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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 73 records · Page 4

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↗

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↗

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)↗

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.↗

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↗

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↗

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↗

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.↗

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↗

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.↗

Characterization and Glass Formation of JSC-1 Lunar and Martian Soil Simulants

The space exploration mission of NASA requires long duration presence of human being beyond the low earth orbit (LEO), especially on Moon and Mars. Developing a human habitat or colony on these planets would require a diverse range of materials, whose applications would range from structural foundations, (human) life support, (electric) power generation to components for scientific instrumentation. A reasonable and cost-effective approach for fabricating the materials needed for establishing a self-sufficient human outpost would be to primarily use local (in situ) resources on these planets. Since ancient times, glass and ceramics have been playing a vital role on human civilization. A long term project on studying the feasibility of developing glass and ceramic materials using Lunar and Martian soil simulants (JSC-1) as developed by Johnson Space Center has been undertaken. The first step in this on-going project requires developing a data base on results that fully characterize the simulants to be used for further investigations. The present paper reports characterization data of both JSC-1 Lunar and JSC Mars-1 simulants obtained up to this time via x-ray diffraction analysis, scanning electron microscopy, thermal analysis (DTA, TGA) and chemical analysis. The critical cooling rate for glass formation for the melts of the simulants was also measured in order to quantitatively assess the glass forming tendency of these melts. The importance of the glasses and ceramics developed using in-situ resources for constructing human habitats on Moon or Mars is discussed.

Sen, Subhayu↗

Scanning microscopy in microcircuit failure analysis

A three-phase microcircuit failure analysis procedure is presented. In the first phase, the device is nondestructively tested and the data obtained is correlated with the circumstances surrounding the failure event. The device is then subjected to a limited nondestructive analysis dominated by optical and scanning electron microscopy (SEM). Knowing that microcircuits usually fail from manufacturing defects or misuse, the analyst looks for one of the probable mechanisms. The SEM has two special operating modes that provide unique data for nondestructive analysis: voltage contrast and conductive current. These are used with other SEM modes and surface analysis techniques to analyze complicated microcircuits. The last phase includes the destructive procedures. The end product of these analyses is a corrective action that will yield a highly reliable system.

Nicolas, D. P.↗

Comparison of Contamination Model Predictions to LDEF Surface Measurements

Contaminant deposition measurements have been made on species content and depth profiles on three experiments trays from the Long Duration Exposure Facility (LDEF), Auger, Argon sputtering, Electron Spectroscopy for Chemical Analysis (ESCA) and Scanning Electron Microscopy (SEM) analysis. The integrated spacecraft environment model (ISEM) was used to predict the deposition levels of the contaminants measured on the three trays. The details of the modeling and assumptions used are presented along with the predictions for the deposition on select surfaces on the trays. These are compared to the measured results. The trays represent surfaces that have a high atomic oxygen flux, and intermediate oxygen flux, and no oxygen flux. All surfaces received significant solar Ultraviolet flux. It appears that the atomic oxygen was the primary agent that caused significant deposition to occur. Surfaces that saw significant contaminant flux solar UV and no atomic oxygen did not show any appreciable levels of observable deposition. The implications of the atom ic oxygen interaction with contaminant deposits containing silicon contaminant sources is discussed. The primary contaminant sources are DC61104 adhesive and Z306 paint. The results and interpretation of the findings have a potential significant impact on spacecraft surfaces that are exposed to solar UV and atomic oxygen in low Earth orbit.

Gordon, Tim↗

New methods for image collection and analysis in scanning Auger microscopy

While scanning Auger micrographs are used extensively for illustrating the stoichiometry of complex surfaces and for indicating areas of interest for fine point Auger spectroscopy, there are many problems in the quantification and analysis of Auger images. These problems include multiple contrast mechanisms and the lack of meaningful relationships with other Auger data. Collection of multielemental Auger images allows some new approaches to image analysis and presentation. Information about the distribution and quantity of elemental combinations at a surface are retrievable, and particular combinations of elements can be imaged, such as alloy phases. Results from the precipitate hardened alloy Al-2124 illustrate multispectral Auger imaging.

Browning, R.↗

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

This study examined the improvement of microscopy segmentation intersection over union accuracy by transfer learning from a large dataset of microscopy images called MicroNet. Many neural network encoder architectures were trained on over 100,000 labeled microscopy images from 54 material classes. These pre-trained encoders were then embedded into multiple segmentation architectures including UNet and DeepLabV3+ to evaluate segmentation performance on 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% reduction in relative intersection over union 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↗