Identification of Lithium Compounds on Surfaces of Lithium Metal Anode with Machine-Learning-Assisted Analysis of ToF-SIMS Spectra.

Zhao, Yinghan; Otto, Svenja-K; Lombardo, Teo; Henss, Anja; Koeppe, Arnd; Selzer, Michael; Janek, Jürgen; Nestler, Britta

Zhao, Yinghan; Otto, Svenja-K; Lombardo, Teo; Henss, Anja; Koeppe, Arnd; Selzer, Michael; Janek, Jürgen; Nestler, Britta.

Afiliación

Zhao Y; Institute for Applied Materials - Microstructure Modelling and Simulation, Karlsruhe Institute of Technology, D-76131 Karlsruhe, Germany.
Otto SK; Institute of Physical Chemistry, Justus-Liebig-Universität Giessen, D-35392 Giessen, Germany.
Lombardo T; Institute of Physical Chemistry, Justus-Liebig-Universität Giessen, D-35392 Giessen, Germany.
Henss A; Institute of Physical Chemistry, Justus-Liebig-Universität Giessen, D-35392 Giessen, Germany.
Koeppe A; Institute for Applied Materials - Microstructure Modelling and Simulation, Karlsruhe Institute of Technology, D-76131 Karlsruhe, Germany.
Selzer M; Institute for Applied Materials - Microstructure Modelling and Simulation, Karlsruhe Institute of Technology, D-76131 Karlsruhe, Germany.
Janek J; Institute for Digital Materials Science, Karlsruhe University of Applied Sciences, D-76133 Karlsruhe, Germany.
Nestler B; Institute of Physical Chemistry, Justus-Liebig-Universität Giessen, D-35392 Giessen, Germany.

ACS Appl Mater Interfaces ; 15(43): 50469-50478, 2023 Nov 01.

Article en En | MEDLINE | ID: mdl-37852613

Palabras clave

ToF-SIMS; all-solid-state battery; data science; lithium metal anode; machine-learning-assisted analysis

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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: ACS Appl Mater Interfaces Asunto de la revista: BIOTECNOLOGIA / ENGENHARIA BIOMEDICA Año: 2023 Tipo del documento: Article País de afiliación: Alemania Pais de publicación: Estados Unidos

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