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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 829 records · Page 46
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Experimental evaluation of localized creep deformation in grade 91 steel weldments

Spatially resolved measurement of localized creep deformation in heterogeneous creep resistant steel weldments is crucial but challenging for lifetime assessments of critical steam components in power plants. Here, experimental approaches were established to quantitatively evaluate commonly observed localized creep deformation in multi-pass Grade 91 steel weldments. An in-situ digital image correlation (DIC) system was utilized with a creep testing frame to monitor and measure both full-field strain and localized strain accumulation across the weldments during long-term creep testing at elevated temperatures. The in situ DIC method measured not only the creep deformation behavior of the weld metal, heat affected zone (HAZ), and base metal, but also creep strain evolution for each sub-region within the HAZ itself, including coarse-grained HAZ, fine-grained HAZ (FGHAZ), and intercritical HAZ (ICHAZ). The DIC results revealed that local creep strain in the ICHAZ reached up to 90% strain before final rupture, whereas nominal creep strain measured by the standard extensometer of the tested cross-weld specimen was below 10%, indicative of Type IV cracking of the Grade 91 weld. Microstructural analyses revealed that the faster creep degradation/deformation in the HAZ was caused mainly by accelerated matrix grain recrystallization/growth and a reduced pinning effect from the segregated and coarsened precipitates in the FGHAZ and ICHAZ. The ultimate creep rupture occurred in the ICHAZ owing to its lowest creep resistance induced by the largest recrystallized grain size, the lowest fraction of coincidence site lattice, and the lowest local strain energies/dislocation densities.

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↗
NASA NTRS2005

Evaluation of Microbolometer-Based Thermography for Gossamer Space Structures

In August 2003, NASA's In-Space Propulsion Program contracted with our team to develop a prototype on-board Optical Diagnostics System (ODS) for solar sail flight tests. The ODS is intended to monitor sail deployment as well as structural and thermal behavior, and to validate computational models for use in designing future solar sail missions. This paper focuses on the thermography aspects of the ODS. A thermal model was developed to predict local sail temperature variations as a function of sail tilt to the sun, billow depth, and spectral optical properties of front and back sail surfaces. Temperature variations as small as 0.5 C can induce significant thermal strains that compare in magnitude to mechanical strains. These thermally induced strains may result in changes in shape and dynamics. The model also gave insight into the range and sensitivity required for in-flight thermal measurements and supported the development of an ABAQUS-coupled thermo-structural model. The paper also discusses three kinds of tests conducted to 1) determine the optical properties of candidate materials; 2) evaluate uncooled microbolometer-type infrared imagers; and 3) operate a prototype imager with the ODS baseline configuration. (Uncooled bolometers are less sensitive than cooled ones, but may be necessary because of restrictive ODS mass and power limits.) The team measured the spectral properties of several coated polymer samples at various angles of incidence. Two commercially available uncooled microbolometer imagers were compared, and it was found that reliable temperature measurements are feasible for both coated and uncoated sides of typical sail membrane materials.

Miles, Jonathan J.↗
DOE OSTI2024

Measuring 3D Chemistry at 1 nm Resolution with Fused Multi-Modal Electron Tomography

Measuring the three-dimensional (3D) distribution of chemistry in nanoscale matter is a longstanding challenge for metrological science. The inelastic scattering events required for 3D chemical imaging are too rare, requiring high beam exposure that destroys the specimen before an experiment is completed. Even larger doses are required to achieve high resolution. Thus, chemical mapping in 3D has been unachievable except at lower resolution with the most radiation-hard materials. Here, high-resolution 3D chemical imaging is achieved near or below one-nanometer resolution in an Au-Fe3O4 metamaterial within an organic ligand matrix, Co3O4-Mn3O4 core-shell nanocrystals, and ZnS-Cu0.64S0.36 nanomaterial using fused multi-modal electron tomography. Multi-modal data fusion enables high-resolution chemical tomography often with 99% less dose by linking information encoded within both elastic (HAADF) and inelastic (EDX/EELS) signals. We thus demonstrate that sub-nanometer 3D resolution of chemistry is measurable for a broad class of geometrically and compositionally complex materials.

Schwartz, Jonathan↗
DOE OSTI2025

Boosting Noise2Inverse via enhanced model selection for denoising computed tomography data

Synchrotron-based x-ray tomographic imaging enables the examination of the internal structure of materials at high spatial and temporal resolution. Experimental constraints can impose dose and time limits on the measurements, introducing a higher level of noise and artifacts in the reconstructed images. Deep learning has emerged as a powerful tool to remove noise from reconstructed images. Recently, the Noise2Inverse method was designed specifically for denoising reconstructed images without requiring paired noisy and clean images. This method creates multiple statistically independent reconstructions used to pair the data in which training involves transforming one reconstruction into the other, and vice versa. Originally designed to be used after a fixed number of epochs, we see in practice that this approach may not produce the optimal model and may unnecessarily waste computational resources. Therefore, we propose an alternative method of identifying the best model during training that aligns with the Noise2Inverse method. During validation, we compare the model output of the multiple reconstructions among each other. We hypothesize that the best model is the one that produces images with the highest similarity, implying a convergence in the predicted material properties and absorption values. To compare model outputs, we consider the absolute error, square error, structural similarity index (SSIM), peak signal-to-noise ratio (PSNR), and cosine similarity. We evaluate our method on two simulated tomography datasets and two, real-world, low-contrast, high-energy x-ray tomography datasets. We show our approach is more effective at determining the best model, up to an increase of 12.50% and 12.53% in SSIM and PSNR, respectively, while only requiring a fifth of the training time compared to the original approach.

CT↗
DOE OSTI2024

Imaging 3D chemistry at 1 nm resolution with fused multi-modal electron tomography

Measuring the three-dimensional (3D) distribution of chemistry in nanoscale matter is a longstanding challenge for metrological science. The inelastic scattering events required for 3D chemical imaging are too rare, requiring high beam exposure that destroys the specimen before an experiment is completed. Even larger doses are required to achieve high resolution. Thus, chemical mapping in 3D has been unachievable except at lower resolution with the most radiation-hard materials. Here, high-resolution 3D chemical imaging is achieved near or below one-nanometer resolution in an Au-Fe 3 O 4 metamaterial within an organic ligand matrix, Co 3 O 4 -Mn 3 O 4 core-shell nanocrystals, and ZnS-Cu 0.64 S 0.36 nanomaterial using fused multi-modal electron tomography. Multi-modal data fusion enables high-resolution chemical tomography often with 99% less dose by linking information encoded within both elastic (HAADF) and inelastic (EDX/EELS) signals. We thus demonstrate that sub-nanometer 3D resolution of chemistry is measurable for a broad class of geometrically and compositionally complex materials.

36 MATERIALS SCIENCE↗
NASA NTRS1986

Nondestructive Evaluation (NDE) of structural ceramics by photoacoustic microscopy

Photoacoustic microscopy (PAM) was utilized to detect surface and subsurface defects in structural ceramic materials. A computerized PAM data acquisition, color imaging and analysis system was developed and used. Subsurface simulated cylindrical holes can be detected to about 1 mm below the interrograting surface. Simulated tight surface cracks of 96 microns length and 48 microns depth can be detected in these materials under optimum conditions.

Khandelwal, P. K.↗
NASA NTRS1993

Imaging Spectrometry-Concepts and System Tradeoffs

The concept of imaging spectrometry is finding broad application in scientific instrumentation for earth-based, airborne, and space applications. An imaging spectrometer is characterized by the combination of imaging with complete sampling in the spectral domain. In so doing, material identification can be accomplished and displayed in conjunction with the conventional recognizable image. An image spectrometer incorporates a wide variety of techniques, including focal plane arrays, imaging and spectrometer optics, and spectral dispersing devices. The design of a successful system involves a complex set of tradeoffs incorporating the properties and limitations of the various technologies. For applications in the infrared, additional technologies such as focal plane cooling are required, and the other technologies present more limitations and constraints.

Herring, M.↗
DOE OSTI2022

Multi-frame, ultrafast, x-ray microscope for imaging shockwave dynamics

Inertial confinement fusion (ICF) holds increasing promise as a potential source of abundant, clean energy, but has been impeded by defects such as micro-voids in the ablator layer of the fuel capsules. It is critical to understand how these micro-voids interact with the laser-driven shock waves that compress the fuel pellet. At the Matter in Extreme Conditions (MEC) instrument at the Linac Coherent Light Source (LCLS), we utilized an x-ray pulse train with ns separation, an x-ray microscope, and an ultrafast x-ray imaging (UXI) detector to image shock wave interactions with micro-voids. To minimize the high- and low-frequency variations of the captured images, we incorporated principal component analysis (PCA) and image alignment for flat-field correction. After applying these techniques we generated phase and attenuation maps from a 2D hydrodynamic radiation code (xRAGE), which were used to simulate XPCI images that we qualitatively compare with experimental images, providing a one-to-one comparison for benchmarking material performance. Moreover, we implement a transport-of-intensity (TIE) based method to obtain the average projected mass density (areal density) of our experimental images, yielding insight into how defect-bearing ablator materials alter microstructural feature evolution, material compression, and shock wave propagation on ICF-relevant time scales.

47 OTHER INSTRUMENTATION↗
DOE OSTI2023

Digital Rocks Portal (Digital Porous Media): Connecting data, simulation and community

Digital Rocks Portal (DRP, https://www.digitalrocksportal.org) organizes and preserves imaged datasets and experimental measurements of porous materials in subsurface, and beyond, with the mission to connect them to simulation and analysis, as well as educate the research community. We have over 150 projects represented in more than 200 publications, and an active community that reuses the data, most recently in multiple machine learning applications for automating image analysis as well as the prediction of transport. Such automation is crucial for performing formation evaluation tasks in near-real time. We present benchmark datasets that have played a role in recent machine learning prediction successes in the field. We further discuss the vision for further research advances, educational materials, as well as growth and sustainability plan of this digital rock physics community resource. In particular, we are in the process of expanding into a broader repository of engineered porous materials, specifically those for energy storage and the portal will transition to Digital Porous Media (DPM) in near future.

58 GEOSCIENCES↗
DOE OSTI2025

Image Rotation in Plasmas

Because of the speed of light compared to material motion, the dragging of light is difficult to observe under laboratory conditions. Furthermore we report on the first observation of image rotation, i. e. a dragging by the medium of the wave’s transverse structure, of Alfv´en waves in plasmas. Exploiting the naturally slow group velocity of these waves, significant wave rotation is achieved for modest angular frequency. Control over the rotation of the wave’s structure is demonstrated through the plasma rotation imposed by biased electrodes. Remarkably, experimental results are well reproduced by light dragging theory derived for isotropic media, even if magnetized plasmas are anisotropic. In addition to offering new insights into the fundamental issue of angular momentum coupling between waves and media, these findings also open possibilities for new remote rotation sensing tools.

Alfvén waves↗
NASA NTRS1986

Planetary optical and infrared imaging

Images were acquired of a circumstellar disk of orbiting material around the nearby star beta Pictoris. This material is believed to be associated with the formation of planets and provides a first look at a planetary system much younger than our own. Preliminary work was also done to acquire multicolor photometric data and polarimetry on the beta Pictoris disk as well. A coronagraphic search for other proto-planetary systems is also underway. Coronagraphic imaging provided the first clear images and a determination of the geometric albedo of the rings of Uranus. A search for material around the asteroid Amphitrite showed that the region around the asteroid was free of particles larger than 500 meters in radius or diffuse material with an optical depth larger than .000001. NASA IRTF infrared images of Jupiter and Saturn are providing information on the relative heights and cloud top temperatures of atmospheric features.

Terrile, R. J.↗
DOE OSTI2024

Understanding and Predicting the Spatially Resolved Adsorption Properties of Nanoporous Materials

Using knowledge from statistical thermodynamics and crystallography, we develop an image–image translation model, called SorbIIT, that uses three-dimensional grids of adsorbate–adsorbent interaction energies as input to predict the spatially resolved loading surface of nanoporous materials over a broad range of temperatures and pressures. SorbIIT consists of a closed-form differential model for loading-surface prediction and a U-Net to generate spatial differential distributions from the energy grids. SorbIIT is trained using the energy grids and adsorbate distributions (obtained from high-throughput simulations) of 50 synthesized and 70 hypothetical zeolites and applied for predicting the adsorption of carbon dioxide, hydrogen sulfide, n-butane, 2-methylpropane, krypton, and xenon in other zeolites from 256 to 400 K. In conclusion, employing a quadratic isotherm model for the local differentiation, SorbIIT yields mean R 2 values of 0.998 for total adsorption and 0.6904 for local adsorption with a resolution of 0.2 Å, and a value of 0.721 for the structural similarity of the local loading distribution.

Sun, Yangzesheng↗
DOE OSTI2026

Neutron Scattering in Sodium-Ion Battery Research: Progress and Prospects

Sodium-ion batteries have attracted renewed interest in recent years and are widely studied as a complementary power source to Li-ion batteries for large-scale stationary energy storage and small electric vehicles. Compared with Li-ion batteries, Na-ion batteries offer several advantages, including elemental abundance, lower cost, improved safety, and better low-temperature performance. An in-depth understanding of Na-ion batteries across multiple length and time scales has further accelerated the rapid development of this technology. Among the various advanced characterization techniques used to study Na-ion batteries, neutron scattering has gained significant traction in recent years. It has become a powerful and versatile tool for probing structure, morphology, and sodium-ion dynamics over a wide range of length and time scales. In this article, we will briefly review the development of neutron scattering technology and highlight recent advances in applying neutron-based techniques—including neutron diffraction, total scattering, small-angle scattering, quasi-elastic/inelastic scattering, and neutron imaging—to Na-ion battery materials. We also provide perspectives on future technique developments, particularly in the realm of in situ and operando neutron scattering characterization, and discuss how these approaches could further enhance our understanding of Na-ion battery systems.

Liu, Jue [ORNL] (ORCID:000000024453910X)↗
DOE OSTI2026

In-situ sensor monitoring of multi-class gas porosity formation in laser powder bed fusion using convolutional neural network

In-situ monitoring of defect formation remains a significant challenge in the laser powder bed fusion (LPBF) process. Recent advances have enabled real-time defect detection with machine learning and in-situ sensing technologies; however, most studies focus on binary classification of keyhole pores, limiting nuanced multi-class pore differentiation and formation mechanisms. This work introduces a multi-class pore detection framework (no pore, small pores < 15 µm, and large pores > 15 µm) by leveraging photodiode sensor data alongside high-fidelity synchrotron X-ray imaging. The 15 µm threshold is selected to distinguish between two fundamentally different defect mechanisms, following the physical size-mechanism boundary established by prior high-resolution synchrotron X-ray characterization of Al6061 LPBF. Distinguishing these classes is critical because large keyhole pores are structurally detrimental, whereas small gas pores are often benign, requiring different process control strategies. Thermal emission monitoring data collected simultaneously with high-speed X-ray imaging at the Stanford Synchrotron Radiation Lightsource (SSRL), are correlated with subsurface melt pool dynamics to establish ground truth. Continuous Wavelet Transform (CWT) with optimized parameters converts the photodiode time-series signals into time–frequency images, facilitating feature extraction. Convolutional Neural Networks (CNN) are then applied for real-time multi-class pore classification in an average inference time of 1 ms per signal window. It achieves 79% accuracy and an Area Under the Receiver Operating Characteristic curve (AUC ROC) score of 0.89 with five-fold cross-validation. The results demonstrate that coupling CWT-based feature engineering with CNN architecture enables reliable multi-class pore detection in Al6061 builds using affordable in-situ sensors. This approach advances scalable and affordable quality assurance in additive manufacturing by moving beyond binary defect detection toward more nuanced classification of porosity mechanisms with in-situ sensors and machine learning.

Laser powder bed fusion, Multi-class pores, In-sit↗
DOE OSTI2020

Slip band characteristics in the presence of grain boundaries in nickel-based superalloy

Shear strain profiles along slip bands in a modified Rolls-Royce nickel superalloy (RR1000) were analyzed for a tensile sample deformed by 2%. The strain increased with distance away from a grain boundary (GB), with maximum shear strain towards the center of the grain, indicating that dislocation nucleation generally occurred in the grain interior. The strain gradients in the neighborhood of the GBs were quantified and generally correlated with rotation about the active slip system line direction. This leads to an ability to determine the active slip system in these regions. The dislocation spacing and pileup stresses were inferred. The dislocation spacing closely follows an Eshelby analytical solution for a single ended pileup of dislocations under an applied stress. The distribution of pileup stress values for GBs of a given misorientation angle follows a log-normal distribution, with no correlation between the pileup stress and the GB misorientation angle. Furthermore, there is no observed correlation between various transmissivity factors and slip band pileup stress. Hence it appears that the obstacle strength of any of the observed GBs is adequate to facilitate the dislocation pileups present in the slip bands. However, slip band transmission does correlate with transmissivity factors, with the current study focusing on the Luster and Morris m’-factor. Observation of strain profiles of transmitted bands indicate dislocation nucleation locations.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Unveiling the interaction of nanopatterned void superlattices with irradiation cascades

Nanopatterned microstructures in materials can have a profound impact on materials’ physical and chemical properties. While voids are typically considered as detrimental defects in irradiated materials, the patterning of nanoscale voids causes the formation of void superlattices and provides a highly efficient mechanism for gas storage. Despite the important applications of nanopatterned defect superlattices, how they degrade under irradiation remains unclear. Here we provide direct observation of the evolution of void superlattices under irradiation and elucidate the interaction of void superlattices with irradiation cascades. We reveal that the instability of void superlattices under irradiation is caused by heterogenous void shrinkage and demonstrate the imperative role of mixed 1D/3D diffusion of self-interstitial atoms and injected inert gas atoms on void shrinkage and void superlattice instability. Understanding the degradation mechanisms of nanopatterned microstructures is essential to designing damage-tolerant materials and broadening their applications in extreme environments.

36 MATERIALS SCIENCE↗