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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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ExpertSegmentation: Segmentation for microscopy with domain-informed targets via custom loss
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Multimodal imaging and machine learning to enhance microscope images of shale
A machine learning based image processing workflow is presented to enhance shale source rock microscopic images obtained using diverse imaging platforms. Images were acquired from a 30 μm diameter cylindrical Vaca Muerta shale sample using both nondestructive Transmission X-Ray Microscopy (TXM, alternately referred to as nano computed tomography) and destructive Focused Ion Beam-Scanning Electron Microscopy (FIB-SEM). Output cross-sectional images from each modality were aligned using a combination of manual and automated registration techniques to create a registered image dataset. We then apply this dataset for two image processing tasks: prediction of image cross sections with SEM-like resolution from nondestructive TXM data and repair of charged region artifacts (localized accumulation of electrons) within SEM images. The image processing algorithms for both tasks use deep learning models, specifically image-to-image Convolutional Neural Networks (CNNs) and conditional Generative Adversarial Networks (cGANs). In the image enhancement tasks, we are able to achieve significant qualitative and quantitative improvement in TXM images. Here, the best model reaches an average Peak Signal to Noise Ratio (PSNR) of 15.8 dB. Conditioning on TXM data is also shown to reduce artifacts from SEM charging, achieving an average PSNR of 25.8 dB. Furthermore, our results suggest that properly trained and validated networks are capable of significant enhancement of images obtained using nondestructive techniques, thereby improving interpretation of two- and three-dimensional images while preserving samples for future use.
Reconstructing porous media using generative flow networks
One area of intense scientific interest for the study of sandstones, carbonates, and shale at the pore scale is the use of limited image and petrophysical data to generate multiple realizations of a rock’s pore structure. Such images aid efforts to quantify uncertainty in petrophysical properties, including porosity–permeability transforms. We develop and evaluate a deep learning-based method to synthesize porous media volumes using a so-called generative flow model trained on x-ray microscope images of rock texture and pore structure. These models are optimized on a log-likelihood objective and they synthesize large and realistic three-dimensional images. Further, we demonstrate the rapid generation of sandstone image volumes that display realism as gauged by quantitative comparison of topological features using Minkowski functionals of porosity, specific surface area, and the Euler–Poincaré characteristic (i.e., pore connectivity). We also evaluate the single-phase permeability using Navier–Stokes and lattice Boltzmann methods and show that transport properties of the generated samples match measured trends.
Microstructure and water absorption of ancient concrete from Pompeii: An integrated synchrotron microtomography and neutron radiography characterization
There is renewed interest in using advanced techniques to characterize ancient Roman concrete due to its exceptional durability and low-carbon footprint. In the present work, samples were drilled from the “Hospitium” in Pompeii and were analyzed by synchrotron microtomography (μCT) and neutron radiography to study how the microstructure, including the presence of induced cracks, affects their water adsorption. The water distribution and absorptivity were quantified by neutron radiography. The 3D crack propagation, pore size distribution and orientation, tortuosity, and connectivity were analyzed from μCT results using advanced imaging methods. Porosity was also measured by mercury intrusion porosimetry (MIP) as a reference. Ductile fracture patterns were observed once cracks were introduced. Compared to Portland cement mortar/concrete, the Pompeii samples had relatively high porosity, low connectivity, and a similar coefficient of capillary penetration. In addition, permeability was predicted from models based on percolation theory and pore structure data to evaluate the fluid transport properties. Understanding the microstructure of ancient Pompeii concrete is important because it could inspire the development of modern concrete with high durability.
Method for measurement of TRISO kernel and layer volumes by X-ray computed tomography
Layer dimensions are key parameters for as-fabricated tristructural-isotropic (TRISO) particle fuel as well as for post-irradiation examination of particle performance. Layer thicknesses are typically measured by optical microscopy of the particle cross section near mid-plane, while layer volumes are estimated with serial sectioning and microscopy. This method for measuring layer volumes is limited due to the resolution limit imposed by slice thickness and the effect of the polishing process on delicate irradiated particle microstructures. In this study, image processing software has been developed to segregate the three-dimensional (3D) TRISO particle images provided by x-ray computed tomography (XCT) into high-resolution volumetric data for the kernel, all particle layers, and any internal voids introduced during irradiation. These data can be used to analyze key in-reactor behaviors of TRISO particles such as kernel swelling or buffer shrinkage, which are important inputs for fuel performance modeling.
Microstructure reconstruction of battery polymer separators by fusing 2D and 3D image data for transport property analysis
A new approach to generate high-fidelity 3D microstructure reconstructions by leveraging resolution and sample volume characteristics from 2D and 3D microscopy methods is presented here. This approach is employed to model the microstructure of a highly orthotropic polypropylene separator used in lithium-ion batteries, which have challenging multi-scale features of fibrils (<100 nm) and lamellae (>100 nm) to resolve in 3D. Phase contrast nano X-ray computed tomographic data are used to reconstruct the lamellae phase, while 2D scanning electron microscopy data are used to characterize sub-100 nm microstructure features such as the thin fibrils that are beyond the effective resolution of X-ray computed tomography. Fibril geometries are reconstructed stochastically based on the 2D higher resolution data, and integrated with the lamellae geometries in 3D space. Transport property analyses are performed to investigate the bias of microstructure models without considering the fibrils. A sensitivity study is also conducted to facilitate understanding of the relationship between microstructure characteristics and transport properties.
Compositional inhomogeneity and its effect on the hardness of nanostructured refractory high-entropy alloys
The pronounced compositional inhomogeneity observed in refractory high-entropy alloys (HEAs), as opposed to non-refractory HEAs, has an important influence on their mechanical properties, thereby posing a significant challenge for the development of high-performance refractory HEAs. In this work, by combining transmission electron microscopy imaging, chemical analysis, and nano-hardness tests, we investigate the compositional inhomogeneities in a series of sintered nanostructured HEAs and elucidate their influence on the material's hardness. Further, we reveal that the compositional inhomogeneity of the sintered nanostructured HEAs is temperature- and component- dependent. By correlating the hardness of nanostructured HEAs to the evolving compositional inhomogeneity, our work demonstrates that the compositional inhomogeneity in nanostructured HEAs can be tuned by sintering temperature or alloying towards optimized microstructure and hardness.
Predicting compressive strength of consolidated molecular solids using computer vision and deep learning
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Refractory wettability and surface tension of several nuclear waste glass simulants
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Modeling and experimental validation of a W f /W-fabrication by chemical vapor deposition and infiltration
Tungsten (W) has a unique combination of excellent thermal properties, low sputter yield, low hydrogen retention, and acceptable activation. Therefore, W is presently the main candidate for the first wall material in future fusion devices. However, its intrinsic brittleness and its further embrittlement during operation bears the risk of a sudden and catastrophic component failure. As a countermeasure, tungsten fiber-reinforced tungsten (W f /W) with extrinsic toughening is being developed. A possible synthesis route is chemical vapor deposition (CVD) using heated W fabrics as substrate. The challenge is that the growing CVD-W can isolate domains from precursor access leading to strength-reducing pores. To deepen the process understanding and to optimize the CVD parameters, models were developed with COMSOL Multiphysics and validated experimentally. W deposition rate equations as function of the temperature and the partial pressures of the precursors H 2 and WF 6 were experimentally validated in previous work. In the present article, these equations are applied to obtain partial pressures within the CVD reactor. The results are taken as input for transient simulations in the microscale, in which W coatings, growing onto multiple adjacent W fibers, were simulated via mesh deformation and remeshing. The surface-to-surface contact of the W coatings and the corresponding potential pore formation were simulated by implementing sophisticated deposition rate stop conditions. Within the measuring uncertainties of ≃ ±1%, the models are validated successfully by experimental comparison regarding the deposition rate, pore structure, and relative densities ranging from 0.6 to 0.9.
Blob velocities and sizes in the Alcator C-Mod scrape-off layer for ohmic and high confinement mode plasmas
An improved time delay estimation method is used to calculate the velocity of cross-field blob motion in the scrape-off layer of Alcator C-Mod for an ohmic and two high confinement (H-mode) plasmas; an edge localized mode free and an enhanced D-alpha H-mode. The gas puff imaging data analysis results are interpreted in the framework of a stochastic model that describes the fluctuations as a super-position of uncorrelated blob-like structures. In all confinement modes investigated, the scrape-off layer is dominated by large amplitude, blob-like filaments moving radially outwards with velocities in the range from 400 to 1000 m/s. Blobs in high confinement mode plasmas have similar velocities and sizes as in ohmic plasma, which is consistent with the close similarity of conditionally averaged burst shapes and frequency spectra for the confinement modes investigated.
Nanopore networks in colloidal silica assemblies characterized by XCT for confined fluid flow modeling
Fluid flow through nanopore structures has exhibited different behavior than that described by Darcy's law, derived from pore networks of micrometer scale. Here, pressure assemblies of colloidal silica spheres of specified diameters from 94 to 620 nanometers were prepared as model nano-porous media. The smaller silica spheres tended to form random packing, but some substantial ordering was found in the packing of the larger silica spheres. X-ray computed tomography (XCT), with a voxel resolution of 16 nm, was used to characterize the nanopore networks. The porosity of the Nano-XCT pore network (34%) was less than the total porosity of the assemblies as determined by X-ray attenuation (52%), possibly due to the limits of voxel resolution during segmentation. Fluid flow in the nanopore networks was simulated using the single-phase Lattice Boltzmann Method, and the simulated permeability was compared with the empirical and experimental values. Based on our characterization, although a well-ordered packing of silica spheres was not achieved, it was found that the nanopore networks in the colloidal silica assemblies had pore size distributions corresponding to the particle sizes. The simulated permeability was less than the experimental measurement for water flow, but the complex packing of silica spheres and surface chemistry issues need to be considered in future research.
Understanding the interplay between pilot fuel mixing and auto-ignition chemistry in hydrogen-enriched environment
The diesel-piloted dual-fuel compression ignition combustion strategy is well-suited to accelerate the decarbonization of transportation by adopting hydrogen as a renewable energy carrier into the existing internal combustion engine with minimal engine modifications. Despite the simplicity of engine modification, many questions remain unanswered regarding the optimal pilot injection strategy for reliable ignition with minimum pilot fuel consumption. The present study uses a single-cylinder heavy-duty optical engine to explore the phenomenology and underlying mechanisms governing the pilot fuel ignition and the subsequent combustion of a premixed hydrogen-air charge. The engine is operated in a dual-fuel mode with hydrogen premixed into the engine intake charge with a direct pilot injection of n-heptane as a diesel pilot fuel surrogate. Optical diagnostics used to visualize in-cylinder combustion phenomena include high-speed IR imaging of the pilot fuel spray evolution as well as high-speed HCHO* and OH* chemiluminescence as indicators of low-temperature and high-temperature heat release, respectively. Three pilot injection strategies are compared to explore the effects of pilot fuel mass, injection pressure, and injection duration on the probability and repeatability of successful ignition. The thermodynamic and imaging data analysis supported by zero-dimensional chemical kinetics simulations revealed a complex interplay between the physical and chemical processes governing the pilot fuel ignition process in a hydrogen containing charge. Hydrogen strongly inhibits the ignition of pilot fuel mixtures and therefore requires longer injection duration to create zones with sufficiently high pilot fuel concentration for successful ignition. Results show that ignition typically tends to rely on stochastic pockets with high pilot fuel concentration, which results in poor repeatability of combustion and frequent misfiring. In conclusion, this work has improved the understanding on how the unique chemical properties of hydrogen pose a challenge for maximization of hydrogen’s energy share in hydrogen dual-fuel engines and highlights a potential mitigation pathway.
Site-specific plan-view (S)TEM sample preparation from thin films using a dual-beam FIB-SEM
To fully evaluate the atomic structure, and associated properties of materials using transmission electron microscopy, examination of samples from three non-collinear orientations is needed. This is particularly challenging for thin films and nanoscale devices built on substrates due to limitations with plan-view sample preparation. In this work, a new method for preparation of high-quality, site-specific, plan-view TEM samples from thin-films grown on substrates, is presented and discussed. Here, it is based on using a dual-beam focused ion beam scanning electron microscope (FIB-SEM) system. To demonstrate the method, the samples were prepared from thin films of perovskite oxide BaSnO 3 grown on a SrTiO 3 substrate and metal oxide IrO 2 on a TiO 2 substrate, ranging from 20–80 nm in thicknesses using molecular beam epitaxy. While the method is optimized for the thin films, it can be extended to other site-specific plan-view samples and devices build on wafers. Aberration-corrected STEM was used to evaluate the quality of the samples and their applicability for atomic-resolution imaging and analysis.
Diffusion-Limited Kinetics of Isovalent Cation Exchange in III–V Nanocrystals Dispersed in Molten Salt Reaction Media
The goal of this work is to determine the kinetic factors that govern isovalent cation exchange in III–V colloidal quantum dots using molten salts as the solvent and cation source. We focus on the reactions of InP + GaI 3 → In 1 – x Ga x P and InAs + GaI 3 → In 1–x Ga x As to create technologically important ternary III–V phases. We find that the molten salt reaction medium causes the transformation of nearly spherical InP nanocrystals to tetrahedron-shaped In 1–x Ga x P nanocrystals. Furthermore, we determine that the activation energy for the cation exchange reaction is 0.9 eV for incorporation of Ga into InP and 1.2 eV for incorporation of Ga into InAs, both much lower than the measured values in bulk semiconductors. Next, we use powder XRD simulations to constrain our understanding of the structure of the In 1–x Ga x P nanocrystals. Together our results reveal several important features of molten salt-mediated cation exchange and provide guidance for future development of these materials.
Atomic-Scale Mapping and Quantification of Local Ruddlesden–Popper Phase Variations
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