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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 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Evaluating and Enhancing Iron Removal via Filterable Iron Precipitates Formation during Coal-Waste Bioleaching

Iron removal via jarosite precipitate formation is a commonly used technique in various hydrometallurgical processes. Excess iron removal often becomes essential to an overall metal recovery circuit. This is particularly important to processes involving iron-bearing minerals. A technique, which involved the use of pyrite to generate acid for leaching, for iron removal is critical to enabling the process. Iron removal using CaO or similar reagents is expensive and often results in lost product. In the present study, various compounds that facilitate jarosite formation, namely Na2SO4, NH4OH, KCl, and KOH, were utilized and their effect in precipitation was observed. Visual Minteq assisted simulations were run in order to evaluate favorable conditions for iron removal. Morphology and elemental composition of precipitates were analyzed using scanning electron microscopy equipped with energy-dispersive X-ray spectroscopy, and the phase purity was identified using X-ray diffraction analysis.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2026

A Comparative Study of Thermal Oxidization Resistance of a High-Entropy Metal Boride and a High-Entropy Metal Carbide

We present a systematic study of thermal oxidation resistance of transition metal borides and carbides up to 1300 °C in a dry air environment. A High-Entropy Metal Boride (HEMB), of composition (Hf 0.2 , Mo 0.2 , Nb 0.2 , Ta 0.2 , Zr 0.2 )B 2 , and a similar High-Entropy Metal Carbide (HEMC) (Hf, Mo, Nb, Ta, Zr)C 5 were synthesized from precursor mixtures, under 30 MPa of pressure at a temperature of 1800 °C using a Spark Plasma Sintering Device. The synthesized phases were confirmed via X-ray Diffraction analysis, which showed a pure hexagonal AlB 2 -type structure for HEMB and a face-centered cubic (FCC) structure for HEMC, with lattice parameters, a = 3.10 Å and c = 3.37 Å for HEMB and a = 4.524 Å for HEMC. Oxidation resistance was evaluated using a simultaneous thermogravimetric analysis and differential scanning calorimetry (TGA/DSC) stage in which HEMB and HEMC were heated up to 1300 °C at a rate of 2 °C/min in a dry air environment. Scanning electron microscopy (SEM) was used to analyze the resulting oxidized material. Our study demonstrates that HEMB shows better thermal oxidation resistance as compared to a similar metal composition HEMC at high temperatures.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Microbial Reduction of Geogenic and Synthetic Goethite and Hematite

The microbial reduction of Fe(III) is a major component of Fe cycling in terrestrial and aquatic environments and is affected by the Fe(III) mineralogy of the system. The majority of the research examining the bioreduction of Fe(III) oxides by Fe(III)-reducing bacteria (IRB) has focused on the reduction of poorly crystalline Fe(III) phases, primarily ferrihydrite; however, crystalline Fe(III) oxides like goethite (α-FeOOH) and hematite (α-Fe 2 O 3 ) comprise the majority of Fe(III) oxides in soils. This study examined the bioreduction of goethite and hematite of geogenic and synthetic origin by Shewanella putrefaciens CN2, a well-studied model IRB, in laboratory incubations. Overall, the rate and extent of Fe(II) production were greater for goethite than for hematite, and for geogenic Fe(III) oxides relative to their synthetic analogs. Although there was substantial production of Fe(II) (i.e., >5 mM Fe(II)) in many of the systems, X-ray diffraction analysis of the solids at the end of the incubation did not indicate the formation of any Fe(II)-bearing secondary minerals (e.g., magnetite, siderite, green rust, etc.). The results of this study demonstrate the variability in the extent of bioreduction of geogenic goethite and hematite, and furthermore, that synthetic goethite and hematite may not be good analogs for the biogeochemical behavior of Fe(III) oxides in aquatic and terrestrial environments.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Corrosion of Zinc Cold Spray Coatings in a Wet Sweet and Sour Gas Environment

Internal corrosion is a problem for steel pipelines transporting natural gas or CO 2 containing water and partial pressures of H 2 S higher than 0.3 kPa (0.05 psi). This work aims to mitigate internal corrosion in steel pipelines transporting natural gas containing H 2 S using cold spray coatings. Two types of the cold spray binary metallic coatings (zinc chromium [ZnCr]. zinc niobium [ZnNb]) were studied using electrochemical techniques: potentiodynamic polarization, linear polarization resistance, and electrochemical impedance spectroscopy. The corrosion resistance of cold spray coatings (ZnCr, ZnNb) was evaluated in an environment containing 4 bar CO 2 pressure, simulating the partial pressures found in gas transmission lines over a solution of 3.5 wt% NaCl heated to 40°C. A concentration of 0.003 M Na 2 S 2 O 3 ·5H 2 O, corresponding to H 2 S partial pressures around 0.079 bar (1.146 psi), was used to simulate sour conditions. Postcorrosion surface characterization was performed using a scanning electron microscope (SEM) equipped with an energy-dispersive x-ray spectroscope (EDS) and x-ray diffraction analysis. The data showed that the presence of 0.003 M Na 2 S 2 O 3 ·5H 2 O shifted the corrosion potential to more anodic values and decreased the corrosion current density. Both coatings showed similar behavior after 1 h of exposure in the CO 2 /H 2 S environment, indicating that similar electrochemical reactions were occurring on ZnNb and ZnCr. SEM images and EDS surface analyses for specimens showed a significant change in the surface chemical composition of carbon steel coated with ZnNb and ZnCr after 24 h of immersion. In the presence of thiosulfate (under sour conditions), the formation of corrosion product layers (ZnCO 3 and ZnS) on top of ZnNb and ZnCr coatings increased their corrosion resistance, which helped to reduce their corrosion by a factor of 2. Under a sweet environment, the corrosion rates for steel coated with cold spray coatings after 14 d of exposure are lower than that for galvanized steel by a factor of 5 due to the ZnCO 3 layer formed on top of the coatings. The ZnCO 3 layer formed on the steel surface acts as a physical barrier against corrosion by blocking the diffusion of corrosive species to the surface. No localized attack was observed. ZnCr Cold spray coating with defect showed promising corrosion protection against CO 2 corrosion (sweet corrosion) after 14 d of exposure to a CO 2 environment. Here, the scratch on the coating simulated damage created in service, and it was deep enough to expose the substrate material (steel). The formation of zinc oxide (ZnO) and zinc carbonate (ZnCO 3 ) on the scratch confirmed the cathodic protection of the steel by ZnCr and ZnNb coatings.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Fabrication and Characterization of Iron-Based Catalysts for the Dehydrogenation of Fossil Fuels

For a prosperous and sustainable future, hydrogen is an encouraging solution due to its simple transition for industrial decarbonization and synergy for economic development. Paradoxically, current hydrogen production pathways release substantial amount of greenhouse gases into the atmosphere contributing to climate change. To keep up with increasing demand, hydrogen could be produced through microwave-assisted thermocatalytic dehydrogenation of fossil fuels without emitting carbon dioxide. This requires specified catalysts to meet the requirements of hydrogen yield and selectivity. The objective of the present research is to fabricate, characterize, and compare iron-based alumina (FeAlxOy) catalysts produced via solution combustion synthesis and iron-based catalysts on silicon carbide support (Fe/SiC) produced via incipient wetness impregnation. Heat mode, fuel type, and oxidizer mole ratio were varied for FeAlxOy catalysts, and metal loading was varied for Fe/SiC catalysts. Each resultant product was characterized by X-ray diffraction analysis, scanning electron microscopy, laser diffraction particle size analysis, and Brunauer-Emmett-Teller (BET) surface area analysis. Characterization suggests that fabrication of iron-based alumina nanocomposites through solution combustion synthesis in a muffle furnace using citric acid as the fuel yields the most promising catalyst.

Reyes, Victoria Isabel↗
DOE OSTI2023

Solution combustion synthesis of iron-based alumina catalysts for dehydrogenation of fossil fuels

Microwave-assisted thermocatalytic dehydrogenation of fossil fuels has been proposed as a method for hydrogen production with no CO2 emissions. The byproduct is carbon, which has a commercial value. Iron-based alumina nanocomposites are promising catalysts for this process as they both absorb microwave radiation and catalyze the dehydrogenation. In addition, they may have magnetic properties, which could be used for their separation from the carbon byproduct. Solution combustion synthesis (SCS) is an attractive technique for the fabrication of iron-based alumina nanocomposites because it can produce high specific surface area oxides in a facile manner. However, the effects of the heating mode, the fuel, and the iron/aluminum ratio on the combustion synthesis behavior and the product properties have not been studied yet. In the present work, the iron-based alumina nanocomposites were obtained by SCS using a hotplate, a muffle furnace, and a microwave oven. The initial mixtures were aqueous solutions of iron nitrate, aluminum nitrate, and an organic fuel. The concentrations of the two nitrates were varied in proportion to Fe:Al molar ratios of 1:2, 1:1, and 2:1. Two fuels were tested: citric acid and glycine. The combustion was less vigorous when citric acid was used as the fuel, which is explained by the lower exothermicity of the reaction. X-ray diffraction analysis of the products confirmed the formation of complex oxides of Fe and Al, specifically it detected hercynite and magnetite phases. Brunauer-Emmett-Teller surface area analysis has shown that the powders obtained using citric acid have specific surface areas as high as 276 m2/g, significantly higher than those obtained using glycine. Muffle furnace and hotplate heating led to comparable specific surface areas, while microwave heating resulted in significantly lower specific surface areas. The magnetic properties of the products increase with increasing the Fe/Al molar ratio from 1:2 (no response to a magnetic field) to 1:1 (weak response) and 2:1 (strong response).

solution combustion synthesis, nanoscale oxides, f↗
DOE OSTI2025

Internal-Gelation Production of Uranium Oxide Sol-Gel Particles for Forensic Applications

The purpose of this project was to develop and demonstrate a novel method for the production of uranium oxide microsphere particles with tunable chemical compositions via a sol-gel process using a 3D-printer setup. These particles can serve several purposes in research and development as a forensic training tool or as standard reference materials. A key component of the project was to demonstrate the ability to control physical and chemical parameters of the particles created. First, we demonstrated the ability to employ an internal gelation sol-gel process to create individual uranium oxide particles. The particles were successfully dispensed using a unique 3D-printing setup onto a substrate to react and then were collected and thermally processed. A series of temperatures for the annealing process was tested on individual samples to investigate the effect on the sol-gel chemical composition and physical integrity. Next, we demonstrated the ability to control matrix composition of the particles by separately incorporating fission product isotopes as well as Np-237 into the sol-gel solution. It was shown by gamma-ray spectroscopy that these matrix elements were successfully retained during the gelation process. We studied the retention of the elements across a series of annealing temperatures. Additionally, we demonstrated the ability to quantitatively control the isotopic composition of the particles by altering the U-237/U-238 ratio to a controlled value. Finally, X-ray diffraction analysis (XRD) was used to investigate the oxidation state of the sol-gel after annealing at different temperatures.

37 - INORGANIC, ORGANIC, PHYSICAL AND ANALYTICAL C↗
DOE OSTI2024

Exploring 2D X-ray diffraction phase fraction analysis with convolutional neural networks: Insights from kinematic-diffraction simulations

Abstract Deep-learning models are effective for analyzing the complex information in 2D X-ray diffraction (XRD) patterns. Accurately collecting parameters of the material sample is crucial during model training, significantly impacting model performance. In this study, we employ a kinematic-diffraction simulator to generate simulated 2D XRD patterns for Ti–6Al–4V alloy, allowing precise control of sample parameters. These simulated patterns are used to train convolutional neural networks, predicting $$\upbeta$$ β -phase volume fractions. The training data set consists exclusively of 2D XRD patterns with pure $$\upalpha$$ α - or pure $$\upbeta$$ β -phase, while the testing set incorporates patterns with intermediate phase volume fraction. In particular, we investigate how the architectures of the model influence prediction reliability and computational performance. Experimental results reveal that, with appropriate training, the convolutional neural network accurately detects intermediate phase volume fractions even trained with only pure-phase patterns, achieving a mean square error accuracy of $$9.4 \times 10^{-4}$$ 9.4 × 10 - 4 . Graphical abstract

Yue, Weiqi↗
DOE OSTI2020

Hydration of α-UO 3 following storage under controlled conditions of temperature and relative humidity

Changes in chemical speciation of uranium oxides following storage under varied conditions of temperature and relative humidity are valuable for characterizing material provenance. In this study, subsamples of high purity α-UO 3 were stored under four sets of controlled conditions of temperature and relative humidity over several years, and then measured periodically for chemical speciation. Powder X-ray diffraction (XRD) analysis and extended X-ray absorption fine structure spectroscopy confirm hydration of α-UO 3 to a schoepite-like end product following storage under each of the varied storage conditions, but the species formed during exposure to the lower relative humidity and lower temperature condition follows different trends from those formed under the other three storage conditions (high relative humidity with high or low temperatures, and low relative humidity with a high temperature). Thermogravimetry coupled with XRD analysis was carried out to distinguish desorption pathways of water from the hydrated end products. Density functional theory calculations discern changes in the structure of α-UO 3 following incorporation of 1, 2 or 3 H 2 O molecules or 1, 2 or 3 OH groups into the orthorhombic lattice, revealing differences in lattice constants, U–O bond lengths, and U–U distances. The collective results from this analysis are in contrast to analogous studies that report that U 3 O 8 is oxidized and hydrated in air during storage under high relative humidity conditions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Machine Learning Automated Analysis of Enormous Synchrotron X-ray Diffraction Datasets

X-ray diffraction (XRD) data analysis can be a time-consuming and laborious task. Deep neural network (DNN) based models trained with synthetic XRD patterns have been proven to be a highly efficient, accurate, and automated method for analyzing common XRD data collected from solid samples in ambient environments. However, it remains unclear whether synthetic XRD-based models can be effective in solving micro(μ)-XRD mapping data for in situ experiments involving liquid phases, which always have lower quality and significant artifacts. In this study, we collected μ-XRD mapping data from a LaCl 3 -calcite hydrothermal fluid system and trained two categories of models to analyze the experimental XRD patterns. Here, the models trained solely with synthetic XRD patterns showed low accuracy (as low as 64%) when solving experimental μ-XRD mapping data. However, the accuracy of the DNN models significantly improved (90% or above) when we trained them with a data set containing both synthetic and a small number of labeled experimental μ-XRD patterns. This study highlights the importance of labeled experimental patterns in training DNN models to solve μ-XRD mapping data from in situ experiments involving liquid phases.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Integrated analysis of X-ray diffraction patterns and pair distribution functions for machine-learned phase identification

Abstract To bolster the accuracy of existing methods for automated phase identification from X-ray diffraction (XRD) patterns, we introduce a machine learning approach that uses a dual representation whereby XRD patterns are augmented with simulated pair distribution functions (PDFs). A convolutional neural network is trained directly on XRD patterns calculated using physics-informed data augmentation, which accounts for experimental artifacts such as lattice strain and crystallographic texture. A second network is trained on PDFs generated via Fourier transform of the augmented XRD patterns. At inference, these networks classify unknown samples by aggregating their predictions in a confidence-weighted sum. We show that such an integrated approach to phase identification provides enhanced accuracy by leveraging the benefits of each model’s input representation. Whereas networks trained on XRD patterns provide a reciprocal space representation and can effectively distinguish large diffraction peaks in multi-phase samples, networks trained on PDFs provide a real space representation and perform better when peaks with low intensity become important. These findings underscore the importance of using diverse input representations for machine learning models in materials science and point to new avenues for automating multi-modal characterization.

36 MATERIALS SCIENCE↗
DOE OSTI2020

XRD-PUAT (X-Ray Diffraction - Parameter Uncertainty Analysis Toolkit)

This XRD uncertainty toolkit was built to investigate uncertainty and local least squares topology over a specified parameter space on a single histogram in a gpx GSAS-II file. The parameter uncertainty can be investigated using either a frequentist F-test approach or a Bayesian Inference statistical inversion method on a weighted least squares or peak fit refinements.SAND Number: SAND2020-12227 M Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

Moore, Alexander↗
DOE OSTI2024

DENNIS: a design and analysis tool for dynamic material x-ray diffraction experiments

We present DENNIS (Diffraction Experiment desigN and aNalysiS): a graphical software tool useful for the design and analysis of dynamic x-ray diffraction experiments, such as those performed on the Z Pulsed Power Facility, Thor Pulsed Power Generator, and Dynamic Compression Sector (DCS) of the Advanced Photon Source. DENNIS provides rapid powder and single-crystal diffraction pattern predictions and powder diffraction pattern image integration in three-dimensional geometries. Additional features include crystallographic information file reading, image processing, and synthetic diffraction pattern image generation. We overview the software's capabilities, detail the prediction and integration methodologies, and provide example implementations on Z and DCS experiments.

47 OTHER INSTRUMENTATION↗
DOE OSTI2021

Carbonation of model cement pastes: The mineralogical origin of microstructural changes and shrinkage

This study explored the reactive processes of atmospheric carbonation and the consequences with respect to cementitious materials. Two model pastes were used: hydrated C{sub 3}S (including C-S-H and portlandite) and a paste prepared by hydrating a blend of C{sub 3}S and nanosilica (including C-S-H only). The two pastes were carbonated under accelerated conditions in the laboratory. The resulting mineralogical assemblage was examined using X-ray diffraction, thermogravimetric analysis and nuclear magnetic resonance. The microstructural changes were studied by X-ray tomography and porosimetry, and their macroscopic impacts were evaluated through gas diffusion and shrinkage measurements. The use of model pastes allowed for the evaluation of the change in solid volume induced by the carbonation of C-S-H. C-S-H decalcification and subsequent silica chain polymerisation were found responsible for carbonation shrinkage (and potentially cracking). Finally, the results highlight the protective role of portlandite: portlandite helped in limiting C-S-H decalcification and then reducing carbonation shrinkage and cracking.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Double-walled containment cell

Double-walled containment cells are described as may be used for storage, transport, and examination of a sample held within the cell by use of optical analysis techniques. A double-walled containment cell can include multiple types of windows that can be located as desired on the containment cells and thereby provide for optical access to a sample for multiple optical analysis techniques. Disclosed containment cells can be sized and designed for use with existing optical analysis systems, e.g., laser ablation, X-ray diffraction, spectral analysis (e.g., Raman spectroscopy, infrared spectroscopy, laser-induced breakdown spectroscopy, etc.), imaging analysis, etc.

Villa-Aleman, Eliel↗
DOE OSTI2024

Automated pipeline processing X-ray diffraction data from dynamic compression experiments on the Extreme Conditions Beamline of PETRA III

Presented and discussed here is the implementation of a software solution that provides prompt X-ray diffraction data analysis during fast dynamic compression experiments conducted within the dynamic diamond anvil cell technique. It includes efficient data collection, streaming of data and metadata to a high-performance cluster (HPC), fast azimuthal data integration on the cluster, and tools for controlling the data processing steps and visualizing the data using the DIOPTAS software package. This data processing pipeline is invaluable for a great number of studies. The potential of the pipeline is illustrated with two examples of data collected on ammonia–water mixtures and multiphase mineral assemblies under high pressure. The pipeline is designed to be generic in nature and could be readily adapted to provide rapid feedback for many other X-ray diffraction techniques, e.g. large-volume press studies, in situ stress/strain studies, phase transformation studies, chemical reactions studied with high-resolution diffraction etc.

97 MATHEMATICS AND COMPUTING↗