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| Naji, M. |
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| Motta, Antonella |
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| Aletan, Dirar |
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| Mohamed, Tarek |
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| Ertürk, Emre |
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| Taccardi, Nicola |
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| Kononenko, Denys |
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| Petrov, R. H. | Madrid |
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| Alshaaer, Mazen | Brussels |
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| Bih, L. |
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| Casati, R. |
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| Muller, Hermance |
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| Kočí, Jan | Prague |
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| Šuljagić, Marija |
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| Kalteremidou, Kalliopi-Artemi | Brussels |
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| Azam, Siraj |
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| Ospanova, Alyiya |
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| Blanpain, Bart |
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| Ali, M. A. |
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| Popa, V. |
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| Rančić, M. |
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| Ollier, Nadège |
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| Azevedo, Nuno Monteiro |
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| Landes, Michael |
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| Rignanese, Gian-Marco |
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Henss, Anja
in Cooperation with on an Cooperation-Score of 37%
Topics
Publications (11/11 displayed)
- 2024SEI growth on Lithium metal anodes in solid-state batteries quantified with coulometric titration time analysis
- 2024Lipid-related ion suppression on the herbicide atrazine in earthworm samples in ToF-SIMS and matrix-assisted laser desorption ionization mass spectrometry imaging and the role of gas-phase basicitycitations
- 2023SEI growth on Lithium metal anodes in solid-state batteries quantified with coulometric titration time analysiscitations
- 2023Investigation of the Stability of the Poly(ethylene oxide) | LiNi$_{1‐x‐y}$Co$_x$Mn$_y$O$_2$ Interface in Solid‐State Batteries
- 2023Evaluation and Improvement of the Stability of Poly(ethylene oxide)-based Solid-state Batteries with High-Voltage Cathodes
- 2023Identification of Lithium Compounds on Surfaces of Lithium Metal Anode with Machine-Learning-Assisted Analysis of ToF-SIMS Spectracitations
- 2022In Situ Investigation of Lithium Metal–Solid Electrolyte Anode Interfaces with ToF‐SIMScitations
- 2022Quantification of calcium content in bone by using ToF-SIMS-a first approach
- 2022Advanced Analytical Characterization of Interface Degradation in Ni-Rich NCM Cathode Co-Sintered with LATP Solid Electrolytecitations
- 2021Reaction of Li1.3Al0.3Ti1.7(PO4)3 and LiNi0.6Co0.2Mn0.2O2 in co-sintered composite cathodes for solid-state batteriescitations
- 2013Quantification of calcium content in bone by using ToF-SIMS–a first approach
Places of action
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article
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-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.