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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 OSTI2023

Multimodal Few-Shot Segmentation of Electron Micrographs

Scanning transmission electron microscopy (STEM) is one of the most used methods of analyzing the chemistry and composition of materials. By analyzing microstructures, these microscopes can help scientists better understand the molecular underpinnings of microelectronics, batteries, and more. However, STEM data can be difficult to interpret, so recent developments have been made in applications of machine learning to analyze these images. The PNNL-developed pyCHIP Classifier has achieved results in segmenting STEM these images via few-shot learning, a method which requires little data and human input, perfect for quickly analysis. In my internship I (Eli Meyers) investigated a multimodal improvement of this classifier by incorporating energy dispersive x-ray spectroscopy (EDS) data into the classification process for a more accurate segmentation. Furthermore, I encoded the spectral data by training a mass spectrometry encoder on the EDS data to extract a more meaningful representation of the data.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Microscopy modality transfer of steel microstructures: Inferring scanning electron micrographs from optical microscopy using generative AI

Scanning electron microscopy (SEM) is resource intensive, which limits its throughput in some applications. As an alternative, we propose applying computer vision and machine learning to generate high-quality synthetic SEM micrographs from micrographs obtained using light optical microscopy (LOM). Working with a correlated LOM/SEM dataset of dual-phase steel images, we test generative models of various architectures, including encoder-decoder networks, generative adversarial networks (GANs), and diffusion-based models. We find that the diffusion models significantly outperform other methods on both qualitative and quantitative assessments, while preserving key metallurgical meaning. This work establishes diffusion as the state-of-the-art for microscopy modality transfer and demonstrates the potential of AI-powered microscopy to enhance LOM with micron scale structural recreation.

Computer vision↗
DOE OSTI2025

A comparative analysis of YOLOv8 and U-Net image segmentation approaches for transmission electron micrographs of polycrystalline thin films

Metallic thin films offer a platform to experimentally study the dynamics of microstructural evolution, but the required transmission electron microscopy (TEM)-based imaging generates complex images that are challenging to segment and quantify. This work provides a comparative analysis of a new YOLOv8 model and an established U-Net model for bright-field TEM images of polycrystals, employing a framework leveraging physical observables to evaluate performance against two hand-traced benchmark datasets. This methodology obviates the comparison of large, diversely structured, and manually labeled datasets that are required to assess performance on a per-image/per-pixel basis. It is found that the YOLOv8 model, adapted for real-time instance segmentation, has up to 43× faster inferencing (NVIDIA GeForce RTX 4090) compared to U-Net and reconstructs hand-traced grain size distributions (GSDs) with excellent fidelity, finding mean diameter within 3% for grains near an optimal magnification; for grains that deviate from the optimal pixel-diameter, the size of small- (large)-diameter grains is systematically over- (under)-estimated. This is partially mitigated by including scale-aware augmentations during training. Moreover, when the bias is corrected post-inference by a rigid shift in distribution, the YOLOv8 model reproduces ground truth GSDs with exceptional fidelity, with statistical tests indicating <5% probability that the distributions are distinct. Based on ground truth data, calibration curves pertaining to this shift can be constructed for a given model. This issue is not present in the U-Net model’s results, indicating that for quantitative measurements where the true size of objects is of interest, special procedures must be implemented for YOLO-based models.

36 MATERIALS SCIENCE↗
DOE OSTI2022

A crystal plasticity finite element method modeling of Zircaloy with hydride phases based on scanning electron micrographs

This work developed a microstructure-based finite element model to predict the stress state of alloys with second phase inclusions. Quantitative microstructural details extracted from scanning electron microscopy (SEM) images were used to generate heterogeneous microstructures including the morphology and spatial distribution of hydrides. Generation of digital microstructure was achieved through two steps of tessellations using software Neper and Matlab. The process is demonstrated using an example of Zircaloy material with secondary phases of hydrides dispersed within and stress-strain response of Zircaloy containing hydrides was predicted. The constitutive material model for Zircaloy in this study was based on crystal plasticity theory which considers the hexagonal close-packed (HCP) atomic structure of Zircaloy material. The hydrides were modeled as isotropic elasto-plastic material. A parametric study had been conducted to understand the effect of volume fraction, and lamellae thickness of the hydride phase on the mechanical properties of the overall material. Results can help designers to alter the manufacturing process to obtain the enhanced mechanical properties for components used in nuclear applications made by Zircaloy material.

Kulkarni, Shank S.↗
DOE OSTI2024

SAM-I-Am: Semantic boosting for zero-shot atomic-scale electron micrograph segmentation

Image segmentation is a critical enabler for tasks ranging from medical diagnostics to autonomous driving. However, the correct segmentation semantics — where are boundaries located? what segments are logically similar? — change depending on the domain, such that state-of-the-art foundation models can generate meaningless and incorrect results. Moreover, in certain domains, fine-tuning and retraining techniques are infeasible: obtaining labels is costly and time-consuming; domain images (micrographs) can be exponentially diverse; and data sharing (for third-party retraining) is restricted. To enable rapid adaptation of the best segmentation technology, we propose the concept of semantic boosting: given a zero-shot foundation model, guide its segmentation and adjust results to match domain expectations. Here, we apply semantic boosting to the Segment Anything Model (SAM) to obtain microstructure segmentation for transmission electron microscopy. Our booster, SAM-I-Am, serves as a post-processing engine that extracts geometric and textural features of various intermediate masks to perform mask removal and mask merging operations. We demonstrate a zero-shot performance increase of (absolute) +21.35%, +12.6%, +5.27% in mean IoU, and a -9.91%, -18.42%, -4.06% drop in mean false positive masks across images of three difficulty classes over vanilla SAM (ViT-L).

36 MATERIALS SCIENCE↗
DOE OSTI2024

Machine Learning Correlation of Electron Micrographs and ToF-SIMS for the Analysis of Organic Biomarkers in Mudstone

The spatial distribution of organics in geological samples can be used to determine when and how these organics were incorporated into the host rock. Mass spectrometry (MS) imaging can rapidly collect a large amount of data, but ions produced are mixed without discrimination, resulting in complex mass spectra that can be difficult to interpret. Here, we apply unsupervised and supervised machine learning (ML) to help interpret spectra from time-of-flight-secondary ion mass spectrometry (ToF-SIMS) of an organic-carbon-rich mudstone of the Middle Jurassic of England (UK). It was previously shown that the presence of sterane molecular biomarkers in this sample can be detected via ToF-SIMS (Pasterski, M. J. et al., Astrobiology 2023, 23, 936). We use unsupervised ML on scanning electron microscopy–electron dispersive spectroscopy (SEM-EDS) measurements to define compositional categories based on differences in elemental abundances. We then test the ability of four ML algorithms─k-nearest neighbors (KNN), recursive partitioning and regressive trees (RPART), eXtreme gradient boost (XGBoost), and random forest (RF)─to classify the ToF-SIM spectra using (1) the categories assigned via SEM-EDS, (2) organic and inorganic labels assigned via SEM-EDS, and (3) the presence or absence of detectable steranes in ToF-SIMS spectra. In terms of predictive accuracy and balanced accuracy, KNN was the best performing model and RPART the worst. The feature importance, or the specific features of the ToF-SIM spectra used by the models to make classifications, cannot be determined for KNN, preventing posthoc model interpretation. Nevertheless, the feature importance extracted from the other models was useful for interpreting spectra. In conclusion, we determined that some of the organic ions used to classify biomarker containing spectra may be fragment ions derived from kerogen which is abundant in this mudstone sample.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Development of Multimodal Few-Shot Analytics for Electron Micrographs

Recent advances in materials data analytics have provided new avenues for determining process-structure-property (PSP) linkages in a variety of materials. Machine learning techniques including few-shot learning have increased the efficiency of classifying microscopy images for the purposes of material characterization. Attempts at creating a multimodal approach can provide further improvements to current models and help extract more salient features from data. In this vein, raw spectrum data was taken to provide an additional modality to our current pyCHIP classifier. Modifications in segmentation also show potential in improving the accuracy of the pyCHIP classifier. Classifier output was analyzed using network graphs and unsupervised clustering algorithms such as spectral clustering to detect better segmentation methods than the current “chipping” approach. We suggest that the chip selection process can be automated in the future using a combination of these techniques to enable high-throughput analyses.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Combustion of Silane-Hydrogen-Nitrous oxide Mixtures: Analysis of Laminar Flame Propagation and Condensed Products

The laminar burning rate, the explosion pressure, and the pressure rise coefficient have been measured for silane-nitrous oxide-argon mixtures using the spherically expanding flame technique in a constant volume combustion chamber. For these three parameters, the values obtained were higher than for hydrogen-nitrous oxide-argon and typical hydrocarbon-based mixtures. As an example, a maximum burning rate of 1800 g/m 2 s was measured at 101 kPa, whereas under similar conditions, a maximum burning rate around 950 g/m 2 s has been reported for hydrogen-nitrous oxide-argon mixtures. Here, the condensed combustion products formed in the silane-nitrous oxide-argon flames were sampled and characterized using electron micrograph, electronic diffraction, energy-dispersive spectroscopy, and X-ray powder diffraction. For all equivalence ratios, silica spherical particles with a mean diameter in the range 200-300 nm were observed. In addition, for mixtures with Φ ≥2.2, silicon nanowires were formed. X-ray diffraction experiments showed that the silicon nanowires are composed of a material referred to as “metal silicon” characterized by a cubic structure (lattice parameter: a = 5.425 Å) with the Fm-3m space group.

42 ENGINEERING↗
DOE OSTI2022

Onion-Like nanoparticles of the metal–organic framework UiO-66 synthesized by sequential spike crystal growth

Reproducible size and morphology of metal–organic framework (MOF) particles are essential for tuning these materials for applications like heterogeneous catalysis. Particle size measurements from electron micrographs provide information about the impact of changing synthetic parameters on MOF growth, but reported data are usually limited to averages and statistical distributions. Further elucidating synthetic control parameters could facilitate studies of facet-dependent and non-classical growth mechanisms. Here, in this work, phase-pure nanoparticles of the MOF UiO-66 were synthesized using alternating spikes of zirconium and hafnium-based nodes, forming an onion-like structure with alternating layers of Zr-UiO-66 and Hf-UiO-66. In conventional, bright-field transmission electron micrographs, Hf-UiO-66 and Zr-UiO-66 layers appeared darker and lighter, respectively. Alternating layers of Hf-UiO-66 and Zr-UiO-66 were also apparent in elemental maps obtained using scanning transmission electron microscopy with energy-dispersive X-ray spectroscopy. Average particle sizes increased monotonically with each spike, and no Zr-only or Hf-only particles were observed after the first spike, indicating that new material was consistently incorporated onto existing seeds. Surface area of the onion-like particles and their uptake of Ni 2+ from solution were both between the values measured for Zr-UiO-66 and Hf-UiO-66. Results demonstrate the use of sequential spikes of nodes in monitoring crystal growth in MOFs.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Geometric frustration in the myosin superlattice of vertebrate muscle

Geometric frustration results from an incompatibility between minimum energy arrangements and the geometry of a system, and gives rise to interesting and novel phenomena. Here, we report geometric frustration in a native biological macromolecular system---vertebrate muscle. We analyse the disorder in the myosin filament rotations in the myofibrils of vertebrate striated (skeletal and cardiac) muscle, as seen in thin-section electron micrographs, and show that the distribution of rotations corresponds to an archetypical geometrically frustrated system---the triangular Ising antiferromagnet. Spatial correlations are evident out to at least six lattice spacings. The results demonstrate that geometric frustration can drive the development of structure in complex biological systems, and may have implications for the nature of the actin--myosin interactions involved in muscle contraction. Identification of the distribution of myosin filament rotations with an Ising model allows the extensive results on the latter to be applied to this system. It shows how local interactions (between adjacent myosin filaments) can determine long-range order and, conversely, how observations of long-range order (such as patterns seen in electron micrographs) can be used to estimate the energetics of these local interactions. Furthermore, since diffraction by a disordered system is a function of the second-order statistics, the derived correlations allow more accurate diffraction calculations, which can aid in interpretation of X-ray diffraction data from muscle specimens for structural analysis.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Relation between sampling, sensitivity and precision in strain mapping using the Geometric Phase Analysis method in Scanning Transmission Electron Microscopy

The sensitivity and the precision of the Geometric Phase Analysis (GPA) method for strain characterization is a topic widely discussed in the literature and is usually difficult to quantify. Indeed, the GPA precision is intricately linked to the resolution of the strain maps defined when masking the periodic reflections in Fourier space. In this study an additional parameter, sampling, is proposed to be analyzed regarding the precision of GPA by developing the concept of a phase noise in the GPA equations. Both experimentally and theoretically, the following article demonstrates how the precision, and the sensitivity of the GPA method is improved when using a larger pixel spacing to record an electron micrograph in Scanning Transmission Electron Microscopy (STEM). In conclusion, the counterintuitive concept of increasing the field of view to improve the GPA precision results in an extension of the application of strain characterization methods in STEM towards low deformation levels.

47 OTHER INSTRUMENTATION↗
DOE OSTI2022

Understanding the Influence of Receptive Field and Network Complexity in Neural Network-Guided TEM Image Analysis

Abstract Trained neural networks are promising tools to analyze the ever-increasing amount of scientific image data, but it is unclear how to best customize these networks for the unique features in transmission electron micrographs. Here, we systematically examine how neural network architecture choices affect how neural networks segment, or pixel-wise separate, crystalline nanoparticles from amorphous background in transmission electron microscopy (TEM) images. We focus on decoupling the influence of receptive field, or the area of the input image that contributes to the output decision, from network complexity, which dictates the number of trainable parameters. For low-resolution TEM images which rely on amplitude contrast to distinguish nanoparticles from background, we find that the receptive field does not significantly influence segmentation performance. On the other hand, for high-resolution TEM images which rely on both amplitude and phase-contrast changes to identify nanoparticles, receptive field is an important parameter for increased performance, especially in images with minimal amplitude contrast. Rather than depending on atom or nanoparticle size, the ideal receptive field seems to be inversely correlated to the degree of nanoparticle contrast in the image. Our results provide insight and guidance as to how to adapt neural networks for applications with TEM datasets.

42 ENGINEERING↗
DOE OSTI2021

Multi-scale investigation of heterogeneous swift heavy ion tracks in stannate pyrochlore

Er 2 Sn 2 O 7 pyrochlore was irradiated with swift heavy Au ions (2.2 GeV), and the induced structural modifications were systematically examined using complementary characterization techniques including transmission electron microscopy (TEM), X-ray diffraction (XRD), and neutron total scattering with pair distribution function (PDF) analysis. Each technique probes different aspects and length scales of the transformed material regions. TEM revealed a core–shell ion track structure—an amorphous core surrounded by a disordered, anion-deficient fluorite shell—which was confirmed by XRD. Neutron total scattering, with sensitivity to the oxygen sublattice, provided relative fractions of amorphous and disordered fluorite phases and confirmed the presence of a defective pyrochlore phase, which largely maintains its structural ordering but is clearly distinct from the pristine pyrochlore matrix. This defect-rich pyrochlore phase forms a halo extending radially beyond the well-characterized core–shell track morphology observed in electron micrographs. Despite their differing long-range periodicity, the short-range structures of the amorphous, disordered, and defective pyrochlore phases are all modeled well with a weberite-type configuration. Evolution of the phase fractions with increasing ion fluence was examined to ascertain the phase-to-phase pathways that occur during primary and secondary ion impact. Furthermore, this approach extends knowledge about the multi-scale response of stannate pyrochlores to swift heavy ion irradiation in the electronic energy loss regime and improves existing track-overlap models.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Coherence in Polycrystalline Thin Films of Twisted Molecular Crystals

Helicoidal crystallites in rhythmically banded spherulites manifest spectacular optical patterns in small molecules and polymers. It is shown that concentric optical bands indicating crystallographic orientations typically lose coherence (in-phase twisting) with growth from the center of nucleation. Here, coherence is shown to increase as the twist period decreases for seven molecular crystals grown from the melt. This dependence was correlated to crystallite fiber thickness and length, as well as crystallite branching frequency, a parameter that was extracted from scanning electron micrographs, and supported by numerical simulations. Hole mobilities for 2,5-didodecyl-3,6-di(thiophen-2-yl)pyrrolo[3,4-c]pyrrole-1,4(2H,5H)-dione (DPP-C12) measured by using organic field-effect transistors demonstrated that more incoherent boundaries between optical bands in spherulites lead to higher charge transport for films with the same twist period. This was rationalized by combining our growth model with electrodynamic simulations. This work illustrates the emergence of complexity in crystallization processes (spherulite formation) that arises in the extra variable of helicoidal radial twisting. The details of the patterns analyzed here link the added complexity in crystal growth to the electronic and optical properties of the thin films.

36 MATERIALS SCIENCE↗
DOE OSTI2021

THF co-solvent pretreatment prevents lignin redeposition from interfering with enzymes yielding prolonged cellulase activity

Background: Conventional aqueous dilute sulfuric acid (DSA) pretreatment of lignocellulosic biomass facilitates hemicellulose solubilization and can improve subsequent enzymatic digestibility of cellulose to fermentable glucose. However, much of the lignin after DSA pretreatment either remains intact within the cell wall or readily redeposits back onto the biomass surface. This redeposited lignin has been shown to reduce enzyme activity and contribute to rapid enzyme deactivation, thus, necessitating significantly higher enzyme loadings than deemed economical for biofuel production from biomass. Results: In this study, we demonstrate how detrimental lignin redeposition on biomass surface after pretreatment can be prevented by employing Co-solvent Enhanced Lignocellulosic Fractionation (CELF) pretreatment that uses THF–water co-solvents with dilute sulfuric acid to solubilize lignin and overcome limitations of DSA pretreatment. We first find that enzymatic hydrolysis of CELF-pretreated switchgrass can sustain a high enzyme activity over incubation periods as long as 5 weeks with enzyme doses as low as 2 mg protein/g glucan to achieve 90% yield to glucose. A modified Ninhydrin-based protein assay revealed that the free-enzyme concentration in the hydrolysate liquor, related to enzyme activity, remained unchanged over long hydrolysis times. DSA-pretreated switchgrass, by contrast, had a 40% drop in free enzymes in solution during incubation, providing evidence of enzyme deactivation. Furthermore, measurements of enzyme adsorption per gram of lignin suggested that CELF prevented lignin redeposition onto the biomass surface, and the little lignin left in the solids was mostly integral to the original lignin–carbohydrate complex (LCC). Scanning electron micrographs and NMR characterization of lignin supported this observation. Conclusions: Enzymatic hydrolysis of solids from CELF pretreatment of switchgrass at low enzyme loadings was sustained for considerably longer times and reached higher conversions than for DSA solids. Analysis of solids following pretreatment and enzymatic hydrolysis showed that prolonged cellulase activity could be attributed to the limited lignin redeposition on the biomass surface making more enzymes available for hydrolysis of more accessible glucan.

09 BIOMASS FUELS↗
DOE OSTI2021

Uranium Oxide Synthetic Pathway Discernment through Unsupervised Morphological Analysis

We present a novel unsupervised machine learning method for quantitative representation of scanning electron micrographs and its applications and performance for nuclear forensic analysis of uranium ore concentrates. The method uses a vector quantizing variational autoencoder followed by a histogram operation to encode a micrograph into a single dimensional representation, called the latent vector. The method requires no extant labeling of the data and can be applied over large datasets of micrographs with minimal human interaction. The representations generated are broadly descriptive of each micrograph and the microstructure of the material imaged. In the case of uranium ore concentrate analysis, the representations were amenable to processing reagent and ore concentrate species classification with accuracy of 81:8%, which is competitive with state-of-the-art supervised networks. The representations were also used to classify previously unseen processing routes, were able to classify imaging parameters such as magnification (to 76:0% accuracy), were able to classify fine grained process parameters such as calcining temperature (to 74:4% accuracy), and their informatic properties indicate that they are generally descriptive of the image represented. This method can be applied across microstructure analysis fields to perform quantitative analysis without the need for labor intensive and possibly biased human analysis.

Scanning Electron Microscopy, Vector Quantizing Va↗