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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 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

A-site occupancy effects on structure, ionic conductivity, and thermodynamic stability of KxMgx/2Ti8−x/2O16

Abstract K x Mg x /2 Ti 8− x /2 O 16 (1.54 ≤ x ≤ 2) hollandite samples synthesized using a conventional solid-state route were used to investigate the effect of structure, ionic conductivity, and thermodynamic stability as a function of A-site (K) occupancy. The ionic conductivity, as measured using AC impedance spectroscopy, decreased as the A-site occupancy (and K content) increased. Titanate hollandite, of the form K x Mg x /2 Ti 8− x /2 O 16 , was measured using high-temperature oxide melt solution calorimetry in sodium molybdate (3Na 2 O $$\bullet$$ ∙ 4MoO 3 ) solvent at 804 $$^\circ{\rm C}$$ ∘ C . The enthalpies of formation are strongly exothermic indicating that they are thermodynamically stable relative to their constituent oxides. Previous studies have demonstrated increased stability with an increase in Cs content at fixed A-site occupancy. This work demonstrated an increase in the stability for the K-bearing hollandite samples with an increase in K content and tunnel occupancy. The measured enthalpies of formation (∆ H f,ox ) were in good agreement with density functional theory (DFT) predictions. Graphical abstract

Cassell, Nakeshma S.↗
DOE OSTI2019

Effect of structure and composition on the electronic excitation induced amorphization of La2Ti2−xZrxO7 ceramics

Abstract Understanding the response of ceramics operating in extreme environments is of interest for a variety of applications. Ab initio molecular dynamic simulations have been used to investigate the effect of structure and B -site (=Ti, Zr) cation composition of lanthanum-based oxides (La 2 B 2 O 7 ) on electronic-excitation-induced amorphization. We find that the amorphous transition in monoclinic layered perovskite La 2 Ti 2 O 7 occurs for a lower degree of electronic excitation than for cubic pyrochlore La 2 Zr 2 O 7 . While in each case the formation of O 2 -like molecules drives the structure to an amorphous state, an analysis of the polyhedral connection network reveals that the rotation of TiO 6 octahedra in the monoclinic phase can promote such molecule formation, while such octahedral rotation is not possible in the cubic phase. However, once the symmetry of the cubic structure is broken by substituting Ti for Zr, it becomes less resistant to amorphization. A compound made of 50% Ti and 50% Zr (La 2 TiZrO 7 ) is found to be more resistant in the monoclinic than in the cubic phase, which may be related to the lower bandgap of the cubic phase. These results illustrate the complex interplay of structure and composition that give rise to the radiation resistance of these important functional materials.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Experimental evidence of plasmarons and effective fine structure constant in electron-doped graphene/h-BN heterostructure

Electron-electron interaction is fundamental in condensed matter physics and can lead to composite quasiparticles called plasmarons, which strongly renormalize the dispersion and carry information of electron-electron coupling strength as defined by the effective fine structure constant $α$ $^{*}_{ee}$. Although h-BN with unique dielectric properties has been widely used as an important substrate for graphene, so far there is no experimental report of plasmarons in graphene/h-BN yet. Here, we report direct experimental observation of plasmaron dispersion in graphene/h-BN heterostructures through angle-resolved photoemission spectroscopy (ARPES) measurements upon in situ electron doping. Characteristic diamond-shaped dispersion is observed near the Dirac cone in both 0° (aligned) and 13.5° (twisted) graphene/h-BN, and the electron-electron interaction strength $α$ $^{*}_{ee}$ is extracted to be $α$ $^{*}_{ee}$ ≈ 0.9 ± 0.1, highlighting the important role of electron-electron interaction. Our results suggest graphene/h-BN as an ideal platform for investigating strong electron-electron interaction with weak dielectric screening, and lays fundamental physics for gate-tunable nano-electronics and nano-plasmonics.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2019

Structure–Property Effects in the Generation of Transient Aqueous Benzoic Acid Anhydrides by Carbodiimide Fuels

The design of dissipative systems, which operate out-of-equilibrium by consuming chemical fuels, is difficult. As yet, there are a few examples of privileged fuel chemistries that can be broadly applied in abiotic systems in the same way that ATP hydrolysis is exploited throughout biochemistry. The key issue is that designing nonequilibrium systems is inherently about balancing the relative rates of coupled reactions. The use of carbodiimides as fuels to generate transient aqueous carboxylic anhydrides has recently been used in examples of new nonequilibrium materials and supramolecular assemblies. Here, we explore the kinetics of formation and decomposition of a series of benzoic anhydrides generated from the corresponding acids and EDC under typical conditions (EDC = N-(3-dimethylaminopropyl)-N'-ethylcarbodiimide hydrochloride). The reactions can be described by a simple mechanism that merges known behavior for the two processes independently. Structure–property effects in these systems are dominated by differences in the anhydride decomposition rate. The kinetic parameters allow trends in concentration-dependent properties to be simulated, such as reaction lifetimes, peak anhydride concentrations, and yields. For key properties, there are diminishing returns with the addition of increasing amounts of fuel. These results should provide useful guidelines for the design of functional systems making use of this chemistry.

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

Effects of Structural Constraints on Excited-State Properties in Dimeric Cu(I) Diimine Complexes

Copper(I) bis-diimine complexes have played important roles in light-activated processes that can lead to their potential applications in photocatalysis and chemical sensing. Their metal-to-ligand charge-transfer (MLCT) excited-state properties are tunable by various structural factors. Dimeric Cu(I) complexes with connecting diimine derivative ligands offer another structural tuning platform for the excited-state properties. Here, we investigate excited-state properties in two covalently connected dimeric Cu(I)'s with varying structural constraints exerted by the number of carbons in the polyethylene bridge (C0 and C4) connecting the two copper(I) diimine moieties. An interesting feature of Cu(I) diimine complexes is their ability to flatten following a photoinduced structural change. Herein, we observe larger structural constraints and more structural rearrangement required upon excitation of the longer bridged complex C4 to achieve a conformation toward a more flattened tetrahedral coordination geometry compared to the shorter bridged C0. Vibrational wavepacket analysis of these complexes further supports the effect of these structural constraints where we observe a more rapid dephasing of the C0 complex, as opposed to the C4 complex, despite similar normal mode vibrations. The experimental results were supplemented by TDDFT calculations. In conclusion, the studies provide insight into using metal-metal interactions through constraints to tune excited-state dynamics and pathways.

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

Electronic structure and effective mass of pristine and Cl-doped CsPbBr 3

Organic–inorganic lead halide perovskites (LHPs) have attracted great interest owing to their outstanding optoelectronic properties. Typically, the underlying electronic structure would determinate the physical properties of materials. But as for now, limited studies have been done to reveal the underlying electronic structure of this material system, comparing to the huge amount of investigations on the material synthesis. The effective mass of the valance band is one of the most important physical parameters which plays a dominant role in charge transport and photovoltaic phenomena. In pristine CsPbBr 3 , the Fröhlich polarons associated with the Pb–Br stretching modes are proposed to be responsible for the effective mass renormalization. In this regard, it would be very interesting to explore the electronic structure in doped LHPs. Here, we report high-resolution angle-resolved photoemission spectroscopy (ARPES) studies on both pristine and Cl-doped CsPbBr 3 . The experimental band dispersions are extracted from ARPES spectra along both $\overline{\Gamma}$–$\overline{M}$–$\overline{\Gamma}$ and $\overline{X}$–$\overline{M}$–$\overline{X}$ high symmetry directions. DFT calculations are performed and directly compared with the ARPES data. Our results have revealed the band structure of Cl-doped CsPbBr 3 for the first time, which have also unveiled the effective mass renormalization in the Cl-doped CsPbBr 3 compound. Doping dependent measurements indicate that the chlorine doping could moderately tune the renormalization strength. These results will help understand the physical properties of LHPs as a function of doping.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Co doping effect on structural and optical properties of nickel oxide (NiO) thin films via spray pyrolysis

Undoped and cobalt doped nickel oxide thin films (Co:NiO) at different doping concentrations were deposited on a glass substrate at constant substrate temperature by spray pyrolysis. The doping percentage increased from 0 to 10% and the effects of doping content on structural, morphological and optical properties were explored by X-ray diffraction, atomic force microscopy and UV–visible spectroscopy, respectively. X-ray diffraction configurations proved the NiO thin films were polycrystalline in nature having a cubic crystal structure. The regularly used parameters such as crystallite size, micro-strain, dislocation density and texture coefficient are presented from structural data. The average crystallite size is found between 10 and 14 nm with cobalt doping (Co) concentration. The band gap energies were increased with the effect of Co doping concentration in the range between 2.10 and 2.50 eV. The various optical constants as a function of photon energy are modified with Co doping concentration.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2021

Effects of Al:Si and (Al + Na):Si ratios on the properties of the international simple glass, part I: Physical properties

Understanding composition-structure-property relationships of high-alumina nuclear waste glasses are important for vitrification of nuclear waste at the Hanford Site. Two series of glasses were designed, one with varying Al:Si ratios and the other with (Al+Na):Si ratios based on the International Simple Glass (ISG, a simplified nuclear waste model glass), with Al2O3 ranging from 0 to 23 mol % (0 to 32 wt. %). The glasses were synthesized and characterized using electron probe microanalysis, X-ray photoelectron spectroscopy, small angle X-ray scattering, high temperature oxide melt solution calorimetry, and infrared spectroscopy. Glasses were crystal free, and the lowest Na2O and Al2O3 glass formed an immiscible glass phase. Evolution of various properties - glass transition temperature, percentage of 4-coordinated B, enthalpy of glass formation - and infrared spectroscopy results indicate that structural effects differ based on the glass series.

Nuclear Waste Glass↗
DOE OSTI2020

Interference of electrical double layers: Confinement effects on structure, dynamics, and screening of ionic liquids

Ionic liquids are widely used as electrolytes in electronic devices in which they are subject to nanoconfinement within nanopores or nanofilms. Because the intrinsic width of an electrical double layer is on the order of several nanometers, nanoconfinement is expected to fundamentally alter the double layer properties. Furthermore, in confined systems, a large portion of the ions are interfacial, e.g., at the electrode interface, leading to significant deviations of electrostatic screening and ion dynamics as compared to bulk properties. In this article, we systematically investigate the interference between electrical double layers for nanoconfined ionic liquids and the resulting influence on the structure, dynamics, and screening behavior. We perform molecular dynamics simulations for the ionic liquids [BMIm + ][BF$^-_4$] and [BMIm + ][PF$^-_6$] confined between two flat electrodes at systematic separation distances between 1.5 nm and 4.5 nm for both conducting and insulating boundary conditions. We find that while ion dynamics is expectedly slower than in the bulk (by ~2 orders of magnitude), there is an unexpected non-linear trend with the confinement length that leads to a local maximum in dynamic rates at ~3.5–4.5 nm confinement. We show that this nonlinear trend is due to the ion correlation that arises from the interference between opposite double layers. We further evaluate confinement effects on the ion structure and capacitance and investigate the influence of electronic polarization of the ionic liquid on the resulting properties. This systematic evaluation of the connection between electrostatic screening and structure and dynamics of ionic liquids in confined systems is important for the fundamental understanding of electrochemical supercapacitors.

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

Computational Analysis of the Effect of Structured Packing Design on Absorption Column Hydrodynamics for Post-Combustion Carbon Capture Applications

Solvent based post-combustion carbon capture technologies have a potential for reducing carbon emissions from fossil-fuel-fired power plants and industrial sources where CO2 emissions are inherently harder to mitigate, such as steel or cement industries. A prominent technology to achieve this is by retrofitting absorption columns to the existing infrastructure. While these systems have been among the less costly alternatives for carbon capture, they still impose a considerable energy penalty to the operation of power plants or industrial facilities. The optimization of CO2 capture rate in solvent-based absorption process is complex as it depends on several factors including CO2 solubility, solvent reaction kinetics and temperature effects on the solubility, reaction rates, surface tension, and thermophysical properties of the solvent and the flue gas. The overall heat and mass transfer also depends on the hydrodynamics, which in turn, is affected by the packing geometry. In the current work, we systematically quantify the effects the design of the structured packing has on the column hydrodynamics, by performing detailed CFD simulations for different geometrical configurations and operating conditions. We then obtain relationships between the key hydrodynamic metrics, such as liquid holdup, interfacial area, wetted area, and pressure drop to the parameters defining the packing geometries and identify new more effective packing designs for the given operating conditions.

Shah, Yash Girish↗
DOE OSTI2020

Impact of Individual Structural Defects in GaAs Solar Cells: A Correlative and In Operando Investigation of Signatures, Structures, and Effects

Defects usually degrade device performance. Thus, many techniques and effort are devoted to studying semiconductor defects. However, it is rarely known: i) how individual defects affect device performance; ii) how the impact depends on the device operating conditions; iii) how the impact varies from one defect to another; and iv) how these variations are correlated to the microscopic-scale defect structure. To address these crucial questions, an array of correlative and spatially resolved techniques, including electroluminescence, photoluminescence, Raman, I–V characteristics, and high-resolution electron microscopy, are used to characterize dislocation defects in GaAs solar cells. Significantly, the study is carried out in a series mode and in operando. This approach provides quantitative and definitive correlation between the atomistic structure of defects and their explicit effects on device performance, thus giving unprecedented insight into defect physics.

dislocation defects↗
DOE OSTI2018

The Effect of Structural Inhomogeneities and Gas-Forming Impurities on the Functional Properties of High Purity Rare-Earth Metals

Rare-earth metals (REM) gadolinium and dysprosium, and also the Gd–H and Dy–H systems in which magnetic order–order and order–disorder phase transitions are accompanied by significant magnetocaloric effect, have been studied. The materials have been prepared in various structural states. It is stated that there is a substantial difference of their functional properties in the dependence on the features of the structural state and also on the existence of a gas-forming impurity. It has been proved experimentally and theoretically that the Curie temperatures of Gd samples with grain sizes of ~200 nm increases as a result of hydrogenation, while the magnetic phase transition temperatures of Dy are almost unchanged Inhomogeneities.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2019

Quantitative Structure–Property Relationship (QSPR) Analysis of ZrO 2 -Containing Soda-Lime Borosilicate Glasses

Quantitative structure–property relationship (QSPR) analysis is a promising approach to correlate structural features with properties of glass materials that lack long-range order and usually have complex structures. By using carefully chosen descriptors based on structural models generated from molecular dynamics (MD) simulations, correlations with properties and insights on glass behaviors can be obtained. Zirconia can significantly alter glass properties including chemical durability, even in a small amount, and hence plays an important role in vitrification of nuclear waste where long-term chemical durability is desired. In this study, borosilicate glasses with the composition of xZrO 2 –(61 – x)SiO 2 –17B 2 O 3 –18Na 2 O–4CaO with x = 0, 1, 2, 4, 6, and 8 were simulated using classical MD simulations with the recently developed composition-dependent potentials. Short-range (e.g., bond distance and coordination numbers) and medium-range (e.g., Qn distribution, network connectivity, and ring-size distribution) structural features altered by ZrO 2 were obtained and analyzed. The use of a descriptor (Fnet descriptor) that combines short-range structural characteristics, from MD simulations, and the cation-oxygen single bond strength was found to provide excellent linear correlations with the density and initial dissolution rate of these glasses. Furthermore, the results show that by combining MD simulations and QSPR analysis the composition and structural effect on the properties of complex multicomponent glasses can be elucidated, thus suggesting that this is a promising approach for future glass research and new composition design.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Machine learning for deep elastic strain engineering of semiconductor electronic band structure and effective mass

Abstract The controlled introduction of elastic strains is an appealing strategy for modulating the physical properties of semiconductor materials. With the recent discovery of large elastic deformation in nanoscale specimens as diverse as silicon and diamond, employing this strategy to improve device performance necessitates first-principles computations of the fundamental electronic band structure and target figures-of-merit, through the design of an optimal straining pathway. Such simulations, however, call for approaches that combine deep learning algorithms and physics of deformation with band structure calculations to custom-design electronic and optical properties. Motivated by this challenge, we present here details of a machine learning framework involving convolutional neural networks to represent the topology and curvature of band structures in k -space. These calculations enable us to identify ways in which the physical properties can be altered through “deep” elastic strain engineering up to a large fraction of the ideal strain. Algorithms capable of active learning and informed by the underlying physics were presented here for predicting the bandgap and the band structure. By training a surrogate model with ab initio computational data, our method can identify the most efficient strain energy pathway to realize physical property changes. The power of this method is further demonstrated with results from the prediction of strain states that influence the effective electron mass. We illustrate the applications of the method with specific results for diamonds, although the general deep learning technique presented here is potentially useful for optimizing the physical properties of a wide variety of semiconductor materials.

Tsymbalov, Evgenii↗
DOE OSTI2022

Physics-informed graph neural networks for predicting cetane number with systematic data quality analysis

Designing alternative fuels for advanced compression ignition engines necessitates a predictive model for cetane number (CN). In this study, the physics-informed graph neural networks are introduced for a reliable CN prediction by considering molecular features pertinent to the physical properties of molecules that affect CN. The reliability of measured data is another key factor to consider for improving the predictive model. Various experimental instruments for measuring CN exist, including standard and non-standard methods. In this regard, a systematic data quality analysis was carried out for the total 630 CNs collected from literature and new measurements in this study using Advanced Fuel Ignition Delay Analyzer (AFIDA). The results from this data curation process were reflected in the model by imposing lower sample weights on the data coming from less reliable measurement techniques. This approach effectively maximized the prediction accuracy while incorporating data from all available sources. Using the sample weights decreased the mean absolute error (MAE) up to 0.8 CN units. The accuracy was also improved by introducing the CN-related physical properties (the number of hydrogen bond donors and acceptors); the test set MAE is 5.74 and 7.01 for the model with and without such properties, respectively. Investigating molecular structural effects on CN was also carried out to gain chemical insights into factors used to design new fuel candidates. The dimensionality reduction analysis of feature vectors showed a clear clustering in terms of functional groups and CN and the structural effect derived from the model was consistent with the physicochemical insights. Finally, this physics-informed model and data curation would be helpful for accurate CN prediction and inform rational fuel design.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

Effect of structural disordering on magnetic and magneto-optical properties of F e 3 Si

The electronic, magnetic, transport, and magneto-optical properties of the D0 3 and amorphous Fe 3 Si are investigated by using the first-principles calculations. A strong correlation between local magnetization and atomic arrangement is established. Here, the amorphization significantly alters the spin polarization and the magneto-optical Kerr rotation spectrum but has a little influence on anomalous Hall conductivity. Analyses in band structures and interband matrix elements provide clear insights for the understanding of these results.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Invertible Temper Modeling using Normalizing Flows and the Effects of Structure Preserving Loss

Advanced manufacturing research and development is typically small-scale, owing to costly experiments associated with these novel processes. Deep learning techniques could help accelerate this development cycle but frequently struggle in small-data regimes like the advanced manufacturing space. While prior work has applied deep learning to modeling visually plausible advanced manufacturing microstructures, little work has been done on data-driven modeling of how microstructures are affected by heat treatment, or assessing the degree to which synthetic microstructures are able to support existing workflows. We propose to address this gap by using invertible neural networks (normalizing flows) to model the effects of heat treatment, e.g., tempering. The model is developed using scanning electron microscope imagery from samples produced using shear-assisted processing and extrusion (ShAPE) manufacturing. This approach not only produces visually and topologically plausible samples, but also captures information related to a sample’s material properties or experimental process parameters. We also demonstrate that topological data analysis, used in prior work to characterize microstructures, can also be used to stabilize model training, preserve structure, and improve downstream results. We assess directions for future work and identify our approach as an important step towards end-to-end deep learning system for accelerating advanced manufacturing research and development.

Howland, Sylvia↗