’s complex, nonlinear image formation, accounting for sample thickness and curvature. This physically realistic framework enables exploration of a broad parameter space impractical to sample experimentally. We demonstrate that the trained model has accurate predictions for experimental single-crystal silicon samples, achieving performance comparable to established measurement techniques. This work highlights the critical role of robust, simulation-based training in overcoming the limitations of real-world imaging artifacts and inconsistent sample geometries. By integrating machine learning with numerical simulations, we offer an efficient and scalable framework for quantitative TEM analysis, paving the way for more sophisticated 3D characterization of complex materials.

Dynamical diffraction↗
DOE OSTI2022

Multi-scale imaging of high-pressure hydrogen induced damage in EPDM rubber using X-ray microcomputed tomography, helium-ion microscopy and transmission electron microscopy

Ethylene propylene diene (EPDM) rubber has gained increasing interest for use in hydrogen infrastructure due to its excellent sealing performance and low temperature properties. However, severe structural damage has been observed in EPDM O-rings after exposure to high-pressure hydrogen. The origination and propagation mechanisms of this damage are poorly understood. Here, to address this knowledge gap, multi-scale imaging leveraging X-ray micro-computed tomography (micro-CT), helium ion microscopy (HeIM), and transmission electron microscopy (TEM) were used in this work to study a series of sulfur-cured ethylene propylene diene (EPDM) rubber materials with varying additives that were exposed to different hydrogen environments. Micro-CT captured the substantial structural damage due to hydrogen exposure; it revealed an association between zinc oxide (ZnO) particles and damage initiation. Further studies by TEM and scanning TEM with energy dispersive X-ray spectroscopy (EDS) were focused on these particles at the micro-to nano-scale range. TEM indicated that hydrogen causes void formation at the interface between ZnO and the rubber matrix. HeIM enabled imaging of surface morphology of the material at high resolution pre- and post-hydrogen exposure while providing information on chemical composition and that cannot be captured by either micro-CT or TEM.

08 HYDROGEN↗
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↗
DOE OSTI2022

Towards automating structural discovery in scanning transmission electron microscopy *

Abstract Scanning transmission electron microscopy is now the primary tool for exploring functional materials on the atomic level. Often, features of interest are highly localized in specific regions in the material, such as ferroelectric domain walls, extended defects, or second phase inclusions. Selecting regions to image for structural and chemical discovery via atomically resolved imaging has traditionally proceeded via human operators making semi-informed judgements on sampling locations and parameters. Recent efforts at automation for structural and physical discovery have pointed towards the use of ‘active learning’ methods that utilize Bayesian optimization with surrogate models to quickly find relevant regions of interest. Yet despite the potential importance of this direction, there is a general lack of certainty in selecting relevant control algorithms and how to balance a priori knowledge of the material system with knowledge derived during experimentation. Here we address this gap by developing the automated experiment workflows with several combinations to both illustrate the effects of these choices and demonstrate the tradeoffs associated with each in terms of accuracy, robustness, and susceptibility to hyperparameters for structural discovery. We discuss possible methods to build descriptors using the raw image data and deep learning based semantic segmentation, as well as the implementation of variational autoencoder based representation. Furthermore, each workflow is applied to a range of feature sizes including NiO pillars within a La:SrMnO 3 matrix, ferroelectric domains in BiFeO 3 , and topological defects in graphene. The code developed in this manuscript is open sourced and will be released at github.com/nccreang/AE_Workflows .

47 OTHER INSTRUMENTATION↗
DOE OSTI2023

Grain Boundary Plane Measurement Using Transmission Electron Microscopy Automated Crystallographic Orientation Mapping for Atom Probe Tomography Specimens

Abstract Grain boundaries are critical in determining the properties of materials, including mechanical stability, conductivity, and corrosion resistance. The specific properties of materials depend not only on the misorientation of the crystals, the three most commonly characterized parameters, but also on the angle of the grain boundary plane between the two crystals, the final two parameters in the five-parameter macroscopic description of the grain boundary. The method presented here allows for the direct measurement of all five parameters of the grain boundary in a transmission electron microscopy specimen of various morphologies. This is especially applicable to atom probe specimens, where only a single-tilt axis is generally available, allowing the crystallographic description to be matched to the detailed chemical data available in the atom probe tomography. This method provides a platform for efficient grain boundary analysis in unique samples, saving operator time and allowing for ease of acquisition and interpretation in comparison with traditional electron diffraction methods.

Materials Science↗
DOE OSTI2022

Transmission electron microscopy characterization of the fuel-cladding chemical interactions in HT9 cladded U-10Zr fuel

Fuel cladding chemical interaction (FCCI) is a key phenomenon needs to be better understood to establish the design basis for U-10Zr metallic fuel performance. Characterizing the microstructure and chemical composition of FCCI at micron and sub-micron scale is critically important toward a more mechanistic understanding of FCCI phenomenon and its potential effects on cladding integrity and metallic fuel performance. This paper, by using transmission electron microscopy, investigated the FCCI region in HT9 cladded U-10Zr fuel irradiated to 5.7% FIMA burnup at a peak inner cladding temperature of 615 °C in Fast Flux Test Facility (FFTF). Four distinct layers are identified in the FCCI region. The migration of Fe into the fuel side leads to the formation of several U-Zr-Fe ternary phases, including ?-Fe0.5Zr0.32U0.18, e-Fe0.3Zr0.4U0.3, and ?-Fe0.06Zr0.23U0.71, mingled with UFe2, U6Fe, and U phase at various Fe penetration depth up to ~ 150 µm. On the cladding side, grain coarsening and significant lanthanides infiltration along grain boundaries are observed. Laves phase, (Fe,Cr)2(Mo,W), which typically does not exist in fresh HT9, is identified in a wide radial range in the cladding. The typical HT9 martensitic lath structure and pre-existing M23C6 precipitates disappear, partially or completely, depending on the radial distance from the fuel-cladding interface. Those microstructural and compositional changes could cause mechanical degradation in the HT9 cladding. The present characterization results will improve the understanding of FCCI phenomenon and facilitate the development of microstructure-informed FCCI modeling for metallic fuel.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2026

The ABCs of phase retrieval: Connecting the acronyms of scanning transmission electron microscopy

High-resolution scanning transmission electron microscopy (S/TEM) is an indispensable tool for characterizing the structure and properties of materials down to the atomic scale. Conventional S/TEM imaging, however, is limited by the phase problem, whereby the phase of the electron exit wave is lost upon detection. Recent advances in diffractive imaging and 4D-STEM have enabled a range of phase-retrieval techniques that computationally reconstruct the missing information encoded in the phase of the transmission function. These approaches offer improved dose efficiency and enhanced sensitivity to weakly scattering signals, extending quantitative imaging to beam-sensitive materials composed of light elements. In this work, we introduce the phase problem in electron microscopy and survey the diverse landscape of phase-retrieval techniques used in the field. Despite their many acronyms and algorithmic variations, these techniques share a common physical and mathematical foundation. We present a unified framework that connects these seemingly distinct methods, from parallax imaging and tilt-corrected bright-field (tcBF-STEM), to aberration-corrected bright-field (acBF-STEM), optimum bright-field (OBF-STEM) and single-sideband (SSB) ptychography, as well as first-moment integrated center of mass techniques (iCOM) and iterative ptychographic algorithms. Based on these insights, we discuss the opportunities and practical limitations of applying these methods across different materials systems, detector designs, and microscope configurations.Graphical abstractRepresentative electron microscopy configurations used for phase retrieval and diffractive imaging in S/TEM: (a) Zernike phase-contrast transmission electron microscopy (TEM), (b) small-convergence-angle four-dimensional scanning transmission electron microscopy (4D-STEM) for nanobeam-based phase reconstruction methods, and (c) large-convergence-angle 4D-STEM for ptychographic and related diffractive imaging techniques reviewed in this work.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Deep learning-based noise filtering toward millisecond order imaging by using scanning transmission electron microscopy

Application of scanning transmission electron microscopy (STEM) to in situ observation will be essential in the current and emerging data-driven materials science by taking STEM’s high affinity with various analytical options into account. As is well known, STEM’s image acquisition time needs to be further shortened to capture a targeted phenomenon in real-time as STEM’s current temporal resolution is far below the conventional TEM’s. However, rapid image acquisition in the millisecond per frame or faster generally causes image distortion, poor electron signals, and unidirectional blurring, which are obstacles for realizing video-rate STEM observation. Here we show an image correction framework integrating deep learning (DL)-based denoising and image distortion correction schemes optimized for STEM rapid image acquisition. By comparing a series of distortion corrected rapid scan images with corresponding regular scan speed images, the trained DL network is shown to remove not only the statistical noise but also the unidirectional blurring. This result demonstrates that rapid as well as high-quality image acquisition by STEM without hardware modification can be established by the DL. The DL-based noise filter could be applied to in-situ observation, such as dislocation activities under external stimuli, with high spatio-temporal resolution.

47 OTHER INSTRUMENTATION↗
DOE OSTI2026

Machine learning pipeline for denoising low signal-to-noise ratio and out-of-distribution transmission electron microscopy datasets

High-resolution transmission electron microscopy (HRTEM) is crucial for observing material’s structural and morphological evolution at Angstrom scales, but the electron beam can alter these processes. Devices such as CMOS-based direct-electron detectors operating in electron-counting mode can be utilized to substantially reduce the electron dosage. However, the resulting images often lead to a low signal-to-noise ratio, which requires frame integration that sacrifices temporal resolution. Several machine learning (ML) models have been recently developed to successfully denoise HRTEM images. Yet, these models are often computationally expensive, and their inference speeds on GPUs are outpaced by the imaging speed of advanced detectors, precluding in situ analysis. Furthermore, the performance of these denoising models on datasets with imaging conditions that deviate from the training datasets has not been evaluated. To mitigate these gaps, we propose a new self-supervised ML denoising pipeline specifically designed for time-series HRTEM images. This pipeline integrates a blind-spot convolution neural network with pre-processing and post-processing steps, including drift correction and low-pass filtering. Results demonstrate that our model outperforms various other ML and non-ML denoising methods in noise reduction and contrast enhancement, leading to improved visual clarity of atomic features. Additionally, the model is drastically faster than U-Net-based ML models and demonstrates excellent out-of-distribution generalization. The model’s computational inference speed is in the order of milliseconds per image, rendering it suitable for application in in-situ HRTEM experiments.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Quantitative phase retrieval and characterization of magnetic nanostructures via Lorentz (scanning) transmission electron microscopy

Magnetic materials phase reconstruction using Lorentz transmission electron microscopy (LTEM) measurements have traditionally been achieved using longstanding methods such as off-axis holography (OAH) fast-Fourier transform technique and the transport-of-intensity equation (TIE). The increase in access to processing power alongside the development of advanced algorithms have allowed for phase retrieval of nanoscale magnetic materials with greater efficacy and resolution. Specifically, reverse-mode automatic differentiation (RMAD) and the extended electron ptychography iterative engine (ePIE) are two recent developments of phase retrieval that can be applied to analyzing micro-to-nano- scale magnetic materials. This work evaluates phase retrieval using TIE, RMAD, and ePIE in simulations of Permalloy (Ni 80 Fe 20 ) nanoscale islands, or nanomagnets. Extending beyond simulations, we demonstrate total phase retrieval and image reconstructions of a NiFe nanowire using OAH and RMAD in LTEM and ePIE in Lorentz-mode-4D scanning transmission electron microscopy experiments and determine the saturation magnetization through corroborations with micromagnetic modeling. Finally, we demonstrate the efficacy of these methods in retrieving the total phase and highlight its use in characterizing and analyzing the proximity effect of the magnetic nanostructures.

Lorentz transmission electron microscopy↗
DOE OSTI2025

End-to-End Automated Segmentation Framework for Four-Dimensional Scanning Transmission Electron Microscopy Data

Four-dimensional scanning transmission electron microscopy (4D-STEM) is powerful for rapidly characterizing arrays of nanoparticles produced via high-throughput synthesis. However, such 4D-STEM datasets typically contain thousands of nanoparticles, each characterized by thousands of diffraction patterns spatially distributed across the nanoparticle, necessitating efficient and comprehensive analysis. We propose an end-to-end segmentation framework to automatically segment each nanoparticle into regions with distinct composition/orientation of crystal grains, using only the 4D-STEM data. Bragg disk information is extracted in a physics-informed manner from the diffraction patterns at each spatial location and combined with the real space coordinates to form feature vectors. These feature vectors are then used as inputs to a Gaussian mixture model (GMM) to segment the nanoparticle into distinct regions. We also develop two visualization tools based on the GMM outputs to infer the interface transition and the degree of superposition. Our framework comprehensively integrates machine learning tools and physics knowledge, and provides a basis for substantially compressing enormous 4D-STEM datasets, e.g., by replacing the full 4D-STEM dataset for each nanoparticle with only a single set of Bragg disk features for each distinct crystal grain identified in the nanoparticle. In this article, we demonstrate the power of our framework by presenting results for real, complex datasets.

47 OTHER INSTRUMENTATION↗
DOE OSTI2023

Design of Electrostatic Aberration Correctors for Scanning Transmission Electron Microscopy

Abstract In a scanning transmission electron microscope (STEM), producing a high-resolution image generally requires an electron beam focused to the smallest point possible. However, the magnetic lenses used to focus the beam are unavoidably imperfect, introducing aberrations that limit resolution. Modern STEMs overcome this by using hardware aberration correctors comprised of many multipole elements, but these devices are complex, expensive, and can be difficult to tune. We demonstrate a design for an electrostatic phase plate that can act as an aberration corrector. The corrector is comprised of annular segments, each of which is an independent two-terminal device that can apply a constant or ramped phase shift to a portion of the electron beam. We show the improvement in image resolution using an electrostatic corrector. Engineering criteria impose that much of the beam within the probe-forming aperture be blocked by support bars, leading to large probe tails for the corrected probe that sample the specimen beyond the central lobe. We also show how this device can be used to create other STEM beam profiles such as vortex beams and probes with a high degree of phase diversity, which improve information transfer in ptychographic reconstructions.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2024

Facile Tensile Testing Platform for In Situ Transmission Electron Microscopy of Nanomaterials

In situ tensile testing using transmission electron microscopy (TEM) is a powerful technique to probe structure-property relationships of materials at the atomic scale. In this work, a facile tensile testing platform for in situ characterization of materials inside a transmission electron microscope is demonstrated. The platform consists of: 1) a commercially available, flexible, electron-transparent substrate (e.g., TEM grid) integrated with a conventional tensile testing holder, and 2) a finite element simulation providing quantification of specimen-applied strain. The flexible substrate (carbon support film of the TEM grid) mitigates strain concentrations usually found in free-standing films and enables in situ straining experiments to be performed on materials that cannot undergo localized thinning or focused ion beam lift-out. The finite element simulation enables direct correlation of holder displacement with sample strain, providing upper and lower bounds of expected strain across the substrate. The tensile testing platform is validated for three disparate material systems: sputtered gold-palladium, few-layer transferred tungsten disulfide, and electrodeposited lithium, by measuring lattice strain from experimentally recorded electron diffraction data. The results show good agreement between experiment and simulation, providing confidence in the ability to transfer strain from holder to sample and relate TEM crystal structural observations with material mechanical properties.

2D materials↗
DOE OSTI2022

Discovering the nanoscale origins of localized corrosion in additive manufactured stainless steel 316L by liquid cell transmission electron microscopy

Here, we use liquid cell scanning transmission electron microscopy (STEM) to directly characterize the nanoscale origins of corrosion initiation in Additive manufacturing (AM) 316 L stainless steel. Under applied anodic potentials, we found that the dislocation cellular boundaries were preferentially corroded and that regions of localize corrosion occurred along the cellular boundaries. We directly observed the earliest stages of corrosion by controlling the biasing parameters to decelerate the corrosion processes. The results show that highly localized corrosion occurs via inclusion dissolution along dislocation cell boundaries. More widespread corrosion initiates at the dislocation cell boundaries and spreads throughout the dislocation networks.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Identification of the glassy state in nanoparticles by transmission electron microscopy

Identification of amorphous phases in nanoparticles by atomic-resolution transmission electron microscopy (TEM) requires analyses such as tilt-angle-dependent TEM imaging and single-nanoparticle electron diffraction, rather than relying on a single TEM image. Here, the disordered structures of amorphous nanoparticles offer unique atomic configurations and properties that differ from the properties of their crystalline counterparts. These characteristics have motivated the exploration of such materials for mechanical, sensing and catalytic applications. In addition, the formation of amorphous metal nanoparticles is an important endeavour to understand the process of vitrification and the nature of the glassy state. Atomic-resolution transmission electron microscopy (TEM) is increasingly being used as a tool for characterizing structures of nanoparticles produced by vitrification. In this Comment, we discuss the pitfalls of using TEM for ascertaining whether nanoparticles are amorphous. We also make recommendations of best practices.

Alcorn, Francis M. [Sandia National Laboratories (↗
DOE OSTI2024

Structural Complexities in Sodium Ion Conductive Antiperovskite Revealed by Cryogenic Transmission Electron Microscopy

Here we use low-dose cryogenic transmission electron microscopy (cryo-TEM) to investigate the atomic-scale structure of antiperovskite Na 2 NH 2 BH 4 crystals by preserving the room-temperature cubic phase and carefully monitoring the electron dose. Via quantitative analysis of electron beam damage using selected area electron diffraction, we find cryogenic imaging provides 6-fold improvement in beam stability for this solid electrolyte. Cryo-TEM images obtained from flat crystals revealed the presence of a new, long-range-ordered supercell with a cubic phase. The supercell exhibits doubled unit cell dimensions of 9.4 Å × 9.4 Å as compared to the cubic lattice structure revealed by X-ray crystallography of 4.7 Å × 4.7 Å. The comparison between the experimental image and simulated potential map indicates the origin of the supercell is a vacancy ordering of sodium atoms. This work demonstrates the potential of using cryo-TEM imaging to study the atomic-scale structure of air- and electron-beam-sensitive antiperovskite-type solid electrolytes.

36 MATERIALS SCIENCE↗