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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 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
NASA NTRS2017

Thermochemistry of CaO-MgO-Al2O3-SiO2 (CMAS) and Advanced Thermal and Environmental Barrier Coating Systems

CaO-MgO-Al2O3-SiO2 (CMAS) oxides are constituents in a broad number of materials and minerals which have recently inferred to discussions in materials science, planetary science, geochemistry and cosmochemistry communities. In materials science, there is increasing interest in the degradation studies of thermal (TBC) and environmental (EBC) barrier coatings of gas turbines by molten CMAS. CMAS minerals usually are carried by the intake air into gas turbines, e.g. in aircraft engines, and their deposits react at high temperatures (1000C) with the coating materials. This causes degradation and accelerated failure of the static and rotating components of the turbine engines. We discuss some preliminary results of the reactions between CMAS and Rare-Earth (RE Y, Yb, Dy, Gd, Nd and Sm) oxide stabilized ZrO2 or HfO2 systems, and the stability of the resulting oxides and silicates. Plasma sprayed hollow tube samples ( 2.2 mm and 26 mm height) were half filled with CMAS powder, wrapped and sealed with platinum foil, and heat treated at 1310 C for 5h. Samples were characterized by differential scanning calorimetry, X-ray diffraction and cross section electron microscopy analysis.

degradation↗
NASA NTRS2017

Thermochemistry of Calcium-Magnesium-Aluminum-Silicate (CMAS) and Components of Advanced Thermal and Environmental Barrier Coating Systems

There is increasing interest in the degradation mechanism studies of thermal and environmental barrier coatings (TEBCs) of gas turbines by molten CaO-MgO-Al(exp. 2)O(exp. 3)-SiO(exp. 2) CMAS). CMAS minerals are usually referred as silicon-containing sand dust and volcano ash materials that are carried by the intake air into gas turbines, e.g. in aircraft engines, and their deposits often react at high temperatures (greater than 1200 degrees C) with the engine turbine coating systems and components. The high temperature reactions causes degradation and accelerated failure of the static and rotating components of the turbine engines. We discuss some results of the reactions between the CMAS and Rare-Earth (RE = Y, Yb, Dy, Gd, Nd and Sm) - oxide stabilized ZrO(exp. 2) or HfO(exp. 2) systems, and the stability of the resulting oxides and silicates. Plasma sprayed hollow tube samples (outside diameter = 4.7 mm, wall thickness = 0.76 mm and = 26 mm height) were half filled with CMAS powder, wrapped and sealed with platinum foil, and heat treated at 1310 degrees C for 5h. Samples were characterized by differential scanning calorimetry (DSC), X-ray diffraction, and cross-section electron microscopy analysis and energy dispersive X-ray spectroscopy. It was found that CMAS penetrated the samples at the grain boundaries and dissolved the TEBC materials to form silicate phases containing the rare-earth elements. Furthermore, it was found that apatite crystalline phases were formed in the samples with total rare-earth content higher than 12 mol% in the reaction zone for the ZrO(exp. 2) system. In general, samples with the nominal compositions (30YSZ), HfO(exp. 2)-7Dy(exp. 2)O(exp. 2) and ZrO(exp. 2)-9.5Y(exp. 2)O(exp. 3)-2.25Gd(exp. 2)O(exp. 3)-2.25Yb(exp. 2)O(exp. 3) exhibited lower reactivity or more resistance to CMAS than the other coating compositions of this work.

degradation↗
NASA NTRS2017

High Temperature Degradation of Advanced Thermal and Environmental Barrier Coatings (TEBCs) by CaO-MgO-Al2O3-SiO2 (CMAS)

There is increasing interest in the degradation studies of thermal and environmental barrier coatings (TEBCs) of gas turbines by molten CaO-MgO-Al2O3-SiO2 (CMAS). CMAS minerals are usually referred as silica-containing sand dust and volcano ash materials that are carried by the intake air into gas turbines, e.g. aircraft engines. The low-melting deposits react at high temperatures (1000C) with the coating materials. This causes degradation and accelerated coating failure of the static and rotating components of the turbine engines. We discuss some preliminary results of the reactions between CMAS and Rare-Earth (RE Y, Yb, Dy, Gd, Nd and Sm) oxide stabilized ZrO2 or HfO2 systems, and the stability of the resulting oxides and silicates. Plasma sprayed hollow tube samples (outer diameter 4.7 mm, wall thickness 0.76 mm and 26 mm height) were half filled with CMAS powder, wrapped and sealed with platinum foil, and heat- treated at 1310 C for 5h. Samples were characterized by differential scanning calorimetry, X-ray diffraction and cross section electron microscopy analysis.

Thermal and environmental barrier coatings↗
NASA NTRS2017

Characterization of the NEXT Hollow Cathode Inserts After Long-Duration Testing

Hollow dispenser cathode inserts are a critical element of electric propulsion systems, and should therefore be well understood during long term operation to ensure reliable system performance. This work destructively investigated cathode inserts from the NEXT long-duration test which demonstrated 51,184 hours of high-voltage operation, 918 kg of propellant throughput, and 35.5 MN-s of total impulse. The characterization methods used include scanning electron microscopy with energy dispersive spectroscopy and X-ray diffraction. Microscopy analysis has been performed on fractured surfaces, emission surfaces, and metallographically polished cross-sections of post-test inserts and unused inserts. Impregnate distribution, etch region thickness, impregnate chemical content, emission surface topography, and emission surface phase identification are the primary factors investigated.

life (durability)↗
NASA NTRS2017

Characterization of the NEXT Hollow Cathode Inserts After Long-Duration Testing

Hollow dispenser cathode inserts are a critical element of electric propulsion systems, and should therefore be well understood during long term operation to ensure reliable system performance. This work destructively investigated cathode inserts from the NEXT long-duration test which demonstrated 51,184 hours of high-voltage operation, 918 kg of propellant throughput, and 35.5 MN-s of total impulse. The characterization methods used include scanning electron microscopy with energy dispersive spectroscopy and X-ray diffraction. Microscopy analysis has been performed on fractured surfaces, emission surfaces, and metallographically polished cross-sections of post-test inserts and unused inserts. Impregnate distribution, etch region thickness, impregnate chemical content, emission surface topography, and emission surface phase identification are the primary factors investigated.

Mackey, Jonathan A.↗
NASA NTRS2022

Microstructure Segmentation with Deep Learning Encoders Pre-Trained on a Large Microscopy Dataset

This study examined the improvement of microscopy segmentation accuracy by transfer learning from a large dataset of microscopy images called MicroNet. Many neural network encoder architectures, including VGG, Inception, and ResNet, were trained on over 100,000 labelled microscopy images from 54 classes. These pre-trained encoders were then embedded into multiple segmentation architectures including U-Net and DeepLabV3+ to evaluate segmentation performance on newly created benchmark microscopy datasets. Compared to ImageNet pre-training, models pre-trained on MicroNet generalized better to out-of-distribution micrographs taken under different imaging and sample conditions and were more accurate with less training data. When training with only a single Ni-superalloy image, pre-training on MicroNet produced a 72.2 percent reduction in relative segmentation error. These results suggest that transfer learning from large in-domain datasets generate models with learned feature representations that are more useful for downstream tasks and will likely improve any microscopy image analysis technique that can leverage pre-trained encoders.

machine learning↗
DOE OSTI2026

Irradiation Testing of Additively Manufactured Materials for LWR Applications: Alloy 718 and 316L Stainless Steel

The objective of this NSUF Project is to assess the changes in irradiated additively manufactured (AM) material properties as compared to non-irradiated material. Type 316L stainless steel and Alloy 718 samples were produced using Direct Metal Laser Melting (DMLM) fabrication. Materials produced from this fabrication method have several potential applications within the nuclear industry as reactor internal repair parts, fuel debris resistant filters, or fuel spacers within existing light water reactors (LWRs). AM materials have been shown to achieve equivalent mechanical behavior in simulated reactor environments as compared to wrought materials, but have significantly more flexibility when it comes to unique design features. The increased component design flexibility makes these AM materials an attractive choice for both current LWR applications as well as for small modular reactor (SMR) designs. Prior to use of these materials in reactor fleet operation, the industry as a whole must evaluate the effects of irradiation on their material properties. Standard 0.4 inch thick Compact Tension specimens and SSJ3 type tensile bars were neutron irradiated at the Advanced Test Reactor to ~1 dpa for the purpose of performing a variety of mechanical tests in a range of simulated environments applicable to LWRs. For the ductile austenitic Type 316L stainless steel, the irradiated data will be used to confirm that the AM process produces materials with properties that are equivalent to wrought materials under testing conditions applicable to LWR operation. Transmission electron microscopy analysis was also performed in order to understand microstructural and microchemical changes induced in each material in response to neutron irradiation. If possible, data collected from these AM 316L samples will be used to remove fluence limits from specifications of ASME code cases for this alloy, which will give vendors much more flexibility in building future components.

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 OSTI2024

Generalization Across Experimental Parameters in Neural Network Analysis of High-Resolution Transmission Electron Microscopy Datasets

Neural networks are promising tools for high-throughput and accurate transmission electron microscopy (TEM) analysis of nanomaterials, but are known to generalize poorly on data that is “out-of-distribution” from their training data. Given the limited set of image features typically seen in high-resolution TEM imaging, it is unclear which images are considered out-of-distribution from others. Here, we investigate how the choice of metadata features in the training dataset influences neural network performance, focusing on the example task of nanoparticle segmentation. We train and validate neural networks across curated, experimentally collected high-resolution TEM image datasets of nanoparticles under various imaging and material parameters, including magnification, dosage, nanoparticle diameter, and nanoparticle material. Overall, we find that our neural networks are not robust across microscope parameters, but do generalize across certain sample parameters. Additionally, data preprocessing can have unintended consequences on neural network generalization. Our results highlight the need to understand how dataset features affect deployment of data-driven algorithms.

42 ENGINEERING↗
DOE OSTI2025

Image registration for accurate electrode deformation analysis in operando microscopy of battery materials

Operando imaging techniques have become increasingly valuable in both battery research and manufacturing. However, the reliability of these methods can be compromised by instabilities in the imaging setup and operando cells, particularly when utilizing high-resolution imaging systems. The acquired imaging data often include features arising from both undesirable system vibrations and drift, as well as the scientifically relevant deformations occurring in the battery sample during cell operation. For meaningful analysis, it is crucial to distinguish and separately evaluate these two factors. To address these challenges, we employ a suite of advanced image-processing techniques. These include fast Fourier transform analysis in the frequency domain, power spectrum-based assessments for image quality, as well as rigid and non-rigid image-registration methods. These techniques allow us to identify and exclude blurred images, correct for displacements caused by motor vibrations and sample holder drift and, thus, prevent unwanted image artifacts from affecting subsequent analyses and interpretations. Additionally, we apply optical flow analysis to track the dynamic deformation of battery electrode materials during electrochemical cycling. This enables us to observe and quantify the evolving mechanical responses of the electrodes, offering deeper insights into battery degradation. Together, these methods ensure more accurate image analysis and enhance our understanding of the chemomechanical interplay in battery performance and longevity.

Sun, Tianxiao↗
DOE OSTI2020

Current measurements in the intermittent-contact mode of atomic force microscopy using the Fourier method: a feasibility analysis

Atomic force microscopy (AFM) is an important tool for measuring a variety of nanoscale surface properties, such as topography, viscoelasticity, electrical potential and conductivity. Some of these properties are measured using contact methods (static contact or intermittent contact), while others are measured using noncontact methods. Some properties can be measured using different approaches. Conductivity, in particular, is mapped using the contact-mode method. However, this modality can be destructive to delicate samples, since it involves continuously dragging the cantilever tip on the surface during the raster scan, while a constant tip–sample force is applied. In this paper we discuss a possible approach to develop an intermittent-contact conductive AFM mode based on Fourier analysis, whereby the measured current response consists of higher harmonics of the cantilever oscillation frequency. Such an approach may enable the characterization of soft samples with less damage than contact-mode imaging. To explore its feasibility, we derive the analytical form of the tip–sample current that would be obtained for attractive (noncontact) and repulsive (intermittent-contact) dynamic AFM characterization, and compare it with results obtained from numerical simulations. Although significant instrumentation challenges are anticipated, the modelling results are promising and suggest that Fourier-based higher-harmonics current measurement may enable the development of a reliable intermittent-contact conductive AFM method.

14 SOLAR ENERGY↗
DOE OSTI2024

A new paradigm in electron microscopy: Automated microstructure analysis utilizing a dynamic segmentation convolutional neutral network

Over the past half century, the transmission electron microscope enabled insight into the fundamental arrangements and structures of materials. State-of-the-art electron microscopes can acquire large image datasets across multiple imaging modalities. However, the manual annotation process for feature or defect quantification may not be feasible with the modern microscope. Convolutional neural networks emerged to characterize individual microstructural features from an image in a cost-effective, consistent manner. However, many of these neural network approaches rely on thousands to hundreds of thousands of manual annotations of each feature type across hundreds of images to train the network for adequate performance. This work focused on the development and application of a pixel-wise defect detection machine-learning dynamic segmentation convolutional neural network with associated automated acquisition and postprocessing to identify microstructural features rapidly and quantitatively from a small initial dataset incorporating multiple imaging modes. The approach was demonstrated for characterization of superalloy 718 from both single image acquisition on multiple detectors to in-situ evolution captured with a single detector on a standard desktop computer to demonstrate the low barrier to entry required for widespread adoption. Pixel-by-pixel class identification was excellent with strong identification of chemically distinct phases, structurally distinct phases, and defect structures, thus demonstrating the new paradigm of machine learning-assisted characterization.

36 MATERIALS SCIENCE↗
NASA NTRS1997

Low Temperature Synthesis, Chemical and Electrochemical Characterization of LiNi(x)Co(1-x)O2 (0 less than x less than 1)

A new method of synthesis for the solid solution cathode materials LiNi(x)Co(1-x)O2 (0 less than x less than 1) involving enhanced reactions at temperatures less than or equal to 700 deg. C, between metal oxy-hydroxide precursors MOOH (M = Ni, Co) and Li-salts (Li2CO3, LiOH, and LiNO3) has been investigated. The effects of synthesis conditions and sources of Li, on phase purity, microstructure, and theoretical electrochemical capacity (total M(3+) content) are characterized by powder X-ray diffraction analysis, scanning electron microscopy, chemical analysis and room temperature magnetic susceptibility. An attempt has been made to correlate the electrochemical properties with the synthesis conditions and microstructure.

Nanjundaswamy, K. S.↗
NASA NTRS1999

Electron Microscopy and Image Analysis for Selected Materials

This particular project was completed in collaboration with the metallurgical diagnostics facility. The objective of this research had four major components. First, we required training in the operation of the environmental scanning electron microscope (ESEM) for imaging of selected materials including biological specimens. The types of materials range from cyanobacteria and diatoms to cloth, metals, sand, composites and other materials. Second, to obtain training in surface elemental analysis technology using energy dispersive x-ray (EDX) analysis, and in the preparation of x-ray maps of these same materials. Third, to provide training for the staff of the metallurgical diagnostics and failure analysis team in the area of image processing and image analysis technology using NIH Image software. Finally, we were to assist in the sample preparation, observing, imaging, and elemental analysis for Mr. Richard Hoover, one of NASA MSFC's solar physicists and Marshall's principal scientist for the agency-wide virtual Astrobiology Institute. These materials have been collected from various places around the world including the Fox Tunnel in Alaska, Siberia, Antarctica, ice core samples from near Lake Vostoc, thermal vents in the ocean floor, hot springs and many others. We were successful in our efforts to obtain high quality, high resolution images of various materials including selected biological ones. Surface analyses (EDX) and x-ray maps were easily prepared with this technology. We also discovered and used some applications for NIH Image software in the metallurgical diagnostics facility.

Williams, George↗
NASA NTRS2007

Paleomagnetic Analysis Using SQUID Microscopy

Superconducting quantum interference device (SQUID) microscopes are a new generation of instruments that map magnetic fields with unprecedented spatial resolution and moment sensitivity. Unlike standard rock magnetometers, SQUID microscopes map magnetic fields rather than measuring magnetic moments such that the sample magnetization pattern must be retrieved from source model fits to the measured field data. In this paper, we presented the first direct comparison between paleomagnetic analyses on natural samples using joint measurements from SQUID microscopy and moment magnetometry. We demonstrated that in combination with apriori geologic and petrographic data, SQUID microscopy can accurately characterize the magnetization of lunar glass spherules and Hawaiian basalt. The bulk moment magnitude and direction of these samples inferred from inversions of SQUID microscopy data match direct measurements on the same samples using moment magnetometry. In addition, these inversions provide unique constraints on the magnetization distribution within the sample. These measurements are among the most sensitive and highest resolution quantitative paleomagnetic studies of natural remanent magnetization to date. We expect that this technique will be able to extend many other standard paleomagnetic techniques to previously inaccessible microscale samples.

Weiss, Benjamin P.↗