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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 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Multiscale Strategy for Predicting Radiation Chemistry in Polymers

A primary mode for radiation damage in polymers arises from ballistic electrons that induce electronic excitations, yet subsequent chemical mechanisms are poorly understood. We develop a multiscale strategy to predict this chemistry starting from subatomic scattering calculations. Nonadiabatic molecular dynamics simulations sample initial bond-breaking events following the most likely excitations, which feed into semiempirical simulations that approach chemical equilibrium. Application to polyethylene reveals a mechanism explaining the low propensity to cross-link in crystalline samples.

36 MATERIALS SCIENCE↗
DOE OSTI2017

Predicting a Drug’s Membrane Permeability: A Computational Model Validated With in Vitro Permeability Assay Data

Membrane permeability is a key property to consider during the drug design process, and particularly vital when dealing with small molecules that have intracellular targets as their efficacy highly depends on their ability to cross the membrane. In this work, we describe the use of umbrella sampling Molecular Dynamics (MD) computational modeling to comprehensively assess the passive permeability profile of a range of compounds through a lipid bilayer. The model was initially calibrated through in vitro validation studies employing a Parallel Artificial Membrane Permeability Assay (PAMPA). The model was subsequently evaluated for its quantitative prediction of permeability profiles for a series of custom synthesized and closely related compounds. The results exhibited substantially improved agreement with the PAMPA data, relative to alternative existing methods. Our work introduces a computational model that underwent progressive molding and fine-tuning as a result of its synergistic collaboration with numerous in vitro PAMPA permeability assays. The presented computational model introduces itself as a useful, predictive tool for permeability prediction.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Solvent-Mediated Control of Nanocellulose Dispersion: An Integrated Computational and Experimental Investigation

Fibrillated cellulose derived from forestry feedstocks represents a renewable and high-strength materials platform for circular bioeconomies. However, its practical implementation is hindered by the irreversible aggregation of nanocellulose architectures, including cellulose nanofibers (CNFs). Solvent-based dispersion offers a simple and practical route to prevent CNF aggregation. Here, in this work, we integrate classical and enhanced sampling molecular dynamics (MD) simulations with experimental suspension rheology and atomic force microscopy (AFM) to elucidate how solvent environments tune CNF–CNF interactions and dispersion stability. CNF–CNF contact free energies computed from MD simulations reveal reduced aggregation in acetone/water, γ-valerolactone (GVL)/water, and tetrahydrofuran (THF)/water and pure acetone compared with pure water, reflecting stronger CNF-solvent relative to inter-CNF interactions. Correspondingly, CNF-solvent suspensions in these solvent systems exhibit stronger inter-fibril network structures and enhanced recovery compared to water, indicating improved CNF-solvent affinity. Liquid cell AFM imaging in acetone–water mixtures and in pure acetone further confirm the presence of well-dispersed CNFs. By combining multiscale computation with targeted experiments, this study establishes a rational framework for solvent design to achieve stable nanocellulose dispersions for high-strength biobased materials and efficient bioenergy conversion.

cellulose↗
DOE OSTI2025

Electric Field’s Dueling Effects through Dehydration and Ion Separation in Driving NaCl Nucleation at Charged Nanoconfined Interfaces

Investigating nucleation in charged nanoconfined environments under electric fields is crucial for many scientific and engineering applications. Here we study the nucleation of NaCl from aqueous solution near charged surfaces using machine-learning-augmented enhanced sampling molecular dynamics simulations. Our simulations successfully drive phase transitions between the liquid and solid phases of NaCl. The solid phase is stabilized under electric fields, particularly at an intermediate surface charge density. We examine which physical characteristics drive the nucleation of NaCl from aqueous solutions and find that the removal of solvent water from Cl– at the solid precursor surface plays a more critical role than the accumulation of ions. Our simulations reveal the competing effects of electric fields on nucleation processes: they facilitate the removal of water, promoting nucleation, but also promote the separation of ion pairs, thereby hindering nucleation. Here, this work provides a framework for studying nucleation processes in nanoconfined environments under electric fields and provides physical insights for the design of electrochemistry materials.

Electric fields↗
DOE OSTI2019

Evidence for Crystalline Structure in Dynamically-Compressed Polyethylene up to 200 GPa

Abstract We investigated the high-pressure behavior of polyethylene (CH 2 ) by probing dynamically-compressed samples with X-ray diffraction. At pressures up to 200 GPa, comparable to those present inside icy giant planets (Uranus, Neptune), shock-compressed polyethylene retains a polymer crystal structure, from which we infer the presence of significant covalent bonding. The A 2 /m structure which we observe has previously been seen at significantly lower pressures, and the equation of state measured agrees with our findings. This result appears to contrast with recent data from shock-compressed polystyrene (CH) at higher temperatures, which demonstrated demixing and recrystallization into a diamond lattice, implying the breaking of the original chemical bonds. As such chemical processes have significant implications for the structure and energy transfer within ice giants, our results highlight the need for a deeper understanding of the chemistry of high pressure hydrocarbons, and the importance of better constraining planetary temperature profiles.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Elemental zoning enhances mass transport in zeolite catalysts for methanol to hydrocarbons

Mass transport limitations in zeolite catalysts pose major hurdles for their optimal performance in diverse chemical reactions. Most approaches to reduce these restrictions focus on the synthesis of either hierarchical or nanosized zeolites. Here we demonstrate that the existence of a siliceous, catalytically inactive exterior rim on ZSM-5 particles dramatically reduces the diffusion limitations, which leads to an enhanced catalyst lifetime for the methanol-to-hydrocarbon reaction. In this study, our findings reveal that binary inorganic and organic structure-directing agents enable a one-pot synthesis of Si-zoned ZSM-5 catalysts with diffusion properties that are characteristic of particles with a much smaller size. Operando ultraviolet–visible light diffuse reflectance spectroscopy reveals a marked reduction in external coking among Si-zoned samples. Molecular dynamics simulations to assess the diffusion of methanol and benzene in siliceous pores and in those with Brønsted acids reveal substantially reduced transport limitations in zoned regions, consistent with the improved catalyst activity of Si-zoned zeolites relative to that of ZSM-5 with a homogeneous acid-site distribution.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2018

Citrate stabilized gold nanoparticles interfere with amyloid fibril formation: D76N and ΔN6 β2-microglobulin variants

Here, protein aggregation including the formation of dimers and multimers in solution, underlies an array of human diseases such as systemic amyloidosis which is a fatal disease caused by misfolding of native globular proteins damaging the structure and function of affected organs. Different kind of interactors can interfere with the formation of protein dimers and multimers in solution. A very special class of interactors are nanoparticles thanks to the extremely efficient extension of their interaction surface. In particular citrate-coated gold nanoparticles (cit-AuNPs) were recently investigated with amyloidogenic protein β 2 -microglobulin (β 2 m). Here we present the computational studies on two challenging models known for their enhanced amyloidogenic propensity, namely ΔN6 and D76N β 2 m naturally occurring variants, and disclose the role of cit-AuNPs on their fibrillogenesis. The proposed interaction mechanism lies in the interference of the cit-AuNPs with the protein dimers at the early stages of aggregation, that induces dimer disassembling. As a consequence, natural fibril formation can be inhibited. Relying on the comparison between atomistic simulations at multiple levels (enhanced sampling molecular dynamics and Brownian dynamics) and protein structural characterisation by NMR, we demonstrate that the cit-AuNPs interactors are able to inhibit protein dimer assembling. As a consequence, the natural fibril formation is also inhibited, as found in experiment.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

Active learning of polarizable nanoparticle phase diagrams for the guided design of triggerable self-assembling superlattices

Polarizable nanoparticles are of interest in materials science because of their rich and complex phase behavior that can be used to engineer nanostructured materials with long-range crystalline order. To understand and rationally navigate the design space of polarizable nanoparticles for self-assembling highly ordered superlattices, we developed a coarse-grained computational model to describe the nanoparticle-nanoparticle interactions in implicit solvent and employ the computationally efficient image method to model many-body polarization interactions. We conducted high-throughput virtual screening over a five-dimensional particle design space spanned by temperature, particle size, particle charge, particle dielectric, and solvent dielectric using enhanced sampling molecular dynamics calculations within an active learning framework to efficiently map out the regions of thermodynamic stability of the self-assembled aggregates. We validate our predictions in comparisons against small angle x-ray scattering measurements of gold nanoparticles surface functionalized with metal chalcogenide ligands. Lastly, we use our validated phase maps to computationally design switchable nanostructured materials capable of triggered assembly and disassembly as a function of temperature and solvent dielectric with potential applications as sensors, smart windows, optoelectronic devices, and in medical diagnostics.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2016

Predicting kinetic nanocrystal shapes through multi-scale theory and simulation: Polyvinylpyrrolidone-mediated growth of Ag nanocrystals

In the shape-controlled synthesis of colloidal Ag nanocrystals, structure-directing agents, particularly polyvinylpyrrolidone (PVP), are known to be a key additive in making nanostructures with well-defined shapes. Although many Ag nanocrystals have been successfully synthesized using PVP, the mechanism by which PVP actuates shape control remains elusive. Here, we present a multi-scale theoretical framework for kinetic Wulff shape predictions that accounts for the chemical environment, which we used to probe the kinetic influence of the adsorbed PVP film. Within this framework, we use umbrella-sampling molecular dynamics simulations to calculate the potential of mean force and diffusion coefficient profiles of Ag atom deposition onto Ag(100) and Ag(111) in ethylene glycol solution with surface-adsorbed PVP. We use these profiles to calculate the mean-first passage times and implement extensive Brownian dynamics simulations, which allows the kinetic effects to be quantitatively evaluated. Our results show that PVP films can regulate the flux of Ag atoms to be greater towards Ag(111) than Ag(100). PVP’s preferential binding towards Ag(100) over Ag(111) gives PVP its flux-regulating capabilities through the lower free-energy barrier of Ag atoms to cross the lower-density PVP film on Ag(111) and enhanced Ag trapping by the extended PVP film on Ag(111). Under kinetic control, {100}-faceted nanocrystals will be formed when the Ag flux is greater towards Ag(111). Furthermore, the predicted kinetic Wulff shapes are in agreement with the analogous experimental system.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2018

Phase separation and large deviations of lattice active matter

Off-lattice active Brownian particles form clusters and undergo phase separation even in the absence of attractions or velocity-alignment mechanisms. Arguments that explain this phenomenon appeal only to the ability of particles to move persistently in a direction that fluctuates, but existing lattice models of hard particles that account for this behavior do not exhibit phase separation. Here we present a lattice model of active matter that exhibits motility-induced phase separation in the absence of velocity alignment. Using direct and rare-event sampling of dynamical trajectories, we show that clustering and phase separation are accompanied by pronounced fluctuations of static and dynamic order parameters. In conclusion, this model provides a complement to off-lattice models for the study of motility-induced phase separation.

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

Material coherence from trajectories via Burau eigenanalysis of braids

In this paper, we provide a numerical tool to study a material’s coherence from a set of 2D Lagrangian trajectories sampling a dynamical system, i.e., from the motion of passive tracers. We show that eigenvectors of the Burau representation of a topological braid derived from the trajectories have levelsets corresponding to components of the Nielsen–Thurston decomposition of the dynamical system. One can thus detect and identify clusters of space–time trajectories corresponding to coherent regions of the dynamical system by solving an eigenvalue problem. Unlike previous methods, the scalable computational complexity of our braid-based approach allows the analysis of large amounts of trajectories.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Deriving effective electrode–ion interactions from free-energy profiles at electrochemical interfaces

Understanding ion adsorption at electrified metal–electrolyte interfaces is essential for accurate modeling of electrochemical systems. Here, in this study, we systematically investigate the free energy profiles of Na + , Cl − , and F − ions at the Au(111)–water interface using enhanced sampling molecular dynamics with both classical force fields and machine-learned interatomic potentials (MLIPs). Our classical metadynamics results reveal a strong dependence of predicted ion adsorption on the Lennard-Jones parameters, highlighting that—without due care—standard mixing rules can lead to qualitatively incorrect descriptions of ion–metal interactions. We present a systematic methodology for tuning the cross term LJ parameters to control adsorption energetics in agreement with more accurate models. As a surrogate for an ab initio model, we employed the recently released Universal Models for Atoms MLIP, which validates classical trends and displays strong specific adsorption for chloride, weak adsorption for fluoride, and no specific adsorption for sodium, in agreement with experimental and theoretical expectations. By integrating molecular-level adsorption free energies into continuum models of the electric double layer, we show that specific ion adsorption substantially alters the interfacial ion population, the potential of zero charge, and the differential capacitance of the system. Our results underscore the critical importance of force field parameterization and advanced interatomic potentials for the predictive modeling of ion-specific effects at electrified interfaces and provide a robust framework for bridging molecular simulations and continuum electrochemical models.

Roncoroni, Fabrice [Lawrence Berkeley National Lab↗
DOE OSTI2022

Velocity dispersions of clusters in the Dark Energy Survey Y3 redMaPPer catalogue

ABSTRACT We measure the velocity dispersions of clusters of galaxies selected by the red-sequence Matched-filter Probabilistic Percolation (redMaPPer) algorithm in the first three years of data from the Dark Energy Survey (DES), allowing us to probe cluster selection and richness estimation, λ, in light of cluster dynamics. Our sample consists of 126 clusters with sufficient spectroscopy for individual velocity dispersion estimates. We examine the correlations between cluster velocity dispersion, richness, X-ray temperature, and luminosity, as well as central galaxy velocity offsets. The velocity dispersion–richness relation exhibits a bimodal distribution. The majority of clusters follow scaling relations between velocity dispersion, richness, and X-ray properties similar to those found for previous samples; however, there is a significant population of clusters with velocity dispersions that are high for their richness. These clusters account for roughly 22 per cent of the λ < 70 systems in our sample, but more than half (55 per cent) of λ < 70 clusters at z > 0.5. A couple of these systems are hot and X-ray bright as expected for massive clusters with richnesses that appear to have been underestimated, but most appear to have high velocity dispersions for their X-ray properties likely due to line-of-sight structure. These results suggest that projection effects contribute significantly to redMaPPer selection, particularly at higher redshifts and lower richnesses. The redMaPPer determined richnesses for the velocity dispersion outliers are consistent with their X-ray properties, but several are X-ray undetected and deeper data are needed to understand their nature.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Detection of the large-scale tidal field with galaxy multiplet alignment in the DESI Y1 spectroscopic survey

We explore correlations between the orientations of small galaxy groups, or ‘multiplets’, and the large-scale gravitational tidal field. Using data from the Dark Energy Spectroscopic Instrument (DESI) Y1 survey, we detect the intrinsic alignment (IA) of multiplets to the galaxy-traced matter field out to separations of $100\,h^{-1}$ Mpc. Unlike traditional IA measurements of individual galaxies, this estimator is not limited by imaging of galaxy shapes and allows for direct IA detection beyond redshift $z=1$. Multiplet alignment is a form of higher order clustering, for which the scale-dependence traces the underlying tidal field and amplitude is a result of small-scale ($\lt 1h^{-1}$ Mpc) dynamics. Within samples of bright galaxies, luminous red galaxies (LRG) and emission-line galaxies, we find similar scale-dependence regardless of intrinsic luminosity or colour. This is promising for measuring tidal alignment in galaxy samples that typically display no IA. DESI’s LRG mock galaxy catalogues created from the A BACUS S UMMIT N -body simulations produce a similar alignment signal, though with a 33 per cent lower amplitude at all scales. An analytic model using a non-linear power spectrum (NLA) only matches the signal down to 20 $h^{-1}$ Mpc. Our detection demonstrates that galaxy clustering in the non-linear regime of structure formation preserves an interpretable memory of the large-scale tidal field. Multiplet alignment complements traditional two-point measurements by retaining directional information imprinted by tidal forces, and contains additional line-of-sight information compared to weak lensing. This is a more effective estimator than the alignment of individual galaxies in dense, blue, or faint galaxy samples.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Measurement of turbulent velocity and bounds for thermal diffusivity in laser shock compressed foams by x-ray photon correlation spectroscopy

Experimental benchmarking of transport coefficients under extreme conditions is required for validation of differing theoretical models. To date, measurement of transport properties of dynamically compressed samples remains a challenge with only a limited number of studies able to quantify transport in high pressure and temperature matter. x-ray photon correlation spectroscopy utilizes coherent x-ray sources to measure time correlations of density fluctuations, thus providing measurements of length and timescale-dependent transport properties. Here, we present a first-of-a-kind experiment to conduct x-ray photon correlation spectroscopy in laser shock compression experiments. We report measurement of the turbulent velocity in the wake of a laser driven supersonic shock and place an upper bound on thermal diffusivity in a solid density plasma on nanosecond timescales.

High-energy-density plasmas↗
DOE OSTI2019

Nonvolatile Ionic Modification of the Dzyaloshinskii-Moriya Interaction

The possibility of tuning the Dzyaloshinskii-Moriya interaction (DMI) by electric (E)-field gating in ultrathin magnetic materials has opened up new perspectives in terms of controlling the stabilization of chiral spin structures. The most recent efforts have used voltage-induced charge redistribution at the interface between a metal and an oxide to modulate the DMI. This approach is attractive for active devices but tends to be volatile, making it energy-demanding, and it is limited by Coulomb screening in the metal. Here we demonstrate nonvolatile E-field manipulation of the DMI by ionic-liquid gating of Pt/Co/Hf ⁢O 2 ultrathin films. The E-field effect on the DMI scales with the E-field exposure time, and we propose that it is linked to the migration of oxygen species from the Hf⁢ O 2 layer into the Co and Pt layers and subsequent anchoring. Here, this effect permanently changes the properties of the material, showing that E fields can be used not only for local gating in devices but also as a highly scalable materials design tool for postgrowth tuning of the DMI.

Domain walls↗
DOE OSTI2021

A Statistical Evaluation of WRF-LES Trace Gas Dispersion Using Project Prairie Grass Measurements

In recent years, new measurement systems have been deployed to monitor and quantify methane emissions from the natural gas sector. Large-eddy simulation (LES) has complemented measurement campaigns by serving as a controlled environment in which to study plume dynamics and sampling strategies. However, with few comparisons with controlled-release experiments, the accuracy of LES for modeling natural gas emissions is poorly characterized. In this paper, we evaluate LES from the Weather Research and Forecasting (WRF) Model against Project Prairie Grass campaign measurements and surface layer similarity theory. Using WRF-LES, we simulate continuous emissions from 30 near-surface trace gas sources in two stability regimes: strong convection and weak convection. We examine the impact of grid resolutions ranging from 6.25 to 52 m in the horizontal dimension on model results. We evaluate performance in a statistical framework, calculating fractional bias and conducting Welch’s t tests. WRF-LES accurately simulates observed surface concentrations at 100 m and beyond under strong convection; simulated concentrations pass t tests in this region irrespective of grid resolution. However, in weakly convective conditions with strong winds, WRF-LES substantially overpredicts concentrations—the magnitude of fractional bias often exceeds 30%, and all but one t test fails. The good performance of WRF-LES under strong convection correlates with agreement with local free convection theory and a minimal amount of parameterized turbulent kinetic energy. The poor performance under weak convection corresponds to misalignment with Monin–Obukhov similarity theory and a significant amount of parameterized turbulent kinetic energy.

17 WIND ENERGY↗
DOE OSTI2025

Homomorphic data compression for real time photon correlation analysis

The construction of highly coherent X-ray sources, combined with next-generation detectors that are larger and faster, has enabled new research opportunities across the scientific landscape. Among the techniques that benefit most from these advancements is X-ray photon correlation spectroscopy (XPCS), where faster acquisition unlocks the ability to study faster dynamics within samples. However, faster acquisition on larger detectors also introduces unprecedented challenges for online data processing and offline data storage. Such challenges are particularly prominent for XPCS, where real time analyses require simultaneous calculation of all the previously acquired data in the time series. We present a homomorphic compression scheme to effectively reduce the computational time and memory space required for XPCS analysis. Leveraging similarities in the mathematical expression between a matrix-based compression algorithm and the correlation calculation, our approach allows direct operation on the compressed data without their decompression. The offline compression scheme extends storage capacity by a factor of 40 while preserving key features in the lossy compressed data. Meanwhile, the online compression scheme reduces the computational time to below 1 ms, enabling real time calculation of the correlation functions at kHz framerate. Our demonstration of a homomorphic compression of scientific data provides an effective solution to the big data challenge at coherent light sources. Beyond the example shown in this work, the framework can be extended to facilitate real-time operations directly on a compressed data stream for other techniques.

36 MATERIALS SCIENCE↗