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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 433 records · Page 24
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Facile hermetic TEM grid preparation for molecular imaging of hydrated biological samples at room temperature

Abstract Although structures of vitrified supramolecular complexes have been determined at near-atomic resolution, elucidating in situ molecular structure in living cells remains a challenge. Here, we report a straightforward liquid cell technique, originally developed for real-time visualization of dynamics at a liquid-gas interface using transmission electron microscopy, to image wet biological samples. Due to the scattering effects from the liquid phase, the micrographs display an amplitude contrast comparable to that observed in negatively stained samples. We succeed in resolving subunits within the protein complex GroEL imaged in a buffer solution at room temperature. Additionally, we capture various stages of virus cell entry, a process for which only sparse structural data exists due to their transient nature. To scrutinize the morphological details further, we used individual particle electron tomography for 3D reconstruction of each virus. These findings showcase this approach potential as an efficient, cost-effective complement to other microscopy technique in addressing biological questions at the molecular level.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

PySAGES: flexible, advanced sampling methods accelerated with GPUs

Abstract Molecular simulations are an important tool for research in physics, chemistry, and biology. The capabilities of simulations can be greatly expanded by providing access to advanced sampling methods and techniques that permit calculation of the relevant underlying free energy landscapes. In this sense, software that can be seamlessly adapted to a broad range of complex systems is essential. Building on past efforts to provide open-source community-supported software for advanced sampling, we introduce PySAGES, a Python implementation of the Software Suite for Advanced General Ensemble Simulations (SSAGES) that provides full GPU support for massively parallel applications of enhanced sampling methods such as adaptive biasing forces, harmonic bias, or forward flux sampling in the context of molecular dynamics simulations. By providing an intuitive interface that facilitates the management of a system’s configuration, the inclusion of new collective variables, and the implementation of sophisticated free energy-based sampling methods, the PySAGES library serves as a general platform for the development and implementation of emerging simulation techniques. The capabilities, core features, and computational performance of this tool are demonstrated with clear and concise examples pertaining to different classes of molecular systems. We anticipate that PySAGES will provide the scientific community with a robust and easily accessible platform to accelerate simulations, improve sampling, and enable facile estimation of free energies for a wide range of materials and processes.

Chemistry↗
DOE OSTI2021

Effects of interlayer confinement and hydration on capacitive charge storage in birnessite

Nanostructured birnessite exhibits high specific capacitance and nearly ideal capacitive behaviour in aqueous electrolytes, rendering it an important electrode material for low-cost, high-power energy storage devices. The mechanism of electrochemical capacitance in birnessite has been described as both Faradaic (involving redox) and non-Faradaic (involving only electrostatic interactions). To clarify the capacitive mechanism, we characterized birnessite’s response to applied potential using ex situ X-ray diffraction, electrochemical quartz crystal microbalance, in situ Raman spectroscopy and operando atomic force microscope dilatometry to provide a holistic understanding of its structural, gravimetric and mechanical responses. These observations are supported by atomic-scale simulations using density functional theory for the cation-intercalated structure of birnessite, ReaxFF reactive force field-based molecular dynamics and ReaxFF-based grand canonical Monte Carlo simulations on the dynamics at the birnessite–water–electrolyte interface. We show that capacitive charge storage in birnessite is governed by interlayer cation intercalation. We conclude that the intercalation appears capacitive due to the presence of nanoconfined interlayer structural water, which mediates the interaction between the intercalated cation and the birnessite host and leads to minimal structural changes.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Hot-Carrier Transfer at Photocatalytic Silicon/Platinum Interfaces

Interfacial charge transfer from silicon to heterogeneous catalysts plays a key role in silicon-based photoelectrochemical systems. In general, prior to interfacial charge transfer, carriers that are generated by photons with energies above the bandgap dissipate the excess kinetic energy via hot-carrier cooling, and such energy loss limits the maximum power conversion efficiency. The excess energy of hot-carriers, however, could be utilized through hot-carrier transfer from silicon to the catalysts, but such hot-carrier extraction has not yet been demonstrated. In this work, we exploit transient reflection spectroscopy to interrogate charge transfer at the interface between silicon and platinum. Additionally, quantitative modeling of the surface carrier kinetics indicates that the velocity of charge transfer from silicon to platinum exceeds 2.6 x 10 7 cm s -1 , corresponding to an average carrier temperature of extracted carriers of ~600 K, two times higher than the lattice temperature. The charge transfer velocity can be controllably reduced by inserting silica spacing layers between silicon and platinum.

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

Prediction of carbon nanostructure mechanical properties and the role of defects using machine learning

Graphene-based nanostructures hold immense potential as strong and lightweight materials, however, their mechanical properties such as modulus and strength are difficult to fully exploit due to challenges in atomic-scale engineering. This study presents a database of over 2,000 pristine and defective nanoscale CNT bundles and other graphitic assemblies, inspired by microscopy, with associated stress–strain curves from reactive molecular dynamics (MD) simulations using the reactive INTERFACE force field (IFF-R). These 3D structures, containing up to 80,000 atoms, enable detailed analyses of structure-stiffness-failure relationships. By leveraging the database and physics- and chemistry-informed machine learning (ML), accurate predictions of elastic moduli and tensile strength are demonstrated at speeds 1,000 to 10,000 times faster than efficient MD simulations. Hierarchical Graph Neural Networks with Spatial Information (HS-GNNs) are introduced, which integrate chemistry knowledge. HS-GNNs as well as extreme gradient boosted trees (XGBoost) achieve forecasts of mechanical properties of arbitrary carbon nanostructures with only 3 to 6% mean relative error. The reliability equals experimental accuracy and is up to 20 times higher than other ML methods. Predictions maintain 8 to 18% accuracy for large CNT bundles, CNT junctions, and carbon fiber cross-sections outside the training distribution. The physics- and chemistry-informed HS-GNN works remarkably well for data outside the training range while XGBoost works well with limited training data inside the training range. The carbon nanostructure database is designed for integration with multimodal experimental and simulation data, scalable beyond 100 nm size, and extendable to chemically similar compounds and broader property ranges. The ML approaches have potential for applications in structural materials, nanoelectronics, and carbon-based catalysts.

Winetrout, Jordan J.↗
DOE OSTI2024

Electronic conductivity in metal-graphene composites: the role of disordered carbon structures, defects, and impurities

Abstract This paper explores the transport properties of aluminum-carbon composite material via ab initio methods. Interfacial and electronic dynamics of the aluminum-graphene interface structure were investigated using models of amorphous graphene added to an aluminum matrix. We examine the impact on electronic conduction caused by the presence of nitrogen impurities within the interfacial amorphous graphene layer. We elucidate the conduction mechanisms by using a projection of the electronic conductivity into space.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Microstructural Pattern Formation during Far-from-Equilibrium Alloy Solidification

We introduce a new phase-field formulation of rapid alloy solidification that quantitatively in- corporates non-equilibrium effects at the solid-liquid interface over a very wide range of interface velocities. Simulations identify a new dynamical instability of dendrite tip growth driven by solute trapping at velocities approaching the absolute stability limit. They also reproduce the formation of the widely observed banded microstructures, revealing how this instability triggers transitions between dendritic and microsegregation-free solidification. Predicted band spacings agree quantita- tively with observations in rapidly solidified Al-Cu thin films.

36 MATERIALS SCIENCE↗
DOE OSTI2020

FICH: Feature instability cavitation history [Slides]

We seek to improve our ability to predict and understand the Richtmyer-Meshkov (RM) unstable dynamics of explosively shocked isolated features interfaced with vacuum or gases, and an HE products cavity. Explosively shocked features form as 2D or 3D RM phenomena. Liquid cavitation cause cavitation bubbles when shockwaves collide, and cavitation waves from wave interactions with isolated features form that source mass into the RM unstable sheets or spikes, and the cavitation waves may slice through bulk material into the HE products cavity.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
DOE OSTI2023

MST-8 Colloquium: The Effect of Microstructure on the Spall Strength and Damage of 1045 Steel [Slides]

Three different microstructures of the same steel alloy were investigated to study the influence of microstructure on spall strength and damage. Microstructure influences spall behavior within the explored dynamic regime. Second phase morphology, interphase interface characteristics, and grain boundary character shown to influence spall behavior. Continued understanding of microstructure’s influence on spall and damage needed for targeted material design.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Keeping LAMMPS cutting edge

Since its inception 30 years ago, LAMMPS has grown to be a world-class molecular dynamics code and a cornerstone of computational materials science research. This project aimed to keep LAMMPS at the forefront of molecular dynamics simulations by adapting LAMMPS to the latest developments in machine learning technology and hardware. Initially, the project set out to provide a unified implementation of active learning for efficient training data generation in LAMMPS, but the research trajectory pivoted to address more immediate and impactful opportunities. On the hardware side, recent record-breaking molecular dynamics simulations were developed on the Cerebras wafer-scale AI chip, and this project has developed an interface between LAMMPS and the hardware-specific molecular dynamics code to accelerate and simplify development and user adoption. On the software side, PyTorch’s Ahead-of-Time (AOT) compilation features promised increased performance for state-of-the-art equivariant neural network potentials, and this project laid the groundwork for their adoption in LAMMPS, resulting in a nearly 20x acceleration in extreme cases. Combined with a comprehensive benchmark study of LAMMPS across all current exascale systems, this project has reinforced LAMMPS’s role as a versatile, high-performance tool for current and future materials science applications.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Machine Learning Enabled Advanced Electron Tomography for Resolving Chemical Inhomogeneity and Materials Dynamics in Lithium-Ion Battery Electrodes

The objective of this project is to develop machine learning-assisted electron microscopy, together with three-dimensional, cryogenic, and in-situ imaging techniques, for resolving chemical inhomogeneity and materials dynamics in lithium battery electrodes and interfaces. The project aims to expand the spatial, temporal, and dimensional resolution of transmission electron microscopy and to enable quantitative analysis of beam-sensitive battery materials.

25 ENERGY STORAGE↗
DOE OSTI2024

Toward understanding the dimensional crossover of canonical spin-glass thin films

Spin-glass thin films exhibit many features different from the bulk. The freezing temperatures of spin-glass films are suppressed for reduced thickness and follow the Kenning relation. The dynamics are altered near the vacuum interface. These phenomena are closely related to the lower critical dimension of spin glasses, the spin-glass correlation length, and the dimensional crossover from d = 3 to d = 2. In this article, we review the experimental facts and theoretical perspectives for spin-glass thin films. We focus on canonical spin-glass systems with the Ruderman–Kittel–Kasuya–Yosida (RKKY) interaction between magnetic impurities in a nonmagnetic host. Open questions to be addressed are emphasized.

interfacial effects↗
DOE OSTI2026

Dynamic Interfacial Design in Adaptive Hybrid Materials Enables Reversible and Tunable Mechano-Optic Smart Responses

Next-generation polymeric materials are shifting toward adaptive and interactive behaviors of living systems; however, designing materials that can reversibly modulate optical properties under mechanical deformation while maintaining mechanical robustness remains a key challenge. Here, we report a mechanically robust vitrimer-based adaptive hybrid material (AHM) that exhibits a stretch-induced reversible transparency-to-opacity transition, enabled by the integration of dynamic interactions at the polymer–silica nanoparticle interface and controlled nanoparticle self-assembly. The AHM combines boronic ester–functionalized polystyrene-b-poly(ethylene-co-butylene)-b-polystyrene (S-Bpin) with diol-functionalized silica nanoparticles (diol-SiNPs) to form a hybrid network hosting both dynamic boronic ester and hydrogen-bonding interactions. These reversible linkages facilitate controlled nanoparticle self-assembly and enable strain-induced nanoparticle alignment/aggregation. Upon stretching, SiNP-rich domains align and aggregate within the polymer matrix, while local modulus mismatch between stiff aggregated SiNP/borylated-styrene-rich regions and the softer elastomeric midblock induces surface microwrinkle formation. These internal aggregates and surface wrinkles cooperatively enhance light scattering, producing the opaque state under strain. Furthermore, the tailored AHM exhibits high toughness, thermomechanical stability, reprocessability, and programmable shape-memory behavior. This work presents a dynamic interfacial design strategy for mechanically robust, optically reconfigurable, and reusable soft materials for adaptive optics, smart windows, sensing, soft robotics, and circular smart-material platforms.

adaptive hybrid materials↗
DOE OSTI2022

On the mechanisms of ion adsorption to aqueous interfaces: air-water vs. oil-water

The adsorption of ions to water-hydrophobe interfaces influences a wide range of phenomena, including chemical reaction rates, ion transport across biological membranes, and electrochemical and many catalytic processes; hence, developing a detailed understanding of the behavior of ions at water-hydrophobe interfaces is of central interest. Here, we characterize the adsorption of the chaotropic thiocyanate anion (SCN − ) to two prototypical liquid hydrophobic surfaces, water-toluene and water-decane, by surface-sensitive nonlinear spectroscopy and compare the results against our previous studies of SCN − adsorption to the air-water interface. For these systems, we observe no spectral shift in the charge transfer to solvent spectrum of SCN − , and the Gibb’s free energies of adsorption for these three different interfaces all agree within error. We employed molecular dynamics simulations to develop a molecular-level understanding of the adsorption mechanism and found that the adsorption for SCN − to both water-toluene and water-decane interfaces is driven by an increase in entropy, with very little enthalpic contribution. This is a qualitatively different mechanism than reported for SCN − adsorption to the air-water and graphene-water interfaces, wherein a favorable enthalpy change was the main driving force, against an unfavorable entropy change.

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

Advanced Power-Hardware-in-the-Loop Evaluation of Inverter-Based Resources (IBRs)

Power-hardware-in-the-loop evaluation of IBRs has become more and more important as it provides reliable testing results to investigate the real responses of inverters with interconnected systems. A successful laboratory PHIL testing gives confidence of the hardware system to be deployed and de-risk technology integration prior to field deployment. So far, there are two important applications for PHIL evaluation: (1) test stability and functionality of large utility inverters into the system when it interconnects to the distribution systems/microgrids; and (2) test the collective grid service that inverters can provide to the grid. For the first application, the PHIL evaluation has high requirements for the stability and accuracy of the PHIL interface as the close-loop in digital real time simulator (DRTS) should replicate the actual current and voltage dynamics in the inverter. This is challenging because of the delays, sensing errors, nonlinearities of inverters, and hardware bandwidth limitations of the elements in the HIL loop. For the second application, multiple inverters will be tested resulting in multiple PCCs, which naturally causes competing dynamics and oscillations among hardware inverters if traditional PHIL interface is used. Therefore, new PHIL interface should be developed to compromise between stability and accuracy and represent the dispatched grid services for the hardware inverters. In this presentation, we will share our latest work in developing PHIL interface in these two applications to address the two key challenges.

DERMS↗
DOE OSTI2023

Operando characterization and regulation of metal dissolution and redeposition dynamics near battery electrode surface

Mn dissolution has been a longstanding, ubiquitous issue that negatively impacts the performance of Mn-based battery materials. Mn dissolution involves complex chemical and structural transformations at the electrode-electrolyte interface. The continuously evolving electrodeelectrolyte interface has posed great challenges for characterizing the dynamic interfacial process and quantitatively establishing the correlation with battery performance. In this study, we visualize and quantify the temporally and spatially resolved Mn dissolution/redeposition (D/R) dynamics of electrochemically operating Mn-containing cathodes. The particle-level and electrode-level analyses reveal that the D/R dynamics is associated with distinct interfacial degradation mechanisms at different states of charge. Our results statistically differentiate the contributions of surface reconstruction and Jahn-Teller distortion to the Mn dissolution at different operating voltages. Introducing sulfonated polymers (Nafion) into composite electrodes can modulate the D/R dynamics through trapping the dissolved Mn species and rapidly establishing the local Mn D/R equilibrium. This work represents an inaugural effort to pinpoint the chemical and structural transformations responsible for Mn dissolution via an operando synchrotron study and develops an effective method to regulate Mn interfacial dynamics for improving battery performance.

25 ENERGY STORAGE↗
DOE OSTI2021

Interfacial Atomistic Mechanisms of Lithium Metal Stripping and Plating in Solid–State Batteries

All-solid-state batteries based on a Li metal anode represent a promising next-generation energy storage system, but are currently limited by low current density and short cycle life. Further research to improve the Li metal anode is impeded by the lack of understanding in its failure mechanisms at lithium–solid interfaces, in particular, the fundamental atomistic processes responsible for interface failure. Here, using large-scale molecular dynamics simulations, the first atomistic modeling study of lithium stripping and plating on a solid electrolyte is performed by explicitly considering key fundamental atomistic processes and interface atomistic structures. In the simulations, the interface failure initiated with the formation of nano-sized pores, and how interface structures, lithium diffusion, adhesion energy, and applied pressure affect interface failure during Li cycling are observed. By systematically varying the parameters of solid-state lithium cells in the simulations, the parameter space of applied pressures and interfacial adhesion energies that inhibit interface failure during cycling are mapped to guide selection of solid-state cells. Furthermore, this study establishes the atomistic modeling for Li stripping and plating, and predicts optimal solid interfaces and new strategies for the future research and development of solid-state Li-metal batteries.

25 ENERGY STORAGE↗