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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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64 records · Page 4
All sourcesNASA NTRSDOE OSTI
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↗
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↗
DOE OSTI2025

Computed Tomography Scanning and Geophysical Measurements of the Clinton Formation in Ohio

Computed tomography and multi-sensor core logging of core material from twelve Ohio wells held by the Ohio Department of Natural Resources, Division of Geological Survey to make publicly available core information from the Early Silurian Clinton Formation in Eastern Ohio. Describes data available can be accessed from NETL’s Energy Data eXchange (EDX) online system, https://edx.netl.doe.gov/dataset/ct-and-geophysical-data-of-ohio-clinton-sands.

58 GEOSCIENCES↗
DOE OSTI2025

Cyclotron resonance accelerators for industrial applications

Here, this paper describes novel configurations for cyclotron resonance acceleration of electrons and ions that have several attractive features including: a compact robust room-temperature single-cell RF cavity as the accelerator structure; and continuous high current accelerated un-bunched beam output with self-scanning, obviating need for a separate beam scanner. An electron accelerator version, the electron Cyclotron Resonance Accelerator (eCRA), is under development to be an efficient source for high power electron and x-ray beams for medical, research, sterilization, and National Security applications, so as to replace radioactive materials. An ion accelerator version, the ion Cyclotron Auto-Resonance Accelerator (iCARA) is described here, suggesting its potential to produce, as an example, a high-current multi-MeV beam of deuterons which could be highly competitive with that produced either with linacs or cyclotrons. Such a deuteron beam could produce a high flux of fast neutrons via deuteron stripping, for applications including the transmutation of used nuclear fuel, material studies relevant for a fusion reactor inner wall, tritium breeding and medical isotope production. For the high-current, high efficiency simulated performance for eCRA and iCARA as described in this paper, the particle beams produced may not exhibit the low emittance values that are important for most discovery research. Rather, the beams could be useful for industrial applications where higher emittance and some energy spread can be tolerated, in favor of high beam power.

43 PARTICLE ACCELERATORS↗
DOE OSTI2025

Nanoscopic Plugs Block Hydrogen Crossover in Submicron Thick Proton-Conducting SiO 2 Membranes for Water Electrolysis

Zero-gap electrolyzers based on submicron thick proton-conducting oxide membranes (POMs) represent a promising approach to increasing the efficiency of H 2 production from water electrolysis while moving away from conventional perfluorosulfonic acid (PFSA) membranes. A critical barrier to the commercialization of such electrolyzers is that the ultrathin nature of POMs, which is necessary to achieve low cell resistance, makes them more susceptible to defects that can lead to unacceptably high rates of H 2 crossover. Herein, we demonstrate an approach to mitigate this problem through selective deposition of carbon-containing silicon oxide (SiO x C y ) “nanoplugs” into the defects of submicron thick SiO 2 membranes using a facile electrochemically mediated deposition process. Selective deposition of nanoplugs within the defects was verified by multiple characterization techniques, while scanning electrochemical microscopy (SECM) was used to confirm selective plugging of H 2 -crossover hotspots associated with defects at identical locations. Thanks to the use of nanoplugs, the H 2 permeance of 250 nm thick SiO 2 membranes was reduced by 5 to 6 orders of magnitude compared to the unmodified atomic layer deposition (ALD) SiO 2 membranes while having negligible impact on the ionic resistance of the membrane. These plug-modified membranes also enabled safe and stable operation of a zero-gap full cell electrolysis cell, in contrast to cells lacking nanoplugs that produced anode effluent streams having H 2 concentrations near or exceeding the lower flammability limit (LFL) of H 2 . Furthermore, beyond water electrolysis, this defect-sealing strategy has the potential to be broadly implemented in other applications, such as fuel cells and flow batteries, offering a versatile solution to mitigate crossover-related performance losses.

ALD SiO2↗