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

ABSTRACT: Detailed knowledge about contamination and passivation compounds on the surface of lithium metal anodes (LMAs) is essential to enable their use in all-solid-state batteries (ASSBs). Time-of-flight secondary ion mass spectrometry (ToF-SIMS), a highly surface-sensitive technique, can be used to reliably characterize the surface status of LMAs. However, as ToF-SIMS data are usually highly complex, manual data analysis can be difficult and time-consuming. In this study, machine learning techniques, especially logistic regression (LR), are used to identify the characteristic secondary ions of 5 different pure lithium compounds. Furthermore, these models are applied to the mixture and LMA samples to enable identification of their compositions based on the measured ToF-SIMS spectra. This machine-

SUBMITTER: Zhao Y 

PROVIDER: S-EPMC10623505 | biostudies-literature | 2023 Nov

REPOSITORIES: biostudies-literature