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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 163 records · Page 9
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Combining Deep Learning and scatterControl for High-Throughput X-ray CT Based Non-Destructive Characterization of Large-Scale Casted Metallic Components

X-ray computed tomography (XCT) is essential for nondestructive evaluation and quality control of large-scale metal components. XCT imaging, however, faces significant challenges from metal artifacts, particularly those caused by Compton scattering, which degrade image quality and obscure critical details. Hardware-based solutions (e.g. scatterControl) offer advancements by intercepting scattered photons and reducing artifacts, but they can be time-consuming and require additional processing. Here, we propose modifying and leveraging a novel deep learning (DL) framework, Simurgh, to enhance and accelerate scatter correction in XCT. By combining scatterControl with DL-based artifact removal, we demonstrate significant reduction in scan time while producing high-quality reconstructions. Through extensive evaluation on industrial XCT data, we show that our methods reduce scan time by up to more than 10 x while preserving flaw detectability. Quantitative analysis across multiple segmentation techniques confirms that Simurgh-based reconstructions consistently outperform traditional Feldkamp-Davis-Kress, model-based iterative reconstruction, and commercial DL models in both pixel-level and task-specific evaluations, enabling scalable, high-throughput XCT workflows for characterization of large scale components in applications such as casting and metal additive manufacturing.

Complex metal parts↗
DOE OSTI2024

WARE 1-9 Well

Computed tomography and core logger data described in Technical Report "Computed Tomography Scanning and Geophysical Measurements of the J. F. WARE Gas Unit 1-9 Well in Smith County, Texas" by Brinza et al. , 2024.

CoreCM↗
DOE OSTI2025

Reducing the Energy Consumption of Magnetic Resonance Imaging and Computed Tomography Scanners: Integrating Ecodesign and Sustainable Operations

This review aims to provide valuable insights into how energy consumption in magnetic resonance imaging (MRI) and computed tomography (CT) scanners can be effectively monitored, managed, and reduced, thereby contributing to more sustainable medical imaging practices. Demand for advanced imaging technologies such as MRI and CT scanners continues to increase, and understanding the resultant impact on greenhouse gas emissions requires a thorough evaluation of their energy consumption. Here, this review examines the energy monitoring and consumption characteristics of MRI and CT scanners, highlighting potential approaches for energy savings. An overview of MRI and CT principles, hardware components, and their associated energy consumption is provided. After addressing the technical aspects, the hardware and software requirements essential for accurate energy metering are detailed. Baseline measurements of energy consumption data are then provided as a foundation to understand current usage patterns and identify areas for improvement. Ongoing efforts to reduce energy consumption are categorized into 3 main strategies: operations, scanner design enhancements, and active scanning techniques, including accelerated MRI protocols. Ultimately, we emphasize that achieving sustainability in medical imaging requires collaboration across disciplines. By incorporating eco-friendly design in new imaging equipment, we can reduce the environmental impact, promote sustainability, and set a health care industry standard for a healthier planet.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2025

Influence of gradation on failure mode of saturated sand at particle scale

Extensive research has been reported in the literature to characterize the failure mode of dry sand using various experimental techniques such as surface optical imaging, photo-elastic materials, three-dimensional (3D) computed tomography (CT), and 3D synchrotron micro-computed tomography (SMT). However, there is a limited literature about the behavior of saturated sand. This paper presents the results of axisymmetric triaxial compression (ATC) experiments that were conducted on saturated sand specimens. The behavior of specimens composed of a uniform sand with grain size between US sieves #40 and #50 is compared to specimens conducted on the same sand that has a wider gradation. 3D SMT technique was used to acquire 3D scans while shearing the specimens to probe localized events that are completely missed or misinterpreted when analyzing ATC measurements based on global standard measurements. The results show a higher effective principal stress ratio (EPSR) for the non-uniform specimen and a thicker shear band when compared to uniform specimen.

Elnur, Mohammed [University of Tennessee]↗
DOE OSTI2025

Surface Local Impurity Scattering as a Probe for Topological Kondo Insulators

Shortly after the discovery of topological band insulators, topological Kondo insulators (TKIs) is also theoretically predicted. The latter has ignited revival interest in the properties of Kondo insulators. Currently, the feasibility of topological nature in SmB 6 is intensively analyzed by several complementary probes. Here by starting with a minimal-orbital Anderson lattice model, the local electronic structure is explored in a Kondo insulator. It is showed that the two strong topological regimes sandwiching the weak topological regime give rise to a single Dirac cone, which is located near the center or corner of the surface Brillouin zone. It is further found that, when a single impurity is placed on the surface, low-energy resonance states are induced in the weak scattering limit for the strong TKI regimes and the resonance level moves monotonically across the hybridization gap with the strength of impurity scattering potential; while low energy states can only be induced in the unitary scattering limit for the weak TKI regime, where the resonance level moves universally toward the center of the hybridization gap. These impurity-induced low-energy quasiparticles will lead to characteristic signatures in scanning tunneling microscopy/spectroscopy, which has recently found success in probing into exotic properties in heavy fermion systems.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Gas pore correlations in laser powder bed fusion of Al6061

Additive manufacturing (AM) of metal materials based on powder bed fusion technology is widely used now in many industries. A known limitation of this type of manufacturing is the formation of gas pores in the bulk material. Here we present a combined X-ray imaging and mid-infrared pyrometry study of pore formation in side-by-side tracks of Al6061 for different processing conditions using both in-situ and post-processing analysis. By carefully quantifying the distributions and correlations of pore positions, we show that an existing pore in one track often catalyzes the formation of another pore in an adjacent track. In a raster scan strategy commonly used to construct bulk material, this phenomenon has the result of forming subsurface perforations, or lines of pores transverse to the scanning direction in a rastered patch. If controlled, this effect can be eliminated to improve the yield strength of the build, or exploited to create programmable failures for specific purposes.

36 MATERIALS SCIENCE↗
NASA NTRS1989

Fatigue crack growth in a unidirectional SCS-6/Ti-15-3 composite

An investigation was conducted to characterize and model the fatigue crack growth (FCG) behavior of a SCS-6/Ti-15-3 metal matrix composite. Part of the study was conducted using a fatigue loading stage mounted inside a scanning electron microscope (SEM). This unique facility allowed high magnification viewing of the composite fatigue processes and measurement of the near crack tip displacements. The unidirectional composite was tested in the (0)8 (i.e., longitudinal) and (90)8 (i.e., transverse) orientations. For comparison purposes unreinforced matrix material produced by the identical process as the reinforced material was also tested. The results of the study reveal that the fatigue crack growth behavior of the composite is a function of specimen geometry, fiber orientation and the interaction of local stress fields with the highly anisotropic composite. In the case of (0)8 oriented single edge notch (SEN) specimens and (90)8 oriented compact tension (CT) specimens, the crack growth was normal to the loading direction. However, for the (0)8 CT specimens the crack grew mostly parallel to the loading and the fiber direction. The unusual fatigue behavior of the (0)8 CT specimens was attributed to the specimen geometry and the associated high tensile bending stresses perpendicular to the fiber direction. These stresses resulted in preferential cracking in the weak interface region perpendicular to the fiber direction. The interface region, and in particular the carbon coating surrounding the fiber proved to be the composites weakest link. In the (0)8 SEN the crack growth was confined to the matrix leaving behind unbroken fibers which bridged the cracked surfaces. As the crack grew longer, more fibers bridged the crack resulting in a progressive decrease in the crack growth rates and eventual crack arrest. The actual near crack tip displacement measurements were used in a proposed formulation for a bridging-corrected effective crack driving force, delta K(sub eff). This parameter was able to account for most of the experienced bridging and correlated the (0)8 SEN fatigue crack growth data reasonably well.

Kantzos, Peter↗
DOE OSTI2024

Sleepy Hollow Reagan Unit 86A Well Data

Computed tomography data described in the technical report series "Computed Tomography Scanning and Geophysical Measurements of the Integrated Mid-Continent Stacked Carbon Storage Hub Sleepy Hollow Reagan Unit 86A Well" by Thomas Paronish; Mathias Pohl; Alexis Parker; Rhiannon Schmitt; Johnathan Moore; Richard Spaulding; Igor Haljasmaa; Dustin Crandall; Valarie Smith; Andrew Duguid; and R. M. Joeckel

AS↗
NASA NTRS2002

Uses of Computed Tomography in the NASA Materials Science Program

Computed Tomography (CT) has proved to be of inestimable use in providing a rapid evaluation of a variety of samples from Mechanics of Granular Materials (MGM) to electronic materials (Ge-Si alloys) to space grown materials such as meteorites. The system at Kennedy Space Center (KSC), because of its convenient geographical location, is ideal for examining samples immediately after returning to Earth. It also has the advantage of the choice of fluxes, and in particular the use of a radioactive cobalt source, which is basically monochromatic. This permits a reasonable measurement of density to be made from which chemical composition can be determined. Due to the current dearth of long duration space grown materials, the CT instrument has been used to characterize materials in preparation for flight, to determine thermal expansion values, and to examine long duration space grown materials, i.e. meteorites. The work will first describe the establishment of the protocol for obtaining the optimum density readings for any material. This will include both the effects of the hardware or instrumental parameters that can be controlled, and the techniques used to process the CT data. Examples will be given of the compositional variation along single crystals of germanium-silicon alloys. Density variation with temperature has been measured in preparation for future materials science experiments; this involved the fabrication and installation of a single zone furnace incorporating a heat pipe to ensure of high temperature uniformity. At the time of writing the thermal expansion of lead has been measured from room temperature to 900 C. Three methods are available. Digital radiography enable length changes to be determined. Prior to melting the sample is small than the container and the diameter change can be measured. Most critical, however, is the density change in solid, through the melting region, and in the liquid state. These data are needed for engineering purposes to aid in the design of containment cartridges, and for enabling fluid flow calculations. A second sample, with the lead alloyed with antimony is ready for scanning. This corresponds to the planned composition of Dr. Poirier's flight experiment. Finally, three-dimensional results will be shown of the structure of a two-phase metallic meteorite of metal and sulfide, in which the iron-nickel phase has coarsened during slow cooling over hundreds of millions of years.

Engel, H. Peter↗
NASA NTRS2003

Uses of Computed Tomography for Characterizing Materials Grown Terrestrially and in Microgravity

Tomography (CT) has proved to be of inestimable use in providing a rapid evaluation of a variety of samples from Mechanics of Granular Materials (MGM) to electronic materials (Ge-Si alloys) to space grown materials such as meteorites. The system at Kennedy Space Center (KSC), because of its convenient geographical location, is ideal for examining samples before launch and immediately after returning to Earth. It also has the advantage of the choice of fluxes, and in particular the use of a radioactive cobalt source, which is basically monochromatic. This permits a reasonable measurement of density to be made from which chemical composition can be determined. Due to the current dearth of long duration space grown materials, the CT instrument has been used: (1) to characterize materials in preparation for flight, (2) to determine thermal expansion values, and (3) to examine long duration space grown materials, i.e. meteorites. This work will first describe the establishment of the protocol for obtaining the optimum density readings for any material. Included will be the effects of the hardware or instrumental parameters that can be controlled, and the techniques used to process the CT data. Examples will be given of the compositional variation along a single crystal of Ge-Si alloy. Density variation with temperature has been measured in preparation for future materials science experiments; this involved the fabrication at MSFC and installation of a single zone furnace at KSC incorporating a heat pipe to ensure high temperature uniformity. At the time of writing the thermal expansion of lead (Pb) has been measured from room temperature to 900 C. Three methods are available. Digital radiography enables length changes to be determined. Prior to melting the sample is smaller than the container and the diameter change can be measured. Most critical, however, is the density change in solid, through the melting region, and in the liquid state. These data are needed for engineering purposes to aid in the design of containment cartridges, and for enabling fluid flow calculations. A second sample, with the Pb alloyed with Sb is ready for scanning. This corresponds to the planned composition of Dr. Poirier s flight experiment. Other materials pertinent to NASA programs such as Al-Cu (Trivedi), CdTe (Banish), HgCdTe (Lehoczky) will be examined below and above the melting point. Finally, three-dimensional results will be shown of the structure of a two-phase metallic meteorite of metal and sulfide, in which the Fe-Ni phase has coarsened during slow cooling over hundreds of millions of years.

Source record↗
DOE OSTI2024

Statistical analysis of HAADF-STEM images to determine the surface coverage and distribution of immobilized molecular complexes

The surface immobilization of molecular catalysts is attractive because it combines the benefits of homogeneous and heterogeneous catalysis. However, determining the surface coverage and distribution of a molecular catalyst on a solid support is often challenging, inhibiting our ability to design improved catalytic systems. Here, in this work, we demonstrate that the combination of scanning transmission electron microscopy (STEM) and image analysis of the individual positions of heavy atoms in transition metal complexes via a convolutional neural network (CNN) allows statistically robust determination of the surface coverage and distribution of immobilized molecular catalysts. These observations provide information about how changes in the functionalization conditions, attachment group, and structure of the molecular catalyst affect the surface coverage and distribution, providing insight into the chemical mechanism of surface immobilization. The method could be generally valuable for correlating the surface coverage and distribution to the activity, selectivity, and stability of a catalytic system.

HAADF-STEM↗
DOE OSTI2025

Enhancing synchrotron radiation micro-CT images using deep learning: an application of Noise2Inverse on bone imaging

In bone-imaging research, in situ synchrotron radiation micro-computed tomography (SRµCT) mechanical tests are used to investigate the mechanical properties of bone in relation to its microstructure. Low-dose computed tomography (CT) is used to preserve bone's mechanical properties from radiation damage, though it increases noise. To reduce this noise, the self-supervised deep learning method Noise2Inverse was used on low-dose SRµCT images where segmentation using traditional thresholding techniques was not possible. Simulated-dose datasets were created by sampling projection data at full, one-half, one-third, one-fourth and one-sixth frequencies of an in situ SRµCT mechanical test. After convolutional neural networks were trained, Noise2Inverse performance on all dose simulations was assessed visually and by analyzing bone microstructural features. Visually, high image quality was recovered for each simulated dose. Lacunae volume, lacunae aspect ratio and mineralization distributions shifted slightly in full, one-half and one-third dose network results, but were distorted in one-fourth and one-sixth dose network results. Following this, new models were trained using a larger dataset to determine differences between full dose and one-third dose simulations. Significant changes were found for all parameters of bone microstructure, indicating that a separate validation scan may be necessary to apply this technique for microstructure quantification. Noise present during data acquisition from the testing setup was determined to be the primary source of concern for Noise2Inverse viability. While these limitations exist, incorporating dose calculations and optimal imaging parameters enables self-supervised deep learning methods such as Noise2Inverse to be integrated into existing experiments to decrease radiation dose.

Obata, Yoshihiro (ORCID:0000000303659129)↗
DOE OSTI2026

Using 2.5D super-resolution to improve flaw detection in metal additive manufacturing parts

Industrial X-ray computed tomography (XCT) enables non-destructive inspection of additively manufactured (AM) parts, but high-resolution scanning requires long acquisition times and significant computational resources, limiting throughput in production environments. Super-resolution techniques can recover high-resolution information from low-resolution scans, but existing methods face a trade-off between 2D approaches that ignore inter-slice information and 3D methods that are computationally prohibitive for practical deployment. To address this trade-off, we propose a 2.5D deep learning-based super-resolution approach that uses seven neighbouring low-resolution slices to super-resolve the centre slice. This work evaluates the method on real XCT scans of steel AM parts, comparing reconstruction quality and flaw detection performance of 2D, 2.5D, and 3D ESRGAN-based super-resolution methods. Results demonstrate that 2.5D super-resolution significantly improves detection of small, process-induced flaws (e.g. porosity) compared to 2D methods, while avoiding the prohibitive computational burden of full 3D approaches. These findings provide initial evidence of 2.5D super-resolution as a practical, deployable solution for improving flaw detection in high-throughput industrial XCT inspection.

X-ray CT↗
DOE OSTI2026

Influence of Air and Ethanol Dehydration on Structure, Behavior, and Function of Type I Collagen Scaffolds

Ethanol dehydration is a common step in both scaffold manufacturing and tissue processing, yet the influence of ethanol on collagen is not well understood. This study examined the effects of dehydration, via ethanol treatment and air drying, on collagen structure, behavior, mechanics, and rehydration capacity. Multiple material characterization methods were used including Fourier Transform infrared spectroscopy (FTIR), Raman spectroscopy, scanning electron microscopy, thermogravimetric analysis, small/medium angle x‐ray scattering, volumetric swelling analysis, and tensile testing. Ethanol dehydration removed bulk water from scaffolds, making them stronger and stiffer, but also showed loss of molecular water. This molecular water appears to act as a collagen stabilizer, resulting in less thermally stable scaffolds. The loss of molecular water is also evident in the molecular d‐spacing. Secondary structure of scaffolds was also altered by ethanol, resulting in significantly enhanced rehydration capacity. Bulk water, both before and after rehydration, largely determined mechanical properties, which did not correlate with other structural measures such as FTIR. While rehydration largely returned collagen spacing to pre‐ethanol treated state, structural alterations seen in FTIR cannot be recovered. These results have implications for not only collagen scaffolds, but in many tissue engineering and processing applications.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2025

Unusual Electrochemical Activity of Thin SiO 2 Layers Leads to Instability of Molecular Attachment in Hybrid Photoelectrodes

Hybrid photoelectrodes, comprised of a light-absorbing semiconductor and a surface-integrated molecular catalyst, are attractive for applications in artificial photosynthesis, since they combine the advantages of broadband semiconductor light absorption with the selectivity of molecular catalysis. A widely used class of hybrid photoelectrodes is based on Si substrates passivated by a thin (<3 nm) layer of silicon oxide, which is commonly prepared by controlled chemical or thermal oxidation, resulting in chemical oxide (ChO) or thermal oxide (ThO) layers, respectively. However, the electrochemical stability of these oxide layers, and the chemical stability of the semiconductor-molecule assembly in hybrid photoelectrodes, are not well understood, with evidence that covalently-bound molecules detach from the oxide surface upon application of cathodic bias. We have examined the intrinsic electrochemical reactivity of silicon oxide layers and how it affects the attachment of molecular monolayers. We determined that the surface of Si|ThO is primarily terminated with hydrophobic siloxane moieties, whereas that of Si|ChO contains a higher concentration of hydrophilic silanol groups. Initial high current densities for Si|ChO under applied bias up to -2 V vs. Ag/AgCl, decrease during repeated cyclic voltammetry scans, due to the consumption of surface-bound water. This is manifested by a reversible wave around -0.5 V in CH 3 CN solution, and a similar pH-dependent wave in water, revealing the pK a of the silanol groups to be ~4. Here, our combined observations support the electrochemically-induced dehydration of the SiO 2 surface, which converts silanol groups to siloxanes and proceeds through an H-atom intermediate that is most likely stabilized by pentavalent Si. We propose that similar reactivity is responsible for the electrochemical loss of alkylsiloxane-attached molecules under cathodic bias, which has important implications for the choice of catalyst attachment strategy in hybrid photoelectrodes.

14 SOLAR ENERGY↗
NASA NTRSDate not supplied

Multiscale Modeling of Reconstructed Tricalcium Silicate using NASA Multiscale Analysis Tool

To study microstructure characteristics of cementitious materials hydrated in space; previously, cement binder formations were processed under microgravity conditions and was further compared against ground-based experiments. For accurate estimation of process-structure-property linkage, particularly on samples hydrated in the microgravity environment, it is desired to have a high-fidelity volumetric representation of the microstructure. However, owing to small sample size and high porosity of the space-returned samples, conventional experimental characterization techniques are not viable. Hence, a deep learning-based reconstruction algorithm was employed to obtain high fidelity 3D volumes from sparse high resolution 2D Scanning Electron Microscopy (SEM) images, as inputs to micromechanics-based modeling. This machine learning-based reconstruction methodology validated against low-order statistical descriptors, captured the microstructural topology of both sample types (ground, 1g and microgravity, μg). Due to the lack of gravity, hydration products of the samples processed in space differed from those processed-on ground. Such AI-generated virtual samples were analyzed in a multiscale recursive micromechanics approach using the NASA Multiscale Analysis Tool (NASMAT). Here, we present a methodology to rapidly integrate and evaluate these AI-generated volumes in NASMAT. The synthesized microstructural volumes are directly employed as Representative Volume Elements (RVEs) to preserve the fidelity (1 pixel = 0.54 m). Invariably, analysis of such largescale problems (5123 voxels) requires huge amount of computational resources. By taking advantage of the NASMAT architecture, we also focused on systematic multiscale integration of these AI-reconstructed virtual volumes to reduce the computational demands. In this work, this methodology is demonstrated on the ground-based, 1g samples. The estimated stiffness value of 15.90 GPa is comparable to experimentally obtained modulus of hydrated tricalcium silicate sample. The workflow presented here paves the way for utilizing the NASMAT tool to perform multiscale analyses of other multi-phase material systems using either 3D virtual datasets synthesized using AI or obtained via micro-CT.

Machine Learning↗
DOE OSTI2025

Quantitative Imaging of Cobalt Phthalocyanine Distribution on Carbon Nanotubes: A Deep Learning Approach to Catalyst Characterization

Electrochemical reduction of carbon dioxide (CO 2 ) offers a pathway to valuable products, with catalysts playing a crucial role. This study investigates the distribution of cobalt tetraaminophthalocyanine (CoPc-NH 2 ) immobilized on carbon nanotubes (CNTs), utilizing high-angle annular dark-field scanning transmission electron microscopy (HAADF-STEM) to characterize CoPc-NH 2 distribution. A challenge in the quantitative HAADF-STEM analysis is the introduction of bias from manual Co atom identification. To address this, we developed and trained a convolutional neural network (CNN) using a data set generated from images of CoPc-NH 2 /CNT samples with varying Co loadings. The CNN, implemented in TensorFlow and Keras, facilitated Co atom detections. Analysis of the CNN-generated data confirmed a correlation between Co loading and surface density, consistent with findings from UV–vis spectroscopy. Furthermore, the application of Ripley’s L(d) function highlighted the presence of slight Co atom clustering. Furthermore, this work demonstrates the utility of the combined HAADF-STEM and CNN approach for providing spatially resolved information about catalyst distribution on nonplanar supports, revealing structural details that are typically lost through other characterization methods.

HAADF-STEM↗
DOE OSTI2025

Charting the chemical space of Zintl phases with graph neural networks and bonding insights

A large number of Zintl phases have been discovered by solid-state chemists driven by empirical knowledge, chemical intuition and in some cases, through serendipitous accidents. These discoveries have only scratched the surface, given the vast compositional and structural diversity that Zintl phases can accommodate. The large chemical space of Zintl phases, as well as intermetallic compounds in general, remain under-explored. Here, we use graph neural networks and the upper bound energy minimization approach to efficiently scan a large chemical space of >90 000 hypothetical Zintl phases and accurately discover 1810 new thermodynamically stable phases with 90% precision, as validated with first-principles calculations. We show that our approach is more than 2× more accurate in predicting DFT stability than M3GNet (40% precision) on the same dataset. Using a random forest model and SHAP analysis, we demonstrate the critical role of ionic bonding in the thermodynamic stability of Zintl phases. Our results not only expand the known chemical landscape of Zintl phases but also highlight the efficacy of machine learning frameworks combined with domain knowledge in uncovering chemically meaningful insights across complex intermetallics.

36 MATERIALS SCIENCE↗