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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 181 records · Page 10
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Predicting the Rate of Homogeneous Intermetallic Nucleation within Steep Composition Gradients

Simulations of isothermal homogeneous nucleation from deeply undercooled amorphous melts exhibit systematic variations in nucleation behavior depending upon the strength of an imposed composition gradient. Data from molecular dynamics (MD) simulations in a model Ni/Al system permit quantification of the nucleation rate of the NiAl-B2 intermetallic phase and indicate that nucleation proceeds in a polymorphous mode. The nucleation rate decreases with increasing gradient, and nucleation is completely suppressed above a critical gradient. Based on an argument of crystal nucleus stability, a simple estimate provides good prediction of the critical gradient. Here, a modified classical nucleation model parameterized with thermodynamic and kinetic quantities calculated independently predicts the nucleation rates and their variation with the imposed gradient, matching the MD results very well.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Accurate Dehydrogenation Enthalpies Dataset for Liquid Organic Hydrogen Carriers

This contribution presents a comprehensive extension of the QM9 dataset (originally at 133 K molecules) with the calculation of G4MP2 enthalpies for 9,841 molecules, featuring up to nine heavy atoms. We present QM9-LOHC, a (de)hydrogenation dataset of 10,373 reactions, including a minimum of 5.5% weight hydrogen storage capacity in line with the Department of Energy standards for Liquid Organic Hydrogen Carriers (LOHC). By utilizing the accurate quantum chemical method G4MP2 we expand the QM9 database and explore new avenues for the exploration of hydrogen storage technologies (electrochemical LOHCs, alkali metal-LOHCs, and mixtures of LOHCs). The QM9-LOHC dataset, with its focus on reactions that vary only by hydrogen saturation levels, provides a needed data resource for advancing the design and optimization of both conventional and innovative LOHC systems, and high-fidelity data for molecular discovery.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

CF + excitation in the interstellar medium

The detection of CF + in interstellar clouds potentially allows astronomers to infer the elemental fluorine abundance and the ionization fraction in ultraviolet-illuminated molecular gas. Because local thermodynamic equilibrium (LTE) conditions are hardly fulfilled in the interstellar medium (ISM), the accurate determination of the CF + abundance requires one to model its non-LTE excitation via both radiative and collisional processes. Here, we report quantum calculations of rate coefficients for the rotational excitation of CF + in collisions with para- and ortho-H 2 (for temperatures up to 150 K). As an application, we present non-LTE excitation models that reveal population inversion in physical conditions typical of ISM photodissociation regions (PDRs). We successfully applied these models to fit the CF + emission lines previously observed toward the Orion Bar and Horsehead PDRs. The radiative transfer models achieved with these new rate coefficients allow the use of CF + as a powerful probe to study molecular clouds exposed to strong stellar radiation fields.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Recent Advances in Small Angle X-ray Scattering for Superlattice Study

Small-angle x-ray scattering is used for the structure determination of superlattice for its superior resolution, nondestructive nature, and high penetration power of x rays. With the advent of high brilliance x-ray sources and innovative computing algorithms, there have been notable advances in small angle x-ray scattering analysis of superlattices. High brilliance x-ray beams have made data analyses less model-dependent. Additionally, novel data acquisition systems are faster and more competitive than ever before, enabling a more accurate mapping of the superlattices' reciprocal space. Fast and high-throughput computing systems and algorithms also make possible advanced analysis methods, including iterative phasing algorithms, non-parameterized fitting of scattering data with molecular dynamics simulations, and the use of machine learning algorithms. As a result, solving nanoscale structures with high resolutions has become an attainable task. In this review, we highlight new developments in the field and introduce their applications for the analysis of nanoscale ordered structures, including nanoparticle supercrystals, nanoscale lithography patterns, and supramolecular self-assemblies. Particularly, we highlight the reciprocal space mapping techniques and the use of iterative phase retrieval algorithms. We also cover coherent-beam-based small angle x-ray scattering techniques such as ptychography and ptycho-tomography in view of the traditional small angle x-ray scattering perspective.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

The importance of accounting for the Tolman correction to surface tension for nucleation and growth modeling of Fe clusters

Gibbs free energies of clusters are required for predictive modeling of cluster growth during condensation of a cooling vapor. Here, we present a straightforward method of calculating free energies of cluster formation using the data from molecular dynamics (MD) simulations. We apply this method to iron clusters having from 2 to 100 atoms. The energies obtained are verified by comparing to an MD-simulated equilibrium cluster size distribution in a sub-saturated vapor. We show that these free energies differ significantly from those obtained with a commonly used spherical cluster approximation, which relies on a surface tension coefficient of a flat surface, as it is used in the classical nucleation theory (CNT). We show that the spherical cluster approximation in CNT can be improved by using a cluster-size-dependent Tolman correction for the surface tension. The Tolman length and effective surface tension values were derived for iron clusters, and they significantly differ from the commonly used experimentally measured values. This improved approximation does not account for geometric magic number effects responsible for spikes and troughs in densities of neighbor cluster sizes. Nonetheless, it allows to more accurately model cluster formation from a cooling vapor. It better reproduces the condensation timeline, overall shape of the cluster size distribution, average cluster size, and the distribution width. In contrast, using a constant surface tension coefficient (as done in CNT) resulted in incorrect condensation dynamics and cluster size distributions. The analytical expression for cluster nucleation rate from CNT was updated to account for the size-dependence of cluster surface tension.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Meta-GGA performance in solids at almost GGA cost

A recent modification, r 2 SCAN, of the strongly constrained and appropriately normed (SCAN) meta-generalized gradient approximation (GGA) exchange-correlation functional mostly eliminates numerical instabilities and attendant integration grid sensitivities exhibited by SCAN. Here, we show that the successful deorbitalization of SCAN to SCAN-L (SCAN with density Laplacian dependence) carries over directly to yield r 2 SCAN-L. A major benefit is that the high iteration counts that hindered the use of SCAN-L are eliminated in r 2 SCAN-L. It therefore is a computationally much faster meta-GGA than its orbital-dependent antecedent. In this work, validation data for molecular heats of formation, bond lengths, and vibration frequencies (G3/99X, T96-R, and T82-F test sets, respectively) and on lattice constants, and cohesive energies (for 55 solids) and bulk moduli (for 40 solids) are provided. In addition, we show that the overmagnetization of bcc Fe, hcp Co, and fcc Ni persists in r 2 SCAN but does not appear in r 2 SCAN-L. Distinct from SCAN, both r 2 SCAN and r 2 SCAN-L give the correct nonmagnetic ground state for bcc V.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2021

Anomalous thermoelectricity at the two-dimensional structural transition of SnSe monolayers

The thermoelectric figure of merit ZT comprises electronic and vibrational contributions that drastically change across phase transitions, and the most common theoretical ab initio approach to thermoelectricity fails to describe the evolution of ZT across finite-temperature structural transitions in its entirety. Furthermore, while the thermoelectric behavior of bulk SnSe has been extensively studied, SnSe monolayers have been experimentally realized only recently, and the existent prediction of thermoelectricity on this two-dimensional material is unreliable because it misses its structural transition altogether. SnSe monolayers (and similar GeS, GeSe, GeTe, SnS, and SnTe monolayers) experience a temperature-induced two-dimensional Pnm2 1 → P4/nmm structural transition precipitated by the softening of vibrational modes, and we describe their thermoelectric properties across the phase transition, using molecular dynamics data to inform both electronic and vibrational coefficients directly and within the same footing. Similar to recent experimental observations pointing to an overestimated ZT past the transition temperature in bulk SnSe, we find a smaller ZT on SnSe monolayers when compared to its value predicted by the standard paradigm, due to the dramatic changes in the electrical conductivity and lattice thermal conductivity as the structural transition ensues. Here, the process described here lends a strong focus to both the vibrational and electronic evolutions throughout the structural transition, and it applies to thermoelectric materials undergoing thermally driven solid-to-solid structural phase transitions in one, two, and three dimensions.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2024

Heterogeneous Cluster Energetics and Nonlinear Thermodynamic Response in Supercritical Fluids

Microstructural heterogeneities arising from molecular clusters directly affect the nonlinear thermodynamic properties of supercritical fluids. We present a physical model to elucidate the relation between energy exchange and heterogeneous cluster dynamics during the transition from liquidlike to gaslike conditions. By analyzing molecular-dynamics data and employing physical principles, the model considers contributions from three key processes, namely, changing cluster density, cluster separation, and transfer of molecules between clusters. We show that the proposed model is consistent with the energetics at subcritical conditions and can be used to explain the nonlinear behavior of thermodynamic response functions, including the peak in the isobaric heat capacity.

42 ENGINEERING↗
DOE OSTI2023

Artificial Neural Network Models for Octane Number and Octane Sensitivity: A Quantitative Structure Property Relationship Approach to Fuel Design

Octane sensitivity (OS), defined as the research octane number (RON) minus the motor octane number (MON) of a fuel, has gained interest among researchers due to its effect on knocking conditions in internal combustion engines. Compounds with a high OS enable higher efficiencies, especially within advanced compression ignition engines. RON/MON must be experimentally tested to determine OS, requiring time, funding, and specialized equipment. Thus, predictive models trained with existing experimental data and molecular descriptors (via quantitative structure-property relationships (QSPRs)) would allow for the preemptive screening of compounds prior to performing these experiments. Here, the present work proposes two methods for predicting the OS of a given compound: using artificial neural networks (ANNs) trained with QSPR descriptors to predict RON and MON individually to compute OS (derived octane sensitivity (dOS)), and using ANNs trained with QSPR descriptors to directly predict OS. Twenty-five ANNs were trained for both RON and MON and their test sets achieved an overall 6.4% and 5.2% error, respectively. Twenty-five additional ANNs were trained for both dOS and OS; dOS calculations were found to have 15.3% error while predicting OS directly resulted in 9.9% error. A chemical analysis of the top QSPR descriptors for RON/MON and OS is conducted, highlighting desirable structural features for high-performing molecules and offering insight into the inner mathematical workings of ANNs; such chemical interpretations study the interconnections between structural features, descriptors, and fuel performance showing that connectivity, structural diversity, and atomic hybridization consistently drive fuel performance.

09 BIOMASS FUELS↗
DOE OSTI2022

Planar 2D wireframe DNA origami

Two-dimensional (2D) DNA origami is widely used for applications ranging from excitonics to single-molecule biophysics. Conventional, single-layer 2D DNA origami exhibits flexibility and curvature in solution; however, that may limit its suitability as a 2D structural template. In contrast, 2D wireframe DNA origami rendered with six-helix bundle edges offers local control over duplex orientations with enhanced in-plane rigidity. Here, we investigate the 3D structure of these assemblies using cryo–electron microscopy (cryo-EM). 3D reconstructions reveal a high degree of planarity and homogeneity in solution for polygonal objects with and without internal mesh, enabling 10-Å resolution for a triangle. Coarse-grained simulations were in agreement with cryo-EM data, offering molecular structural insight into this class of 2D DNA origami. Our results suggest that these assemblies may be valuable for 2D material applications and geometries that require high structural fidelity together with local control over duplex orientations, rather than parallel duplex assembly.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

De novo transcriptome in roots of switchgrass ( Panicum virgatum L. ) reveals gene expression dynamic and act network under alkaline salt stress

Background: Soil salinization is a major limiting factor for crop cultivation. Switchgrass is a perennial rhizomatous bunchgrass that is considered an ideal plant for marginal lands, including sites with saline soil. Here we investigated the physiological responses and transcriptome changes in the roots of Alamo (alkaline-tolerant genotype) and AM314/MS-155 (alkaline-sensitive genotype) under alkaline salt stress. Results: Alkaline salt stress significantly affected the membrane, osmotic adjustment and antioxidant systems in switchgrass roots, and the ASTTI values between Alamo and AM-314/MS-155 were divergent at different time points. A total of 108,319 unigenes were obtained after reassembly, including 73,636 unigenes in AM-314/MS-155 and 65,492 unigenes in Alamo. A total of 10,219 DEGs were identified, and the number of upregulated genes in Alamo was much greater than that in AM-314/MS-155 in both the early and late stages of alkaline salt stress. The DEGs in AM-314/MS-155 were mainly concentrated in the early stage, while Alamo showed greater advantages in the late stage. These DEGs were mainly enriched in plant-pathogen interactions, ubiquitin-mediated proteolysis and glycolysis/gluconeogenesis pathways. We characterized 1480 TF genes into 64 TF families, and the most abundant TF family was the C2H2 family, followed by the bZIP and bHLH families. A total of 1718 PKs were predicted, including CaMK, CDPK, MAPK and RLK. WGCNA revealed that the DEGs in the blue, brown, dark magenta and light steel blue 1 modules were associated with the physiological changes in roots of switchgrass under alkaline salt stress. The consistency between the qRT-PCR and RNA-Seq results confirmed the reliability of the RNA-seq sequencing data. A molecular regulatory network of the switchgrass response to alkaline salt stress was preliminarily constructed on the basis of transcriptional regulation and functional genes. Conclusions: Alkaline salt tolerance of switchgrass may be achieved by the regulation of ion homeostasis, transport proteins, detoxification, heat shock proteins, dehydration and sugar metabolism. These findings provide a comprehensive analysis of gene expression dynamic and act network induced by alkaline salt stress in two switchgrass genotypes and contribute to the understanding of the alkaline salt tolerance mechanism of switchgrass and the improvement of switchgrass germplasm.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

mystic : software for autonomous discovery and design under uncertainty

Throughout the diverse range of science and engineering applications, there is a growing desire to develop computational methods that can reliably predict the behavior of complex systems. Specifically, there is a strategic need for tools that can robustly forecast the behavior of complex physical systems, where data may be high-dimensional, noisy, or sparse, and models of the system may be time-dependent or include uncertainty. We use mystic to build tools that leverage statistical learning, physics-informed learning, and active learning in the efficient generation of reliably predictive surrogates for complex physical systems. mystic is a robust, proven, open-source optimization and uncertainty quantification toolkit with over a decade of use in the design and optimization of neutron instrumentation, solar-powered drones, and gasguns, and in iterative tuning of models for Raman spectroscopy and elastoplastic materials strength. Recent developments have focused on automated learning of statistically robust surrogates under uncertainty, with applications in materials in extreme environments, nanostructures, materials simulations and strength models, and the failure of shielding under particle radiation. In 2020, McKerns demonstrated active learning of optimally robust surrogates with respect to new simulated data for molecular dynamics simulations of materials mixing in warm dense matter, and is currently applying active learning to the automated steering of particle accelerator beams and the optimal design and control of quantum optical sensor instrumentation.

42 ENGINEERING↗
DOE OSTI2023

Combining Molecular Simulations and Machine Learning to Comprehensively Explore Adsorption Space (Final Report)

This is the final report for the project "Combining Molecular Simulations and Machine Learning to Comprehensively Explore Adsorption Space". The central aim of this project was to develop modeling tools that vastly increase the diversity of molecules for which reliable adsorption isotherms in nanoporous materials are available. Within the project, molecular simulation data for a diverse set of examples was combined with machine-learning (ML) methods to rapidly predict adsorption equilibria in crystalline nanoporous materials (primarily MOFs).

36 MATERIALS SCIENCE↗
DOE OSTI2023

Design of CO 2 Selective Type 3 Porous Liquids Through Porous Host Morphology

Direct air capture (DAC) of CO 2 is a negative emission technology under development to limit the impacts of climate change. The dilute concentration of CO 2 in the atmosphere (~400 ppm) requires new materials for carbon capture with increased CO 2 selectivity that is not met with current carbon capture materials. Porous liquids (PLs) are an emerging candidate for carbon capture and consists of a combination of solvents and porous hosts that creates a liquid with permanent porosity. The fundamental mechanisms of carbon capture in a PL are relatively unknown. To uncover these mechanisms, PLs were synthesized consisting of three different zeolitic-imidazolate framework (ZIF-8, ZIF-67, or ZIF-69) porous host in a water/glycol/2-methylimidazole solvent. The most stable composition was based on ZIF-8 and exhibited carbon capture following exposure to CO 2 . Density functional theory identified a three-step carbon capture mechanism based on (i) reaction of OH- with ethylene glycol in the solution followed by (ii) formation of 2-hydroxyethyl carbonate, which (iii) further react with OH- to form a carbonate species. This mechanism was validated with experimental nuclear magnetic resonance spectroscopy (NMR) to identify the dissolved carbonate phases and the decrease in the pH during CO 2 exposure. Deuterated samples of the ZIF-8 PLs were synthesized and analyzed via neutron diffraction at the Spallation Neutron Sources at Oak Ridge National Laboratory. Results identified differences in diffraction for PLs pre- and post-CO 2 exposure that will be combined with ab initio molecular dynamics data of the same PL composition to identify how the presence of a solvent-porous host interfaces results in carbon capture.

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

Developing Open-Source Tools for Increasing the Efficiency of Synthetic Aviation Turbine Fuel Certification Process

FuelLib is an open-source Python-based fuel library, developed by NREL, that leverages the group contribution method (GCM) of [1] to systematically estimate the thermodynamic and transport properties of hydrocarbon fuels. FuelLib predicts these properties based on the molecular structure of individual compounds or compound families, using weight percentages of a fuel's composition, typically measured using techniques such as gas chromatography (GC). FuelLib enables property estimation over a wide range of temperatures and pressures of multi-component fuels in the absence of detailed molecular composition data, making it particularly valuable for complex fuel mixtures where detailed experimental characterization of fuel composition is unavailable. These capabilities contribute directly to synthetic aviation turbine fuels (SATF) development, supporting the short-term American Society for Testing and Materials (ASTM) qualification of drop-in fuels while potentially expanding ASTM boundaries to certify a broader range of fuels.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2020

Prediction of Research/Motor Octane Number and Octane Sensitivity Using Artificial Neural Networks

Octane sensitivity (OS), dfined as the research octane number (RON) minus the motor octane number (MON) of a given fuel, has gained interest among researchers due to its apparent ffect on knocking conditions in internal combustion engines. Compounds with a high OS enable higher efficiencies, especially with respect to advanced compression ignition engines. RON/MON must be experimentally tested to determine OS; however, the experimental methods utilized require a substantial amount of time, a significant monetary investment, and specialized equipment. To this end, predictive computational models trained with existing experimental data and molecular properties would allow for the preemptive screening of compounds prior to performing these experiments. The present work proposes two methods for predicting the OS of a given compound: using artficial neural networks (ANNs) trained with quantitative structure-property relationship (QSPR) descriptors to predict RON and MON individually to compute OS from RON/MON predictions (derived octane sensitivity, dOS), and using an ANN trained with QSPR descriptors to directly predict OS. ANNs trained to predict RON and MON achieved test set root-mean-square errors (RMSEs) of 10.499 and 7.551 respectively. dOS calculations were found to have a test set RMSE of 6.432 while predicting OS directly resulted in a test set RMSE of 7.019, showing it is more bene cial to obtain OS from RON/MON predictions than predicting it directly. Furthermore, relationships between individual QSPR descriptors and RON/MON/OS are discussed, highlighting correlations between specfic molecular features and these properties.

09 BIOMASS FUELS↗
DOE OSTI2024

Phase Diagram of Nuclear Pastas in Neutron Star Crusts

Two neural networks were trained to predict, respectively, the Euler characteristic and the curvature of nuclear pastas in neutron star crust conditions generated by molecular dynamics simulations of neutron star matter with 0.1 < x < 0.5, 0.040 fm−3 < ρ < 0.085 fm−3 (0.68 × 1014 g/cm3 < ρ < 1.43 × 1014 g/cm3), and 0.2 MeV < T < 4.0 MeV, where x is proton content, the density is ρ, and the temperature is T. The predictions of the two networks were combined to determine the nuclear pasta phase that is thermodynamically stable at a given x, ρ, and T, and a three-dimensional phase diagram that extrapolated slightly the regions of existing molecular dynamics data was computed. The jungle gym and anti-jungle gym structures are prevalent at high temperature and low density, while the anti-jungle gym and anti-gnocchi structures dominate at high temperature and high density. A diversity of structures exist at low temperatures and intermediate density and proton content. The trained models used in this work are open access and available at a public repository to promote comparison to pastas obtained with other models.

Muñoz, Jorge A. (ORCID:0000000294870079)↗
DOE OSTI2022

Characterization of Interactions between CTX-M-15 and Clavulanic Acid, Desfuroylceftiofur, Ceftiofur, Ampicillin, and Nitrocefin

Cefotaximase-Munich (CTX-M) extended-spectrum beta-lactamases (ESBLs) are commonly associated with Gram-negative, hospital-acquired infections worldwide. Several beta-lactamase inhibitors, such as clavulanate, are used to inhibit the activity of these enzymes. To understand the mechanism of CTX-M-15 activity, we have determined the crystal structures of CTX-M-15 in complex with two specific classes of beta-lactam compounds, desfuroylceftiofur (DFC) and ampicillin, and an inhibitor, clavulanic acid. The crystal structures revealed that Ser70 and five other residues (Lys73, Tyr105, Glu166, Ser130, and Ser237) participate in catalysis and binding of those compounds. Based on analysis of steady-state kinetics, thermodynamic data, and molecular docking to both wild-type and S70A mutant structures, we determined that CTX-M-15 has a similar affinity for all beta-lactam compounds (ceftiofur, nitrocefin, DFC, and ampicillin), but with lower affinity for clavulanic acid. A catalytic mechanism for tested β-lactams and two-step inhibition mechanism of clavulanic acid were proposed. CTX-M-15 showed a higher activity toward DFC and nitrocefin, but significantly lower activity toward ampicillin and ceftiofur. The interaction between CTX-M-15 and both ampicillin and ceftiofur displayed a higher entropic but lower enthalpic effect, compared with DFC and nitrocefin. DFC, a metabolite of ceftiofur, displayed lower entropy and higher enthalpy than ceftiofur. This finding suggests that compounds containing amine moiety (e.g., ampicillin) and the furfural moiety (e.g., ceftiofur) could hinder the hydrolytic activity of CTX-M-15.

59 BASIC BIOLOGICAL SCIENCES↗