Unveiling the Chemical Composition of Halide Perovskite Films Using Multivariate Statistical Analyses

Abstract

The local chemical composition of halide perovskites is a crucial factor in determining their macroscopic properties and their stability. While the combination of scanning transmission electron microscopy (STEM) and energy-dispersive X-ray spectroscopy (EDX) is a powerful and widely used tool for accessing such information, electron-beam-induced damage and complex formulation of the films make this investigation challenging. Here we demonstrate how multivariate analysis, including statistical routines derived from “big data” research, such as principal component analysis (PCA), can be used to dramatically improve the signal recovery from fragile materials. We also show how a similar decomposition algorithm (non-negative matrix factorisation (NMF)) can unravel elemental composition at the nanoscale in perovskite films, highlighting the presence of segregated species and identifying the local stoichiometry at the nanoscale.