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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 1,243 records · Page 69
All sourcesNASA NTRSDOE OSTI
NASA NTRS2005

Combustion Synthesis of Sm0.5Sr0.5CoO3-x and La0.6Sr0.4CoO3-x Nanopowders for Solid Oxide Fuel Cell Cathodes

Nanopowders of Sm0.5Sr0.5CoO(3-x) (SSC) and La0.6Sr0.4CoO(3-x) (LSC) compositions, which are being investigated as cathode materials for intermediate temperature solid oxide fuel cells, were synthesized by a solution-combustion method using metal nitrates and glycine as fuel. Development of crystalline phases in the as-synthesized powders after heat treatments at various temperatures was monitored by x-ray diffraction. Perovskite phase in LSC formed more readily than in SSC. Single phase perovskites were obtained after heat treatment of the combustion synthesized LSC and SSC powders at 1000 and 1200 C, respectively. The as-synthesized powders had an average particle size of ~12 nm as determined from x-ray line broadening analysis using the Scherrer equation. Average grain size of the powders increased with increase in calcination temperature. Morphological analysis of the powders calcined at various temperatures was done by scanning electron microscopy.

Bansal, Narottam P.↗
NASA NTRS2006

Active Metal Brazing and Characterization of Brazed Joints in Titanium to Carbon-Carbon Composites

The Ti-metal/C-C composite joints were formed by reactive brazing with three commercial brazes, namely, Cu-ABA, TiCuNi, and TiCuSiI. The joint microstructures were examined using optical microscopy and scanning electron microscopy (SEM) coupled with energy dispersive spectrometry (EDS). The results of the microstructure analysis indicate solute redistribution across the joint and possible metallurgical bond formation via interdiffusion, which led to good wetting and spreading. A tube-on-plate tensile test was used to evaluate joint strength of Ti-tube/ C-C composite joints. The load-carrying ability was greatest for the Cu-ABA braze joint structures. This system appeared to have the best braze spreading which resulted in a larger braze/C-C composite bonded area compared to the other two braze materials. Also, joint loadcarrying ability was found to be higher for joint structures where the fiber tows in the outer ply of the C-C composite were aligned perpendicular to the tube axis when compared to the case where fiber tows were aligned parallel to the tube axis.

Singh, M.↗
NASA NTRS2011

Sol-Gel Synthesis of La(0.6)Sr(0.4)CoO(3-x) and Sm(0.5)Sr(0.5)CoO(3-x) Cathode Nanopowders for Solid Oxide Fuel Cells

Nanopowders of La(0.6)Sr(0.4)CoO(3-x) (LSC) and Sm(0.5)Sr(0.5)CoO(3-x) (SSC) compositions, which are being investigated as cathode materials for intermediate temperature solid oxide fuel cells (IT-SOFC) with La(Sr)Ga(Mg)O(3-x) (LSGM) as the electrolyte, were synthesized by low-temperature sol-gel method using metal nitrates and citric acid. Thermal decomposition of the citrate gels was followed by simultaneous DSC/TGA methods. Development of phases in the gels, on heat treatments at various temperatures, was monitored by x-ray diffraction. Solgel powders calcined at 550 to 1000 C consisted of a number of phases. Single perovskite phase La(0.6)Sr(0.4)CoO(3-x) or Sm(0.5)Sr(0.5)CoO(3-x) powders were obtained at 1200 and 1300 C, respectively. Morphological analysis of the powders calcined at various temperatures was done by scanning electron microscopy. The average particle size of the powders was approx.15 nm after 700 C calcinations and slowly increased to 70 to 100 nm after heat treatments at 1300 to 1400 C.

Bansal, Narottam P.↗
NASA NTRS2017

The Effects of Fiber Orientation and Adhesives on Tensile Properties of Carbon Fiber Reinforced Polymer Matrix Composite with Embedded Nickel-Titanium Shape Memory Alloys

Nickel-titanium (NiTi) shape memory alloy (SMA) sections were embedded within carbon fiber reinforced polymer matrix composite (CFRPPMC) laminates and their tensile properties were evaluated with simultaneous monitoring of modal acoustic emissions. The test specimens were fabricated in three different layup configurations and two different thin film adhesives were applied to bond the SMA with the PMC. A trio of acoustic sensors were attached to the specimens during tensile testing to monitor the modal acoustic emission (AE) as the materials experienced mechanical failure. The values of ultimate tensile strengths, strains, and moduli were obtained. Cumulative AE energy of events and specimen failure location were determined. In conjunction, optical and scanning electron microscopy techniques were used to examine the break areas of the specimens. The analysis of AE data revealed failure locations within the specimens which were validated from the microscopic images. The placement of 90 deg plies in the outer ply gave the strongest acoustic signals during break as well as the cleanest break of the samples tested. Overlapping 0 deg ply layers surrounding the SMA was found to be the best scenario to prevent failure of the specimen itself.

shape memory alloy↗
DOE OSTI2020

Conversion, Stability, and Selectivity Improvements Through Catalyst Development for the Reductive Etherification Reaction

Economical pathways from lignocellulosic non-food biomass feedstocks to biofuels with appropriate fuel properties are necessary for displacing fossil-fuel derived diesel use in medium-/heavy duty vehicles. Oxygenated compounds have recently shown promise due to low sooting tendencies and autoignition properties and molecules with sufficiently high carbon numbers can meet the requisite standards for water solubility and flash point. Coupling waste biomass derived small molecules into bioblendstock targets through the reductive etherification reaction shows potential for increasing molecular weight without losing the advantages of oxygenates. However, the transition to industrial viability for this process is limited by strong catalyst deactivation in typical flow reactor systems. Strong acid resins such as Amberlyst-15 (A-15) show excellent activity in batch reactions but are limited to low temperatures and are poisoned by the side-product, water. In this talk, I will describe our recent work in catalyst synthesis and testing to select superior options with improved aqueous and thermal stability in reductive etherification flow reactions. Water-tolerant solid acid catalysts have potential to improve the conversion and selectivity to the ether target at temperatures above the degradation point of A-15. Analysis techniques such as physisorption, chemisorption, x-ray diffraction and transmission electron microscopy are being used to characterize the active sites. These techniques are also being used to evaluate degradation of the catalyst after extensive flow reactions in addition to comparative efficiency testing with standardized cyclohexene hydrogenation reactions. Finally, I will discuss a demonstration of the full pathway from bio-derived butyric acid to the performance-advantaged ether product and the additional obstacles from side-products and purification limitations in the preliminary upgrading reactions.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2025

Multiscale Characterization of Additive Manufacturing Components with Computed Tomography, 3D X-ray Microscopy, and Deep Learning

Additive manufacturing (AM) facilitates the creation of complex-geometry parts, driving advancements in lightweight aerospace components, high-efficiency engine cooling channels, and customized medical implants. However, ensuring the quality and reliability of AM parts remains challenging due to internal defects, surface irregularities, porosity, and residual trapped powder, which are often inaccessible to traditional inspection methods. Recent developments in X-ray computed tomography (XCT) and 3D X-ray microscopy (XRM), particularly systems equipped with resolution-at-a-distance (RaaD™) capabilities, enable high-resolution, non-destructive evaluation of AM components across multiple scales, from sub-micrometer to macroscopic levels. This paper explores modern XCT and XRM techniques for multiscale characterization of AM parts, focusing on their ability to detect and analyze defects such as porosity, cracks, inclusions, and surface roughness, while offering insights into defect formation mechanisms, material properties, and process-induced variations. The integration of deep learning (DL) frameworks, including Simurgh, DeepRecon, and DeepScout, enhances XCT/XRM workflows by reducing scan times, improving resolution recovery, and enabling accurate defect detection even with limited projection data. These DL-based methods overcome limitations of traditional reconstruction techniques, enabling faster, more reliable characterization of dense materials like Inconel 718 and novel alloys such as AlCe. Applications include process parameter optimization, high-throughput quality control, and multistage AM process evaluation, with DL-enhanced workflows accelerating analysis times from weeks to days. Correlative imaging approaches further validate XCT and XRM data against scanning electron microscopy (SEM) images of physically sectioned samples, confirming the accuracy of DL-based reconstructions and enabling comprehensive defect analysis. While challenges remain in generalizing DL models to diverse materials and imaging conditions, improvements in resolution, noise reduction, and defect detection highlight the transformative potential of these methods. This multiscale and correlative approach enables precise identification and correlation of microstructural features with the overall performance of AM components. By integrating advanced XCT, XRM, and DL techniques, this paper demonstrates a significant leap forward in AM characterization, offering valuable insights into the relationships between processing parameters, microstructure, and part performance, and driving innovations that enhance the quality and reliability of AM products for demanding industrial applications.

Additive manufacturing↗
DOE OSTI2026

Patch-Based Convolutional Neural Networks for Multiple Microstructural Features Detection in FIB-SEM Micrographs of Irradiated Nuclear Fuel

Focused ion beam scanning electron microscopy (FIB-SEM) tomography has increasingly been utilized for acquiring three-dimensional (3D) microstructure features at the sub-micron scale in irradiated nuclear materials. This technique involves sequential ion beam slicing followed by electron beam imaging and compositional mapping using energy dispersive spectroscopy (EDS). Despite its growing use, several challenges persist. These include the time-intensive nature of data collection of EDS data, difficulties in distinguishing between various microstructures, and issues with image alignment. These challenges currently limit the broader application of FIB-SEM tomography in the field. To overcome these limitations, we propose using convolutional neural networks (CNNs) to automate microstructure identification in SEM images. Our study introduces a new framework for identifying microstructures in irradiated U-10Zr (wt. %) metallic fuel with limited annotated data. The framework includes the creation of a reliable annotated dataset with paired SEM and ground truth data from EDS maps, the applications of CNNs for microstructure identification, and the validation of model performance. Specifically, we employed the Segment Anything Model (SAM) to align SEM images with corresponding EDS maps and focused ion beam (FIB) tomography SEM data. We evaluate several models, including Patch-based U-Net, Attention U-Net, and Residual U-Net, finding that patch-based U-Net exhibits superior segmentation performance and consistency. This approach reduces reliance on EDS detectors and aids in accelerating nuclear material analysis process, highlighting the potential of advanced deep learning techniques to improve microstructural understanding in nuclear material. This is the first framework to integrate SAM and Patch-based CNN models for semantic segmentation of irradiated nuclear materials, with potential applicability to other tomography datasets.

36 - MATERIALS SCIENCE↗
DOE OSTI2024

Fractal analysis on Ag 2 O thin film using a data-driven approach

The synthesis of fractal Ag oxide (Ag 2 O) on the surface of Ag thin film has been achieved at room temperature by using Synchrotron X-ray irradiation. We have performed an automated quantitative analysis of a batch of 1879 fractal Ag 2 O patterns in a scanning electron microscopy (SEM) image within a radius of 2000 mm outward from the center of the X-ray beam. The morphology is similar to that of the diffusion-limited cluster aggregation (DLCA) model. The fractal dimension (D) of Ag 2 O is between 1.7 and 1.5 from the center to the edge. The area distribution density of fractal Ag 2 O follows a quadratic function with radius R. It is found that the branches’ number of fractal Ag 2 O is a key factor affecting the fractal dimension. The more branches the fractal has, the greater the D is. This is the first time that Ag fractal has been investigated by combining automated data analysis methods with batch experimental data. This data-driven approach provides a new research perspective for rationally regulating materials’ fractal morphology and performance.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Circuit-level design principles for transmission-mode microwave impedance microscopy

A recently developed technique of transmission-mode microwave impedance microscopy (T-MIM) has greatly extended the capabilities of standard reflection-mode MIM to novel applications, such as the in operando study of nanoscale electro-acoustic devices. As is common for new techniques, systematic design principles for boosting sensitivity and balancing bandwidth are lacking. Here, we show numerically and analytically that the T-MIM signal is proportional to the reflection-mode voltage enhancement factor η of the circuit, as long as the output impedance of the local voltage source is properly treated. We show that this proportionality holds in the currently achievable “weak sampling” regime and beyond, for which we demonstrate a realistic path with commercially available superconducting components and critically coupled impedance matching networks. Furthermore, we demonstrate that for these next-generation designs, the sensitivity is generally maximized at a slightly different frequency from the unloaded S 11 resonance, which can be explained by the maximum power transfer theorem.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
NASA NTRS1991

Design and analysis of aspherical multilayer imaging X-ray microscope

Spherical Schwarzschild microscopes for soft X-ray applications in microscopy and projection lithography employ two concentric spherical mirrors that are configured such that the third-order spherical aberration and coma are zero. Based on incoherent, sine-wave MTF calculations, the object-plane resolution of a magnification-factor-20 microscope is presently analyzed as a function of object height and numerical aperture of the primary for several spherical Schwarzschild, conic, and aspherical two-mirror microscope configurations.

Shealy, David L.↗
NASA NTRS1992

Design and analysis of soft X-ray imaging microscopes

The spherical Schwarzschild microscope for soft X-ray applications in microscopy and projection lithography consists of two concentric spherical mirrors configured such that the third-order spherical aberration and coma are zero. Since multilayers are used on the mirror substrates for X-ray applications, it is desirable to have only two reflecting surfaces in a microscope. To reduce microscope aberrations and increase the field of view, generalized mirror surface profiles are here considered. Based on incoherent and sine wave modulation transfer function calculations, the object plane resolution of a microscope has been analyzed as a function of the object height and numerical aperture (NA) of the primary for several spherical Schwarzschild, conic, and aspherical Head reflecting two-mirror microscope configurations. The Head microscope with a NA of 0.4 achieves diffraction limited performance for objects with a diameter of 40 microns. Thus, it seems possible to record images with a feature size less than 100 A with a 40x microscope when using 40 A radiation.

Shealy, David L.↗
NASA NTRS1998

Ground Based Program for the Physical Analysis of Macromolecular Crystal Growth

During the past year we have focused on application of in situ Atomic Force Microscopy (AFM) for studies of the growth mechanisms and kinetics of crystallization for different macromolecular systems. Mechanisms of macrostep formation and their decay, which are important in understanding of defect formation, were studied on the surfaces of thaumatin, catalase, canavalin and lysozyme crystals. Experiments revealed that step bunching on crystalline surfaces occurred either due to two- or three-dimensional nucleation on the terraces of vicinal slopes or as a result of uneven step generation by complex dislocation sources. No step bunching arising from interaction of individual steps in the course of the experiment was observed. The molecular structure of the growth steps for thaumatin and lipase crystals were deduced. It was further shown that growth step advance occurs by incorporation of single protein molecules. In singular directions growth steps move by one-dimensional nucleation on step edges followed by lateral growth. One-dimensional nuclei have different sizes, less then a single unit cell, varying for different directions of step movement. There is no roughness due to thermal fluctuations, and each protein molecule which incorporated into the step remained. Growth kinetics for catalase crystals was investigated over wide supersaturation ranges. Strong directional kinetic anisotropy in the tangential step growth rates in different directions was seen. The influence of impurities on growth kinetics and cessation of macromolecular crystals was studied. Thus, for catalase, in addition to pronounced impurity effects on the kinetics of crystallization, we were also able to directly observe adsorption of some impurities. At low supersaturation we repeatedly observed filaments which formed from impurity molecules sedimenting on the surfaces. Similar filaments were observed on the surfaces of thaumatin, canavalin and STMV crystals as well, but the frequency was low compared with catalase crystallization. Cessation of growth of xylanase and lysozyme crystals was also observed and appeared to be a consequence of the formation of dense impurity adsorption layers. Attachment: "An in situ AFM investigation of catalase crystallization", "Atomic force microscopy studies of living cells: visualization of motility, division, aggregation, transformation, and apoptosis", AFM studies on mechanisms of nucleation and growth of macromolecular crystals", and "In situ atomic force microscopy studies of surface morphology, growth kinetics, defect structure and dissolution in macromolecular crystallization".

Malkin, Alexander J.↗
DOE OSTI2020

Revisiting heat treatment and surface activation of GaAs photocathodes: In situ studies using scanning tunneling microscopy and photoelectron spectroscopy

The lifetime of GaAs photocathodes in polarized electron guns is limited due to the delicate activation layer. An atomically clean and smooth GaAs surface is needed to deposit a robust activation layer, such as Cs 2 Te, with longer lifetime compared to traditional (Cs,O) activation. A previous experiment with Cs 2 Te activation on GaAs used heat cleaning temperatures around 400°C to avoid an increase in surface roughness [Bae et al., Appl. Phys. Lett. 112, 154101 (2018)]. Furthermore, high-temperature heat cleaning around 580°C, which results in a relatively contamination-free surface, could be one possible way to improve quantum efficiency. However, one should be cautious about surface roughness degradation during high-temperature heat cleaning. In this paper, we report results of surface roughness measurements on native, heat cleaned, and (Cs,O) activated GaAs photocathodes under vacuum. The results, measured by ultrahigh vacuum scanning tunneling microscopy, show that the surface roughness improves as the heat cleaning temperature is increased, by at least a factor of three for 580°C heat cleaning, compared to the native sample. Activation with (Cs,O) is shown to increase surface roughness by a factor of four compared to a 580°C heat cleaned sample. This confirms that high-temperature heat cleaning can be useful for depositing good quality robust activation layers on GaAs. Additionally, we also report chemical analysis for each step of preparation for p-doped GaAs photocathodes using X-ray photoelectron spectroscopy (XPS), angle-resolved XPS, and ultraviolet photoelectron spectroscopy. Our results indicate that the (Cs,O) activation layer forms a sandwich structure consists of Cs and oxygen. We found no formation of any specific compound such as Cs 2 O or Cs 11 O 3 .

43 PARTICLE ACCELERATORS↗
DOE OSTI2021

Stamping Nanoparticles onto the Electrode for Rapid Electrochemical Analysis in Microfluidics

Electrochemical analysis is an efficient way to study various materials. However, nanoparticles are challenging due to the difficulty in fabricating a uniform electrode containing nanoparticles. We developed novel approaches to incorporate nanoparticles as a working electrode (WE) in a three-electrode microfluidic electrochemical cell. Specifically, conductive epoxy was used as a medium for direct application of nanoparticles onto the electrode surface. Three approaches in this work were illustrated, including sequence stamping, mix stamping, and droplet stamping. Shadow masking was used to form the conductive structure in the WE surface on a thin silicon nitride (SiN) membrane. Two types of nanomaterials, namely cerium oxide (CeO2) and graphite, were chosen as representative nanoparticles. The as-fabricated electrodes with attached particles were characterized using atomic force microscopy (AFM) and time-of-flight secondary ion mass spectrometry (ToF-SIMS). Electrochemical analysis was performed to verify the feasibility of these nanoparticles as electrodes. Nanomaterials can be quickly assessed for their electrochemical properties using these new electrode fabrication methods in a microfluidic cell, offering a passport for rapid nanomaterial electrochemical analysis in the future.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2022

An SECM-Based Spot Analysis for Redoxmer-Electrode Kinetics: Identifying Redox Asymmetries on Model Graphitic Carbon Interfaces

The fundamental process in non-aqueous redox flow battery (NRFB) operation revolves around electron transfer (ET) between a current collector electrode and redox-active organic molecules (redoxmers) in solution. Here, we present an approach utilizing scanning electrochemical microscopy (SECM) to evaluate interfacial ET kinetics between redoxmers and various electrode materials of interest at desired locations. This spot-analysis method relies on the measurement of heterogeneous electron transfer rate constants (k f or k b ) as a function of applied potential (E-E 0 '). As demonstrated by COMSOL simulations, this method enables the quantification of Butler-Volmer kinetic parameters, the standard heterogeneous rate constant, k 0 , and the transfer coefficient, α. Our method enabled the identification of inherent asymmetries in the ET kinetics arising during the reduction of ferrocene-based redoxmers, compared to their oxidation which displayed faster rate constants. Similar behavior was observed on a wide variety of carbon electrodes such as multi-layer graphene, highly ordered pyrolytic graphite, glassy carbon, and chemical vapor deposition-grown graphite films. However, aqueous systems and Pt do not exhibit such kinetic effects. Our analysis suggests that differential adsorption of the redoxmers is insufficient to account for our observations. Displaying a greater versatility than conventional electroanalytical methods, we demonstrate the operation of our spot analysis at concentrations up to 100 mM of redoxmer over graphite films. Looking forward, our method can be used to assess non-idealities in a variety of redoxmer/electrode/solvent systems with quantitative evaluation of kinetics for applications in redox-flow battery research.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

DONUT: physics-aware machine learning for real-time X-ray nanodiffraction analysis

Coherent X-ray scattering techniques are critical for investigating the fundamental structural properties of materials at the nanoscale. While advancements have made these experiments more accessible, real-time analysis remains a significant bottleneck, often hindered by artifacts and computational demands. In scanning X-ray nanodiffraction microscopy, which is widely used to spatially resolve structural heterogeneities, this challenge is compounded by the convolution of the divergent beam with the sample’s local structure. To address this, we introduce DONUT (Diffraction with Optics for Nanobeam by Unsupervised Training), a physics-aware neural network designed for the rapid and automated analysis of nanobeam diffraction data. By incorporating a differentiable geometric diffraction model directly into its architecture, DONUT learns to predict crystal lattice strain and orientation in real-time. Crucially, this is achieved without reliance on labeled datasets or pre-training, overcoming a fundamental limitation for supervised machine learning in X-ray science. We demonstrate experimentally that DONUT accurately extracts all features within the data over 200 times more efficiently than conventional fitting methods.

Materials science↗
DOE OSTI2025

DONUT: Physics-aware Machine Learning for Real-time X-ray Nanodiffraction Analysis

SF-25-088 Coherent X-ray scattering techniques are critical for investigating the fundamental structural properties of materials at the nanoscale. While advancements have made these experiments more accessible, real-time analysis remains a significant bottleneck, often hindered by artifacts and computational demands. In scanning X-ray nanodiffraction microscopy, which is widely used to spatially resolve structural heterogeneities, this challenge is compounded by the convolution of the divergent beam with the sample’s local structure. To address this, we introduce DONUT (Diffraction with Optics for Nanobeam by Unsupervised Training), a physics-aware neural network designed for the rapid and automated analysis of nanobeam diffraction data. By incorporating a differentiable geometric diffraction model directly into its architecture, DONUT learns to predict crystal lattice strain and orientation in real-time. Crucially, this is achieved without reliance on labeled datasets or pre-training, overcoming a fundamental limitation for supervised machine learning in X-ray science. We demonstrate experimentally that DONUT accurately extracts all features within the data over 200 times more efficiently than conventional fitting methods.

Zhou, Tao [Argonne National Laboratory (ANL), Argo↗
DOE OSTI2024

Random forest prediction of crystal structure from electron diffraction patterns incorporating multiple scattering

Diffraction is the most common method to solve for unknown or partially known crystal structures. However, it remains a challenge to determine the crystal structure of a new material that may have nanoscale size or heterogeneities. Here, in this study, we train an architecture of hierarchical random forest models capable of predicting the crystal system, space group, and lattice parameters from one or more unknown two-dimensional electron diffraction patterns. Our initial model correctly identifies the crystal system of a simulated electron diffraction pattern from a 20-nm-thick specimen of arbitrary orientation 67% of the time. We achieve a topline accuracy of 79% when aggregating predictions from ten patterns of the same material but different zone axes. The space group and lattice predictions range from 70% to 90% accuracy and median errors of 0.01-0.5Å, respectively, for cubic, hexagonal, trigonal, and tetragonal crystal systems while being less reliable on orthorhombic and monoclinic systems. We apply this architecture to a four-dimensional scanning transmission electron microscopy scan of gold nanoparticles, where it accurately predicts the crystal structure and lattice constants. These random forest models can be used to significantly accelerate the analysis of electron diffraction patterns, particularly in the case of unknown crystal structures. Additionally, due to the speed of inference, these models could be integrated into live transmission electron microscopy experiments, allowing real-Time labeling of a specimen.

36 MATERIALS SCIENCE↗