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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
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Yet Another Sunshine Mystery: Unexpected Asymmetry in GeV Emission from the Solar Disk

The Sun is one of the most luminous γ-ray sources in the sky and continues to challenge our understanding of its high-energy emission mechanisms. This study provides an in-depth investigation of the solar disk γ-ray emission, using data from the Fermi Large Area Telescope spanning 2008 August to 2022 January. We focus on γ-ray events with energies exceeding 5 GeV, originating from 0°5 angular aperture centered on the Sun, and implement stringent time cuts to minimize potential sample contaminants. We use a helioprojection method to resolve the γ-ray events relative to the solar rotation axes and combine statistical tests to investigate the distribution of events over the solar disk. We found that integrating observations over large time windows may overlook relevant asymmetrical features, which we reveal in this work through a refined time-dependent morphological analysis. We describe significant anisotropic trends and confirm compelling evidence of energy-dependent asymmetry in the solar disk γ-ray emission. Intriguingly, the asymmetric signature coincides with the Sun's polar field flip during the cycle 24 solar maximum, around 2014 June. Our findings suggest that the Sun's magnetic configuration plays a significant role in shaping the resulting γ-ray signature, highlighting a potential link between the observed anisotropies, solar cycle, and the solar magnetic fields. These insights pose substantial challenges to established emission models, prompting fresh perspectives on high-energy solar astrophysics.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Automated Scoring of Morphological Changes in Images of Pentaerythritol Tetranitrate

Recent advances in characterization techniques that generate large datasets of material microstructure images require robust, automated image-processing. We applied an unsupervised anomaly detection method called feature anomaly detection system (FADS) to automatically detect and quantify microstructure changes in images of the explosive pentaerythritol tetranitrate (PETN) aged at various temperatures. We demonstrated the FADS approach on two-dimensional images extracted from computed tomography scans, but the same technique can be readily applied to other imaging modalities. FADS calculates anomaly scores on the basis of differences in filter activations of nominal and test data in pretrained convolutional neural networks. The FADS scores successfully differentiated between pristine PETN and PETN aged at a temperature where material coarsening occurred. Morphological metric analysis of segmented images verified observed trends in FADS scores as a function of aging temperature and aging time, specifically by calculating volume fractions, specific boundary lengths, two-point correlation functions, and local thicknesses. Here, the FADS technique has two important advantages compared to traditional morphological analysis: First, it uses grayscale images as input, rather than images that are segmented to separate the appropriate phases; and second, FADS scores capture any type of changes among image sets, rather than requiring prior knowledge or selection of a relevant set of metrics.

Accelerated aging↗
DOE OSTI2022

Quantitative Morphological Characterization of Carbide Inclusions in Uranium Metal

Uranium carbides (UCs) are prevalent inclusions in U metal that form during melting operations from interactions with crucible walls and the casting chamber atmosphere. Although UCs have been studied extensively since the beginning of U metal foundry operations, there are still unknowns regarding the effects of thermomechanical processing on their sizes and morphology. Here, we present the results of a series of controlled cooling experiments with molten uranium to elucidate the effect of cooling rate on inclusion morphology in a-U. Samples were melted using a vacuum induction melter and manually cooled at rates of 2.5, 1.7, 1.1, 0.8, 0.3 (± 1%) K/s from ~1600 K to <700 K in under 1 hour. Subsequent scanning electron microscopy (SEM) was performed on cross-sections of the samples, revealing a complex mixture of UC morphologies that are indicative of diffusion and growth influenced by the thermal processing of the U matrix. Image analysis using the Morphological Analysis of Materials (MAMA) software showed that UC sizes generally grew larger with slower cooling rates, and the two slowest cooling rates noticeably impacted the inclusion circularity and ellipse aspect ratio. These results indicate that UC morphology is sensitive to short cooling rates (<1 hour) and could therefore be controlled in the production of metallic nuclear fuels. Additionally, inclusion speciation and morphologies could potentially provide forensic clues about processing history of unknown metal samples. Understanding the driving forces involved in UC morphology evolution is beneficial for evaluating metal fuels for next generation nuclear reactors and for identifying signatures for nuclear forensics.

Athon, Matthew T.↗
DOE OSTI2024

Analysis of the Surface Morphology and Chemical Composition of Zr-Nb3Sn Alloys with Zr Different Concentrations

The inclusion of zirconium (Zr) in niobium-tin (Nb3Sn) significantly enhances the performance of Nb3Sn radiofrequency cavities in high magnetic fields. This research project is dedicated to characterizing the surface properties and chemical composition of Zr-doped Nb3Sn. Two different concentrations of Zr were used for doping: approximately 0.5% and 24%. Various spectroscopy techniques were employed to analyze how the surface morphology and chemical composition of Nb3Sn change with increasing Zr content. The findings of this study indicate that the grain size decreases as the Zr concentration increases. In samples with 24% Zr, nanometer-sized particles, likely oxides, were observed. X-ray photoelectron spectroscopy (XPS) data revealed that the thickness of niobium oxides (NbOx) decreases with increasing Zr, while the thickness of tin oxides (SnOx) increases. Additionally, grain size distribution analysis showed that the average grain size is around 5300 nm , with a grain area density of about 165 grains/μm .

Sue, Micah↗
DOE OSTI2024

Analysis of the Surface Morphology and Chemical Composition of Zr-Nb3Sn Alloys with Zr Different Concentrations

The Zr inclusion in Nb3Sn can improve the high magnetic field performance of Nb3Sn radio frequency cavities. The focus of this project is to characterize the surface properties and chemical composition of Zr-Nb3Sn. Nb3Sn was doped with two different concentrations of Zr. ~0.5%, and ~24% Zr. Various spectroscopy techniques were used to examine how the surface morphology and chemical composition changes with increasing Zr fraction in Nb3Sn.

Sue, Micah↗
DOE OSTI2024

Analysis of the Surface Morphology and Chemical Composition of Zr-Nb3Sn Alloys with Different Zr Concentrations

In the field of superconducting science, Nb has been the material of choice for Radio Frequency (RF) Cavities for years. However, in recent years Nb3Sn has shown to be a promising alternative to Nb. Its combination of a significantly high superconducting transition temperature and a much higher value of upper critical magnetic field (Hc2), has been the reason the alloy has been so widely studied. This project aims to develop materials to further improve the high magnetic field performance of Nb3Sn by improving its upper critical magnetic field and critical current density (Jc). One way to improve the Jc and the Hc2 is to create more grain boundaries in the Nb3Sn alloy allowing them to trap more magnetic flux lines in the materials. To do this the grains must be made smaller which will naturally cause the formation of more grain boundaries. A common way to create smaller grains is by doping an impurity in the alloy. In this study, Nb3Sn was doped with Zr to form Zr-Nb3Sn alloys. This project investigates the surface properties and chemical composition of Zr-Nb3Sn alloys with varying zirconium concentrations (~0.5% and ~24% Zr). Using Scanning Electron Microscopy (SEM), Energy Dispersive Spectroscopy (EDS), and X-ray Photoemission Spectroscopy (XPS), allowed for the characterization of the differences in surface morphology and elemental composition between samples. SEM images were quantified using ImageJ giving insight into grain distribution. The results indicate that increased Zr concentration creates smaller grains in the Zr-Nb3Sn alloy. Furthermore, the ~ 24% Zr grew nanoscale particles on the surface that are possible oxides. XPS data revealed that as Zr concentration increased, the total Nb oxides (NbOx) thickness decreased. Conversely, as Zr concentration increased, the surface Sn oxides (SnOx) also increased slightly. Quantification via ImageJ of SEM data showed that the ~ 0.5% Zr sample had a grain size of ~ 5300 nm2. These results show that Zr inclusion leads to smaller grain formations, which is promising in the world of superconducting RF cavities for high magnetic field applications.

Sue, Micah↗
DOE OSTI2023

PhytoOracle: Scalable, modular phenomics data processing pipelines

As phenomics data volume and dimensionality increase due to advancements in sensor technology, there is an urgent need to develop and implement scalable data processing pipelines. Current phenomics data processing pipelines lack modularity, extensibility, and processing distribution across sensor modalities and phenotyping platforms. To address these challenges, we developed PhytoOracle (PO), a suite of modular, scalable pipelines for processing large volumes of field phenomics RGB, thermal, PSII chlorophyll fluorescence 2D images, and 3D point clouds. PhytoOracle aims to ( i ) improve data processing efficiency; ( ii ) provide an extensible, reproducible computing framework; and ( iii ) enable data fusion of multi-modal phenomics data. PhytoOracle integrates open-source distributed computing frameworks for parallel processing on high-performance computing, cloud, and local computing environments. Each pipeline component is available as a standalone container, providing transferability, extensibility, and reproducibility. The PO pipeline extracts and associates individual plant traits across sensor modalities and collection time points, representing a unique multi-system approach to addressing the genotype-phenotype gap. To date, PO supports lettuce and sorghum phenotypic trait extraction, with a goal of widening the range of supported species in the future. At the maximum number of cores tested in this study (1,024 cores), PO processing times were: 235 minutes for 9,270 RGB images (140.7 GB), 235 minutes for 9,270 thermal images (5.4 GB), and 13 minutes for 39,678 PSII images (86.2 GB). These processing times represent end-to-end processing, from raw data to fully processed numerical phenotypic trait data. Repeatability values of 0.39-0.95 (bounding area), 0.81-0.95 (axis-aligned bounding volume), 0.79-0.94 (oriented bounding volume), 0.83-0.95 (plant height), and 0.81-0.95 (number of points) were observed in Field Scanalyzer data. We also show the ability of PO to process drone data with a repeatability of 0.55-0.95 (bounding area).

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Creating ground truth for nanocrystal morphology: a fully automated pipeline for unbiased transmission electron microscopy analysis

Control over colloidal nanocrystal morphology (size, size distribution, and shape) is important for tailoring the functionality of individual nanocrystals and their ensemble behavior. Despite this, traditional methods to quantify nanocrystal morphology are laborious. New developments in automated morphology classification will accelerate these analyses but the assessment of machine learning models is limited by human accuracy for ground truth, causing even unsupervised machine learning models to have inherent bias. Herein, we introduce synthetic image rendering to solve the ground truth problem of nanocrystal morphology classification. By simulating 2D images of nanocrystal shapes via a function of high-dimensional parameter space, we trained a convolutional neural network to link unique morphologies to their simulated parameters, defining nanocrystal morphology quantitatively rather than qualitatively. An automated pipeline then processes, quantitatively defines, and classifies nanocrystal morphology from experimental transmission electron microscopy (TEM) images. Using improved computer vision techniques, 42,650 nanocrystals were identified, assessed, and labeled with quantitative parameters, offering a 600-fold improvement in efficiency over best-practice manual measurements. Further, a classification algorithm was trained with a prediction accuracy of 99.5%, which can successfully analyze a range of concave, convex, and irregular nanocrystal shapes. The resulting pipeline was applied to differentiating two syntheses of nominally cuboidal CsPbBr 3 nanocrystals and uniquely classifying binary nickel sulfide nanocrystal phase based on morphology. This pipeline provides a simple, efficient, and unbiased method to quantify nanocrystal morphology and represents a practical route to construct large datasets with an absolute ground truth for training unbiased morphology-based machine learning algorithms.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2026

Synthesis and application of thermally responsive nanofiber coatings for overtemperature monitoring

This study presents a one-pot synthesis route to organometallic nanofibers based on copper thiolate, exhibiting distinctive chemical and physical characteristics. Electron microscopy analysis of morphology and composition revealed 2-10 μm-long, 50-90 nm-diameter hollow and non-hollow fibers composed of copper, sulfur, oxygen, hydrocarbon, and chlorine. Thermogravimetric analysis showed a pronounced mass loss within 120°C-135°C. To elucidate the thermal responsive pathways, the nanofibers were characterized before and after heating. X-ray photoelectron spectroscopy indicates that an initially mixed Cu(I)/Cu(II) oxidation states transition to predominantly Cu(I) upon heating. A layer of nanofiber was coated on battery pouch foil and evaluated as a candidate thermally sensitive coating. At elevated temperature (100-130°C), nanofiber coating released volatile organic compounds, sulfide and sulfur dioxide as detected using multiple gas sensors. This thermally responsive gas release/sensing approach provides a potential large-area temperature monitoring strategy, which is particularly relevant where direct temperature measurements of individual batteries is impractical. The results established proof of concept for nanofiber-coated battery pouch foil as overtemperature warning platform that can provide alerts when surface temperatures exceed a critical threshold. More broadly, the ability to form interconnected fiber networks positions copper thiolate nanofiber coatings as promising materials for advanced applications.

Ihala Gamaralalage, Chanaka [ORNL] (ORCID:00000002↗
DOE OSTI2022

Utilization of the Critic Subnetwork of a Generative Adversarial Network as Detector of Morphological Material Change in Image Data

The resolution of computed tomography (CT) has become high enough to monitor morphological changes due to aging in materials in long-term applications. For this work, we explored the utility of the critic of a generative adversarial network (GAN) to automatically detect such changes. The GAN was trained with images of pristine Pharmatose, which is used as a surrogate energetic material. It is important to note that images of the material with altered morphology were only used during the test phase. The GAN-generated images reproduced the microstructure of Pharmatose well, although some unrealistic particle fusion was seen. Calculated morphological metrics (volume fraction, interfacial line length, and local thickness) for the synthetic images also showed good agreement with the training data, albeit with signs of mode collapse in the interfacial line length. While the critic exposed changes in particle size, it showed limited ability to distinguish images by particle shape. The detection of shape differences was also a more challenging task for the selected morphological metrics that related to energetic material performance. We further tested the critic with images of aged Pharmatose. Subtle changes due to aging are difficult for the human analyst to detect; but both critic and morphological metrics analysis showed image differentiation.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Computer Vision Pipeline for Image Analysis for Freeze‐Fracture Electron Microscopy: Rosette Cellulose Synthase Complexes Case

In materials science, plant biology, agriculture, and environmental research, the automated analysis of high-magnification, complex microscopy images, such as those generated by freeze-fracture electron microscopy (FF-TEM), remains a critical challenge that limits the scalability of data interpretation. We present a deep learning computer vision pipeline for high-throughput detection and morphological characterization analysis of cellulose synthase complexes (CSCs, or rosettes) in FF-TEM images. The pipeline integrates preprocessing, detection, human-in-the-loop verification, and semantic segmentation to quantify features such as rosette diameter and inter-lobe spacing. The approach was trained and tested on a curated dataset of high-resolution FF-TEM micrographs of Physcomitrium patens, expanded via strategic tiling and augmentation to over 650 images. We compare YOLOv8 and YOLOv9 architectures and demonstrate that YOLOv9 achieves superior performance in both localization accuracy (mAP50-95 = 0.854) and inference speed. The resulting distributions revealed biological variability consistent with prior manual studies, validating the approach for high-throughput applications. Our results show that the pipeline achieves human-expert level accuracy while dramatically reducing analysis time, enabling scalable, reproducible structural characterization of intramembrane protein complexes. The pipeline is broadly applicable to other domains requiring precise interpretation of complex microscopy data and establishes a foundation for future artificial intelligence (AI)-assisted workflows in biological imaging.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Latent Tracks in Ion-Irradiated LiTaO 3 Crystals: Damage Morphology Characterization and Thermal Spike Analysis

Systematic research on the response of crystal materials to the deposition of irradiation energy to electrons and atomic nuclei has attracted considerable attention since it is fundamental to understanding the behavior of various materials in natural and manmade radiation environments. This work examines and compares track formation in LiTaO3 induced by separate and combined effects of electronic excitation and nuclear collision. Under 0.71–6.17 MeV/u ion irradiation with electronic energy loss ranging from 6.0 to 13.8 keV/nm, the track damage morphologies evolve from discontinuous to continuous cylindrical zone. Based on the irradiation energy deposited via electronic energy loss, the subsequently induced energy exchange and temperature evolution processes in electron and lattice subsystems are calculated through the inelastic thermal spike model, demonstrating the formation of track damage and relevant thresholds of lattice energy and temperature. Combined with a disorder accumulation model, the damage accumulation in LiTaO3 produced by nuclear energy loss is also experimentally determined. The damage characterizations and inelastic thermal spike calculations further demonstrate that compared to damage-free LiTaO3, nuclear-collision-damaged LiTaO3 presents a more intense thermal spike response to electronic energy loss owing to the decrease in thermal conductivity and increase in electron–phonon coupling, which further enhance track damage.

36 MATERIALS SCIENCE↗
DOE OSTI2020

H i asymmetries in LVHIS, VIVA, and HALOGAS galaxies

ABSTRACT We present an analysis of morphological, kinematic, and spectral asymmetries in observations of atomic neutral hydrogen (H i) gas from the Local Volume H i Survey (LVHIS), the VLA Imaging of Virgo in Atomic Gas (VIVA) survey, and the Hydrogen Accretion in Local Galaxies Survey. With the aim of investigating the impact of the local environment density and stellar mass on the measured H i asymmetries in future large H i surveys, we provide recommendations for the most meaningful measures of asymmetry for use in future analysis. After controlling for stellar mass, we find signs of statistically significant trends of increasing asymmetries with local density. The most significant trend we measure is for the normalized flipped spectrum residual (Aspec), with mean LVHIS and VIVA values of 0.204 ± 0.011 and 0.615 ± 0.068 at average weighted 10th nearest-neighbour galaxy number densities of log (ρ10/Mpc−3) = −1.64 and 0.88, respectively. Looking ahead to the Widefield ASKAP L-band Legacy All-sky Blind survey on the Australian Square Kilometre Array Pathfinder, we estimate that the number of detections will be sufficient to provide coverage over 5 orders of magnitude in both local density and stellar mass increasing the dynamic range and accuracy with which we can probe the effect of these properties on the asymmetry in the distribution of atomic gas in galaxies.

Reynolds, T. N.↗
DOE OSTI2025

Characterization of the degradation of gamma-irradiated elastomers using Raman spectroscopy

This report presents key findings from Raman spectroscopic analysis of gamma-irradiated rubber samples extracted from a laminated lead-damped rubber (LDR) seismic isolation device. The samples were exposed to gamma radiation from a 60Co source in a Foss Therapy Services gamma irradiator, reaching absorbed doses up to 1600 kGy. A distinct threshold near 400 kGy was identified, beyond which significant spectral changes were observed. Two Raman peaks - at approximately 425 cm-1 and 2440 cm-1 - were tracked as a function of dose using Gaussian fitting. The 425 cm-1 peak, attributed to sulfur–sulfur (S–S) bond stretching (resulting from vulcanization of the rubber), exhibited a dose-dependent upshift, indicating radiation-induced crosslinking within the sulfur-based polymer network. Conversely, the 2440 cm-1 peak, likely associated with vibrational modes of additives or impurities, showed a downward shift with increasing dose, suggesting chain scission and degradation of non-rubber constituents. These results provide first-of-a-kind insights into the microstructural evolution of elastomers under high-dose gamma irradiation and establish a preliminary dose threshold for significant degradation. Future work will incorporate multi-modal characterization—including Fourier Transform Infrared (FTIR) spectroscopy, scanning electrom microscopy (SEM) of the rubber surface morphology, thermogravimetric analysis (TGA) to determine changes in thermal stability, and mechanical testing—to correlate molecular-level changes with macroscopic performance of these elastomers as damping media in seismic isolation devices. These findings are expected to provide regulatory guidance and design criteria for qualifying low-damping rubber seismic isolators in advanced nuclear reactor applications.

36 - MATERIALS SCIENCE↗
DOE OSTI2026

Polymer Deconstruction and Redesign Strategies for Plastics Recycling

Advancing plastics recycling requires both the selective deconstruction of existing polymers and the design of new materials that enable efficient reuse without loss of performance. This perspective highlights an integrated approach that is rooted in polymer chemistry, catalysis, and process engineering which can enable a circular plastics economy. Here, we outline recent advances in catalytic, solvolytic, and enzymatic pathways for plastic deconstruction, and examine the molecular design principles driving next-generation recyclable-by-design and bio-based polymers. Despite these advances, major knowledge gaps remain in understanding the evolution of polymer morphology and catalyst structure during deconstruction, assessing deconstruction processes with realistic polymers, and offering redesigned polymers with competitive cost and environmental advantage over conventional plastics. United States Department of Energy (U.S. DOE) national laboratories offer unique capabilities to address these challenges through in situ and operando characterization, high-throughput experimentation, environmental studies, technoeconomic and life cycle assessment, scale-up support, and collaboration networks. Advances made in understanding plastic deconstruction mechanisms and structure-property correlations of redesigned polymers inform emerging research directions including autonomous experimentation, real-time feedback-enabled process optimization, and protein engineering for enzymatic depolymerization.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Review of multi-faceted morphologic signatures of actinide process materials for nuclear forensic science

Particle morphology is an emerging signature that has the potential to identify the processing history of unknown nuclear materials. Using readily available scanning electron microscopes (SEM), the morphology of nearly any solid material can be measured within hours. Coupled with robust image analysis and classification methods, the morphological features can be quantified and support identification of the processing history of unknown nuclear materials. The viability of this signature depends on developing databases of morphological features, coupled with a rapid data analysis and accurate classification process. With developed reference methods, datasets, and throughputs, morphological analysis can be applied within days to (i) interdicted bulk nuclear materials (gram to kilogram quantities), and (ii) trace amounts of nuclear materials detected on swipes or environmental samples. In conclusion, this review aims to develop validated and verified analytical strategies for morphological analysis relevant to nuclear forensics.

36 MATERIALS SCIENCE↗