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At least 1,315 records · Page 73
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Adoption of image-driven machine learning for microstructure characterization and materials design: A Perspective

Microstructure characterization enables the development of structure-processing-property relationships critical to several research areas within the broad field of materials science, from alloy design to the assessment of corrosion resistance, and failure analysis. Conventional approaches to material characterization have relied on either qualitative inference by the human ex-pert or software applications that can extract high-level features from images, such as boundary segmentation, average grain diameter, etc. Such approaches rely heavily on subject matter expert user intervention and knowledge of what phases or more generally, what microstructural features, are of interest. The recent surge in the adoption of machine learning techniques to address problems in materials engineering has brought with it an increased interest and application of Image Driven Machine Learning (IDML) approaches. In this work, we review the applications of IDML to the field of materials characterization. A canonical hierarchy of stages is defined, which when put sequentially together completes an IDML study: problem definition, dataset building, model selection and training, model evaluation, and integration with existing instrumentation or simulation workflow. The studies reviewed in this work are analyzed from the perspective of each of these stages. Such a review permits agranular assessment of the field, for example the impact of IDML on materials characterization at the nanoscale, the size of a typical dataset required to train a semantic segmentation model on electron microscopy images, ubiquitousness of transfer learning in the domain, etc. Finally, we discuss the importance of interpretability and explainability in the field of IDML for materials characterization, and provide an overview of two emerging techniques in the field: semantic segmentation and generative adversarial networks.

Baskaran, Arun↗
DOE OSTI2022

Real-time X-ray phase-contrast imaging using SPINNet—a speckle-based phase-contrast imaging neural network

X-ray phase-contrast imaging has become indispensable for visualizing samples with low absorption contrast. In this regard, speckle-based techniques have shown significant advantages in spatial resolution, phase sensitivity, and implementation flexibility compared with traditional methods. However, the computational cost associated with data inversion has hindered their wider adoption. By exploiting the power of deep learning, we developed a speckle-based phase-contrast imaging neural network (SPINNet) that significantly improves the imaging quality and boosts the phase retrieval speed by at least 2 orders of magnitude compared to existing methods. To achieve this performance, we combined SPINNet with a coded-mask-based technique, an enhanced version of the speckle-based method. Using this scheme, we demonstrate the simultaneous reconstruction of absorption and phase images on the order of 100 ms, where a traditional correlation-based analysis would take several minutes even with a cluster. In addition to significant improvement in speed, our experimental results show that the imaging and phase retrieval quality of SPINNet outperform existing single-shot speckle-based methods. Furthermore, we successfully demonstrate SPINNet application in x-ray optics metrology and 3D x-ray phase-contrast tomography. Our result shows that SPINNet could enable many applications requiring high-resolution and fast data acquisition and processing, such as in situ and in operando 2D and 3D phase-contrast imaging and real-time at-wavelength metrology and wavefront sensing.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Predicting U 3 O 8 powder processing conditions: An AI/ML approach analyzing deep learning embeddings of SEM micrographs

High-resolution SEM images of uranium-oxide powders encode micro- and nanoscale clues to their synthesis route and calcination temperature. We trained a ResNet-50 model on 11 commercial-scale U₃O₈ classes, ammonium diuranate (ADU) or uranyl peroxide (H₂O₂) precursors calcined at temperatures ranging from 400 to 750 °C and added a 256-D projection head before the classifier to analyze the learned representation. The best of eight seeds reached 92.4 % accuracy on reserved testing data, but our focus is the structure of the embedding space rather than the accuracy and labels. We quantify class relatedness in the original 256-D space using centroid similarity and distributional distances, and we use Uniform Manifold Approximation Projection (UMAP) for visualization. ‘Unknown’ images from different preparation methods, SEM operators, and from the literature localized near the expected classes under a nearest-centroid analysis without retraining, as well as clustered in similar UMAP space. In conclusion, this embedding-centered workflow complements black-box classification by providing quantitative, similarity-based comparisons of U₃O₈ morphologies and reduces storage space by up to 98 % for image data used in millisecond vector search comparisons.

36 MATERIALS SCIENCE↗
DOE OSTI2023

In Situ Imaging of Faujasite Surface Growth Reveals Unique Pathways of Zeolite Crystallization

Zeolite crystallization occurs by complex processes involving a variety of possible mechanisms. The sol gel media used to prepare zeolites leads to heterogeneous mixtures of solution and solid states with diverse solute species. At later stages of zeolite synthesis when growth occurs predominantly from solution, classical two-dimensional nucleation and spreading of layers on crystal surfaces via the addition of soluble species is the dominant pathway. At earlier stages, these processes occur in parallel with nonclassical pathways involving crystallization by particle attachment (CPA). The relative roles of solution- and solid-state species in zeolite crystallization have been a subject of debate. Here, in this work, we investigate the growth mechanism of a commercially relevant zeolite, faujasite (FAU). In situ atomic force microscopy (AFM) measurements reveal that supernatant solutions extracted from a conventional FAU synthesis at various times do not result in growth, indicating that FAU growth predominantly occurs from the solid state through a disorder-to-order transition of amorphous precursors. Elemental analysis shows that supernatant solutions are significantly more siliceous than both the original growth mixture and the FAU zeolite product; however, in situ AFM studies using a dilute clear solution with a lower Si/Al ratio revealed three-dimensional growth of surfaces that is distinct from layer-by-layer and CPA pathways. This unique mechanism of growth differs from those observed in studies of other zeolites. Given that relatively few zeolite frameworks have been the subject of mechanistic investigation by in situ techniques, these observations of FAU crystallization raise the question whether its growth pathway is characteristic of other zeolite structures.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Experimental Investigation of Cavitation-Induced Erosion Using X-Ray Imaging and Tomography

High injection pressure in diesel engines can lead to cavitation-induced erosion in injector nozzles. One important factor affecting the severity of erosion is the fuel and its properties. Traditionally, modeling and simulation studies have used single-component representations of fuels, but realistic fuels feature a multitude of components and can even include volatile additives such as water and alcohol. To provide realistic benchmarks and comparisons, experimental measurements quantifying erosion characteristics were made using ultra-low sulfur diesel (ULSD) and two alternative diesel fuels (ADF). X-ray imaging and computed tomography were used to investigate cavitation-induced erosion onset and progression. Hard X-ray tomography revealed injector internal geometry, including details such as surface marks from the manufacturing process and erosion patterns from repeated injections. Erosion progression was measured using X-ray tomography and imaging performed between injections. The critical erosion site was found to be similar across different fuel blends, while the erosion rate and incubation time were sensitive to the fuel blend. The injector geometry and the erosion characteristics were also prepared for numerical model development and validation.

42 ENGINEERING↗
DOE OSTI2023

In-Situ X-Ray Imaging High Strain Rate Compression of Laminate Al-Graphene Composite and Mechanical Property Characterization

Owing to the opaque nature of the laminated structures, traditional high-speed optical camera cannot be used to detect the dynamic process of sub-surface deformation. In this article, we report a study of using high speed X-ray imaging to study the high strain rate deformation in laminated Al structures. We used a Kolsky bar apparatus to apply dynamic compression and a high-speed synchrotron X-ray phase contrast imaging (PCI) setup to conduct the in situ X-ray imaging study. The in situ X-ray imaging captures the shock wave propagation in the laminated structures. After shock compression, we characterized the microstructures by using transmission electron microscopy (TEM), which demonstrates an increase of dislocation density. In conclusion, the micro-pillar compression tests show that the yield strength at 0.2% offset of laminated Al-graphene composite has a significant increase of 67%, from 30 to 50 MPa, compared to laminate Al after shock loading.

36 MATERIALS SCIENCE↗
DOE OSTI2025

High-resolution high-throughput spatiotemporal strain imaging reveals loss mechanisms in a surface acoustic wave device

Surface acoustic wave devices are key components for processing radio frequency signals in wireless communication because these devices offer simultaneously high performance, compact size and low cost. The optimization of the device structure requires a quantitative understanding of energy conversion and loss mechanisms. Here we use stroboscopic full-field diffraction x-ray microscopy to reveal an unanticipated acoustic loss in a prototypical one-port resonator device. A non-uniform acoustic excitation in the active area was responsible for the substantial end and side leakages observed at the design frequency. Quantitative analysis of the strain amplitude using a wave decomposition method allowed the determination of several key device parameters. This high-resolution high-throughput spatiotemporal strain imaging technique is more generally applicable to the study of dynamic strain modulation in nanoscale acoustic, electronic, optical and quantum devices. The high sensitivity allows precise measurement of the strain modulation with picometer-scale amplitude.

Zhou, Tao [ESRF - The European Synchrotron, Grenob↗
DOE OSTI2021

An integrated manifold learning approach for high-dimensional data feature extractions and its applications to online process monitoring of additive manufacturing

As an effective dimension reduction and feature extraction technique, manifold learning has been successfully applied to high-dimensional data analysis. With the rapid development of sensor technology, a large amount of high-dimensional data such as image streams can be easily available. Thus, a promising application of manifold learning is in the field of sensor signal analysis, particular for the applications of online process monitoring and control using high-dimensional data. The objective of this study is to develop a manifold learning-based feature extraction method for process monitoring of Additive Manufacturing (AM) using online sensor data. Due to the non-parametric nature of most existing manifold learning methods, their performance in terms of computational efficiency, as well as noise resistance has yet to be improved. To address this issue, this study proposes an integrated manifold learning approach termed multi-kernel metric learning embedded isometric feature mapping (MKML-ISOMAP) for dimension reduction and feature extraction of online high-dimensional sensor data such as images. Based on the extracted features with the utilization of supervised classification and regression methods, an online process monitoring methodology for AM is implemented to identify the actual process quality status. Finally, in the numerical simulation and real-world case studies, the proposed method demonstrates excellent performance in both prediction accuracy and computational efficiency.

36 MATERIALS SCIENCE↗
DOE OSTI2023

CSAPR2 Optimized Convective Cell Tracking Data during TRACER

One of the challenges in analyzing convective cell properties is to observe the quick evolution of individual convective cells. While the operational radar data provide a volumetric data set to analyze radar observables of convective precipitation clouds, previous studies also suggested the quick evolution of cell life cycle that might not be captured by conventional radar volume scan strategies that take ~5-7 minutes. Aiming at enhancing our understanding of the links between convective cloud kinematic and microphysical processes as well as life cycles, the Tracking Aerosol Convection Interactions ExpeRiment (TRACER; Jensen et al. 2019) was conducted at Houston, Texas, in 2022. The TRACER campaign deployed the 2nd generation C-band Scanning ARM Precipitation Radar (CSAPR2), which performed frequent updates of range height indicator (RHI) and sector plan position indicator (PPI) scans to track individual convective cells every < 2 minutes, guided by a new cell tracking framework, Multisensor Agile Adaptive Sampling (MAAS; Kollias et al. 2020). This allows for capturing fast-evolving radar observables. We provide the processed CSAPR2 cell tracking data in CfRadial format collected during the TRACER field campaign from June to September 2022. The data files include processed radar variables: noise-masked reflectivity and differential reflectivity corrected for rain attenuation and systematic biases, noise-masked dealiased radial velocity, specific differential phase, locations of target cells (latitude, longitude, radar range), and radar-echo classification. Figure 1 provides an example of a 3D image of CSAPR2 reflectivity from the lowest PPI scan and an RHI scan after data processing.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Wide Dust Gaps in Protoplanetary Disks Induced by Eccentric Planets: A Mass-eccentricity Degeneracy

The tidal perturbation of embedded protoplanets on their natal disks has been widely attributed to be the cause of gap-ring structures in submillimeter images of protoplanetary disks around T Tauri stars. Numerical simulations of this process have been used to propose scaling of characteristic dust-gap width/gap-ring distance with respect to planet mass. Applying such scaling to analyze observed gap samples yields a continuous mass distribution for a rich population of hypothetical planets in the range of several Earth to Jupiter masses. In contrast, the conventional core-accretion scenario of planet formation predicts a bimodal mass function due to (1) the onset of runaway gas accretion above ~20 Earth masses and (2) suppression of accretion induced by gap opening. Here, we examine the dust disk response to the tidal perturbation of eccentric planets as a possible resolution of this paradox. Based on simulated gas and dust distributions, we show the gap-ring separation of Neptune-mass planets with small eccentricities might become comparable to that induced by Saturn-mass planets on circular orbits. Furthermore, this degeneracy may obliterate the discrepancy between the theoretical bimodal mass distribution and the observed continuous gap width distribution. Despite damping due to planet–disk interaction, modest eccentricity may be sustained either in the outer regions of relatively thick disks or through resonant excitation among multiple super Earths. Moreover, the ring-like dust distribution induced by planets with small eccentricities is axisymmetric even in low viscosity environments, consistent with the paucity of vortices in Atacama Large Millimeter/submillimeter Array images.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

In vitro investigation of protein assembly by combined microscopy and infrared spectroscopy at the nanometer scale

The nanoscale structure and dynamics of proteins on surfaces has been extensively studied using various imaging techniques, such as transmission electron microscopy and atomic force microscopy (AFM) in liquid environments. These powerful imaging techniques, however, can potentially damage or perturb delicate biological material and do not provide chemical information, which prevents a fundamental understanding of the dynamic processes underlying their evolution under physiological conditions. Here, we use a platform developed in our laboratory that enables acquisition of infrared (IR) spectroscopy and AFM images of biological material in physiological liquids with nanometer resolution in a cell closed by atomically thin graphene membranes transparent to IR photons. In this work, we studied the self-assembly process of S-layer proteins at the graphene-aqueous solution interface. The graphene acts also as the membrane separating the solution containing the proteins and Ca 2+ ions from the AFM tip, thus eliminating sample damage and contamination effects. The formation of S-layer protein lattices and their structural evolution was monitored by AFM and by recording the amide I and II IR absorption bands, which reveal the noncovalent interaction between proteins and their response to the environment, including ionic strength and solvation. Our measurement platform opens unique opportunities to study biological material and soft materials in general.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Hyperspectral Raman Imaging using a Spatial Heterodyne Spectrometer (SHS)

Savannah River National Laboratory, and the larger community, has limited chemical-specific imaging capabilities, which would be beneficial for remote detection of threat chemicals or use in inaccessible and harsh environments, as well as in-line process monitoring. The project objective is to develop a novel, rugged, highly sensitive spectrometer to support real-time, chemically specific imaging using hyperspectral Raman spectroscopy.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2023

Comparative Analysis of Imaging and Measurements of Micrometer-Scale Fracture Aperture Fields Within a Heterogeneous Rock Using PET and X-ray CT

Knowledge of the spatial distribution of fracture apertures is essential for reliable characterization of flow and transport processes in fractured systems and for better understanding of physicochemical matrix–fracture interactions. Here, we propose and test two image-based methods, thereby extending the current experimental capabilities to characterize aperture size distribution in structurally heterogeneous geologic porous media noninvasively. The first approach utilizes an inversion method based on the dataset acquired from positron emission tomography (PET) and the second approach considers an extension of the classic missing attenuation technique that relies on clinical X-ray computed tomography (X-ray CT). Independent sets of imaging experiments are conducted on a fractured basalt core with heterogeneous matrix properties and aperture distributions to compare the two methodologies. A repeat of each experiment is conducted to verify the proposed workflows. The performance of these two imaging techniques is systematically evaluated through the analysis of signal-to-noise ratio, minimum fracture size detectability, and measurement errors. While both approaches provide a reliable estimation of fracture aperture distributions, PET yields a signal-to-noise ratio that is substantially higher than the corresponding X-ray CT measurements. Furthermore, uncertainties of the aperture values for PET are considerably lower ($\bar {\sigma}_{\text {d}} = 15\%$) compared to those obtained from X-ray CT ($\bar {\sigma}_{\text {d}} = 29\%$), allowing for the detection of minimum aperture sizes of 20 $\mu$m with 70% confidence level. Finally, these approaches provide key experimental tools for better understanding dynamic hydromechanical fracture properties in geologic systems.

58 GEOSCIENCES↗
DOE OSTI2023

Multiscale dynamics of charging and plating in graphite electrodes coupling operando microscopy and phase-field modelling

The phase separation dynamics in graphitic anodes significantly affects lithium plating propensity, which is the major degradation mechanism that impairs the safety and fast charge capabilities of automotive lithium-ion batteries. In this study, we present comprehensive investigation employing operando high-resolution optical microscopy combined with non-equilibrium thermodynamics implemented in a multi-dimensional (1D+1D to 3D) phase-field modeling framework to reveal the rate-dependent spatial dynamics of phase separation and plating in graphite electrodes. Here we visualize and provide mechanistic understanding of the multistage phase separation, plating, inter/intra-particle lithium exchange and plated lithium back-intercalation phenomena. A strong dependence of intra-particle lithiation heterogeneity on the particle size, shape, orientation, surface condition and C-rate at the particle level is observed, which leads to early onset of plating spatially resolved by a 3D image-based phase-field model. Moreover, we highlight the distinct relaxation processes at different state-of-charges (SOCs), wherein thermodynamically unstable graphite particles undergo a drastic intra-particle lithium redistribution and inter-particle lithium exchange at intermediate SOCs, whereas the electrode equilibrates much slower at low and high SOCs. These physics-based insights into the distinct SOC-dependent relaxation efficiency provide new perspective towards developing advanced fast charge protocols to suppress plating and shorten the constant voltage regime.

25 ENERGY STORAGE↗
DOE OSTI2022

Impact of coolant temperature on the combustion characteristics and emissions of a stratified-charge direct-injection spark-ignition engine fueled with E30

The direct injection spark ignition (DISI) engine has received considerable attention due to its potential to increase the power density of traditional spark ignition engines while significantly improving fuel economy through lean, unthrottled combustion. However, the market introduction of DISI engines operated in a lean combustion mode is inhibited by their unsatisfactory emissions, especially during cold start conditions that make proper mixture formation more challenging. Ethanol-blended gasoline, now a widely used fuel, makes the cold start of a DISI engine more difficult, leading to higher HC and soot emissions because of the high latent heat of vaporization of ethanol relative to gasoline. This work investigated the impact of coolant temperature on the characteristics of combustion and emissions in a stratified-charge DISI engine fueled with an E30 fuel (i.e. 30% ethanol in gasoline), while the coolant temperature was alternated between four levels (45, 60, 75, and 90 °C) to simulate different conditions throughout the warm-up process. The experiments showed that the coolant temperature affected the post-spark inflammation time, as well as the speed, intensity, and stability of the combustion process in the engine. When the coolant temperature rose, the engine produced more NOX and less CO, PM and HC. In addition, high-speed direct photography was used to obtain crank-angle resolved images of fuel sprays and flames in the cylinder. As the coolant temperature rose, the liquid spray lengths became shorter, reducing the possibility of wall wetting, and reduced irradiance from soot particles also indicated less nonpremixed combustion. The in-cylinder imaging results are consistent with the observed combustion and emission characteristics and shed light on the underlying processes. Finally, some potential solutions to the emissions challenges faced here could be either raising in-cylinder temperatures by using trapped residuals or modifying the injection schedule, for example by increasing the number of injections or to inject later in the cycle into a higher-density environment.

42 ENGINEERING↗
DOE OSTI2024

Static metrology of the meter-scale deformable heliostat

This paper describes a heliostat metrology system which is developed based on deflectometry, utilizing a static perforated panel instead of conventional dynamic monitor displays to provide incident rays. The developed method is named static screen deflectometry (SSD). This robust and scalable method is especially valuable for outdoor tests of large reflectors used in concentrating solar-thermal power (CSP) systems. The developed method has been successfully demonstrated on a 2.4 𝑚 × 3.3 𝑚 float glass deformable reflector bent to focus sunlight at 113 𝑚 distance throughout a day. From images obtained from a camera at 50𝑚 distance, the reflector surface was measured to an accuracy of less than 1 𝑚𝑟𝑎𝑑 rms slope error in the full test scope.

14 SOLAR ENERGY↗
DOE OSTI2022

Hierarchical deep reinforcement learning reveals a modular mechanism of cell movement

Time-lapse images of cells and tissues contain rich information about dynamic cell behaviours, which reflect the underlying processes of proliferation, differentiation and morphogenesis. However, we lack computational tools for effective inference. Here we exploit deep reinforcement learning (DRL) to infer cell–cell interactions and collective cell behaviours in tissue morphogenesis from three-dimensional (3D) time-lapse images. We use hierarchical DRL (HDRL), known for multiscale learning and data efficiency, to examine cell migrations based on images with a ubiquitous nuclear label and simple rules formulated from empirical statistics of the images. When applied to Caenorhabditis elegans embryogenesis, HDRL reveals a multiphase, modular organization of cell movement. Imaging with additional cellular markers confirms the modular organization as a novel migration mechanism, which we term sequential rosettes. Furthermore, HDRL forms a transferable model that successfully differentiates sequential rosettes-based migration from others. Our study demonstrates a powerful approach to infer the underlying biology from time-lapse imaging without prior knowledge.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Introducing SpaceNet 9 - Cross-Modal Satellite Imagery Registration for Natural Disaster Responses

Computer vision algorithms are increasingly leveraged to accelerate geospatial analysis for disaster response and recovery. As the diversity of remote sensing imagery grows with optical, SAR, and other modalities, a perquisite for analytics is cross-modal image registration. There is a high potential to harness computer vision for this pre-processing requirement toward enabling downstream analytics such as heterogeneous change detection, automated feature extraction, and data fusion. Advancement in these areas has the potential to simplify data wrangling tasks and further accelerate disaster response timelines. The SpaceNet 9 challenge (launching in mid-2024) focuses on addressing the cross-modal image registration problem and demonstrating the utility of such modules on earthquake impacted scenarios. This paper describes the motivation for the SpaceNet 9 and provides a first overview of the dataset, the baseline algorithm, and implications for seeking cross-modal image registration in Earth observation. Code is available at https://github.com/SpaceNetChallenge/SpaceNet9.

Hansch, Ronny↗