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Results for “all-solid-state”

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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60 records · Page 4
All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

Hierarchical Screening for Li-Based Solid Electrolytes Using Fast, Interpretable Machine-Learned Potentials

Li-based solid-state electrolyte materials enable safer, all-solid-state batteries but the computational search for candidates with favorable stability and Li-ion conductivity is challenging due to the size of the search space and the cost of evaluating transport properties with ab initio methods. The prohibitive cost of high-throughput screening with DFT has lead to the development of surrogate models using geometric analysis, empirical potentials, and descriptors for ionic transport. Here, I will discuss a hierarchical screening approach for identifying promising materials using a combination of density functional theory, bond-valence methods, and machine learning potentials generated with the Ultra-Fast Force Fields (UF3) framework. We show how the inexpensive bond-valence method can be used to guide the generation of training samples for machine learning, in addition to filtering candidates.

Materials discovery↗
NASA NTRSDate not supplied

High-Throughput Screening of Li Solid-State Electrolytes With Bond Valence Methods and Graph Neural Networks

Li-based solid-state electrolyte (Li-SSE) materials enable safer, all-solid-state batteries but the computational search for candidates with favorable stability and Li-ion conductivity is challenging due to the size of the search space and the cost of evaluating transport properties with ab initio methods. We present a high-throughput screening approach for Li-SSE materials using a combination of bond-valence methods and graph neural networks. We demonstrate the screening approach with a dataset containing tens of thousands of Li-containing compounds. Furthermore, we combine the machine-learning screening procedure with an isovalent substitution scheme to generate and screen additional Li SSE candidates beyond existing databases. Finally, we discuss relative importances of geometric and bond-valence quantities in the training of graph neural networks, providing insight for future modeling of ionic conductivity in Li-SSE materials.

Materials discovery↗
NASA NTRSDate not supplied

Space Qualification Studies of AOTF Devices Under the MISSE-11 Mission

The acousto-optic tunable filter (AOTF) devices are used in a variety of applications including hyperspectral and spectrapolarimetric based imaging. The AOTF devices provide several advantages including speed and random access to desired spectral bands and enable all-solid-state operation that are attractive for space applications. In this paper, the performance of Tellurium Dioxide (TeO 2 ) and Mercurous Bromide (Hg 2 Br 2 ) AOTF devices in space environment carried out under the NASA’s Materials International Space Station Experiments 11 (MISSE-11) mission is discussed. The purpose of the MISSE-11 mission was to study materials and devices subjected to space conditions for long term while attached to the International Space station (ISS). The TeO 2 AOTF was developed for short wavelength IR (SWIR) operation while the Hg 2 Br 2 AOTF device was developed for long wavelength IR (LWIR) operation. These devices were attached to the ISS platform for more than a year. Pre-flight and post-flight performance characterization were performed on these devices. In the case of TeO 2 AOTF, the preflight efficiency was around 87% with a center frequency at 68MHz while the post-flight efficiency was around 83%. However, the optical transmission of Hg 2 Br 2 AOTF had deteriorated considerably. The details of performance testing and analysis of both AOTF devices are discussed.

Space Qualification↗
NASA NTRSDate not supplied

Screening of Li-Based Solid Electrolytes Using Bond-Valence Methods and Graph Neural Networks

Li-based solid-state electrolyte (Li-SSE) materials enable safer, all-solid-state batteries but the computational search for candidates with favorable stability and Li-ion conductivity is challenging due to the size of the search space and the cost of evaluating transport properties with ab initio methods. We present a high-throughput screening approach for Li-SSE materials using a combination of bond-valence methods and graph neural networks. We demonstrate the screening approach with a dataset containing tens of thousands of Li-containing compounds. Furthermore, we combine the machine-learning screening procedure with an isovalent substitution scheme to generate and screen additional Li SSE candidates beyond existing databases. Finally, we discuss relative importances of geometric and bond-valence quantities in the training of graph neural networks, providing insight for future modeling of ionic conductivity in Li-SSE materials.

Materials discovery↗
NASA NTRSDate not supplied

Fabricating Solid-State Batteries for Electric Aviation

All-solid-state lithium batteries are widely reputed as the next major step in energy storage technology on account their drastically improved safety over wet cells, as solid electrolytes lack the volatile compounds in wet electrolytes that make them combustible. This enhanced safety enables a far wider range of products and vehicles to go fully electric and aligns with future NASA missions that require high performing, reliable, and safe batteries. However, they currently face issues with implementation in consumer products due to the often-poor mechanical properties of the solid electrolyte and relative difficulty to manufacture. Typical solid-state electrolyte challenges were bypassed by incorporating a range of polymeric binders into a solid electrolyte suspension and cast onto a composite carbon-sulfide cathode to create a single bilayer film. The resulting films display superior flexibility and durability compared to solid-electrolyte films typically discussed in literature without compromising the thinness of the films. The bilayer film was then punched into 12.7 cm diameter discs and 4 cm by 5 cm sheets, pressed, and assembled into coin cells and pouch cells respectively.

Batteries↗
NASA NTRSDate not supplied

Cathode Modeling of Solid-State Batteries

The search for safe, reliable, and compact high-capacity energy storage devices has led to increased interest in all-solid-state battery research. The use of solid electrolytes provides enhanced safety and durability due to their reduced flammability and increased mechanical strength compared to organic liquid electrolytes. Still, the use of solid electrolytes remains challenging. Computational modeling plays a substantial role in addressing these challenges. A particle dynamics electromechanical model for simulating electrochemical processes in a solid-state battery cathode will be presented. The model presents cathode microstructure at the particle level as a mixture of ionically conductive solid electrolyte particles, electrically conductive carbon additives, and cathodic reactant particles. After densification, the particle connectivity is analyzed to reconstruct the complex electric network connecting reactant particles with an anodic and cathodic current collectors through the electrolyte and carbon particles. The Kirchhoff’s matrix equation describing this electric network, is solved to obtain values of various critical parameters, such as the overall conductivity of the cathode for lithium ions and electrons, cathodic reactant material utilization, and the distribution of the electric current and voltages within the cathode. In addition, by representing the reactant particles as electrolyte or galvanic microcells governed by the Butler-Volmer electrochemical equation, the overall performance of battery cells during charge or discharge processes, respectively, can be predicted for a given cathodic powder composition. The presented model, executed on a high-performance computing architecture, essentially provides a valuable guidance in designing and developing future solid-state batteries.

solid-state battery↗