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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 145 records · Page 8
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Neutron thermalization in nuclear graphite: A modern story of a classic moderator

Graphite has been of great interest since the dawn of the nuclear era. In this work, we will focus on the thermal neutron scattering of two types of nuclear graphite that have different microstructures, namely, the historic PGA which has a very anisotropic medium-grained needle coke, and the new G347A which is a near-isotropic fine-grained graphite. We report the results of room temperature inelastic neutron scattering measurements for the scattering function, double differential scattering cross-section, and phonon density of states. The obtained results are discussed in conjunction with results extracted from first-principles lattice dynamics simulations of crystalline graphite, using the incoherent approximation and by also including the coherent-one phonon contribution. Excellent agreements have been found between the measured and calculated scattering functions and phonon densities of states. The inclusion of the coherent-one phonon scattering shows a significant improvement of the calculated scattering functions, double differentials, and inelastic scattering cross-sections. We also highlight the physical concerns about the current thermal neutron scattering ENDF/B-VIII of nuclear graphite library and its corresponding phonon density of states. This work aims to provide a new presentation of neutron thermalization by graphite and offers a new set of high quality experimental and computational data for consideration.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2023

A general predictive methodology for fuel-mixture properties up to supercritical conditions

A predictive thermodynamic model is utilized for the calculation of fuel properties of oxymethylene dimethyl ethers (OME 3–4 ), surrogates for gasoline, diesel and aviation fuel, as well as alcohol blends with gasoline and diesel. The alcohols used for these blends are methanol, ethanol, propanol, butanol and pentanol; their mixing ratio ranges from 10 to 50% by volume. The model is based on the Perturbed-Chain Statistical Association Fluid Theory (PC-SAFT) equation of state (EoS) and Vapor Liquid Equilibrium (VLE) calculations at constant temperature, density and composition. The model includes the association term, with the assumption of two association sites (2B scheme), to enable the modeling of alcohols. The pure-component parameters are estimated based on the Group Contribution (GC) method of various sources, as well as a parametrization model specifically designed for the case of OME 3–4 . The results of the computational model for the density, vapor pressure and distillation curves at various conditions, including high-pressure, high-temperature (HPHT), are compared to experimental and computational data available in the literature. In the cases where no measurements are available for the surrogates, experimental data for the corresponding target fuel are used, taking into consideration the inherent deviation in properties between real and surrogate fuel. Overall, the results are in good agreement with the data from the literature, with the average deviation not exceeding 12% for temperature (Kelvin) on the distillation curves, 10% for density and 46% for vapor pressure and the general trend being captured successfully. The use of different pure component parameter estimation techniques can further improve the prediction quality in the cases of OME3–4 and the aviation fuel surrogate, especially for the vapor pressure, leading to an average deviation lower than 18%. These results demonstrate the predictive capabilities of the model, which extend to a wide range of fuel types and pressure/temperature conditions. Through this investigation, the present work aims to establish the limits of applicability of this thermodynamic property prediction methodology.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2020

Reduction of uranyl and uranyl-organic complexes mediated by magnetite and ilmenite: A combined electrochemical AFM and DFT study

Reduction of mobile aqueous uranyl complexes can be hindered by the strength of the uranyl-ligand complex and the resistivity of the complexes to electron transfer. Semiconducting solids, such as magnetite (Fe 3 O 4 ) and ilmenite (FeTiO 3 ), can facilitate otherwise-slow electron transfer between the reductant and oxidant, especially if these species are adsorbed on their surfaces as inner-sphere complexes. Electrons can then be shuttled through the surface or the structure. This process can be facilitated by either finding a new electron transfer path or by weakening or destroying the ligand-uranyl complex. Using in situ electrochemical atomic force microscopy (EC-AFM), we imaged reduction products of uranyl as mediated by magnetite and ilmenite in the absence or presence of the organic ligands, ethylenediaminetetraacetate (EDTA) and oxalate. In situ images of uranyl reduction show gradual nucleation and growth of reduced uranium on the mineral surfaces with decreasing reduction potentials. X-ray photoelectron spectroscopy (XPS) measurements indicate pentavalent uranium as the dominant uranium species resulting from uranyl reduction mediated by magnetite and ilmenite. The reduction potentials of uranyl-organic species relevant to our reaction system, (U VI O 2 ∙oxalate) 0 and (U VI O 2 ∙HEDTA) - , are derived using quantum–mechanical modeling and compared with the midpoint potentials obtained from cyclic voltammetry measurements. The spectroscopic, electrochemical, and computational data suggest that the catalysis of Fe-bearing minerals facilitates the reduction of uranyl-organic complexes. U(VI)-oxalate complexes are redox-active and participate directly in the reduction mediated by magnetite. In the presence of EDTA, reduction of uranyl may proceed in concert with ligand exchange between metals (i.e., U and Fe) on the mineral surface. Finally, the proposed mechanism is the breaking of uranyl-organic bonds upon interaction with the mineral surface followed by the formation of Fe-organic bonds, thereby allowing the reduction of hexavalent uranyl to pentavalent one.

58 GEOSCIENCES↗
DOE OSTI2022

Energy and mechanical properties predictions in Fe-Ni binary system by ab initio calculations

Here, the fcc random solid solutions in Fe-Ni binary system were investigated using ab initio calculations. Density functional theory (DFT) was applied along with supercells generated by the Special Quasi-Random Structure (SQS) method to predict the enthalpy of mixing and magnetic and elastic properties. The results were compared with relevant experimental and computational data produced by other groups. The effect of SQS supercell size and relaxation procedure on the predicted properties of the alloys were also studied. Our calculations show that 16-atoms SQS model provides adequate approach to the mixing energy of the alloy. However, calculation of mechanical properties requires larger supercell. Excellent agreement between experimental and calculated elastic properties was found in fcc region of Fe-Ni system.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Extracting a mixing parameter from 2D radiographic imaging of variable-density turbulent flow

We extract a suitably averaged fluctuating density from the two-dimensional radiographic image of a flow. The X-ray attenuation is given by the Beer–Lambert law which exponentially damps the incident beam intensity by a factor proportional to the density, opacity and thickness of the target. By making reasonable assumptions for the mean density, opacity and effective thickness of the target flow, we estimate the density fluctuation contribution to the attenuation. The extracted density fluctuations averaged across the thickness of the flow in the direction of the beam may be used to form the density–specific-volume correlation $b$. In a statistical description of variable-density turbulence, $b$ quantifies the degree of mixedness. The ability to extract a measure of mixedness from experimental data would be a powerful tool that could be used in the validation of mix models. The scheme proposed is tested for DNS data computed for variable density buoyancy-driven mixing. We quantify the deficits in the extracted value of $b$ due to target thickness, Atwood number and modeled signal noise. This analysis justifies using the proposed scheme to infer the mix parameter from thin targets at moderate to low Atwood numbers. Furthermore, to illustrate how the scheme might be used in a practical problem, we demonstrate its application to a radiographic image of counter-shear flow obtained from experiments at the National Ignition Facility.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2024

Robust Machine Learning Inference from X-ray Absorption Near Edge Spectra through Featurization

X-ray absorption spectroscopy (XAS) is a commonly employed technique for characterizing functional materials. In particular, X-ray absorption near edge spectra (XANES) encode local coordination and electronic information, and machine learning approaches to extract this information are of significant interest. To date, most ML approaches for XANES have primarily focused on using the raw spectral intensities as input, overlooking the potential benefits of incorporating spectral transformations and dimensionality reduction techniques into ML predictions. Here, in this work, we focused on systematically comparing the impact of different featurization methods on the performance of ML models for XAS analysis. We evaluated the classification and regression capabilities of these models on computed data sets and validated their performance on previously unseen experimental data sets. Our analysis revealed an intriguing discovery: the cumulative distribution function feature achieves both high prediction accuracy and exceptional transferability. This remarkably robust performance can be attributed to its tolerance to horizontal shifts in the spectra, which is crucial when validating models using experimental data. While this work exclusively focuses on XANES analysis, we anticipate that the methodology presented here will hold promise as a versatile asset to the broader spectroscopy community.

36 MATERIALS SCIENCE↗
DOE OSTI2024

In the Mix : A Workshop Merging Computational Chemistry and Electrochemistry Alongside Data Science

As chemistry expands to more complex and interdisciplinary areas, a new generation of diverse researchers must engage with science and learn effective cross-disciplinary collaboration and communication. To these ends, we designed and implemented In the Mix, a graduate student-led, two-day workshop for undergraduate students promoting collaborative science in the context of energy storage innovations. Here, the interactive workshop was designed for future and emerging researchers to gain hands-on experience with data science, computational chemistry, and electrochemistry techniques that are critical for developing materials for battery technologies. Participants also visited commercial renewable energy facilities to help them connect discovery-based research with industry and broader societal considerations. The workshop content and structure ensured that participants experienced the interrelatedness of the fields and understood the importance of collaborative research to yield scientific advances with real-world applications. An external team evaluated the workshop and participants’ perceptions of their experiences. While our research context was energy storage, the workshop goals and outcomes are applicable to other contexts. Interdisciplinary, experiential workshops are a key avenue to broadening participation in science and research, and the ideas presented here can be readily modified for other scientific contexts and/or incorporated as broader impact activities.

25 ENERGY STORAGE↗
DOE OSTI2024

Diffusion-Limited Kinetics in Reactive Systems

A proper representation of chemical kinetics is vital to understanding, modeling, and optimizing many important chemical processes. In liquid and surface phases, where diffusion is slow, the rate at which the reactants diffuse together limits the overall rate of many elementary reactions. Commonly, the textbook Smoluchowski theory is utilized to estimate effective rate coefficients in the liquid phase. On surfaces, modelers commonly resort to much more complex and expensive Kinetic Monte Carlo (KMC) simulations. Here, in this study, we extend the Smoluchowski model to allow the diffusing species to undergo chemical reactions and derive analytical formulas for the diffusion-limited rate coefficients for 3D, 2D, and 2D/3D interface cases. With these equations, we are able to demonstrate that when species react faster than they diffuse they can react orders of magnitude faster than predicted by Smoluchowski theory, through what we term “the reactive transport effect”. We validate the derived steady-state equations against particle Monte Carlo (PMC) simulations, KMC simulations, and non-steady-state solutions. Furthermore, using PMC and KMC simulations, we propose corrections that agree with all limits and the computed data for the 2D and 2D/3D interface steady-state equations, accounting for unique limitations in the associated derived equations. Additionally, we derive equations to handle couplings between diffusion-limited rate coefficients in reaction networks. We believe these equations should make it possible to run much more accurate mean-field simulations of liquids, surfaces, and liquid–surface interfaces accounting for diffusion limitations and the reactive transport effect.

Johnson, Matthew Sean↗
DOE OSTI2022

Electrostatic Asymmetry of Wurtzite Nanocrystals and Resulting Photocatalytic Properties

Efficient light absorption and high energy of charge carriers of zinc cadmium sulfide (ZCS) make this semiconductor attractive for many photocatalytic reactions. Despite marked successes in shape-controlled synthesis of ZCS central to their photocatalytic performance, recombination of charge carriers as they migrate through the nanoscale particles results in losses of excitation energy, markedly reducing the photocatalytic activity of ZCS and other heterogeneous photocatalysts. Here, in this study, we show that the electrostatic asymmetry of nanostructures, previously discovered for nearly spherical nanoparticles, also manifests in wurtzite ZCS with planar geometry. The electrostatic asymmetry assists charge separation and substantially increases the yield of photocatalytic reactions in monocrystalline ZCS. The synthesized ZCS nanorods and nanoplates with identical chemical composition were found to have markedly different photocatalytic activity for evolution of hydrogen in water. Despite much smaller specific surface areas, the ~500 nm wide nanoplates displayed a hydrogen evolution rate 12 times higher than the ~35 nm long nanorods, also outperforming other ZCS photocatalysts. Experimental and computational data indicate that the homo- and heterojunction-free ZCS nanoplates with continuous wurtzite lattice behave essentially as nanoscale dipoles with intrinsic dipole moment as high as 48.39 D per unit cell. Electric-field-directed migration of charge carriers stimulates their localization on opposite parts of the nanoplates. Direct imaging of the intraparticle electrical field using off-axis electron holography confirmed their electrostatic asymmetry. Polarization-enhanced charge separation provides a new pathway to efficient and stable photocatalysts for sustainable energy technologies.

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

Origin of Stabilization of Ligand-Centered Mixed Valence Ruthenium Azopyridine Complexes: DFT Insights for Neuromorphic Applications

Redox-driven conductance changes are critical processes in molecular- and coordination-complex-based memristive thin films and devices that are envisioned for neuromorphic technologies, but fundamental mechanisms of conductance switching are not fully understood. Here, we explore charge disproportionation (CD) processes in [Ru II L 2 ](PF 6 ) 2 molecular systems that intrinsically involve interfragment charge transfer (IFCT). Using a combination of ab initio molecular dynamics simulation (AIMD), time-dependent density functional theory (TD-DFT), and density functional theory (DFT) calculations, we investigate the electron transfer mechanisms and the roles of temperature and cell volumetric expansion in facilitating the counterion movements and electronic transitions required for low-cost IFCT and charge redistribution. A detailed analysis of the density of states and TD-DFT calculations highlights that unpaired electrons play a crucial role in low-energy transitions, with the azo (N=N) groups of the ligand serving as the primary sites for electronic transport between molecular fragments, further stabilizing the asymmetric state. Localization of added electrons on azo ligands occurs with negligible change at the Ru centers, supported by atomic volume expansions up to +4.74 bohr 3 , and goes along with a progressive reduction of the HOMO−LUMO gap across redox states, suggesting enhanced conductivity. The TD-DFT analysis reveals a dominant IFCT excitation at 2082.76 nm in the doubly reduced (22) state, while a stabilization energy of 1.20 eV of the asymmetric (13) state relative to the symmetric (22) state is predicted by constrained DFT. Periodic DFT and AIMD simulations emulating a molecular film show that the stabilization of the asymmetric state, relative to a symmetric one, translates in net charge separation values (order of ∼0.33 e) that are strongly linked to increased counterion mobility (average counterion displacements exceeding 0.7 Å per atom during CD events) and the involvement of azo groups in electron redistribution. These findings, which align with previously reported experimental and computational data, provide key insights into the IFCT mechanisms and electronic transport facilitated by azo groups, with important implications for redox-driven memristive and neuromorphic technologies.

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

Pentamethylcyclopentadienyl Metalloradical Iron Complexes Containing Redox Noninnocent α-Diimine-Type Ligands: Synthesis, Molecular, and Electronic Structures

The synthesis and characterization of pentamethylcyclopentadienyl iron complexes bearing the redox non-innocent α-diimine (N,N'-dimesitylbutane-2,3-diimine, Mes DI) and α-iminopyridine (N-mesityl(pyridin-2-yl)ethanimine Mes PI) ligands were explored. One-pot reduction and complexation of the cyclopentadienyl ring was accomplished by treatment of (κ 2 -N,N')FeCl 2 (N,N'= Mes DI or Mes PI) pre-cursors with [C 5 Me 5 ]Li. The resulting iron compounds were characterized by paramagnetic 1 H NMR spectroscopy, magnetic susceptibility measurements, zero-field 57 Fe Mössbauer spectroscopy, low-temperature EPR spectroscopy, and cyclic voltammetry. The combined spectroscopic, structural and DFT computational data supported low-spin iron(III) compounds (S Fe = ½) with π-acidic, formally doubly-reduced chelating ligands.

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

Flash Communication: Properties and Applications of a Pentavalent Bromoantimony Lewis Acid

While normally reluctant toward oxidation, SbBr 3 can be oxidized with 3,4,5,6-tetrachloro-1,2-benzoquinone (o-chloranil) in the presence of a Lewis base like the bromide anion or triphenylphosphine oxide (PPh 3 O) to form the tetrachlorocatecholate (cat Cl ) derivatives [SbBr 4 (cat Cl )] − ([2-Br] − ) and SbBr 3 (cat Cl )•OPPh 3 (2•OPPh 3 ), respectively. Structural, spectroscopic, and computational data indicate that the SbBr 3 (cat Cl ) fragment is similarly, if slightly less, Lewis acidic than the previously reported SbCl 3 (cat Cl ) fragment. The SbBr 3 /o-chloranil system, as well as its previously reported counterpart SbCl 3 /o-chloranil, were tested as Lewis acid catalysts for C−O bond metathesis and polycarbonate depolymerization. These results identified SbX 3 /o-chloranil pairs (X = Cl, Br) as simple main-group platforms for C−O bond cleavage chemistry.

Anions↗
DOE OSTI2024

Exploitation of Pore Structure for Increased CO 2 Selectivity in Type 3 Porous Liquids

CO 2 capture requires materials with high adsorption selectivity and an industrial ease of implementation. To address these needs, a new class of porous materials was recently developed that combines the fluidity of solvents with the porosity of solids. Type 3 porous liquids (PLs) composed of solvents and metal–organic frameworks (MOFs) offer a promising alternative to current liquid carbon capture methods due to the inherent tunability of the nanoporous MOFs. However, the effects of MOF structural features and solvent properties on CO 2 –MOF interactions within PLs are not well understood. Herein experimental and computational data of CO 2 gas adsorption isotherms were used to elucidate both solvent and pore structure influences on ZIF-based PLs. The roles of the pore structure including solvent size exclusion, structural environment, and MOF porosity on PL CO 2 uptake were examined. A comparison of the pore structure and pore aperture was performed using ZIF-8, ZIF-L, and amorphous-ZIF-8. Adsorption experiments here have verified our previously proposed solvent size design principle for ZIF-based PLs (1.8× ZIF pore aperture). Furthermore, the CO 2 adsorption isotherms of the ZIF-based PLs indicated that judicious selection of the pore environment allows for an increase in CO 2 selectivity greater than expected from the individual PL components or their combination. This nonlinear increase in the CO 2 selectivity is an emergent behavior resulting from the complex mixture of components specific to the ZIF-L + 2'-hydroxyacetophenone-based PL.

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

Mechanism of Catechol Oxidation by the Coupled Bicupric Active Site of Tyrosinase: Completion of the Oxygenase/Oxidase Reaction Cycle

Tyrosinase contains a coupled binuclear copper (CBC) active site, which in its bicuprous form (deoxy-Ty) binds O 2 to form a side-on peroxide [Cu(II) 2 O 2 ] 2+ intermediate (oxy-Ty) that performs the regioselective monooxygenation of monophenols to catechols and their subsequent 2e − oxidation to quinones. Previously, we used spectroscopic, kinetic, and computational methods to elucidate the mechanism of the initial steps in the monooxygenation reaction. Herein, we investigated the final step in catecholate formation and provide experimental and computational results elucidating the catechol oxidation reaction, formally a proton-coupled 2e − process. We employed single-turnover stoppedflow absorption to observe the elusive catecholate-Cu(II) 2 OH intermediate via the anaerobic reaction of resting Cu(II) 2 OH Ty (met-Ty) with the catecholate substrate. This intermediate was cryo-trapped and characterized by electron paramagnetic resonance and X-ray absorption spectroscopies. These experimental results were correlated to quantum mechanics/molecular mechanics (QM/MM) and QM calculations to describe the complete catalytic cycle of tyrosinase, revealing that the 2e − oxidation of catechol to quinone involves two steps: (i) coupled 1H + /1e − transfer from the bound monoanionic catecholate to the bridged hydroxide of the met-Ty active site, followed by (ii) the second 1e − transfer. The latter is calculated to be the rate-limiting step in catechol oxidation, confirmed by experimental solvent kinetic isotope effect studies. Our computational data suggest that quinone release from the protein provides the necessary driving force for this reaction. These results establish a detailed description of this oxidase cycle of the tyrosinase mechanism and more broadly provide molecular-level insights into the diverse reactivity of CBC sites in biology.

QM/MM modeling↗
DOE OSTI2025

Intrachain Electron Transport in a Naphthalene Diimide–Bithiophene Copolymer: A Mixed-Valence Approach

The mechanism of electron transport in the polymer P(NDI2OD-T2) (poly(N,N'-bis-2-octyldodecylnaphthalene-1,4,5,8-bis-dicarboximide-2,6-diyl-alt-2,2'-bithiophene-5,5'-diyl), N2200) is investigated. Here, we use spectroelectrochemical measurements on P(NDI2OD-T2), spectroscopic studies of a chemically reduced model compound, 2,2'-(2,2'-bithiophene-5,5'-diyl)-bis(N,N'-di-n-hexylnaphthalene-1,8:4,5-bis(dicarboximide)) (NDI-T2-NDI), and electronic structure calculations to evaluate the microscopic charge-transport parameters. Experimental and computational data suggest that NDI-T2-NDI•– is a class-II mixed-valence compound, strongly supporting the small-polaron hopping model as a charge-transport mechanism for electrons along polymer chains in P(NDI2OD-T2). The electronic coupling between the NDI redox units is at least 21 meV, while the reorganization energy is between 0.45 and 0.56 eV. Using a hopping model, we estimated the mobility and activation energy for electron transport along P(NDI2OD-T2) polymer chains to be 0.15 cm 2 V –1 s –1 and 60 meV, respectively. Our study elucidates a long-standing issue of explaining the coexistence in P(NDI2OD-T2) of localized redox sites with relatively large electron mobilities more usually achieved only in highly conjugated polymers.

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 OSTI2025

A framework to evaluate machine learning crystal stability predictions

The rapid adoption of machine learning in various scientific domains calls for the development of best practices and community agreed-upon benchmarking tasks and metrics. We present Matbench Discovery as an example evaluation framework for machine learning energy models, here applied as pre-filters to first-principles computed data in a high-throughput search for stable inorganic crystals. We address the disconnect between (1) thermodynamic stability and formation energy and (2) retrospective and prospective benchmarking for materials discovery. Alongside this paper, we publish a Python package to aid with future model submissions and a growing online leaderboard with adaptive user-defined weighting of various performance metrics allowing researchers to prioritize the metrics they value most. To answer the question of which machine learning methodology performs best at materials discovery, our initial release includes random forests, graph neural networks, one-shot predictors, iterative Bayesian optimizers and universal interatomic potentials. We highlight a misalignment between commonly used regression metrics and more task-relevant classification metrics for materials discovery. Accurate regressors are susceptible to unexpectedly high false-positive rates if those accurate predictions lie close to the decision boundary at 0 eV per atom above the convex hull. The benchmark results demonstrate that universal interatomic potentials have advanced sufficiently to effectively and cheaply pre-screen thermodynamic stable hypothetical materials in future expansions of high-throughput materials databases.

Riebesell, Janosh↗