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

Change log
Cacovich, Stefania 
Matteocci, Fabio 
Abdi Jalebi, Mojtaba  ORCID logo  https://orcid.org/0000-0002-9430-6371
Di Carlo, Aldo 

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.

big data, chemical composition, hybrid perovskite, multivariate analysis, nanoscale, STEM-EDX
Journal Title
ACS Applied Energy Materials
Conference Name
Journal ISSN
Volume Title
ACS Publications
European Research Council (259619)
Engineering and Physical Sciences Research Council (EP/M005143/1)
European Research Council (756962)
S.C., C.D. and G.D. acknowledge funding from ERC under grant number 25961976 PHOTO EM and financial support from the EU under grant number 77 312483 ESTEEM2. S.C., C.D. and G.D. also thank Dr. Francisco de la Peña and Dr. Pierre Burdet for very helpful discussions regarding Hyperspy and MVA. The CHOSE team gratefully acknowledges the European Union's Horizon 2020 Framework Program for funding Research and Innovation under Grant agreement no. 653296 (CHEOPS). M.A.-J. thanks Nava Technology Limited, Cambridge Materials Limited and EPSRC (grant number: EP/M005143/1) for their funding and technical support. S.D.S. acknowledges support from the Royal Society and Tata Group (UF150033) and the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement number 756962).