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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 1,189 records · Page 66
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Selective NMR observation of the SEI–metal interface by dynamic nuclear polarisation from lithium metal

While lithium metal represents the ultimate high-energy-density battery anode material, its use is limited by dendrite formation and associated safety risks, motivating studies of the solid–electrolyte interphase layer that forms on the lithium, which is key in controlling lithium metal deposition. Dynamic nuclear polarisation enhanced NMR can provide important structural information; however, typical exogenous dynamic nuclear polarisation experiments, in which organic radicals are added to the sample, require cryogenic sample cooling and are not selective for the interface between the metal and the solid–electrolyte interphase. Here we instead exploit the conduction electrons of lithium metal to achieve an order of magnitude hyperpolarisation at room temperature. We enhance the 7 Li, 1 H and 19 F NMR spectra of solid–electrolyte interphase species selectively, revealing their chemical nature and spatial distribution. These experiments pave the way for more ambitious room temperature in situ dynamic nuclear polarisation studies of batteries and the selective enhancement of metal–solid interfaces in a wider range of systems.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Can electric fields drive chemistry for an aqueous microdroplet?

Abstract Reaction rates of common organic reactions have been reported to increase by one to six orders of magnitude in aqueous microdroplets compared to bulk solution, but the reasons for the rate acceleration are poorly understood. Using a coarse-grained electron model that describes structural organization and electron densities for water droplets without the expense of ab initio methods, we investigate the electric field distributions at the air-water interface to understand the origin of surface reactivity. We find that electric field alignments along free O–H bonds at the surface are ~16 MV/cm larger on average than that found for O–H bonds in the interior of the water droplet. Furthermore, electric field distributions can be an order of magnitude larger than the average due to non-linear coupling of intramolecular solvent polarization with intermolecular solvent modes which may contribute to even greater surface reactivity for weakening or breaking chemical bonds at the droplet surface.

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

Operando neutron diffraction reveals mechanisms for controlled strain evolution in 3D printing

Abstract Residual stresses affect the performance and reliability of most manufactured goods and are prevalent in casting, welding, and additive manufacturing (AM, 3D printing). Residual stresses are associated with plastic strain gradients accrued due to transient thermal stress. Complex thermal conditions in AM produce similarly complex residual stress patterns. However, measuring real-time effects of processing on stress evolution is not possible with conventional techniques. Here we use operando neutron diffraction to characterize transient phase transformations and lattice strain evolution during AM of a low-temperature transformation steel. Combining diffraction, infrared and simulation data reveals that elastic and plastic strain distributions are controlled by motion of the face-centered cubic and body-centered cubic phase boundary. Our results provide a new pathway to design residual stress states and property distributions within additively manufactured components. These findings will enable control of residual stress distributions for advantages such as improved fatigue life or resistance to stress-corrosion cracking.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Machine learning approaches for structural and thermodynamic properties of a Lennard-Jones fluid

Predicting the functional properties of many molecular systems relies on understanding how atomistic interactions give rise to macroscale observables. However, current attempts to develop predictive models for the structural and thermodynamic properties of condensed-phase systems often rely on extensive parameter fitting to empirically selected functional forms whose effectiveness is limited to a narrow range of physical conditions. Here, we illustrate how these traditional fitting paradigms can be superseded using machine learning. Specifically, we use the results of molecular dynamics simulations to train machine learning protocols that are able to produce the radial distribution function, pressure, and internal energy of a Lennard-Jones fluid with increased accuracy in comparison to previous theoretical methods. The radial distribution function is determined using a variant of the segmented linear regression with the multivariate function decomposition approach developed by Craven et al. [J. Phys. Chem. Lett. 11, 4372 (2020)]. The pressure and internal energy are determined using expressions containing the learned radial distribution function and also a kernel ridge regression process that is trained directly on thermodynamic properties measured in simulation. The presented results suggest that the structural and thermodynamic properties of fluids may be determined more accurately through machine learning than through human-guided functional forms.

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

Theory and modeling of molecular modes in the NMR relaxation of fluids

Traditional theories of the nuclear magnetic resonance (NMR) autocorrelation function for intra-molecular dipole pairs assume a single-exponential decay, yet the calculated autocorrelation of realistic systems displays a rich, multi-exponential behavior, resulting in anomalous NMR relaxation dispersion (i.e., frequency dependence). We develop an approach to model and interpret the multi-exponential intra-molecular autocorrelation using simple, physical models within a rigorous statistical mechanical development that encompasses both rotational diffusion and translational diffusion in the same framework. Here, we recast the problem of evaluating the autocorrelation in terms of averaging over a diffusion propagator whose evolution is described by a Fokker–Planck equation. The time-independent part admits an eigenfunction expansion, allowing us to write the propagator as a sum over modes. Each mode has a spatial part that depends on the specified eigenfunction and a temporal part that depends on the corresponding eigenvalue (i.e., correlation time) with a simple, exponential decay. The spatial part is a probability distribution of the dipole pair, analogous to the stationary states of a quantum harmonic oscillator. Drawing inspiration from the idea of inherent structures in liquids, we interpret each of the spatial contributions as a specific molecular mode. These modes can be used to model and predict the NMR dipole–dipole relaxation dispersion of fluids by incorporating phenomena on the molecular level. We validate our statistical mechanical description of the distribution in molecular modes with molecular dynamics simulations interpreted without any relaxation models or adjustable parameters: the most important poles in the Padé–Laplace transform of the simulated autocorrelation agree with the eigenvalues predicted by the theory

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

Effects of temperature inversion on densification in chemical vapor infiltration

In a classical chemical vapor infiltration (CVI) process, the competing effects of chemical kinetics and reagent gas transport lead to non–uniform depositions such that outer layers of a preform densify faster leaving the core highly porous. Currently, CVI must be performed at a sufficiently low temperature to achieve good densification quality which leads to high processing time and cost. Volumetric heating of the preform, especially through microwaves, can create temperature inversion such that the core is hotter than the outer surface and potentially, overcome the challenges associated with isothermal CVI. Direct numerical simulations (DNS) of densification under various such temperature distributions indicate that microwave heating in CVI processing can lead to better (uniform) densification of porous preforms. Here the role of key parameters describing the temperature distributions on the densification behavior is investigated. Strategic temporal control of the temperature distribution shows that processing times can be reduced by almost half while maintaining a good densification quality similar to that of low–temperature isothermal processing. Inside–out densification due to the inverted temperature profile is a key distinguishing characteristic of microwave assisted CVI.

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

Understanding the Applicability of Gas/Particle Partitioning Theory to SOA using Novel Chemically-Speciated Partitioning Measurements (Final Report)

The main objective of this research grant project was to investigate the physical-chemical processes and properties of particles and partitioning gases that influence gas/particle (G/P) partitioning and timescales for equilibration, to directly determine the conditions under which equilibrium G/P partitioning theory can be applied to secondary organic aerosol (SOA). SOA constitutes a major component of submicron particles in the atmosphere, which have important implications for air quality, human and ecosystem health, radiation balance, and climate; however SOA formation, evolution and losses are not well understood. Use of novel on-line measurements of chemically-speciated partitioning measurements of both gas-phase and particle-phase components was a central component to advancing these scientific goals. The key approach was to conduct environmental chamber studies with systems of increasing complexity to determine G/P partitioning rates and equilibrium states for chemically-speciated SOA components. The project builds on our previous work on gas-wall partitioning that must be understood in order to study G/P partitioning in chambers, and to help extrapolate the results to be atmospherically relevant. The goal was to employ measurements and modeling to systematically determine when G/P partitioning theory accurately describes the distribution of oxidized organic compounds in the gas and aerosol phases, and when and why it does not (e.g., aerosol phase-state kinetic limitations, non-ideal mixing, phase separation, oligomer formation,), for chemical and physical conditions typically found throughout the continental boundary layer.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

A multiscale insight into the growth of h-BN: effect of the enclosure

Abstract There is a lack of knowledge on the fundamental growth mechanisms governing the characteristics of 2D materials synthesized by the chemical vapor deposition (CVD) technique and their correlation with experimentally controllable parameters, which hindered their wafer-scale synthesis. Here, we pursued an analytical and computational approach to access the system states that are not experimentally viable to address these critical needs. We developed a multiscale computational framework correlating the macroscale heat and mass flow with the mesoscale morphology of the as-grown 2D materials by solving the coupled system of heat/mass transfer and phase-field equations. We used hexagonal boron nitride (h-BN) as our model material and investigated the effect of substrate enclosure on its growth kinetics and final morphology. We revealed a lower concentration with a more uniform distribution on the substrate in an enclosed-growth than open-growth. It leads to a more uniform size distribution of the h-BN islands, consistent with existing experimental investigations.

Materials Science↗
DOE OSTI2024

An ion specific continuum model on ionic surfactant's binary phase diagram in aqueous solution

Here, a continuum aggregation model is proposed to account for specific ion effects, enabling accurate phase diagrams calculations for the micellar, cylindrical and lamellar aggregates of sodium/potassium carboxylate surfactants in aqueous solution across a range of temperatures. Three groups of concentrations at distinctive temperatures are fitted to build empirical temperature dependence given the limited availability of relevant experimental measurements. The specific ion effects are manifested in the aggregates' surface tension as well as in the distributions of counter-ions' concentrations in the vicinity of the aggregates. The aggregates' geometric sizes are well-reproduced. The differential evolution algorithm is applied to address boundary conditions of the electrostatic potential, aggregate size optimization as well as the equilibrium of monomers transferring between the aggregate and aqueous region.

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

Sequence Design of Random Heteropolymers as Protein Mimics

Random heteropolymers (RHPs) have been computationally designed and experimentally shown to recapitulate protein-like phase behavior and function. However, unlike proteins, RHP sequences are only statistically defined and cannot be sequenced. Recent developments in reversible-deactivation radical polymerization allowed simulated polymer sequences based on the well-established Mayo–Lewis equation to more accurately reflect ground-truth sequences that are experimentally synthesized. This led to opportunities to perform bioinformatics-inspired analysis on simulated sequences to guide the design, synthesis, and interpretation of RHPs. We compared batches on the order of 10000 simulated RHP sequences that vary by synthetically controllable and measurable RHP characteristics such as chemical heterogeneity and average degree of polymerization. Our analysis spans across 3 levels: segments along a single chain, sequences within a batch, and batch-averaged statistics. We discuss simulator fidelity and highlight the importance of robust segment definition. Examples are presented that demonstrate the use of simulated sequence analysis for in-silico iterative design to mimic protein hydrophobic/hydrophilic segment distributions in RHPs and compare RHP and protein sequence segments to explain experimental results of RHPs that mimic protein function. To facilitate the community use of this workflow, the simulator and analysis modules have been made available through an open source toolkit, the RHPapp.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Evaporation and Molecular Beam Scattering from a Flat Liquid Jet

An experimental setup for molecular beam scattering from flat liquid sheets has been developed, with the goal of studying reactions at gas–liquid interfaces for volatile liquids. Specifically, a crossed molecular beam instrument that can measure angular and translational energy distributions of scattered products has been adapted for liquid jet scattering. A microfluidic chip is used to create a stable flat liquid sheet inside vacuum from which scattering occurs, and both evaporation and scattering from this sheet are characterized using a rotatable mass spectrometer that can measure product time-of-flight distributions. This article describes the instrument and reports on the first measurements of evaporation of dodecane and Ne from a Ne-doped dodecane flat jet, as well as scattering of Ne from a flat jet of pure dodecane.

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

Magnetic properties of CrX 3 (X = Cl, Br, I) monolayers in excited states

The presence of intrinsic ferromagnetism in a new class of magnetic semiconductors CrX 3 (X = Cl, Br, I) in a reduced dimension is associated with magnetic anisotropy energy (MAE). MAE is relevant for the magnetic order of CrX 3 . The control of magnetism in such 2D materials through external stimuli is gaining importance in view of potential technological applications. Here, we use all-electron density functional theory to investigate the magnetism of monolayers of chromium halides in excited states using a cluster model. We study the magnetization reversal barrier for two types of electronic singly excited states: (a) spin-preserving transitions, and (b) spin-flip transitions. While all the three halides show enhancement of MAE in excited states, the MAEs are significant for CrI 3 due to the large spin–orbit coupling in the iodines. The changes in the MAE in the excited state are influenced by the local charge distribution surrounding the iodine atoms. Using a model system, we also show that the MAE in chromium tri-iodides is strongly influenced by the charge transfer to the iodines in an excited state. Furthermore, controlling the charge transfer to the iodines can potentially be used to control the magnetization reversal barrier.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

AENET–LAMMPS and AENET–TINKER : Interfaces for accurate and efficient molecular dynamics simulations with machine learning potentials

Machine-learning potentials (MLPs) trained on data from quantum-mechanics based first-principles methods can approach the accuracy of the reference method at a fraction of the computational cost. To facilitate efficient MLP-based molecular dynamics and Monte Carlo simulations, an integration of the MLPs with sampling software is needed. Here, we develop two interfaces that link the atomic energy network (ænet) MLP package with the popular sampling packages TINKER and LAMMPS. The three packages, ænet, TINKER, and LAMMPS, are free and open-source software that enable, in combination, accurate simulations of large and complex systems with low computational cost that scales linearly with the number of atoms. Scaling tests show that the parallel efficiency of the ænet–TINKER interface is nearly optimal but is limited to shared-memory systems. The ænet–LAMMPS interface achieves excellent parallel efficiency on highly parallel distributed memory systems and benefits from the highly optimized neighbor list implemented in LAMMPS. We demonstrate the utility of the two MLP interfaces for two relevant example applications: the investigation of diffusion phenomena in liquid water and the equilibration of nanostructured amorphous battery materials.

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

Transformer Leakage Inductance Design Methodology

The leakage inductance exhibited by a transformer depends on its winding geometry, which generally involves the selection of several key design parameters in addition to the winding structure and the interleaving configuration. With few resources explaining the effects of these design choices on the observed leakage inductance, numerous trial-and-error iterations become necessary to realize the desired leakage inductance. This paper explores more than a hundred winding geometries feasible in a 2-winding transformer comprising the same magnetic core, number of turns, and wire gauge, and finds the leakage inductance for each unique design using 2-D finite element method (FEM) simulations in association with the semi-analytical double- 2-D model. These leakage inductances are plotted and further analyzed to understand the effects of different design parameters on the effective leakage inductance. The results presented herein and the conclusions drawn from this research can serve as a valuable resource for future design practitioners from both industry and academia.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

Electrochemical Characterization of Bromine Reduction to Tribromide in Individual Nitrobenzene-in-Water Emulsion Droplets

We report an electrochemical study of bromine reduction to tribromide in a single nitrobenzene (NB) droplet during a single collision event. The feasibility of this study is based on the favorable distribution of Br 2 in NB rather than in water, which was demonstrated by voltammetry at the NB/water interface. The NB-in-water emulsions containing the ionic liquids trihexyltetradecylphosphonium bis(trifluoromethylsulfonyl)amide (ILPA) as the supporting electrolyte and sodium dodecyl sulfate (SDS) as the surfactant were prepared by a high-power ultrasonication. Its single droplet as an attoliter reactor, colliding on a 25 μm Pt UME, was monitored by chronoamperometry. The spike-type current transients of Br 2 reduction can elucidate the size and size distribution of NB, and measure the diffusion in the droplet as analyzed by a bulk electrolysis model. The frequency of collisions can provide diffusion coefficient of the droplets to the UME. Moreover, the i-t decay behavior can be simulated, which fits well with the experimental one.

25 ENERGY STORAGE↗
DOE OSTI2025

Connecting Minds: AI Use Cases to Bridge Power Systems and Large Language Models for Practical Applications

Recent advances in artificial intelligence (AI) and development of large language models (LLMs) present the opportunity to develop a new generation of power systems applications. In contrast with early power system AI applications based on structured numerical data, LLMs offer unique capabilities to perform logical reasoning using text documents, unstructured data, and application programming interface (API) calls to computational software. This paper seeks to bridge the knowledge gap between power systems engineers and LLM developers through a crosscutting explanation of use cases, characteristics, requirements, practical considerations from the perspectives of both LLM capabilities and industry needs. Specific focus is given to applications that can be realistically deployed by electric utilities. After introducing the architecture of LLMs and unique challenges of the power systems domain, this paper proposes twenty representative LLM applications grouped into categories of 1) power system operations, 2) asset management, 3) system planning and analytics, and 4) energy management and protection systems. Five use cases are presented within each category with descriptions of the motivation, objectives, approaches, example inputs / outputs, and benefits of each use case.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Distributed water desalination and purification systems: perspective and future directions

Distributed water treatment and desalination (DWTD) systems are critical for the development of a diverse water portfolio of the desired quality and intended use at the target location. Widespread adoption of DWTD has been hampered given the need for round-the-clock monitoring and the lack of local technical expertise for system management. However, self-adaptive operation, real-time remote monitoring, supervisory control, and asset management of DWTD systems are now feasible with the implementation of advanced local system control, cyberinfrastructure that facilitates real-time cloud-based analytics, data management, and artificial intelligence–powered decision support. Such an approach will introduce transformative virtual networks of DWTD systems to provide needed water to locations that are not served by centralized and satellite water treatment and desalination systems.

Cohen, Yoram [University of California, Los Angele↗
DOE OSTI2024

Innovative dead-time correction and background subtraction for neutron multiplicity measurements using neural networks

Abstract The number of neutrons emitted from a nuclear reaction plays a crucial role in various fields, including nuclear theory, nuclear nonproliferation, nuclear energy and nuclear criticality safety. Accurate determination of neutron multiplicities requires the application of several corrections, with dead-time correction and background subtraction being particularly significant. These corrections become more challenging for neutron detectors with time-dependent neutron capture. In this work, we perform a comprehensive study of three existing methods used for dead-time correction and background subtraction in neutron detectors with time-dependent neutron capture. The methods were tested for dead-times in the range from 0 to 1 μs using a Monte Carlo model simulating the dead-time and background effects in the standard neutron multiplicity probability distribution of $$^{252}$$ 252 Cf. The previous methods showed larger than desired uncertainty or systematic trade off. Those uncertainties prompted the development of a novel approach using neural networks trained with data from Monte Carlo simulations. The Neural Network method enabled the correction of neutron multiplicity probabilities more accurately than the other methods with fractional errors smaller than 3% for multiplicities around the peak of $$^{252}$$ 252 Cf. A similar approach using neural networks could be applied to problems where the system being studied can be accurately simulated without having an accurate analytical description available. The neural network method presented in this paper can be easily expanded if multiplicities greater than 10 are expected.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗