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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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Electronic structure and dynamics analysis of the Cu-Ag binary alloy by a tight-binding parametrization
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Nano-crystals of cerium–hafnium binary oxide: Their size-dependent structure
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Hall–Petch effect in binary and ternary alumina / zirconia / spinel composites
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High energy radiation precursors to the collapse of black holes binaries based on resonating plasma modes
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Rapid microwave-assisted preparation of binary and ternary transition metal sulfide compounds
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Single and binary ion sorption equilibria of monovalent and divalent ions in commercial ion exchange membranes
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Machine Learning Applied to a Variable Charge Atomistic Model for Cu/Hf Binary Alloy Oxide Heterostructures [Machine Learning a Variable Charge Atomistic Model for Cu/Hf Binary Alloy Oxide Heterostructures]
Alloy oxide heterostructures are essential for a wide variety of energy applications, including anticorrosion coatings, bifunctional catalysis, energy storage, as well as emerging platforms for multilevel nonvolatile memory and neuromorphic devices. The key role played by oxide composition, density, and stoichiometry in governing electrochemical reactions, interface structure, and ionic transport in alloy oxides remains largely unknown. This is primarily due to the lack of variable charge interatomic potential models that can adequately describe the various atomic/ionic interactions in alloy oxides within a unified framework. Here, we introduce a charge transfer ionic potential (CTIP) model for Cu/Hf/O alloy system and demonstrate its ability to accurately capture the complex potential energy surface owing to dynamically varying interactions, including metallic (Cu/Hf), ionic (Cu/Hf/Ox), mixed environment (interfaces). We leverage supervised machine learning methods powered by genetic algorithms coupled with local simplex optimization to efficiently navigate through a high-dimensional parameter space and identify an optimum set of an independent set of CTIP parameters. We train our model against an extensive first-principles based data set that includes lattice constants, cohesive energies, equations of state, and elastic constants of various experimentally observed polymorphs of hafnia, copper oxide, hafnium, as well as copper. Our machine-learned CTIP model captures the structure, elastic properties, thermodynamics, and energetic ordering of various polymorphs. It also accurately predicts the surface properties of both oxides and their metals. To demonstrate the suitability of our CTIP model for investigating dynamic processes, we employed it to identify the atomistic scale mechanisms associated with the initial nanoscale oxidation and surface oxide growth on Cu-doped hafnia surfaces. Here, our machine-learned CTIP model can be used to probe the dynamic response of Cu/Hf/O alloy interfaces subjected to external stimuli (e.g., electric field, pressure, temperature, strain, etc.) and a variety of atomistic phenomena including the dynamics of switching in emerging neuromorphic platforms.
Multicomponent Nanomaterials with Complex Networked Architectures from Orthogonal Degradation and Binary Metal Backfilling in ABC Triblock Terpolymers
Selective degradation of block copolymer templates and backfilling the open mesopores is an effective strategy for the synthesis of nanostructured hybrid and inorganic materials. Incorporation of more than one type of inorganic material in orthogonal ways enables the synthesis of multicomponent nanomaterials with complex yet well-controlled architectures; however, developments in this field have been limited by the availability of appropriate orthogonally degradable block copolymers for use as templates. We report the synthesis and self-assembly into cocontinuous network structures of polyisoprene-block-polystyrene-block-poly(propylene carbonate) where the polyisoprene and poly(propylene carbonate) blocks can be orthogonally removed from the polymer film. Through sequential block etching and backfilling the resulting mesopores with different metals, we demonstrate first steps toward the preparation of three-component polymer–inorganic hybrid materials with two distinct metal networks. Multiblock copolymers in which two blocks can be degraded and backfilled independently of each other, without interference from the other, may be used in a wide range of applications requiring periodically ordered complex multicomponent nanoarchitectures.
Template-Free Ordered Mesoporous Silicas by Binary Nanoparticle Assembly
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High-strength magnetically switchable plasmonic nanorods assembled from a binary nanocrystal mixture
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New nanoscale toughening mechanisms mitigate embrittlement in binary nanocrystalline alloys
Inhomogeneous chemical segregation to grain boundaries in nanocrystalline metals can lead to a new toughening mechanism called compositional crack arrest.
Binary nucleation rates for ethanol/water mixtures in supersonic Laval nozzles: Analyses by the first and second nucleation theorems
We performed pressure trace measurements and small angle x-ray scattering measurements to determine the vapor-liquid nucleation rates of EtOH/H{sub 2}O mixtures including pure EtOH and pure H{sub 2}O in two supersonic Laval nozzles with different expansion rates. The nucleation rates varied from 0.9 × 10{sup 17} to 16 × 10{sup 17} cm{sup −3} s{sup −1} over the temperature range of 210 K to 230 K, EtOH activity range of 0 to 11.6, and H{sub 2}O activity range of 0 to 124. The first and second nucleation theorems were applied to the nucleation rates to estimate the sizes, compositions, and excess energies of the critical clusters. The critical clusters contained from 4 to 15 molecules for pure H{sub 2}O and EtOH/H{sub 2}O clusters, and from 16 to 23 molecules for pure EtOH clusters. Comparing the excess energies of the pure H{sub 2}O critical clusters with the results of a quantum-chemistry calculation suggested that the pre-factor of the theoretical nucleation rate is almost constant regardless of the monomer concentration. One possible explanation for this result is that cooling of the critical clusters limits the nucleation rate under the highly supersaturated conditions. The results of the analyses also yielded the relation between the surface energy and the composition of the critical clusters, where the latter are predicted to consist only of surface molecules. Applying this relationship to the EtOH/H{sub 2}O bulk liquid mixtures, we estimated the EtOH mole fraction in the surface layer and found it is higher than that derived from the surface tension based on the Gibbs adsorption equation when the EtOH mole fraction in the liquid is higher than about 0.2 mol/mol. This discrepancy was attributed to the existence of the EtOH depletion layer just below the surface layer of the liquid.
ALMA observations of the T Tauri binary system AS 205: evidence for molecular winds and/or binary interactions
In this study, we present high-resolution millimeter observations of the dust and gas disk of the T Tauri star AS 205 N and its companion, AS 205 S, obtained with the Atacama Large Millimeter Array. The gas disk around AS 205 N, for which infrared emission spectroscopy demonstrates significant deviations from Keplerian motion that has been interpreted as evidence for a disk wind, also displays significant deviations from Keplerian disk emission in the observations presented here. Detections near both AS 205 N and S are obtained in 1.3 mm continuum, {sup 12}CO 2-1, {sup 13}CO 2-1, and C{sup 18}O 2-1. The {sup 12}CO emission is extended up to ∼2'' from AS 205 N, and both {sup 12}CO and {sup 13}CO display deviations from Keplerian rotation at all angular scales. Two possible explanations for these observations hold up best to close scrutiny—tidal interaction with AS 205 S or disk winds (or a combination of the two)—and we discuss these possibilities in some detail.