Time-of-flight secondary ion mass spectrometry as a screening method for the identification of degradation products in lithium-ion batteries—A multivariate data analysis approach

This paper presents a case study for the application of multivariate data analysis (MVA) to time-of-flight secondary ion mass spectrometry (ToF-SIMS) data from sample sets of mainly unknown surface composition. Aged lithium-ion battery (LIB) anodes were used as the test sample set due to their relatively complex composition. For example, LIB samples typically contain a large variety of different and often unidentified degradation products that complicate manual data processing. In this work, principal component analysis (PCA) was applied as a first step to find and classify relevant but unknown peaks in the ToF-SIMS mass spectra. As a result, peak identification was simplified in such a way that the chemical nature of 76% of the characteristic but previously unknown peaks was successfully identified. In a second step, multivariate curve resolution (MCR) was applied to depth profiles of the battery anodes for the first time, and a layered structure of the model samples was successfully determined. This approach also provided an efficient way to compare the layers' structure and the thickness across different samples. In addition to MCR, PCA was used on ToF-SIMS data to investigate all of the layer compositions of the complete sample set simultaneously. It is demonstrated that ToF-SIMS data from rarely characterized data sets can be processed successfully using MVA methods even if a priori knowledge of the sample sets is very limited. With respect to the test samples, the combination ToF-SIMS and MVA proved to be an attractive method to study the influence of different additives (vinylene carbonate, fluoroethylene carbonate, and ethylene sulfite) that appeared in the mass spectra, and was therefore helpful in understanding the formation of different degradation products in LiPF6-containing battery anodes.