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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 163 records · Page 9
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Effect of cholesterol on nano-structural alteration of light-activatable liposomes via laser irradiation: Small angle neutron scattering study

Although the light-activated liposomes have been extensively studied for drug delivery applications, the fundamental mechanism of the drug release based on lipid compositions has not been fully understood. Especially, despite the extensive use of cholesterol in the lipid composition, the role of cholesterol in the light-activated drug release has not been studied. Here, in this study, the influence of cholesterol on drug release from light-responsive drug-encapsulated liposomes after activated by near infrared (NIR) laser was investigated. We prepared methotrexate (MTX)-encapsulated DSPC liposomes consisting of 0 mol% (—Chol) or 35 mol% cholesterol (+Chol), with (+Au) or without gold nanorods (—Au) on the lipid bilayer to compare drug release, morphological changes, and nanostructures after laser irradiations. Transmission electron microscopy (TEM) and small angel neutron scattering (SANS) data revealed that only +Chol +Au liposomes showed partial aggregation of the liposomes after laser irradiation. Similar trends on the drug release and structural change were observed when the liposomes were heated to above chain-transition temperature. Overall, we have found that (1) inclusion of 35 mol% cholesterol enhanced the permeability of lipid bilayers above T c ; (2) the mechanism of laser-activated liposomal drug delivery is disrupting lipid bilayer membranes by the photothermal effect in the presence of plasmonic materials. By understanding the fundamentals of the technology, precise controlled drug release at a targeted site with great stability and repeatability is anticipated.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Microstructure and phase evolution in the U-10Zr fuel investigated by in situ TEM heating experiments

The development of U-Zr metallic nuclear fuel for fast spectrum reactors is impacted by a lack of mechanistic understanding of the fuel behavior evolution under thermal irradiation conditions, despite previous works providing substantial fuel performance data. This work uses in-situ transmission electron microscopy heating experiments to study phase and microstructural evolution in several unirradiated U-10Zr specimens during rapid heating ramps (from room temperature to 1000 °C). The starting a-U + bcc-(Zr, U) eutectic microstructure began to decompose above 600 °C. The decomposition initiated from the bcc (U,Zr) phase. Similar results were observed for all specimens even when fabricated by different routes (e.g., cold rolled or annealed). As a result, the impact of observed microstructure and phase evolutions at high temperatures on fuel fabrication and in-pile fuel transient test was also discussed.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2023

Probing the thermal decomposition of plutonium (III) oxalate with IR and Raman spectroscopy, X-ray diffraction, and electron microscopy

The thermal decomposition of Pu(III) oxalate was analyzed by Raman microspectroscopy, infrared spectroscopy, scanning electron microscopy, and powder X-ray diffraction. These data show that crystalline Pu 2 (C 2 O 4 ) 3 •9H 2 O progressively loses water and oxalate ligands as it is heated, which leads to a decrease in long-range lattice ordering, though minimal changes are observed in gross crystalline morphology. The onset of PuO 2 formation was observed between 200 - 250 °C. Thermal decomposition of oxalate ligands leads to the formation of CO 2 and plutonium oxalate-carbonate moieties, which had not been observed in previously published thermogravimetric measurements of Pu(III) oxalate. Formation of plutonium oxalate-carbonate moieties is believed to be associated with a change in the plutonium oxidation state from 3+ to 4+, which occurs prior to PuO 2 formation. The data provided herein demonstrate the rich spectroscopic nature of a rather underexplored, and technologically relevant, plutonium system. Ideally these results will further future investigations into the Pu(III) oxalate system both experimentally and computationally.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Structural Implications of Interfacial Hydrogen Bonding in Hydrated Wyoming-Montmorillonite Clay

Montmorillonite (MMT) clay - a layered porous nanomaterial used as seals in engineered waste containment barriers for spent nuclear fuel - adopts discrete hydration/swelling states depending upon surrounding water and ion activities and confining pressure. The structure of nanoconfined water and charge-balancing counterions in the clay mineral interlayers dictate the swelling and mechanical behavior of MMT, so a molecular model for this clay with high structural fidelity is required to accurately predict the reliability of long-term nuclear waste storage. Here, we present a molecular model for MMT that is based on high resolution transmission electron microscopy of Wyoming-MMT single crystals. Imaging data unambiguously show a cis-vacant arrangement of structural hydroxyl groups in the octahedral sheet, whereas existing molecular models assume a centrosymmetric trans-vacant configuration for MMT. Furthermore, using atomistic simulations, we find that the cis-vacant arrangement of structural hydroxyl groups significantly affects the structure of adsorbed water yielding a larger population of hydrogen bonds with bridging oxygens on the tetrahedral sheet and weak hydrogen bonding between the hydroxyl groups in the octahedral sheet and water in the clay mineral interlayers. As a result, water adsorbed in the interlayer is more "ice-like", with stronger ordering and lower density, although the diffusivity of the interlayer species is not significantly diminished. Our improved structural model for MMT provides insight into the energetics of water adsorption, which ultimately dictates its pore- to macro-scale swelling, transport, and fracture properties.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Bridging multimodal microscopy for advanced characterization on nuclear fuel using machine learning

Uranium dioxide (UO 2 ), widely used as driver fuel in light water reactors, experiences microstructure and property change by nuclear fission reactions. This paper bridges the characterization of fresh UO 2 fuel at different length scales, serving as a baseline for future post irradiation examination of irradiated UO 2 fuel. To characterize the microstructural change of nuclear fuel, modern approaches cover a wide range of length scales through different characterization techniques, such as mm scale for Synchrotron-based X-ray computed tomography (SXCT) and microscale for focused ion beam (FIB) and scanning electron microscopy (SEM). It is challenging to bridge the data and knowledge of the same sample in different length scales. This paper proposed a deep learning framework leveraging transfer learning to detect microstructural defects, trained from a sparse FIB, SEM, and SXCT images. The proposed model achieved superior performance in defect segmentation on multiscale microscopic data compared to four of the latest deep learning models.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Possibility of an integrated transmission electron microscope: enabling complex in-situ experiments

Abstract Multimodal in-situ experiments are the wave of the future, as this approach will permit multispectral data collection and analysis during real-time nanoscale observation. In contrast, the evolution of technique development in the electron microscopy field has generally trended toward specialization and subsequent bifurcation into more and more niche instruments, creating a challenge for reintegration and backward compatibility for in-situ experiments on state-of-the-art microscopes. We do not believe this to be a requirement in the field; therefore, we propose an adaptive instrument that is designed to allow nearly simultaneous collection of data from aberration-corrected transmission electron microscopy (TEM), probe-corrected scanning transmission electron microscopy, ultrafast TEM, and dynamic TEM with a flexible in-situ testing chamber, where the entire instrument can be modified as future technologies are developed. The value would be to obtain a holistic understanding of the underlying physics and chemistry of the process-structure–property relationships in materials exposed to controlled extreme environments. Such a tool would permit the ability to explore, in-situ, the active reaction mechanisms in a controlled manner emulating those of real-world applications with nanometer and nanosecond resolution. If such a powerful tool is developed, it has the potential to revolutionize our materials understanding of nanoscale mechanisms and transients. Graphical Abstract

36 MATERIALS SCIENCE↗
DOE OSTI2023

Coso Geothermal Spectral Library for Rocks and Minerals

An integrated open mineral spectral library designed to enhance the utility and precision of mineral spectral data for geothermal exploration, developed from a reliable and comprehensive digital dataset for seamless sharing by integrating field data, the USGS spectral library, and pertinent information obtained from Coso geothermal field (Coso) in California. The ASD FieldSpec portable spectrometer was utilized for collecting spectral data, which was subsequently analyzed using the THOR Material Identification tool in ENVI, The Spectral Geologist (TSG) software by CSIRO, and the Fully Constrained Linear Spectral Unmixing algorithm (FCLSU) in MATLAB. Scanning Electron Microscopy (SEM) with a mineralogy-analyzing function was employed to assess the mineral composition of samples, ensuring precise mineralogical analysis. A portable X-ray fluorescence (pXRF) spectrometer was also utilized to gather information on elemental enrichment. A framework for developing spectra data and establishing spectral libraries for various geological cases was proposed within this study. The characteristic spectra of six alteration minerals - alunite, chalcedony, epidote, hematite, kaolinite, and opal - were acquired from Coso samples. The spectral library for the Coso alteration minerals was introduced for further application in academic study or industrial exploration. To browse the Coso Geothermal Spectral data and related figures from spreadsheets: #1 Unzip and store the following items in the same folder. 'Contact Probe Data.zip', 'Sample Photos.zip', and 'Coso spectra of higher-certainity minerals.xlsx'. #2 Open 'Coso spectra of higher-certainity minerals.xlsx'. The hyperlinks in the spreadsheet lead to the folders or figures of: spectra .asd file, spectra ASC II file, spectra plots, and sample photos. The spectra data is raw data without splice correction. Spectra .asd files require particular software to open. (These cannot be opened in GIS software such as ArcGIS.) Spectra ASC II files can be opened in a text editor or spread sheet program.

15 GEOTHERMAL ENERGY↗
DOE OSTI2024

Generalization Across Experimental Parameters in Neural Network Analysis of High-Resolution Transmission Electron Microscopy Datasets

Neural networks are promising tools for high-throughput and accurate transmission electron microscopy (TEM) analysis of nanomaterials, but are known to generalize poorly on data that is “out-of-distribution” from their training data. Given the limited set of image features typically seen in high-resolution TEM imaging, it is unclear which images are considered out-of-distribution from others. Here, we investigate how the choice of metadata features in the training dataset influences neural network performance, focusing on the example task of nanoparticle segmentation. We train and validate neural networks across curated, experimentally collected high-resolution TEM image datasets of nanoparticles under various imaging and material parameters, including magnification, dosage, nanoparticle diameter, and nanoparticle material. Overall, we find that our neural networks are not robust across microscope parameters, but do generalize across certain sample parameters. Additionally, data preprocessing can have unintended consequences on neural network generalization. Our results highlight the need to understand how dataset features affect deployment of data-driven algorithms.

42 ENGINEERING↗
DOE OSTI2020

BinaryCIF and CIFTools—Lightweight, efficient and extensible macromolecular data management

3D macromolecular structural data is growing ever more complex and plentiful in the wake of substantive advances in experimental and computational structure determination methods including macromolecular crystallography, cryo-electron microscopy, and integrative methods. Efficient means of working with 3D macromolecular structural data for archiving, analyses, and visualization are central to facilitating interoperability and reusability in compliance with the FAIR Principles. We address two challenges posed by growth in data size and complexity. First, data size is reduced by bespoke compression techniques. Second, complexity is managed through improved software tooling and fully leveraging available data dictionary schemas. To this end, we introduce BinaryCIF, a serialization of Crystallographic Information File (CIF) format files that maintains full compatibility to related data schemas, such as PDBx/mmCIF, while reducing file sizes by more than a factor of two versus gzip compressed CIF files. Moreover, for the largest structures, BinaryCIF provides even better compression—factor ten and four versus CIF files and gzipped CIF files, respectively. Herein, we describe CIFTools, a set of libraries in Java and TypeScript for generic and typed handling of CIF and BinaryCIF files. Together, BinaryCIF and CIFTools enable lightweight, efficient, and extensible handling of 3D macromolecular structural data.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Automated identification of deformation twin systems in Mg WE43 from SEM DIC

In this study, the application of machine learning and computer vision approaches to microscale deformation data for the automated identification of deformation twinning systems, and their associated twin area, is introduced. Deformation data was obtained during in-situ SEM compression testing of a WE43 Mg alloy using digital image correlation (DIC) modified for use with electron microscopy. A 5.7 mm × 3.4 mm area of interest was analyzed, generating ~100 million data points of deformation. Experimental twin trace directions were determined by applying k-means clustering, morphological thinning, and Hough transforms to the deformation data. The identification of deformation twinning was achieved by consideration of the twin trace direction, its strain value, and its evolution. The deformation map was divided into areas corresponding to individual active twin systems, enabling the analysis of microstructure dependence of deformation behavior and twinning activity. The performance of the proposed twinning identification approach was evaluated by accuracy, precision, and sensitivity metrics. The effect of the tuning parameters used in the algorithm on the performance is also discussed.

36 MATERIALS SCIENCE↗
DOE OSTI2022

DISTRIBUTION OF ION-EXCHANGEABLE RARE EARTH ELEMENTS AND CRITICAL MINERALS IN COAL-HOSTED CLAYS OF THE POWDER RIVER BASIN, WY

Increasing demand for a more robust domestic supply of rare earth elements (REE) and critical minerals (CM) has led to significant investigation into unconventional sources. Coal and coal byproducts from the Powder River Basin (PRB) of Wyoming are potential sources of REE but lack complete characterization of mineral hosts including ion-adsorption clays (IACs). IACs, which provide a substantial portion of the global REE supply, form when hydrated REE complexes are physiosorbed to negatively charged sites on clay surfaces. Typical IAC minerals include kaolinite, illite, and smectite. REE complexes are easily leached from IACs via ion exchange, making them economically attractive. However, research regarding the partitioning behaviors of strategic metals into PRB coal-hosted clays is limited. This project aims to quantify the modes of occurrence and distribution of REE and CM in clay minerals derived from PRB coals. Strategic metals investigated in this project have historic production or promising deposits within the PRB and/or high anticipated supply risk, and include: REE (lanthanides and Y), V, Ti, platinum group elements (PGE; Pt, Pd, Rh, Ru, Ir, Os), Al, Co, Ga, Zr, and W. Semi-quantitative compositional data from portable x-ray fluorescence spectroscopy (pXRF) on coal core from the PRB is consistent with the known correlation between REE and Al content, suggesting increased REE abundance will be found in clay-rich units. pXRF data also identifies elevated V, Ti, Co, and Zr correlated with Al. Preliminary x-ray diffraction (XRD) data indicates kaolinite, illite, and dickite are abundant minerals in overburden and clay-rich partings. Quantitative analysis via inductively coupled plasma mass spectrometry (ICP-MS) provides insights into REE and CM fractionation in coals as we compare trace metal occurrence to variations in mineralogy. Additionally, we apply scanning electron microscopy (SEM) and energy dispersive x-ray spectroscopy (EDS) data to correlate clay size and morphology to trace metal occurrence. Our findings supplement a broader effort to couple REE and CM extraction with existing domestic coal production and contribute to the fundamental understanding of adsorption processes that occur in low-temperature basin environments.

Stuart, Sophia↗
DOE OSTI2020

RockFlow: Fast Generation of Synthetic Source Rock Images Using Generative Flow Models

Image-based evaluation methods are a valuable tool for source rock characterization. The time and resources needed to obtain images has spurred development of machine-learning generative models to create synthetic images of pore structure and rock fabric from limited image data. While generative models have shown success, existing methods for generating 3D volumes from 2D training images are restricted to binary images and grayscale volume generation requires 3D training data. Shale characterization relies on 2D imaging techniques such as scanning electron microscopy (SEM), and grayscale values carry important information about porosity, kerogen content, and mineral composition of the shale. Here, we introduce RockFlow, a method based on generative flow models that creates grayscale volumes from 2D training data. We apply RockFlow to baseline binary micro-CT image volumes and compare performance to a previously proposed model. We also show the extension of our model to 2D grayscale data by generating grayscale image volumes from 2D SEM and dual modality nanoscale shale images. The results show that our method underestimates the porosity and surface area on the binary baseline datasets but is able to generate realistic grayscale image volumes for shales. With improved binary data preprocessing, we believe that our model is capable of generating synthetic porous media volumes for a very broad class of rocks from shale to carbonates to sandstone.

58 GEOSCIENCES↗
DOE OSTI2022

Cluster Analysis of Combined EDS and EBSD Data to Solve Ambiguous Phase Identifications

A common problem in analytical scanning electron microscopy (SEM) using electron backscatter diffraction (EBSD) is the differentiation of phases with distinct chemistry but the same or very similar crystal structure. X-ray energy dispersive spectroscopy (EDS) is useful to help differentiate these phases of similar crystal structures but different elemental makeups. However, open, automated, and unbiased methods of differentiating phases of similar EBSD responses based on their EDS response are lacking. This paper describes a simple data analytics-based method, using a combination of singular value decomposition and cluster analysis, to merge simultaneously acquired EDS + EBSD information and automatically determine phases from both their crystal and elemental data. I use hexagonal TiB 2 ceramic contaminated with multiple crystallographically ambiguous but chemically distinct cubic phases to illustrate the method. Code, in the form of a Python 3 Jupyter Notebook, and the necessary data to replicate the analysis are provided as Supplementary material.

47 OTHER INSTRUMENTATION↗
DOE OSTI2023

RadVolViz: An Information Display-Inspired Transfer Function Editor for Multivariate Volume Visualization

In volume visualization transfer functions are widely used for mapping voxel properties to color and opacity. Typically, volume density data are scalars which require simple 1D transfer functions to achieve this mapping. If the volume densities are vectors of three channels, one can straightforwardly map each channel to either red, green or blue, which requires a trivial extension of the 1D transfer function editor. Here, we devise a new method that applies to volume data with more than three channels. These types of data often arise in scientific scanning applications, where the data are separated into spectral bands or chemical elements. Our method expands on prior work in which a multivariate information display, RadViz, was fused with a radial color map, in order to visualize multi-band 2D images. In this work, we extend this joint interface to blended volume rendering. The information display allows users to recognize the presence and value distribution of the multivariate voxels and the joint volume rendering display visualizes their spatial distribution. We design a set of operators and lenses that allow users to interactively control the mapping of the multivariate voxels to opacity and color. This enables users to isolate or emphasize volumetric structures with desired multivariate properties. Furthermore, it turns out that our method also enables more insightful displays even for RGB data. We demonstrate our method with three datasets obtained from spectral electron microscopy, high energy X-ray scanning, and atmospheric science.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Modeling Nanoconfinement Effects Using Active Learning

Predicting the spatial configuration of gas in nanopores of is relevant in applications such as fluid flow forecasting and hydrocarbon reserves estimation. For example, shale reservoirs have suffered from computationally intractable multiscale problems, since fluid properties such as viscosity, density, and adsorption must be calculated by using expensive molecular dynamics (MD) simulations within each nanopore, whereas flow through these connected nanopores must be simulated at the micrometer scale. We utilize machine learning techniques to quickly and accurately model nanoscale confinement effects as an important step toward bridging the nano and micro scales. Our workflow is based on building and training physics-based deep-neural-networks models by learning from a database of MD calculations. The model accounts for the adsorption phenomenon by predicting the statistical distribution of gas inside nanopores. Because large databases of MD calculations are expensive to create, we investigate active learning (AL) as a data set construction strategy. In this workflow, new data are selected based on the model uncertainty via the query-by-committee approach. We show that our workflow obtains accurate models that generalize to real scanning electron microscopy geometries with 1/10th of the number of MD calculations required vs random data set generation. Our method enables the possibility of modeling nanoconfinement effects at the mesoscale, where complex connected sets of nanopores affect flow.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Confined Room-Temperature Ferromagnetism in Kagome (Fe 3 Sn 2 /CoSn) Superlattices

We reveal the presence of robust ferromagnetism in topological kagome (Fe 3 Sn 2 /CoSn) ×n superlattices via polarized neutron reflectivity (PNR) and magnetization measurements. Using molecular beam epitaxy, we have synthesized superlattices of alternating ferromagnetic Fe 3 Sn 2 and paramagnetic CoSn layers, the interfacial integrity of which is confirmed using scanning transmission electron microscopy. Magnetization depth profiles, obtained by fitting the PNR data, confirm the presence of ferromagnetism in the confined Fe 3 Sn 2 layers at room temperature, with minimal thickness dependence down to 2 nm. A net magnetization is found in the near-surface region of CoSn, which we attribute to surface oxidation. Our results demonstrate that superlattice engineering is a means to confine ferromagnetism in kagome metals, enabling future studies of thickness-dependent anomalous Hall and Nernst effects as well as spin-dependent transport and tunneling in device structures.

kagome metals↗
DOE OSTI2022

Fabrication, thermal analysis, and heavy ion irradiation resistance of epoxy matrix nanocomposites loaded with silane-functionalized ceria nanoparticles

This paper describes a detailed understanding of how nanofillers function as radiation barriers within the polymer matrix, and how their effectiveness is impacted by factors such as composition, size, loading, surface chemistry, and dispersion. Here, we designed a comprehensive investigation of heavy ion irradiation resistance in epoxy matrix composites loaded with surface-modified ceria nanofillers, utilizing tandem computational and experimental methods to elucidate radiolytic damage processes and relate them to chemical and structural changes observed through thermal analysis, vibrational spectroscopy, and electron microscopy. A detailed mechanistic examination supported by FTIR spectroscopy data identified the bisphenol A moiety as a primary target for degradation reactions. Results of computational modeling by the Stopping Range of Ions in Matter (SRIM) Monte Carlo simulation were in good agreement with damage analysis from surface and cross-sectional SEM imaging. All metrics indicated that ceria nanofillers reduce the damage area in polymer nanocomposites, and that nanofiller loading and homogeneity of dispersion are key to effective damage prevention. The results of this study represent a significant pathway for engineered irradiation tolerance in a diverse array of polymer nanocomposite materials. Numerous areas of materials science can benefit from utilizing this facile and effective method to extend the reliability of polymer materials.

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

Integrative illustration of a JCVI-syn3A minimal cell

Abstract Data from genomics, proteomics, structural biology and cryo-electron microscopy are integrated into a structural illustration of a cross section through an entire JCVI-syn3.0 minimal cell. The illustration is designed with several goals: to inspire excitement in science, to depict the underlying scientific results accurately, and to be feasible in traditional media. Design choices to achieve these goals include reduction of visual complexity with simplified representations, use of orthographic projection to retain scale relationships, and an approach to color that highlights functional compartments of the cell. Given that this simple cell provides an attractive laboratory for exploring the central processes needed for life, several functional narratives are included in the illustration, including division of the cell and the first depiction of an entire cellular proteome. The illustration lays the foundation for 3D molecular modeling of this cell.

Goodsell, David S.↗