SEARCH · Search NASA
Results for “materials imaging”
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.
Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.
Constructing Self-Labeled Materials Imaging Datasets from Open Access Scientific Journals with EXSCLAIM!
Due to recent improvements in image resolution and acquisition speeds, materials microscopy is experiencing an explosion in imaging data. Yet, despite the volume of images generated, the overall accessibility landscape is highly fragmented, as researchers who do release images to the public, often only do so as snapshots of their larger private dataset in context of scientific journal publications. The effort to automatically consolidate images and descriptive information from web-based platforms has garnered broad attention from the computer vision, language technologies, and chemistry/materials informatics communities. However, these methods are problematic for scientific figures because over 30% of figures are compound in nature, and it is the individual images themselves, paired with relevant context, that are necessary for construction a proper labeled dataset. To this end, we outline in this paper the design of a software pipeline for the automatic EXtraction, Separation, and Caption-based natural Language Annotation of IMages from scientific figures (EXSCLAIM!). Successful consolidation of materials imaging across literature sources will enhance navigation and searchability of materials microscopy images for both novice and experienced researchers, as well as establish the framework necessary for users to search by images, text, or some combination of both.
Scaling deep learning for material imaging with a pseudo 3D model for domain transfer
The recent introduction of deep learning methods for image processing has greatly advanced the characterization of materials using three-dimensional (3D) X-ray imaging techniques. However, deep learning models often have difficulty performing consistently across images owing to unavoidable variations in imaging conditions, which create inconsistencies even for the same material. As a result, networks must frequently be retrained for new datasets, limiting their applicability and generalization. Thus, it is critical to reduce the variations between images to enable a single model to process multiple datasets. Herein, we introduce P3T-Net, a pseudo-3D domain transfer network that transfers diverse 3D images into a uniform domain before processing using deep learning models. Remarkably, P3T-Net enables the reuse of previously trained networks for processing new images and considerably reduces the computational cost of transferring 3D images across domains. These unique capabilities were demonstrated in the following scenarios: (i) image enhancement of fast scans for geological rock and hydrogen fuel cells, (ii) enhancement of images to match the quality of multi-source imaging for lithium-ion batteries, (iii) accurate segmentation of images captured under different conditions, and (iv) tera-scale 3D transfer (10 11 voxels) on a single GPU. Overall, the proposed approach addresses cross-domain inconsistencies across various materials and conditions, thereby enabling more robust and generalizable deep learning solutions for a wide range of material imaging tasks.
Evanescent Microwave Probes Using Coplanar Waveguide and Stripline for Super-Resolution Imaging of Materials
An evanescent field microwave imaging probe based on half-wavelength, microwave transmission line resonators is described. Optimization of the probe tip design, the coupling gap, and the data analysis has resulted in images of metal lines on semiconductor substrates with 2.6 microns spatial resolution and a minimum detectable line width of 0.4 microns at 1 GHz.
Deep Learning and Structural Imaging of Materials
Deep learning has had a transformative effect on numerous domains and is actively utilized by many scientists in data-intensive fields such as high-energy physics and cosmology. Materials science, and in particular, the structural imaging of materials with electrons and X-rays are projected to enter the age of scientific data torrents, positioning them as new application spaces for modern artificial intelligence. In this contribution, we provide a synopsis on the foundations and latest progress in deep learning and present an in-depth application of utilizing modern deep artificial neural networks in scanning transmission electron microscopy to extract structural material properties. We use this case study to expose the strengths of deep learning-based models and discuss their current limitations, in the process highlighting their potential use in other data-intensive structural imaging modalities.
Application of a deep learning semantic segmentation model to helium bubbles and voids in nuclear materials
Imaging nanoscale radiation-induced defects using the transmission electron microscope (TEM) is a key factor in the successful implementation of materials for nuclear energy structural applications. Analyzing each defect in a TEM micrograph is currently a manual task. To identify the defects in a single image can take anywhere from 15 min to an hour and a project can require the analysis of anywhere from tens to ≥ 100 images. Here, we use artificial intelligence (AI) models to automate this task. For simplification, we evaluated images with only a single type of defect; helium bubbles. Additionally, we performed semantic segmentation of these helium bubble defects in electron microscopy images of irradiated FeCrAl alloys using a deep learning DefectSegNet model. This model, which was previously used to classify crystal defects, is inspired by the classic DenseNet and U-Net image segmentation models. It claims high spatial resolution, but has poor performance at object boundaries. Our paper improves the DefectSegNet model’s application by adding two new features. First, the DefectSegNet model is applied not only to perform calculation pixel-wise but also object (or feature) wise. Because object-wise metrics are directly relevant to our final goal of detecting bubbles, whereas pixel-wise classification is only an intermediate step, it is an important part of our study. Second, a distance map loss (DML) function has been added to increase its performance at object boundaries. It is crucial to accurately represent defects boundaries, especially bubbles, in order to track the bubble-induced swelling caused by irradiation. The boundary-focused DML function is also compared to other loss functions like Cross-entropy, Weighted Binary Cross Entropy (WBCE), Dice and Intersection over Union (IOU). Finally, by incorporating new features, we found a marked improvement on segmentation quality and better shape preservation at the boundaries and areas of the bubbles.
Quantitative Scanning Transmission Electron Microscopy for Materials Science: Imaging, Diffraction, Spectroscopy, and Tomography
Scanning transmission electron microscopy (STEM) is one of the most powerful characterization tools in materials science research. Due to instrumentation developments such as highly coherent electron sources, aberration correctors, and direct electron detectors, STEM experiments can examine the structure and properties of materials at length scales of functional devices and materials down to single atoms. STEM encompasses a wide array of flexible operating modes, including imaging, diffraction, spectroscopy, and 3D tomography experiments. This review outlines many common STEM experimental methods with a focus on quantitative data analysis and simulation methods, especially those enabled by open source software. The hope is to introduce both classic and new experimental methods to materials scientists and summarize recent progress in STEM characterization. The review also discusses the strengths and weaknesses of the various STEM methodologies and briefly considers promising future directions for quantitative STEM research.
STEM Imaging of Materials: Final Report for DE-FG02-08ER46547
This project supported research advancing the state of the art in scanning transmission electron microscopy (STEM) imaging and characterization of materials, especially electronic materials. The project began with studies of point defects in materials using high-resolution Z-contrast STEM, then evolved to including methods in four-dimensional (4D) STEM and machine learning. Highlights include: (1) the discovery of stable, p-type ZnO via Sb doping, (2) demonstration that the high performance of InGaN LEDs is not due to In composition fluctuations as previously proposed, (3) the demonstration of the first sub-pm precision high-resolution STEM images using non-rigid registration of a series of STEM images, (4) the extension of high-precision STEM to low dose imaging and spectrum imaging, (5) discovery of a synthesis method for twisted spiral growth of transition-metal dichalcogenide materials via chemical vapor deposition, and (6) development of machine-learning methods for analysis and denoising of enormous 4D STEM data sets.
Interpretation of defects in field ion images - Fcc materials.
Computer shell model simulating dislocation configurations selection in field ion images of fcc structures
Imaging natural materials with a quasi-microscope
A Viking lander camera with auxilliary optics mounted inside the dust post was evaluated to determine its capability for imaging the inorganic properties of granular materials. During mission operations, prepared samples would be delivered to a plate positioned within the camera's field of view and depth of focus. The auxiliary optics would then allow soil samples to be imaged with an 11 pm pixel size in the broad band (high resolution, black and white) mode, and a 33 pm pixel size in the multispectral mode. The equipment will be used to characterize: (1) the size distribution of grains produced by igneous (intrusive and extrusive) processes or by shock metamorphism, (2) the size distribution resulting from crushing, chemical alteration, or by hydraulic or aerodynamic sorting; (3) the shape and degree of grain roundness and surface texture induced by mechanical and chemical alteration; and (4) the mineralogy and chemistry of grains.
Thermographic Imaging of Material Loss in Boiler Water-Wall Tubing by Application of Scanning Line Source
Localized wall thinning due to corrosion in utility boiler water-wall tubing is a significant inspection concern for boiler operators. Historically, conventional ultrasonics has been used for inspection of these tubes. This technique has proven to be very manpower and time intensive. This has resulted in a spot check approach to inspections, documenting thickness measurements over a relatively small percentage of the total boiler wall area. NASA Langley Research Center has developed a thermal NDE technique designed to image and quantitatively characterize the amount of material thinning present in steel tubing. The technique involves the movement of a thermal line source across the outer surface of the tubing followed by an infrared imager at a fixed distance behind the line source. Quantitative images of the material loss due to corrosion are reconstructed from measurements of the induced surface temperature variations. This paper will present a discussion of the development of the thermal imaging system as well as the techniques used to reconstruct images of flaws. The application of the thermal line source coupled with the analysis technique represents a significant improvement in the inspection speed for large structures such as boiler water-walls. A theoretical basis for the technique will be presented which explains the quantitative nature of the technique. Further, a dynamic calibration system will be presented for the technique that allows the extraction of thickness information from the temperature data. Additionally, the results of applying this technology to actual water-wall tubing samples and in situ inspections will be presented.
Detection of defects in atomic-resolution images of materials using cycle analysis
The automated detection of defects in high-angle annular dark-field Z-contrast (HAADF) scanning-transmission-electron microscopy (STEM) images has been a major challenge. Here, we report an approach for the automated detection and categorization of structural defects based on changes in the material’s local atomic geometry. The approach applies geometric graph theory to the already-found positions of atomic-column centers and is capable of detecting and categorizing any defect in thin diperiodic structures (i.e., “2D materials”) and a large subset of defects in thick diperiodic structures (i.e., 3D or bulk-like materials). Despite the somewhat limited applicability of the approach in detecting and categorizing defects in thicker bulk-like materials, it provides potentially informative insights into the presence of defects. The categorization of defects can be used to screen large quantities of data and to provide statistical data about the distribution of defects within a material. This methodology is applicable to atomic column locations extracted from any type of high-resolution image, but here we demonstrate it for HAADF STEM images.
In situ plant materials hyperspectral imaging by multimodal scattering near-field optical microscopy
Plant cells are elaborate three-dimensional polymer nano-constructs with complex chemistry. The bulk response of plants to light, in the far-field, is ultimately encoded by optical scattering from these nano-constructs. Their chemical and physical properties may be acquired through their interaction with a modulated nano-tip using scattering scanning near-field optical microscopy. Here, using this technique, we present 20 nm spatial resolution mechanical, spectral and optical mappings of plant cell walls. We first address the problem of plant polymers tracking through pretreatment and processing. Specifically, cellulose and lignin footprints are traced within a set of delignified specimen, establishing the factors hindering complete removal of lignin, an important industrial polymer. Furthermore, we determine the frequency dependent dielectric function ${\epsilon }(\omega)={(n+ik)}^{2}$ of plant material in the range 28 ≤ ω ≤ 58 THz, and show how the environmental chemical variation is imprinted in the nanoscale variability of n and k . This nanometrology is a promise for further progress in the development of plant-based (meta-)materials.
Spectral gratings-based phase-contrast imaging for materials characterization
Spectral X-ray detectors provide a direct observation of the energy spectrum of a transmitted X-ray beam, both for typical and phase-contrast gratings-based systems. The sensitivity of a gratings-based X-ray system to small-angle deflections from a given particle size is dependent on X-ray energy. Therefore, spectral (energy-sensitive) detectors could be sensitive to particle size, even with a broad spectrum from a commercial X-ray generator. Furthermore, these detectors allow direct observation of how the X-ray spectrum is changing as the beam is passing through an object and gratings, and how this affects grating visibilities used to determine the presence of small-angle deflections. This is a particular issue for higher-energy systems where artifacts from beam hardening are common. We present results exploring the particle-size dependent signatures that are available from a spectral gratings-based phase-contrast X-ray imaging system, and the feasibility of observing them with lab-based, broad-spectrum X-ray generators.
Imaging Special Nuclear Material using a Handheld Dual Particle Imager
Not Available
Two-Color X-Ray Imaging for Material Differentiation in Multiphase Flows
Explore the source record for details and available documents.