Hidden information in principal component analysis of ToF-SIMS data: On the use of correlation loadings for the identification of significant signals and structure elucidation

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

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.

Interfacial Chemistry Investigation of Initial Fouling Conditions in Isocyanate Production: The Antifouling Performance of AISI 316L Stainless Steel

The fouling of AISI 316L stainless steel during themanufacture of polymeric methylene diphenyl diisocyanate (pMDI) has been investigated. Studies have been carried out using a laboratory-based rig that simulates the process chemistry of the production plant. A variety of solution concentrations and treatment times have been employed to represent different stages in the production process. Following exposure, steel coupons have been removed and studied by X-ray photoelectron spectroscopy (XPS) and time-of-flight secondary ion mass spectrometry (ToF-SIMS). The thickness of the fouling layer, determined by XPS, is found to vary inversely with exposure time and solution concentration. This is a result of the solubility of the different pMDI derivatives that have been formed at different stages, and a reaction scheme is developed to explain these inverse relationships. ToF-SIMS indicates the formation of metal chlorides as a result of the initial treatment of the steel in the reaction vessel with hydrogen chloride. Fragment ions characteristic of reacted and unreacted pMDI (at m/z = 106 and 132 au, respectively) were used as an indicator of the degree of reacted isocyanate groups within the fouling layer and show a decrease with increasing exposure time, as a result of the formation of intermediates such as amines, ureas, carbodiimides, and uretonimines. The ToF-SIMS data was also processed by principal component analysis (PCA). This generally reinforced the conclusions reached by XPS and ToF-SIMS but, in addition, gave confidence in the repeatability of the analyses with the repeat data (of four analyses) clustering very tightly in the PCA score plots.