Maximizing information obtained from secondary ion mass spectra of organic thin films using multivariate analysis

Abstract

Time-of-flight secondary ion mass spectrometry (ToF-SIMS) can give a detailed description of the surface chemistry and structure of organic materials. The high mass resolution and high mass range mass spectra obtainable from modern ToF-SIMS instruments offer the ability to rapidly obtain large amounts of data. Distillation of that data into usable information presents a significant problem in the analysis of ToF-SIMS data from organic materials. Multivariate data analysis techniques have become increasingly common for assisting with the interpretation of complex ToF-SIMS data sets. This study presents an overview of principal component analysis (PCA) and partial least squares regression (PLSR) for analyzing the ToF-SIMS spectra of alkanethiol self-assembled monolayers (SAMs) adsorbed onto gold substrates and polymer molecular depth profiles obtained using an SF 5 + primary ion beam. The effect of data pretreatment on the information obtained from multivariate analysis of these data sets has been explored. Multivariate analysis is an important tool for maximizing the information obtained from the ToF-SIMS spectra of organic thin films.