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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 1,135 records · Page 63
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Ultrahigh coulombic efficiency electrolyte enables Li||SPAN batteries with superior cycling performance

Raising the coulombic efficiency of lithium metal anode cycling is the deciding step in realizing long-life rechargeable lithium batteries. Here, we designed a highly concentrated salt/ether electrolyte diluted in a fluorinated ether: 1.8 M LiFSI in DEE/BTFE (diethyl ether/bis(2,2,2-trifluoroethyl)ether), which realized an average coulombic efficiency of 99.37% at 0.5 mA cm -2 and 1 mAh cm -2 for more than 900 cycles. This electrolyte also maintained a record coulombic efficiency of 98.7% at 10 mA cm -2 , indicative of its ability to provide fast-charging with high cathode loadings. Morphological studies reveal dense, dendrite free Li depositions after prolonged cycling, while surface analyses confirmed the formation of a robust LiF-rich SEI layer on the cycled Li surface. Moreover, we discovered that this ether-based electrolyte is highly compatible with the low-cost, high-capacity SPAN (Sulfurized polyacrylonitrile) cathode, where the constructed Li||SPAN cell exhibited reversible cathode capacity of 579 mAh g -1 and no capacity decay after 1200 cycles. A cell where a high areal loading SPAN electrode (>3.5 mAh cm -2 ) is paired with only onefold excess Li was constructed and cycled at 1.75 mA cm -2 , maintaining a coulombic efficiency of 99.30% for the lithium metal. Computational simulations revealed that at saturation, the Li-FSI complex forms contact ion pairs, with a first solvation shell comprising DEE molecules, and a second solvation shell with a mix of DEE/BTFE. This study provides a path to enable high energy density Li||SPAN batteries with stable cycling.

36 MATERIALS SCIENCE↗
DOE OSTI2023

A reduced-order model of concentration polarization in reverse osmosis systems with feed spacers

Feed spacers in reverse osmosis systems generate complex fluid flows that limit computational fluid dynamics (CFD) simulations to small length and time scales. That limits our ability to simulate mineral scaling and other membrane fouling phenomena, which occur over longer length and time scales. Thus motivated, we develop a reduced model that replaces the CFD simulation of the velocity field with an analytical model that mimics spacers. This focuses the remaining numerical effort on simulating the advection–diffusion equation governing solute transport. We motivate and validate the model with CFD simulations and bench-scale experiments of spacer filaments in three different arrangements, including cases of unsteady vortex shedding. We show that the model produces a roughly 10,000-fold speedup compared to CFD, and accurately reproduces CFD predictions of not only the average and maximum concentrations, but also the local concentration distribution along the membrane. Here, we also demonstrate the model for simulating a feed channel with a length-to-height ratio of 200. The model provides a simple testbed for exploratory studies of multispecies transport, precipitation, and membrane fouling phenomena for which simulating spacers is often prohibitive.

Computational fluid dynamics↗
DOE OSTI2022

Plasma-assisted chemical-looping combustion: Mechanistic insights into low temperature methane oxidation with CuO

The low-temperature oxidation of CH 4 by CuO in a coaxial, fixed bed, double dielectric barrier discharge (DBD) reactor was investigated with time-dependent species measurements by an electron-ionization molecular beam mass spectrometer (EI-MBMS). In the experiment, 10% methane carried by noble gasses was flown at 50 sccm through 1 g CuO dispersed in quartz wool both under plasma and non-plasma conditions, while time-dependent gas-phase species profiles were collected. Plasma conditions were explored from 300 to 600 °C while the non-plasma conditions were set from 300 to 900 °C. Mechanistic insights into the oxidation of CH 4 by CuO with plasma discharge at lower temperatures (≤ 600 °C) were obtained by quantifying the fuel oxidation, intermediate species, and CO 2 production in comparison to the non-plasma conditions. Here, we observed significant enhancement of fuel oxidation from the plasma discharge between 400 and 500 °C. The CO 2 production at 500 °C with plasma was greater than that at 700 °C without plasma, reducing fuel oxidation temperature by 200+ °C. During tests, three distinct reaction stages were observed: a gas-phase transport limited stage, a surface reaction limited kinetic stage, and an oxygen ion diffusion limited stage. It was observed that plasma greatly improved the reactivity of the second stage at low temperature. In addition, no carbon deposits were observed on the resultant particles, even under the presence of plasma. M. species such as C 4 H 2 and C 6 H 6 not previously observed or predicted in CuO/CH 4 chemical looping were observed, with some species such as CH 3 OH only becoming detectable as total flowrate was increased from 50 to 1500sccm. A non-plasma reaction pathway for CH 4 based the observed species from the MBMS spectrum and previous predictions from reactive molecular dynamics simulations was created, providing a framework from which more complex plasma CuO mechanisms can be crafted from.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2023

How Does Bending the Uranyl Unit Influence Its Spectroscopy and Luminescence?

We report bent uranyl complexes can be formed with chloride ligands and 1,10-phenanthroline (phen) ligands bound to the equatorial and axial planes of the uranyl(VI) moiety, as revealed by the crystal structures, IR and Raman spectroscopy, and quantum-chemical calculations. With the goal of probing the influence of chloride and phenanthroline coordination enforcing the bending on the absorption and emission spectra of this complex, spin–orbit time-dependent density functional theory calculations for the bare uranyl complexes as well as for the free UO 2 Cl 2 subunit and the UO 2 Cl 2 (phen) 2 complex were performed. The emission spectra have been fully simulated by ab initio methods and compared to experimental photoluminescence spectra, recorded for the first time for UO 2 Cl 2 (phen) 2 . Notably, the bending of uranyl in UO2Cl2 and UO 2 Cl 2 (phen) 2 triggers excitations of the uranyl bending mode, yielding a denser luminescence spectrum.

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

Polyelectrolyte Complex Coacervation across a Broad Range of Charge Densities

Polyelectrolyte complex coacervates of homologous (co)polyelectrolytes with a near-ideally random distribution of a charged and neutral ethylene oxide comonomer were synthesized. The unique platform provided by these building blocks enabled an investigation of the phase behavior across charge fractions 0.10 ≤ f ≤ 1.0. Experimental phase diagrams for f = 0.30–1.0 were obtained from thermogravimetric analysis of complex and supernatant phases and contrasted with molecular dynamics simulations and theoretical scaling laws. At intermediate to high f, a dependence of polymer weight fraction in the salt-free coacervate phase (w P,c ) of w P,c ~ f 0.37±0.01 was extracted; this trend was in good agreement with accompanying simulation predictions. Below f = 0.50, w P,c was found to decrease more dramatically, qualitatively in line with theory and simulations predicting an exponent of 2/3 at f ≤ 0.25. Preferential salt partitioning to either coacervate or supernatant was found to be dictated by the chemistry of the constituent (co)polyelectrolytes.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Topological Data Analysis for Particulate Gels

Soft gels, formed via the self-assembly of particulate materials, exhibit intricate multiscale structures that provide them with flexibility and resilience when subjected to external stresses. Here, this work combines particle simulations and topological data analysis (TDA) to characterize the complex multiscale structure of soft gels. Our TDA analysis focuses on the use of the Euler characteristic, which is an interpretable and computationally scalable topological descriptor that is combined with filtration operations to obtain information on the geometric (local) and topological (global) structure of soft gels. We reduce the topological information obtained with TDA using principal component analysis (PCA) and show that this provides an informative low-dimensional representation of the gel structure. We use the proposed computational framework to investigate the influence of gel preparation (e.g., quench rate, volume fraction) on soft gel structure and to explore dynamic deformations that emerge under oscillatory shear in various response regimes (linear, nonlinear, and flow). Our analysis provides evidence of the existence of hierarchical structures in soft gels, which are not easily identifiable otherwise. Moreover, our analysis reveals direct correlations between topological changes of the gel structure under deformation and mechanical phenomena distinctive of gel materials, such as stiffening and yielding. In summary, we show that TDA facilitates the mathematical representation, quantification, and analysis of soft gel structures, extending traditional network analysis methods to capture both local and global organization.

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

Unlocking the potential: machine learning applications in electrocatalyst design for electrochemical hydrogen energy transformation

Machine learning (ML) is rapidly emerging as a pivotal tool in the hydrogen energy industry for the creation and optimization of electrocatalysts, which enhance key electrochemical reactions like the hydrogen evolution reaction (HER), the oxygen evolution reaction (OER), the hydrogen oxidation reaction (HOR), and the oxygen reduction reaction (ORR). This comprehensive review demonstrates how cutting-edge ML techniques are being leveraged in electrocatalyst design to overcome the time-consuming limitations of traditional approaches. ML methods, using experimental data from high-throughput experiments and computational data from simulations such as density functional theory (DFT), readily identify complex correlations between electrocatalyst performance and key material descriptors. Leveraging its unparalleled speed and accuracy, ML has facilitated the discovery of novel candidates and the improvement of known products through its pattern recognition capabilities. This review aims to provide a tailored breakdown of ML applications in a format that is readily accessible to materials scientists. Hence, we comprehensively organize ML-driven research by commonly studied material types for different electrochemical reactions to illustrate how ML adeptly navigates the complex landscape of descriptors for these scenarios. We further highlight ML's critical role in the future discovery and development of electrocatalysts for hydrogen energy transformation. Potential challenges and gaps to fill within this focused domain are also discussed. As a practical guide, we hope this work will bridge the gap between communities and encourage novel paradigms in electrocatalysis research, aiming for more effective and sustainable energy solutions.

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

Experimental and theoretical study of energy transfer in a chromophore triad: What makes modeling dynamics successful?

Simulation of electronic dynamics in realistically large molecular systems is a demanding task that has not yet achieved the same level of quantitative prediction already realized for its static counterpart. This is particularly true for processes occurring beyond the Born–Oppenheimer regime. Non-adiabatic molecular dynamics (NAMD) simulations suffer from two convoluted sources of error: numerical algorithms for dynamics and electronic structure calculations. While the former has gained increasing attention, particularly addressing the validity of ad hoc methodologies, the effect of the latter remains relatively unexplored. Indeed, the required accuracy for electronic structure calculations to reach quantitative agreement with experiment in dynamics may be even more strict than that required for static simulations. In this study, we address this issue by modeling the electronic energy transfer in a donor–acceptor–donor (D–A–D) molecular light harvesting system using fewest switches surface hopping NAMD simulations. In the studied system, time-resolved experimental measurements deliver complete information on spectra and energy transfer rates. Subsequent modeling shows that the calculated electronic transition energies are “sufficiently good” to reproduce experimental spectra but produce over an order of magnitude error in simulated dynamical rates. We further perform simulations using artificially shifted energy gaps to investigate the complex relationship between transition energies and modeled dynamics to understand factors affecting non-radiative relaxation and energy transfer rates.

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

Spallation Neutron Source Hydrogen Relief Analysis for the Cryogenic Moderator System

The Spallation Neutron Source at Oak Ridge National Laboratory operates the Cryogenic Moderator System (CMS) which provides hydrogen cooling at 20K to three neutron moderators. Each hydrogen loop is protected by multiple burst discs and reclosing relief valves. As a result of the Proton Power Upgrade project, the CMS moderator loops will hold more hydrogen and thus require piping modifications. Operational experience has demonstrated that transient pressure increases in these hydrogen loops occasionally result in the rupture of the burst discs. The causes for pressure transients in the system that might lead to hydrogen venting are varied and complex, for example loss of vacuum or cooling. Dynamic simulation and analysis of the parameters considered as the worst-case relief venting scenario were performed and will be presented.

47 OTHER INSTRUMENTATION↗
DOE OSTI2026

Fast and accurate calculation of EXAFS Debye-Waller factors in U⁢O2 using the dynamical matrix method

Theoretical modeling of bonding dynamics in metal oxides is required for predicting their thermal conductivity, catalytic activity, and mechanical properties. A primary challenge is the scarcity of experimental methods for validating theoretical predictions of these atomic-scale dynamics. This work presents a workflow that uses experimental extended x-ray absorption fine structure (EXAFS) data collected at high temperatures to validate an interatomic force field for uranium dioxide (UO2), an important model material. The validated force field is then used to drive computationally intensive molecular dynamics (MD) simulations and as input for the much faster dynamical matrix Debye-Waller (DMDW) method. The predicted values of the Debye-Waller factors from the DMDW calculations are in good agreement with those obtained from the MD simulations, with residual pair-specific differences attributable to quantum zero-point motion at low temperatures and lattice anharmonicity at high temperatures. We further show that theoretical EXAFS spectra constructed directly from DMDW-derived Debye-Waller factors reproduce the experimental data (at relatively low temperatures) with accuracy comparable to full MD-EXAFS, providing an additional validation of the choice of the potential. This study establishes a validated, rapid computational pathway for modeling bond dynamics, naturally incorporating quantum nuclear\\\\r\\\\nstatistics absent in classical simulations, which are essential for the mechanistic understanding of complex oxide materials.

58 GEOSCIENCES↗
DOE OSTI2024

Computing with a Chemical Reservoir

Contemporary computation is expensive, with large language models and artificial intelligence becoming more common in daily life. However, high-performance computing is reaching the limits in speed and energy expenditure, and domain science requires ever-increasing computational capacity, with simulations and data analysis pipelines ever-growing in complexity. As we progress towards post-exascale computation, with the associated high energy costs, new methods of energy-conscious computation are required. Novel analog and hybrid digital-analog systems can overcome these challenges, and chemical reactions offer a promising avenue. Computers based on chemistry can provide compact desktop devices with immense computational power. These devices are readily scalable by considering greater reaction systems or vessels, meeting the high-performance requirements for scientific workflows. In this article, we present ChemComp, a compilation pipeline for the conversion of ordinary differential equations into implementable chemical reactions. We then demonstrate the solving capabilities of ChemComp by emulating a potential chemical reservoir device. We leverage the multi-layer intermediate representation (MLIR) compiler framework to implement an expressive chemical reaction abstraction and propose a path for chemical reaction networks (CRNs) to represent mathematical problems effectively. Combined, we demonstrate a potential workflow that can harness chemistry’s computing power to create energy-efficient, high-performance computation systems for contemporary computing needs.

artificial intelligence↗
DOE OSTI2025

A molecular conveyor belt-associated protein controls the rotational direction of the bacterial type 9 secretion system

ABSTRACT Many bacteria utilize the type 9 secretion system (T9SS) for gliding motility, surface colonization, and pathogenesis. This dual-function motor supports both gliding motility and protein secretion, where rotation of the T9SS plays a central role. Fueled by the energy of the stored proton motive force and transmitted through the torque of membrane-anchored stator units, the rotary T9SS propels an adhesin-coated conveyor belt along the bacterial outer membrane like a molecular snowmobile, thereby enabling gliding motion. However, the mechanisms controlling the rotational direction and gliding motility of T9SS remain elusive. Shedding light on this mechanism, we find that in the gliding bacteriumFlavobacterium johnsoniae, deletion of the C-terminus of the conveyor belt-associated protein GldJ controls and, in fact, reverses the rotational direction of T9SS from counterclockwise (CCW) to clockwise (CW). This suggests that the interface between the conveyor belt-associated protein GldJ and the T9SS ring protein GldK plays an important role in controlling the directionality of T9SS, potentially by modulating its interaction with the stator complex GldLM, which drives motor rotation. Combined with MD simulation of the T9SS stator units GldLM, we suggest a “tri-component gearset” model where GldJ controls the rotational direction of its driver, the T9SS, thus providing adaptive sensory feedback to influence the motility of the gliding bacterium. IMPORTANCE The type 9 secretion system (T9SS) is fundamental to bacterial gliding motility, pathogenesis, and surface colonization. Our findings reveal that the C-terminal region of the conveyor belt-associated protein GldJ functions as a molecular switch which is capable of reversing the rotational direction of T9SS. Through the coordinated actions of the T9SS stator units (akin to a driving motor), the GldK ring (the gear that converts rotational energy into linear movement), and GldJ, this machinery forms a smart conveyor belt system reminiscent of flexible or cognitive mechanical conveyors. Such advanced conveyors can alter their direction to adapt to shifting demands. Here, we show that the bacterial T9SS similarly adjusts its rotational bias based on feedback from the conveyor belt-associated protein GldJ. This dual-role feedback mechanism underscores an evolved, controllable biological snowmobile, offering new avenues for studying how bacteria fine-tune motility in dynamic environments.

Microbiology↗
DOE OSTI2021

ACAT

A Physics-Informed Machine Learning (PIML) framework for better system vulnerability assessment and faster corrective action recommendation. This framework consists of deriving physics-informed priors and smart sampling algorithms to help reduce the data samples of grid models and the dimension of simulation outputs, yielding small yet representative subset of the complex system, and both supervised and unsupervised machine learning (ML) algorithms for designing corrective actions

Chen, Yousu↗
DOE OSTI2026

Roadrunner

SAND2026-17073O Roadrunner software provides a comprehensive platform for simulating the mechanical behavior of crystalline materials under various loading conditions, allowing users to investigate the effects of dislocation slip hardening and damage evolution. Developed as a fork of the Multiphysics Object Oriented Simulation Environment (MOOSE) software from Idaho National Laboratory, Roadrunner is optimized for high-performance computing and can simulate large-scale problems, enabling researchers to explore complex scenarios. Its applications include material design and optimization in aerospace and automotive industries, investigation of failure mechanisms in structural materials, and development of predictive models for crystalline materials under various loading conditions. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

Lim, Hojun [Sandia National Lab. (SNL-CA), Livermo↗
DOE OSTI2022

Simulation of ENSO Teleconnections to Precipitation Extremes over the United States in the High-Resolution Version of E3SM

Abstract We evaluate the simulated teleconnection of El Niño–Southern Oscillation (ENSO) to winter season precipitation extremes over the United States in a long (98 years) 1950 control high-resolution version (HR; 25-km nominal atmosphere model horizontal resolution) of the U.S. Department of Energy’s (DOE) Energy Exascale Earth System Model version 1 (E3SMv1). The model bias and spatial pattern of ENSO teleconnections to mean and extreme precipitation in HR overall are similar to the low-resolution model’s (LR; 110 km) historical simulation (four-member ensemble, 1925–59). However, over the southeastern United States (SE-U.S.), HR produces stronger El Niño–associated extremes, reducing LR’s model bias. Both LR and HR produce weaker than observed increase in storm track activity during El Niño events there, but HR improves the ENSO-associated variability of moisture transport over SE-U.S. During El Niño, stronger vertical velocities in HR produce stronger large-scale precipitation, causing larger latent heating of the troposphere that pulls in more moisture from the Gulf of Mexico into the SE-U.S. This positive feedback also contributes to the stronger mean and extreme precipitation response in HR. Over the Pacific Northwest, LR’s bias of stronger than observed La Niña associated extremes is amplified in HR. Both models simulate stronger than observed moisture transport from the Pacific Ocean into the region during La Niña years. The amplified HR bias there is due to stronger orographically driven vertical updrafts that create stronger large-scale precipitation, despite weaker La Niña–induced storm track activity. Significance Statement New high-resolution Earth system models (ESMs) solve mathematical equations of fluid flow at much smaller spatial scales than prevalent ESMs, and thus are prohibitively expensive to compute. However, they can be useful for simulating accurate details of regional climate extremes that are driven by naturally occurring climate oscillations like El Niño–Southern Oscillation (ENSO). Here, we evaluate the simulation of ENSO-driven precipitation extremes over the United States in the high-resolution version of the U.S. Department of Energy’s new Energy Exascale Earth System Model version 1. We find that the high-resolution model improves upon its low-resolution counterpart over the southeastern United States by producing a better transport of moisture into the region from the Gulf of Mexico during El Niño. Over the U.S. Pacific Northwest, the high-resolution model simulates the atmospheric flow in more detail over the complex mountainous terrain. However, it also brings in more moisture from the Pacific Ocean just like the low-resolution model. This causes it to produce precipitation extremes during La Niña years there that are stronger than that observed in the real world.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Infrared Optical Constants from Pressed Pellets of Powders: I. Improved n and k Values of (NH4)(2)SO(4)from Single-Angle Reflectance

In combination with other parameters, the real, n(?), and imaginary, k(?), components of the complex refractive index, = n + ik, can be used to simulate the optical properties of a material in different forms, e.g. its infrared spectra. But obtaining reliable values of the optical constants n/k for solid materials is challenging due to the lack of optical quality specimens, usually crystals, large enough to measure. An alternative to crystals is to press the powder into a uniform disk. We have produced pellets from ammonium sulfate, (NH4)2SO4, powder and derived the pellets’ n and k values via single-angle reflectance using a specular reflectance device in combination with a Fourier transform infrared spectrometer. The single-angle technique measures amplitude of light reflected from the material as a function of wavelength over a wide spectral domain; the optical constants are determined from the reflectance data using the Kramers-Kronig relationship. We investigate several parameters associated with the pellets and pellet formation and their effects upon delivering the most reliable n/k values. Parameters studied include pellet diameter, mass and density (void space), drying, grinding, sieving and particle size in the pellet formation, as well as pressing pressure and duration. Of these parameters, using size-selected mixtures of dried, small (< 100 µm) particles and pressing at = 10 tons for at least 30 minutes were found key to forming highly reflective samples. Comparison to two sets of previous literature n(?) and k(?) values obtained from crystalline (NH4)2SO4 both as crystal reflectance as well as extinction spectra of aerosols measured in a flow tube show reasonable agreement, but suggests the present values, as confirmed from two independent techniques, represent a substantial improvement for n/k values for (NH4)2SO4, also demonstrating promise to measure the optical constants of other materials.

Optical constants, index of refraction, single-ang↗
DOE OSTI2021

DNS/LES Study of Representative Wall-Bounded Turbulent Flows using SIERRA/Fuego

This report summarizes a series of SIERRA/Fuego validation efforts of turbulent flow models on canonical wall-bounded configurations. In particular, direct numerical simulations (DNS) and large eddy simulations (LES) turbulence models are tested on a periodic channel, a periodic pipe, and an open jet for which results are compared to the velocity profiles obtained theoretically or experimentally. Velocity inlet conditions for channel and pipe flows are developed for application to practical simulations. To show this capability, LES is performed over complex terrain in the form of two natural hills and the results are compared with other flow solvers. The practical purpose of the report is to document the creation of inflow boundary conditions of fully developed turbulent flows for other LES calculations where the role of inflow turbulence is critical.

42 ENGINEERING↗