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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

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 OSTI2018

Chemical and Electronic Structure Analysis of a SrTiO3 (001) / p-Ge (001) Hydrogen Evolution Photocathode

Germanium is a small-gap semiconductor that efficiently absorbs visible light, resulting in photoexcited electrons predicted to be sufficiently energetic to reduce H2O and evolve H2 gas. In order to protect the surface from corrosion and prevent surface charge recombination in contact with aqueous pH 7 electrolyte, we grew epitaxial SrTiO3 layers of different thicknesses on p-Ge (001) surfaces. 4 nm SrTiO3 allows photogenerated electrons to reach the surface and evolve H2 gas, while 13 nm SrTiO3 blocks these electrons. Ambient pressure X-ray photoelectron spectroscopy indicates the surface readily dissociates H2O to form OH species, which may impact surface band bending.

electrocatalysis, photoelectrochemistry, hydrogen ↗
DOE OSTI2026

Self-driving thin film laboratory: autonomous epitaxial atomic-layer synthesis via real-time computer vision analysis of electron diffraction

Emerging materials science platforms with the ability to make autonomous decisions on the fly are fundamentally changing the outlook and protocols for materials optimization and discovery. Because AI-driven self-navigating schemes can effectively reduce the total number of iterations needed to arrive at the "answer" (i.e. the best stochiometric composition for a desired physical property, optimum materials processing parameters, etc.) by significant margins, they have the potential to revolutionize materials and chemical manufacturing processes at large in research laboratory settings as well as in industrial plants. Here, we demonstrate a successful implementation of real-time closed-loop autonomous navigation of a multi-dimensional materials synthesis parameter space for fabricating phase-pure epitaxial films of a metastable phase of a functional oxide in a combinatorial pulsed laser deposition chamber. Sequential epitaxial growth iterations in search of the optimized recipe to stabilize the desired crystal phase were performed using frame-by-frame quantitative computer vision analysis of reflection high-energy electron diffraction (RHEED) images of the unit-cell level film being deposited. The autonomous scheme regularly resulted in > 30-fold reduction in the number of required experiments compared to a comprehensive mapping of the parameter space. The real-time workflow developed here can be readily extended to a variety of thin film synthesis platforms opening the door for self-driving atomic-level materials design as well as autonomous optimization of semiconductor manufacturing.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Machine Learning Based Software Package for Reproducible Data Analysis for Electron Microscopy

Advances in hardware and computation technologies are enabling large hyperspectral imaging during Electron Microscopy (EM), resulting in datasets being produced at rapidly increasing spatial and temporal resolutions. This presents exciting opportunities for microstructure analysis, but also poses great challenges in data analysis. Under this STTR program, QuesTek Innovations LLC, a leader in the field of integrated computational materials engineering (ICME), collaborated with Lawrence Livermore National Laboratory and Argonne National Laboratory. The team developed an open-source machine learning (ML) powered tool for EM data analysis of multiple systems and data types, and collected EM data for ML model development using the cutting-edge PicoProbe equipment at Argonne National Laboratory.

Lin, Yu↗
DOE OSTI2020

Atomic resolution convergent beam electron diffraction analysis using convolutional neural networks

Two types of convolutional neural network (CNN) models, a discrete classification network and a continuous regression network, were trained to determine local sample thickness from convergent beam diffraction (CBED) patterns of SrTiO 3 collected in a scanning transmission electron microscope (STEM) at atomic column resolution. Acquisition of atomic resolution CBED patterns for this purpose requires careful balancing of CBED feature size in pixels, acquisition speed, and detector dynamic range. The training datasets were derived from multislice simulations, which must be convolved with incoherent source broadening. Sample thicknesses were also determined using quantitative high-angle annular dark-field (HAADF) STEM images acquired simultaneously. The regression CNN performed well on sample thinner than 35 nm, with 70% of the CNN results within 1 nm of HAADF thickness, and 1.0 nm overall root mean square error between the two measurements. The classification CNN was trained for a thicknesses up to 100 nm and yielded 66% of CNN results within one classification increment of 2 nm of HAADF thickness. Our approach depends on methods from computer vision including transfer learning and image augmentation.

36 MATERIALS SCIENCE↗
DOE OSTI2022

A comparison of carbon impurities in pre- and post-melt uranium Part 1: Scanning electron microscopy analysis

Although C impurities in U have been studied for decades, fundamental questions regarding their incorporation, migration, and overall transformation during metal processing still exist. In two written Parts, we compare the chemical speciation, distribution, crystallography, and morphologies (size, shape, etc.) of carbon-containing impurities in U metal both before and after melting using high resolution analytical electron microscopy (AEM). This first Part demonstrates the variability of carbide inclusions in depleted U (DU) metal with respect to their chemical phases and morphologies as observed by scanning electron microscopy (SEM). Here, a variety of inclusion types, such as the pill-shaped U monocarbide (UC), previously only hypothesized, are summarized. Additionally, delineation between hopper-shaped U carbonitride U(C,N) and dendritic UC inclusions is discussed in relationship to archived literature.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Real-time 3D analysis during electron tomography using tomviz

The demand for high-throughput electron tomography is rapidly increasing in biological and material sciences. However, this 3D imaging technique is computationally bottlenecked by alignment and reconstruction which runs from hours to days. We demonstrate real-time tomography with dynamic 3D tomographic visualization to enable rapid interpretation of specimen structure immediately as data is collected on an electron microscope. Using geometrically complex chiral nanoparticles, we show volumetric interpretation can begin in less than 10 minutes and a high-quality tomogram is available within 30 minutes. Real-time tomography is integrated into tomviz, an open-source and cross-platform 3D data analysis tool that contains intuitive graphical user interfaces (GUI), to enable any scientist to characterize biological and material structure in 3D.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Creating ground truth for nanocrystal morphology: a fully automated pipeline for unbiased transmission electron microscopy analysis

Control over colloidal nanocrystal morphology (size, size distribution, and shape) is important for tailoring the functionality of individual nanocrystals and their ensemble behavior. Despite this, traditional methods to quantify nanocrystal morphology are laborious. New developments in automated morphology classification will accelerate these analyses but the assessment of machine learning models is limited by human accuracy for ground truth, causing even unsupervised machine learning models to have inherent bias. Herein, we introduce synthetic image rendering to solve the ground truth problem of nanocrystal morphology classification. By simulating 2D images of nanocrystal shapes via a function of high-dimensional parameter space, we trained a convolutional neural network to link unique morphologies to their simulated parameters, defining nanocrystal morphology quantitatively rather than qualitatively. An automated pipeline then processes, quantitatively defines, and classifies nanocrystal morphology from experimental transmission electron microscopy (TEM) images. Using improved computer vision techniques, 42,650 nanocrystals were identified, assessed, and labeled with quantitative parameters, offering a 600-fold improvement in efficiency over best-practice manual measurements. Further, a classification algorithm was trained with a prediction accuracy of 99.5%, which can successfully analyze a range of concave, convex, and irregular nanocrystal shapes. The resulting pipeline was applied to differentiating two syntheses of nominally cuboidal CsPbBr 3 nanocrystals and uniquely classifying binary nickel sulfide nanocrystal phase based on morphology. This pipeline provides a simple, efficient, and unbiased method to quantify nanocrystal morphology and represents a practical route to construct large datasets with an absolute ground truth for training unbiased morphology-based machine learning algorithms.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2018

Strain Field Analysis in Electronic Components by ESPI: Bad Thermal Contact and Damage Evaluation

Designing electronic components and devices is a complex task that must include also considerations about thermo-mechanical behavior of the component itself. This aspect, in fact, can affect the reliability of the device as a consequence of the thermal stresses that are introduced in the component while it is working. Stress concentration can arise, in fact, due to different thermal expansion coefficients of the materials involved. Determining the thermo-mechanical response by numerically methods is not simple and not always possible so that experimental methods are preferable. In this paper, specifically, the electronic speckle pattern interferometry technique was adopted to measure full-field strain of the component during exercise. By using an optical technique it is possible to get information about the behavior of the component without being in contact with the component itself so that the dissipation coefficient is not altered. The system was able to detect gradients of deformation in the component itself connected to the different distribution of internal current in the component. Moreover, it was observed, that the presence of bad thermal contact between the package and the heat sink can be revealed because it results in a different thermos-mechanical behavior of the component. Finally, a critical defect was introduced in the component and it was observed the way the presence of damage affects the in-plane displacement of the component. Very huge differences were observed leading to the consideration that this kind of approach could be conveniently adopted also as a damage detection tool.

42 ENGINEERING↗
DOE OSTI2019

Visualizing quantum phenomena at complex oxide interfaces: An atomic view from scanning transmission electron microscopy

Complex oxide interfaces have been one of the central focuses in condensed matter physics and material science. Over the past decade, aberration corrected scanning transmission electron microscopy and spectroscopy has proven to be invaluable to visualize and understand the emerging quantum phenomena at an interface. In this paper, we briefly review some recent progress in the utilization of electron microscopy to probe interfaces. Furthermore, we discuss several important challenges for electron microscopy to advance our understanding on interface phenomena, from the perspective of variable temperature, magnetism, electron energy loss spectroscopy analysis, electronic symmetry, and defects probing.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗