PMID- 37852613
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231105
IS  - 1944-8252 (Electronic)
IS  - 1944-8244 (Print)
IS  - 1944-8244 (Linking)
VI  - 15
IP  - 43
DP  - 2023 Nov 1
TI  - Identification of Lithium Compounds on Surfaces of Lithium Metal Anode with 
      Machine-Learning-Assisted Analysis of ToF-SIMS Spectra.
PG  - 50469-50478
LID - 10.1021/acsami.3c09643 [doi]
AB  - 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-learning-based analysis approach shows good performance in 
      identifying characteristic ions of the analyzed compounds that fit well with 
      their chemical nature. Moreover, satisfying accuracy in identifying the 
      compositions of unseen new samples is achieved. In addition, the scope and 
      limitations of such a strategy in practical applications are discussed. This work 
      presents a robust analytical method that can assist researchers in simplifying 
      the analysis of the studied lithium compound samples, offering the potential for 
      broader applications in other material systems.
FAU - Zhao, Yinghan
AU  - Zhao Y
AUID- ORCID: 0000-0001-9440-138X
AD  - Institute for Applied Materials - Microstructure Modelling and Simulation, 
      Karlsruhe Institute of Technology, D-76131 Karlsruhe, Germany.
FAU - Otto, Svenja-K
AU  - Otto SK
AD  - Institute of Physical Chemistry, Justus-Liebig-Universitat Giessen, D-35392 
      Giessen, Germany.
FAU - Lombardo, Teo
AU  - Lombardo T
AD  - Institute of Physical Chemistry, Justus-Liebig-Universitat Giessen, D-35392 
      Giessen, Germany.
FAU - Henss, Anja
AU  - Henss A
AUID- ORCID: 0000-0001-5009-6512
AD  - Institute of Physical Chemistry, Justus-Liebig-Universitat Giessen, D-35392 
      Giessen, Germany.
FAU - Koeppe, Arnd
AU  - Koeppe A
AUID- ORCID: 0000-0002-4833-1306
AD  - Institute for Applied Materials - Microstructure Modelling and Simulation, 
      Karlsruhe Institute of Technology, D-76131 Karlsruhe, Germany.
FAU - Selzer, Michael
AU  - Selzer M
AD  - Institute for Applied Materials - Microstructure Modelling and Simulation, 
      Karlsruhe Institute of Technology, D-76131 Karlsruhe, Germany.
AD  - Institute for Digital Materials Science, Karlsruhe University of Applied 
      Sciences, D-76133 Karlsruhe, Germany.
FAU - Janek, Jurgen
AU  - Janek J
AUID- ORCID: 0000-0002-9221-4756
AD  - Institute of Physical Chemistry, Justus-Liebig-Universitat Giessen, D-35392 
      Giessen, Germany.
FAU - Nestler, Britta
AU  - Nestler B
AD  - Institute for Applied Materials - Microstructure Modelling and Simulation, 
      Karlsruhe Institute of Technology, D-76131 Karlsruhe, Germany.
AD  - Institute for Digital Materials Science, Karlsruhe University of Applied 
      Sciences, D-76133 Karlsruhe, Germany.
LA  - eng
PT  - Journal Article
DEP - 20231018
PL  - United States
TA  - ACS Appl Mater Interfaces
JT  - ACS applied materials & interfaces
JID - 101504991
SB  - IM
PMC - PMC10623505
OTO - NOTNLM
OT  - ToF-SIMS
OT  - all-solid-state battery
OT  - data science
OT  - lithium metal anode
OT  - machine-learning-assisted analysis
COIS- The authors declare no competing financial interest.
EDAT- 2023/10/19 00:44
MHDA- 2023/10/19 00:45
PMCR- 2023/11/03
CRDT- 2023/10/18 19:43
PHST- 2023/10/19 00:45 [medline]
PHST- 2023/10/19 00:44 [pubmed]
PHST- 2023/10/18 19:43 [entrez]
PHST- 2023/11/03 00:00 [pmc-release]
AID - 10.1021/acsami.3c09643 [doi]
PST - ppublish
SO  - ACS Appl Mater Interfaces. 2023 Nov 1;15(43):50469-50478. doi: 
      10.1021/acsami.3c09643. Epub 2023 Oct 18.