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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Electron microscopy data on irradiation effects in glassy carbon, nuclear graphite, pyrolytic carbon, and carbon fibers

Glassy carbon, a monoatomic allotrope of carbon, is a candidate material for components in fission nuclear power systems due to its radiation tolerance. This article presents comprehensive electron microscopy data revealing the effects of neutron and electron irradiation on glassy carbon. For comparison, additional data are provided for pyrolytic graphite and carbon fibers, materials that exhibit similar structural behavior under irradiation. In situ electron irradiation experiments further illustrate the real-time microstructural evolution of glassy carbon during exposure. The dataset is organized into five parts: (1) transmission electron microscopy (TEM) micrographs of as-received and neutron-irradiated glassy carbon; (2) TEM micrographs of neutron-irradiated graphite; (3) TEM micrographs of unirradiated and irradiated carbon–carbon composites; (4) TEM micrographs of pyrolytic carbon specimens in both conditions; (5) scanning transmission electron microscopy (STEM) micrographs of as-received and neutron-irradiated glassy carbon and (6) in situ electron irradiation data of a glassy carbon particle. These datasets provide valuable insights into radiation-induced structural changes in carbon-based materials relevant to nuclear applications.

36 MATERIALS SCIENCE↗
DOE OSTI2024

CryoTransformer: a transformer model for picking protein particles from Cryo-EM micrographs

Cryo-electron microscopy (cryo-EM) is a powerful technique for determining the structures of large protein complexes. Picking single protein particles from cryo-EM micrographs (images) is a crucial step in reconstructing protein structures from them. However, the widely used template-based particle picking process requires some manual particle picking and is labor-intensive and time-consuming. Though machine learning and artificial intelligence (AI) can potentially automate particle picking, the current AI methods pick particles with low precision or low recall. The erroneously picked particles can severely reduce the quality of reconstructed protein structures, especially for the micrographs with low signal-to-noise-ratio (SNR).

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Additive Manufacturing of Poly(phenylene Sulfide) Aerogels via Simultaneous Material Extrusion and Thermally Induced Phase Separation

Abstract Additive manufacturing (AM) of aerogels increases the achievable geometric complexity, and affords fabrication of hierarchically porous structures. In this work, a custom heated material extrusion (MEX) device prints aerogels of poly(phenylene sulfide) (PPS), an engineering thermoplastic, via in situ thermally induced phase separation (TIPS). First, pre‐prepared solid gel inks are dissolved at high temperatures in the heated extruder barrel to form a homogeneous polymer solution. Solutions are then extruded onto a room‐temperature substrate, where printed roads maintain their bead shape and rapidly solidify via TIPS, thus enabling layer‐wise MEX AM. Printed gels are converted to aerogels via postprocessing solvent exchange and freeze‐drying. This work explores the effect of ink composition on printed aerogel morphology and thermomechanical properties. Scanning electron microscopy micrographs reveal complex hierarchical microstructures that are compositionally dependent. Printed aerogels demonstrate tailorable porosities (50.0–74.8%) and densities (0.345–0.684 g cm −3 ), which align well with cast aerogel analogs. Differential scanning calorimetry thermograms indicate printed aerogels are highly crystalline (≈43%), suggesting that printing does not inhibit the solidification process occurring during TIPS (polymer crystallization). Uniaxial compression testing reveals that compositionally dependent microstructure governs aerogel mechanical behavior, with compressive moduli ranging from 33.0 to 106.5 MPa.

Chemistry↗
DOE OSTI2020

Briquetting characteristics of woody and herbaceous biomass blends: Impact on physical properties, chemical composition, and calorific value

Abstract Intercropping (or polyculture) is gaining a lot of interest as it increases yields, allows better utilization of resources (water and nutrients), and provides better land management. The objective of this study is to understand the briquetting characteristics of woody and herbaceous biomass blends, which might result from intercropping. Tests were conducted on blends of lodgepole pine, switchgrass, and corn stover biomass using three hammer mill screen sizes (i.e., 4.8, 12.7, and 19.05 mm), three blend moistures (12%, 15%, and 18%, w.b.), and two‐ and three‐blend ratios (i.e., 1:1 and 1:1:1) using a pilot‐scale hydraulic briquette press. The briquette properties, such as unit and bulk density, durability rating after 5 days of storage (as they tend to attain stable density), and the energy consumption of the process were measured. The results indicate that moisture content of 12% and 15% (w.b.) and smaller hammer mill screen size of 4.8 mm increased the unit and bulk densities to >750 and >430 kg/m 3 and moisture content (15% and 18% (w.b.)) and hammer mill screen size of 12.7 and 19.05 mm produced briquettes with durability rating values of >95%. The higher moisture content of 18% (w.b.) and the hammer mill screen size of 12.7 and 19.05 mm had the highest energy consumption. The proximate and ultimate analysis showed that energy content improved by blending lodgepole pine with both corn stover and switchgrass and also showed increased calorific value and reduced ash content. Scanning electron microscopy micrographs showed interlocking of particles and cracks in the briquettes. © 2020 Society of Chemical Industry and John Wiley & Sons, Ltd

Tumuluru, Jaya Shankar↗
DOE OSTI2020

D614G Spike Mutation Increases SARS CoV-2 Susceptibility to Neutralization

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spike protein acquired a D614G mutation early in the pandemic that confers greater infectivity and is now the globally dominant form. To determine whether D614G might also mediate neutralization escape that could compromise vaccine efficacy, sera from spike-immunized mice, nonhuman primates, and humans were evaluated for neutralization of pseudoviruses bearing either D614 or G614 spike. In all cases, the G614 pseudovirus was moderately more susceptible to neutralization. The G614 pseudovirus also was more susceptible to neutralization by receptor-binding domain (RBD) monoclonal antibodies and convalescent sera from people infected with either form of the virus. Here, negative stain electron microscopy revealed a higher percentage of the 1-RBD “up” conformation in the G614 spike, suggesting increased epitope exposure as a mechanism of enhanced vulnerability to neutralization. Based on these findings, the D614G mutation is not expected to be an obstacle for current vaccine development.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

Experimental investigations of Critical Heat Flux re-occurrence on post-CHF surfaces

Under the extreme environments of light water reactors, the cladding materials demonstrate evolutionary transformations in terms of material compositions, surface morphologies, and thermal-physical properties. This study investigates how the temperature overshooting of Critical Heat Flux (CHF) affects the re-occurrence of CHF on the post-CHF surfaces. Experimental results reveal that the CHF difference between as-received and post-CHF surfaces can be significant, which depends on materials. CHF is enhanced on post-CHF Stainless Steel (SS 316) specimens while it deteriorates on post-CHF Inconel-600 specimens. In comparison with the as-received surfaces, the post-CHF surface of SS 316 is more hydrophobic, while Inconel 600 is more hydrophilic. The scanning electron microscope micrographs of post-CHF surfaces show that surface morphologies that are structured by oxide crystals are formed on SS 316 samples but not on Inconel 600 samples. The physical rationale behind the CHF difference could be boiling heat transfer conjugated with thermal-physical proprieties of investigated surface materials and surface morphologies features. In conclusion, the results imply that it is indispensable to bookkeep the evolutionary changes of cladding surface materials, especially for cladding candidates of Accident Tolerant Fuel since operation histories can leave footprints on cladding surfaces, thus resulting in CHF re-occurrence differences.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2021

A study of real-world micrograph data quality and machine learning model robustness

Abstract Machine-learning (ML) techniques hold the potential of enabling efficient quantitative micrograph analysis, but the robustness of ML models with respect to real-world micrograph quality variations has not been carefully evaluated. We collected thousands of scanning electron microscopy (SEM) micrographs for molecular solid materials, in which image pixel intensities vary due to both the microstructure content and microscope instrument conditions. We then built ML models to predict the ultimate compressive strength (UCS) of consolidated molecular solids, by encoding micrographs with different image feature descriptors and training a random forest regressor, and by training an end-to-end deep-learning (DL) model. Results show that instrument-induced pixel intensity signals can affect ML model predictions in a consistently negative way. As a remedy, we explored intensity normalization techniques. It is seen that intensity normalization helps to improve micrograph data quality and ML model robustness, but microscope-induced intensity variations can be difficult to eliminate.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Automated Grain Boundary (GB) Segmentation and Microstructural Analysis in 347H Stainless Steel Using Deep Learning and Multimodal Microscopy

Austenitic 347H stainless steel offers superior mechanical properties and corrosion resistance required for extreme operating conditions such as high temperature. The change in microstructure due to composition and process variations is expected to impact material properties. Identifying microstructural features such as grain boundaries thus becomes an important task in the process-microstructure-properties loop. Applying convolutional neural network (CNN)-based deep learning models is a powerful technique to detect features from material micrographs in an automated manner. In contrast to microstructural classification, supervised CNN models for segmentation tasks require pixel-wise annotation labels. However, manual labeling of the images for the segmentation task poses a major bottleneck for generating training data and labels in a reliable and reproducible way within a reasonable timeframe. Microstructural characterization especially needs to be expedited for faster material discovery by changing alloy compositions. Here, in this study, we attempt to overcome such limitations by utilizing multimodal microscopy to generate labels directly instead of manual labeling. We combine scanning electron microscopy images of 347H stainless steel as training data and electron backscatter diffraction micrographs as pixel-wise labels for grain boundary detection as a semantic segmentation task. The viability of our method is evaluated by considering a set of deep CNN architectures. We demonstrate that despite producing instrumentation drift during data collection between two modes of microscopy, this method performs comparably to similar segmentation tasks that used manual labeling. Additionally, we find that naïve pixel-wise segmentation results in small gaps and missing boundaries in the predicted grain boundary map. By incorporating topological information during model training, the connectivity of the grain boundary network and segmentation performance is improved. Finally, our approach is validated by accurate computation on downstream tasks of predicting the underlying grain morphology distributions which are the ultimate quantities of interest for microstructural characterization.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Variant selection in laser powder bed fusion of non-spherical Ti-6Al-4V powder

The presence of α/α' on prior β/β grain boundaries directly impacts the final mechanical properties of the titanium alloys. The β/β grain boundary variant selection of titanium alloys has been assumed to be unlikely owing to the high cooling rates in laser powder bed fusion (L-PBF). However, we hypothesize that powder characteristics such as morphology (non-spherical) and particle size (50–120 µm) could affect the initial variant selection in L-PBF processed Ti-6Al-4V alloy by locally altering the cooling rates. Despite the high cooling rate found in L-PBF, the results showed the presence of β/β grain boundary α' lath growth inside two adjacent prior β grains. Electron backscatter diffraction micrographs confirmed the presence of β/β grain boundary variant selection, and synchrotron X-ray high-speed imaging observation revealed the role of the “shadowing effect” on the locally decreased cooling rate because of keyhole depth reduction and the consequent β/β grain boundary α' lath growth. The self-accommodation mechanism was the main variant selection driving force, and the most abundant α/α boundary variant was type 4 (63.26°//[ $\bar{10}$ 5 5 $\bar{3}$ ]). The dominance of Category II α lath clusters associated with the type 4 α/α boundary variant was validated using the phenomenological theory of martensite transformations and analytical calculations, from which the stress needed for the β→α' transformation was calculated.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Characterization of Virgin, Re-used, and Oxygen-reduced Copper Powders processed by the Plasma Spheroidization Process

We report that fabrication of parts with high mechanical properties heavily depend on the quality of powder deployed in the fabrication process. Copper powder in three different powder types were spheroidized using radio-frequency inductively coupled plasma (ICP) spheroidization process (TekSphero-15 system). The characterized powders include virgin powder as purchased from the powder manufacturer, powder used in electron beam powder bed fusion (EB-PBF) process, and reconditioned powder, which was used powder that underwent an oxygen-reduction treatment. The goal of spheroidizing these powder types was to evaluate the change in powder morphology, the possibility of enhancing the powder properties back to their as-received conditions, and assess oxygen reduction of the powder lots given their initial oxygen contents. Also, to investigate the impact of re-spheroidization on powder properties, the second round of spheroidization was performed on the already used-spheroidized powder. The impact of powder type on powder sphericity and particle size distribution was evaluated using the image analysis of scanning electron microscope (SEM) micrographs and laser diffraction, respectively. The spheroidized powder showed higher sphericity and more uniform particle size distribution overall. Depending on the powder collection bin, second round of spheroidization affected the powder sphericity differently. The possibility of deploying the plasma spheroidization process as an alternative oxygen-reduction technique was also investigated through tracking the powders’ oxygen content using inert gas fusion method before and after the spheroidization. The plasma spheroidized powder showed less oxygen content than the hydrogen-treated powder. The second round of spheroidization caused no change in oxygen content. The correlation between oxygen-reduction and created cracks was discussed and compared between plasma spheroidization and hydrogen-treatment. The plasma spheroidization process created a powder with higher sphericity, uniform particle size, and less oxygen content.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2022

Austenitic parent grain reconstruction in martensitic steel using deep learning

In this work we develop a deep convolutional architecture to estimate the prior austenite structure from observed martensite electron backscatter diffraction micrographs. A novel data augmentation strategy randomizes the global reference coordinate system which makes it possible to train our model from only four micrographs. The model is much faster than algorithmic approaches and generalizes well when applied to micrographs of a different material. Empirical evidence suggests the efficacy of the model depends on the scale of the microstructure and receptive field of the vision model. Furthermore, this work demonstrates that modern computer vision approaches are well suited for capturing complex spatial-orientation patterns present in orientation imaging micrographs.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Deep Neural Network Algorithm for CMC Microstructure Characterization and Variability Quantification

Microstructure characterization and variability quantification are crucial for understanding ceramic matrix composites (CMCs) mechanical behavior and deformation mechanisms across length scales. Traditionally, analyses of the micrographs obtained from microscopy are labor-intensive. However, with the vast improvement in computer vision (CV) and deep learning (DL), an automated algorithm can be designed to extract essential microstructure variability from micrographs which can then be used to construct a statistically representative volume element (SRVE). The DL-based algorithm spans the taxonomy of microstructure analyses, including semantic segmentation of microstructure constituents, secondary phases, matrix/fiber interface, and defects, and quantifying the microstructure variability in terms of probability distributions. In this work, C/SiNC and SiC/SiNC CMCs microstructures are semantically segmented through a deep convolutional neural network, followed by variability quantification through the implementation of a fully connected regression layer, hence forming a deep regression network. The deep regression network operates in a feedforward regime, in which the neuron output signal traverses through the network in a unidirectional manner. The weight tensor associated with each layer is updated through a backpropagation stochastic gradient descent approach. The input gray-scale image obtained through in-house scanning electron microscope and confocal microscope micrographs is augmented through affine transformations to increase the training set size, which is then processed through four strided convolutional layers. This compresses the image resolution by half at each layer while increasing the image depth by applying different filters (image encoding). The class activation maps (CAMs) corresponding to the applied filters highlight the key architectural features and assist with the semantic segmentation of the microstructure.

Hamza, Mohamed H.↗
DOE OSTI2020

Mechanisms of paint bake response in resistance spot-welded first and third generation AHSS

Implementation of 3rd generation advanced high strength steels (3GAHSS) in automotive structures requires careful consideration of joining process implications. Due to the presence of increased carbon equivalent in 3GAHSS such as 980 HF relative to commonly-applied 1st generation AHSS such as 980 DP, the resultant properties of the weld nugget and heat affected zones in resistance spot welded must be engineered to provide consistent and acceptable performance, specifically in cross-tension loading mode. Although the as-welded cross-tension strength (CTS) of 980 HF is less than 980 DP, there is a significant improvement of the CTS after paint baking in 980 HF welds. 980 DP steel welds do not exhibit such marked improvement upon baking. The mechanism of the strong and beneficial effect of paint baking on 980 HF in comparison to 980 DP is discussed in light of its effect on residual stress distributions and microstructures. Although residual stress (strain) reduction is observed via synchrotron x-ray diffraction for all tested steels, this effect is demonstrated as secondary to the performance restoration of the weld joint in cross tension. High-resolution electron channeling contrast imaging (ECCI) micrographs reveal that paint baking affects a more beneficial degree of martensite tempering in 980 HF as compared to 980 DP. The martensite tempering is also evidenced by both a reduction in nano-hardness of critical microstructural features and re-emergence of ductile fracture behavior following the paint baking process. The tempering of martensite is concluded to be the dominant mechanism in improving cross tension strength of 3GAHSS welds following paint baking.

36 MATERIALS SCIENCE↗
DOE OSTI2024

CryoSegNet: accurate cryo-EM protein particle picking by integrating the foundational AI image segmentation model and attention-gated U-Net

Picking protein particles in cryo-electron microscopy (cryo-EM) micrographs is a crucial step in the cryo-EM-based structure determination. However, existing methods trained on a limited amount of cryo-EM data still cannot accurately pick protein particles from noisy cryo-EM images. The general foundational artificial intelligence–based image segmentation model such as Meta’s Segment Anything Model (SAM) cannot segment protein particles well because their training data do not include cryo-EM images. Here, we present a novel approach (CryoSegNet) of integrating an attention-gated U-shape network (U-Net) specially designed and trained for cryo-EM particle picking and the SAM. The U-Net is first trained on a large cryo-EM image dataset and then used to generate input from original cryo-EM images for SAM to make particle pickings. CryoSegNet shows both high precision and recall in segmenting protein particles from cryo-EM micrographs, irrespective of protein type, shape and size. On several independent datasets of various protein types, CryoSegNet outperforms two top machine learning particle pickers crYOLO and Topaz as well as SAM itself. The average resolution of density maps reconstructed from the particles picked by CryoSegNet is 3.33 Å, 7% better than 3.58 Å of Topaz and 14% better than 3.87 Å of crYOLO. It is publicly available at https://github.com/jianlin-cheng/CryoSegNet

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

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

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

36 - MATERIALS SCIENCE↗
DOE OSTI2023

A large expert-curated cryo-EM image dataset for machine learning protein particle picking

Cryo-electron microscopy (cryo-EM) is a powerful technique for determining the structures of biological macromolecular complexes. Picking single-protein particles from cryo-EM micrographs is a crucial step in reconstructing protein structures. However, the widely used template-based particle picking process is labor-intensive and time-consuming. Though machine learning and artificial intelligence (AI) based particle picking can potentially automate the process, its development is hindered by lack of large, high-quality labelled training data. To address this bottleneck, we present CryoPPP, a large, diverse, expert-curated cryo-EM image dataset for protein particle picking and analysis. It consists of labelled cryo-EM micrographs (images) of 34 representative protein datasets selected from the Electron Microscopy Public Image Archive (EMPIAR). The dataset is 2.6 terabytes and includes 9,893 high-resolution micrographs with labelled protein particle coordinates. The labelling process was rigorously validated through 2D particle class validation and 3D density map validation with the gold standard. The dataset is expected to greatly facilitate the development of both AI and classical methods for automated cryo-EM protein particle picking.

42 ENGINEERING↗