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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 1,621 records · Page 90
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Silver Nanoparticles Functionalized Nanosilica Grown over Graphene Oxide for Enhancing Antibacterial Effect

The continuous growth of multidrug-resistant bacteria due to the overuse of antibiotics and antibacterial agents poses a threat to human health. Silver nanoparticles, silica-based materials, and graphene-based materials have become potential antibacterial candidates. In this study, we developed an effective method of enhancing the antibacterial property of graphene oxide (GO) by growing nanosilica (NS) of approximately 50 nm on the graphene oxide (GO) surface. The structures and compositions of the materials were characterized through powdered X-ray diffraction (P-XRD), transmission electron microscopy (TEM), scanning electron microscopy coupled with energy dispersive X-ray spectroscopy (SEM-EDS), ultraviolet–visible spectroscopy (UV–VIS), dynamic light scattering (DLS), Raman spectroscopy (RM), Fourier-transform infrared spectroscopy (FTIR), Brunauer–Emmet–Teller (BET) surface area, and pore size determination. The silver nanoparticles (AgNPs) with an average diameter of 26 nm were functionalized on the nanosilica (NS) surface. The composite contained approximately 3% of silver nanoparticles. The silver nanoparticles on nanosilica supported over graphene oxide (GO/NS/AgNPs) exhibited a 7-log reduction of Escherichia coli and a 5.2-log reduction of Bacillus subtilis within one hour of exposure. Both GO/NS and GO/NS/AgNPs exhibited substantial antimicrobial effects against E. coli and B. subtilis.

36 MATERIALS SCIENCE↗
NASA NTRS1988

Optical fiber methods for the NDE of smart skins and structures

The recent use of optical fiber waveguides as sensors in and on materials and structures is briefly reviewed. Recent progress in the application of fiber optic sensors for composite cure monitoring, in-service static and dynamic materials evaluation, and material degradation determination are outlined. The integration of sensors, actuators, and control system electronics for achieving active material and structural response is described.

Claus, R. O.↗
DOE OSTI2022

Characterizing Dynamic Structure in Battery Electrodes by Time-Resolved Cryo-TEM

In recent years, cryogenic transmission electron microscopy (cryo-TEM) has enabled high-resolution characterization of sensitive battery materials by minimizing electron beam-induced artifacts and damage. Success of this technique relies on the preparation of thin, rapidly frozen samples, generally by disassembling batteries under inert atmosphere, transferring materials of interest to a TEM grid, and finally plunge freezing into a cryogen. In material degradation studies, this extensive time between electrochemical cycling and cryo-TEM characterization leaves room for structural relaxation, diffusion, and other dynamic processes that make it difficult to precisely correlate the imaged structure with the native structure that evolves during battery cycling or aging. Here, we present a method to integrate battery cycling with fast preparation of electrode samples for cryo-TEM. This enables higher fidelity between the structures characterized and the electrochemical state of interest, which we use to study deformation in silicon nanoparticle anodes for lithium-ion batteries.

CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSICS↗
DOE OSTI2020

Benchmarking materials property prediction methods: the Matbench test set and Automatminer reference algorithm

Abstract We present a benchmark test suite and an automated machine learning procedure for evaluating supervised machine learning (ML) models for predicting properties of inorganic bulk materials. The test suite, Matbench, is a set of 13 ML tasks that range in size from 312 to 132k samples and contain data from 10 density functional theory-derived and experimental sources. Tasks include predicting optical, thermal, electronic, thermodynamic, tensile, and elastic properties given a material’s composition and/or crystal structure. The reference algorithm, Automatminer, is a highly-extensible, fully automated ML pipeline for predicting materials properties from materials primitives (such as composition and crystal structure) without user intervention or hyperparameter tuning. We test Automatminer on the Matbench test suite and compare its predictive power with state-of-the-art crystal graph neural networks and a traditional descriptor-based Random Forest model. We find Automatminer achieves the best performance on 8 of 13 tasks in the benchmark. We also show our test suite is capable of exposing predictive advantages of each algorithm—namely, that crystal graph methods appear to outperform traditional machine learning methods given ~10 4 or greater data points. We encourage evaluating materials ML algorithms on the Matbench benchmark and comparing them against the latest version of Automatminer.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Reconfigurable Pinwheel Artificial-Spin-Ice and Superconductor Hybrid Device

The ability to control the potential landscape in a medium of interacting particles could lead to intriguing collective behavior and innovative functionalities. In this work, we utilize spatially reconfigurable magnetic potentials of a pinwheel artificial-spin-ice (ASI) structure to tailor the motion of superconducting vortices. The reconstituted chain structures of the magnetic charges in the pinwheel ASI and the strong interaction between magnetic charges and superconducting vortices allow significant modification of the transport properties of the underlying superconducting thin film, resulting in a reprogrammable resistance state that enables a reversible and switchable vortex Hall effect. Our results highlight an effective and simple method of using ASI as an in situ reconfigurable nanoscale energy landscape to design reprogrammable superconducting electronics, which could also be applied to the in situ control of properties and functionalities in other magnetic particle systems, such as magnetic skyrmions.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2021

Application of electron backscatter diffraction techniques to quantify effects of aging on sub-grain and spatial heterogeneity in NMC cathodes

Identification and evaluation of structural heterogeneity in spent cathode materials is crucial to developing appropriate remediation strategies for novel recycling processes. Native heterogeneities may be exacerbated during the cell's operational lifetime, as sub-particle-scale variations induce anisotropic expansion and contraction upon cycling. Structural transformations resulting from repeated cycling and calendar aging predominantly occur at the secondary particle surface and at the grain boundaries (GBs) between primary particles. However, the diffusion and stress build up around and across GBs are poorly understood. In this study, electron backscatter diffraction (EBSD) is employed to track sub-grain lattice structure across a statistically relevant number of Li(Ni0.33Mn0.33Co0.33)O2 (NMC-111) particles. Specifically, differences in lattice misorientation – measured as the deviation from the grain's average orientation – are tracked as a function of position within the electrode (near current collector, middle, and near separator) and as a function of electrochemical cycling. Further, a novel method of structural analysis is developed, offering insight into sub-grain diffusion behavior by comparing lattice misorientation near the grain boundary versus in the grain bulk. The present results suggest that electrode-scale spatial heterogeneity in lattice structure is induced by initial manufacturing conditions, and that radial gradients in lattice misorientation evolve at the primary particle scale with repeated electrochemical cycling.

25 ENERGY STORAGE↗
DOE OSTI2023

Crystallographic variant mapping using precession electron diffraction data

In this work, we developed three methods to map crystallographic variants of samples at the nanoscale by analyzing precession electron diffraction data using a high-temperature shape memory alloy and a VO2 thin film on sapphire as the model systems. The three methods are (I) a user-selecting-reference pattern approach, (II) an algorithm-selecting-reference-pattern approach, and (III) a k-means approach. In the first two approaches, Euclidean distance, Cosine, and Structural Similarity (SSIM) algorithms were assessed for the diffraction pattern similarity quantification. We demonstrated that the Euclidean distance and SSIM methods outperform the Cosine algorithm. We further revealed that the random noise in the diffraction data can dramatically affect similarity quantification. Denoising processes could improve the crystallographic mapping quality. With the three methods mentioned above, we were able to map the crystallographic variants in different materials systems, thus enabling fast variant number quantification and clear variant distribution visualization. The advantages and disadvantages of each approach are also discussed. We expect these methods to benefit researchers who work on martensitic materials, in which the variant information is critical to understand their properties and functionalities.

Crystallographic variant mapping↗
DOE OSTI2022

Physics Discovery in Nanoplasmonic Systems via Autonomous Experiments in Scanning Transmission Electron Microscopy

Abstract Physics‐driven discovery in an autonomous experiment has emerged as a dream application of machine learning in physical sciences. Here, this work develops and experimentally implements a deep kernel learning (DKL) workflow combining the correlative prediction of the target functional response and its uncertainty from the structure, and physics‐based selection of acquisition function, which autonomously guides the navigation of the image space. Compared to classical Bayesian optimization (BO) methods, this approach allows to capture the complex spatial features present in the images of realistic materials, and dynamically learn structure–property relationships. In combination with the flexible scalarizer function that allows to ascribe the degree of physical interest to predicted spectra, this enables physical discovery in automated experiment. Here, this approach is illustrated for nanoplasmonic studies of nanoparticles and experimentally implemented in a truly autonomous fashion for bulk‐ and edge plasmon discovery in MnPS 3 , a lesser‐known beam‐sensitive layered 2D material. This approach is universal, can be directly used as‐is with any specimen, and is expected to be applicable to any probe‐based microscopic techniques including other STEM modalities, scanning probe microscopies, chemical, and optical imaging.

42 ENGINEERING↗
DOE OSTI2022

Synthesis of the Candidate Topological Compound Ni 3 Pb 2

Spin-orbit coupling enables the realization of topologically nontrivial ground states. As spin-orbit coupling increases with increasing atomic number, compounds featuring heavy elements, such as lead, offer a pathway toward creating new topologically nontrivial materials. Here, by employing a high-pressure flux synthesis method, we synthesized single crystals of Ni 3 Pb 2 , the first structurally characterized bulk binary phase in the Ni-Pb system. Combining experimental and theoretical techniques, we examined structure and bonding in Ni 3 Pb 2 , revealing the impact of chemical substitutions on electronic structure features of importance for controlling topological behavior. From these results, we determined that Ni 3 Pb 2 completes a series of structurally related transition-metal-heavy main group intermetallic materials that exhibit diverse electronic structures, opening a platform for synthetically tunable topologically nontrivial materials.

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

Design of facilitated dissociation enables timing of cytokine signalling

Protein design has focused on the design of ground states, ensuring that they are sufficiently low energy to be highly populated. Designing the kinetics and dynamics of a system requires, in addition, the design of excited states that are traversed in transitions from one low-lying state to another. This is a challenging task because such states must be sufficiently strained to be poorly populated, but not so strained that they are not populated at all, and because protein design methods have focused on generating near-ideal structures. Here we describe a general approach for designing systems that use an induced-fit power stroke to generate a structurally frustrated and strained excited state, allosterically driving protein complex dissociation. X-ray crystallography, double electron–electron resonance spectroscopy and kinetic binding measurements show that incorporating excited states enables the design of effector-induced increases in dissociation rates as high as 5,700-fold. We highlight the power of this approach by designing rapid biosensors, kinetically controlled circuits and cytokine mimics that can be dissociated from their receptors within seconds, enabling dissection of the temporal dynamics of interleukin-2 signalling.

deformation dynamics↗
DOE OSTI2024

Fast-Charging Li 4 Ti 5 O 12 Anode Driven by Light

Lithium-ion batteries with fast charging capabilities are crucial for widespread adoption of electric vehicles, as they will increase acceptance of the technology and reduce range anxiety for drivers. To achieve this, advanced fast charging technologies and alternative methods to enhance the slow electrochemical processes are desperately required. This study applied light-emitting diode (LED) photo-assisted fast charging to Li 4 Ti 5 O 12 (LTO), a large bandgap cubic spinel material, to enhance charging speed through electronic-structure coupling. UV LED-generated light irradiation on to the electrode interface boosted the ion diffusion and lithium cation addition by a factor of 1.3 during electrochemical lithiation, resulting in a 30% decrease in charging time under ambient conditions. Red LED light was not effective in accelerating the reaction as it does not provide sufficient energy. The first-principles calculations support the concept that UV illumination generates charges by overcoming the material’s bandgap and effectively reducing it through the introduction of additional titanium defect states.

25 ENERGY STORAGE↗
DOE OSTI2026

Data Summarization and Inference at Scale

This is the final report for the DOE ASCR grant SC-0022260, Data Summarization and Inference at Scale, PI: Alex Pothen, Purdue University. The goal of the project was to solve data-intensive and compute-intensive problems in the physical sciences, engineering, information science, data science, etc. by designing and implementing new algorithms that could work with a subset of the data. The four subgoals were: (a) The solution of problems where the data is too large to be stored in the memory of a computer. In this streaming model of computation, the data arrives as a stream of elements to the computer, each element is processed as it arrives, and a decision is made to discard the data or to store it; only a small subset of the data proportional to the size of the output solution is stored, and when all the data has been streamed, a solution to the problem is computed from the stored subset. (b) The use of machine learning methods to compute solutions to data-intensive problems. The use of GPUs is critical to obtain high performance on machine learning tasks, but their memory sizes are smaller relative to that of CPUs. For large-scale problems, the data is sampled many times, and small samples are used with repetition, for robustness, to compute solutions to inference tasks. This sampling reduces the memory required to solve the problem, but attention is needed to avoid slow convergence to the solutions, and reduced accuracy of inference. We propose submodular optimization, Large Language Models, and physics-informed neural networks to enable GPU computations here. (c) Modeling and visualization of high-dimensional data using interpretable features. Clinical proteomic data sets from immunology for the detection of cancer and other diseases are temporal and high-dimensional, and algorithms for visualizing these data sets using clinically interpretable features are lacking. We propose methods that compute distances based on the optimal transportation problem and graph edit distances to address this problem. We also propose the use of optimal transport-based distances, spatial statistics, and network structure to classify image data sets, We apply these algorithms to electron micrographs of the peripheral nervous system in the digestive tract. (d) The design of data-intensive algorithms on emerging architectures, specifically, noisy, intermediate-scale quantum (NISQ) devices. Quantum computers offer the possibility of exploring large solution spaces due to the principle of superposition, but current quantum computers are limited by few qubits, short coherence times due to noise, poor interconections among the qubits, etc. We propose the use of the divide and conquer paradigm to solve large-scale problems, wherein collections of small subproblems are solved on the quantum devices, and the solutions to the subproblems are integrated into a solution for the original problem on a classical computer.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Designing carbon wetting layers with inorganic salts as additives for enhanced adhesion and molten sodium wettability in electrochemical application

ß” alumina solid-state electrolyte (BASE) is considered one of the most promising materials for sodium batteries due to its high ionic conductivity and exceptional stability with molten sodium. However, at lower operating temperatures, the poor physical contact at the liquid-solid interface between molten sodium and BASE results in high interfacial resistance. In this study, we explore the optimization of carbon wetting layers with various salt additives to enhance adhesion and improve molten sodium wettability. Using a spray-coating method, carbon layers incorporating sodium hexametaphosphate (Na6(PO3)6, SHMP) as an additive demonstrate superior adhesion, mechanical stability, and enhanced wettability with molten sodium. Scanning Electron Microscopy (SEM) analysis reveals that SHMP preserves the porous carbon network required for efficient sodium transport while minimizing structural defects such as cracks and voids. Electrochemical testing using Na symmetric cells confirms that SHMP-modified layers significantly reduce interfacial resistance and overpotential, outperforming No salts counterparts. These findings highlight that the strategic incorporation of suitable additives, such as SHMP, in the design of microscale carbon layers could significantly enhance the performance of molten sodium batteries at lower operating temperatures.

Han, Henry H.↗
NASA NTRS1994

Venus cloud bobber mission: A long term survey of the Venusian surface

We have examined the Venus Balloon concept in order to further develop the ideas and concepts behind it, and to creatively apply them to the design of the major Venus Balloon components. This report presents our models of the vertical path taken by the Venus Balloon and the entry into Venusian atmosphere. It also details our designs of the balloon, gondola, heat exchanger, power generator, and entry module. A vehicle is designed for a ballistic entry into the Venusian atmosphere, and an atmospheric model is created. The model is then used to set conditions. The shape and material of the vehicle are optimized, and the dimensions of the vehicle are then determined. Equipment is chosen and detailed that will be needed to collect and transmit information and control the mission. A gondola is designed that will enable this sensitive electronic equipment to survive in an atmosphere of very high temperature and pressure. This shape and the material of the shell are optimized, and the size is minimized. Insulation and supporting structures are designed to protect the payload equipment and to minimize mass. A method of cooling the gondola at upper altitudes was established. Power needs of the gondola equipment are determined. Power generation options are discussed and two separate thermoelectric generation models are outlined.

Wai, James↗
NASA NTRS1997

Computational Nanotechnology Program

The objectives are: (1) development of methodological and computational tool for the quantum chemistry study of carbon nanostructures and (2) development of the fundamental understanding of the bonding, reactivity, and electronic structure of carbon nanostructures. Our calculations have continued to play a central role in understanding the outcome of the carbon nanotube macroscopic production experiment. The calculations on buckyonions offer the resolution of a long controversy between experiment and theory. Our new tight binding method offers increased speed for realistic simulations of large carbon nanostructures.

Scuseria, Gustavo E.↗
NASA NTRS2012

Multiscale Modeling of Grain Boundaries in ZrB2: Structure, Energetics, and Thermal Resistance

A combination of ab initio, atomistic and finite element methods (FEM) were used to investigate the structures, energetics and lattice thermal conductance of grain boundaries for the ultra high temperature ceramic ZrB2. Atomic models of idealized boundaries were relaxed using density functional theory. Information about bonding across the interfaces was determined from the electron localization function. The Kapitza conductance of larger scale versions of the boundary models were computed using non-equilibrium molecular dynamics. The interfacial thermal parameters together with single crystal thermal conductivities were used as parameters in microstructural computations. FEM meshes were constructed on top of microstructural images. From these computations, the effective thermal conductivity of the polycrystalline structure was determined.

Lawson, John W.↗
NASA NTRS2011

Digital Calibration of TR Modules for Real-time Digital Beamforming SweepSAR Architectures

Real-time digital beamforming, combined with lightweight, large aperture reflectors, enable SweepSAR architectures such as that of the proposed DESDynI [Deformation, Ecosystem Structure, and Dynamics of Ice] SAR [Synthetic Aperture Radar] Instrument (or DSI). These new instrument concepts require new methods for calibrating the multiple channels, which must be combined on-board, in real-time. The calibration of current state-of-the-art Electronically Steered Arrays typically involves pre-flight TR (Transmit/Receive) module characterization over temperature, and in-flight correction based on temperature, which ignores the effects of element aging and drifts unrelated to temperature. We are developing new methods for digitally calibrating digital beamforming arrays to reduce development time, risk and cost of precision calibrated TR modules for array architectures by accurately tracking modules' characteristics through closed-loop Digital Calibration, thus tracking systematic changes regardless of temperature. The benefit of this effort is that it would enable a new class of lightweight radar architecture, Digital Beamforming with SweepSAR, providing significantly larger swath coverage than conventional SAR architectures for solid earth and biomass remote sensing, while reducing mission mass and cost. This new instrument concept requires new methods for calibrating the multiple channels, which must be combined on-board, in real-time.

Real-time digital beamforming↗
NASA NTRS2026

The Trajectory of Recent Solid State Fusion Results

Both NASA and Google have explored and funded Low Energy Nuclear Reaction (LENR) aka Solid-State Fusion or Lattice Confinement Fusion (LCF) research. NASA has funded efforts since 1989, and Google Research began in 2014. Google, and researchers initially-funded by Google, published significant scientific papers in Nature, Nature Communications and the Journal of Applied Physics. NASA began a significant set of LENR-triggering programs in 2012 resulting in papers in Physical Review C, the Journal of Electroanalytical Chemistry and the Journal of Condensed Matter Nuclear Science. Both NASA and Google engaged researchers across fields of nuclear physics, chemistry, electrochemistry, material science and more. NASA built upon early novel gas pumping experiments then followed the patented work of the US Navy SPAWAR (US8,419,919, “System and Method to Generate Particles”) and experiments with the Naval Surface Warfare Centers. Google supported researchers at Lawrence Berkeley National Laboratory (LBNL), the University of British Columbia (UBC), MIT and others. This resulted in patent applications and two granted patents (US10264661B2, “Target structure for enhanced electron screening” and US10566094B2 “Enhanced electron screening through plasmon oscillations”). These separate efforts, unknown to the researchers at the time, provided the impetus for the DoE ARPA-E LENR program followed by the DARPA DSO “Mechanisms for Amplification of Fusion Reaction Rates in Solids” (MARRS) program. This document briefly describes the overlapping NASA and Google Research efforts in plasma loading and electron screening emphasizing the results of the latest paper in Nature Communications. The papers and patents cited are listed.

electron screening↗