Unveiling the chemical composition of halide perovskite films using Multivariate Statistical Analyses
Stefania Cacovich a d, Fabio Matteocci b, Mojtaba Abdi-Jalebi c, Samuel D. Stranks c, Aldo Di Carlo b, Caterina Ducati a, Giorgio Divitini a
a Department of Materials Science and Metallurgy, University of Cambridge, 27 Charles Babbage Road, Cambridge CB3 0FS, United Kingdom
b CHOSE - Centre for Hybrid and Organic Solar Energy, University of Rome ‘‘Tor Vergata’’, Via del Politecnico, 1, Roma, Italy
c Cavendish Laboratory, Department of Physics, University of Cambridge, JJ Thomson Avenue, Cambridge CB3 0HE, UK.
d IPVF, Institut Photovoltaïque d’Ile-de-France, 30 RD 128, 91120 Palaiseau, France
Proceedings of International Conference on Hybrid and Organic Photovoltaics (HOPV19)
Roma, Italy, 2019 May 12th - 15th
Organizers: Prashant Kamat, Filippo De Angelis and Aldo Di Carlo
Poster, Giorgio Divitini, 249
Publication date: 11th February 2019

The advancement of perovskite-based materials towards realistic, scalable applications requires a deep understanding of the local chemical and physical properties at the nanoscale. It is necessary to shed light on the principles underlying opration and to characterise the deposition or manufacturing methods. For commercial applications, it is also imperative to study long-term operation of devices in realistic conditions. Additionally, the poor stability often observed in hybrid perovskite films not only prevents easy commercialisation - it also complicates scientific research: commonly used optical and analytical characterisation tools can induce reversible or irreversible structural/chemical changes in the perovskite films through the use of high energy photon or electron beams.
In this work we propose an approach that combines the acquisition of high-resolution chemical maps by scanning transmission electron microscopy (STEM) with dedicated MultiVariate Analysis (MVA) methods that improve the signal/noise ratio (SNR) and identify correlations between the spatial distribution of elements. The correlations are determined from statistical analysis in an operator-agnostic fashion, and can be represented as maps describing the spatial distribution of chemical compounds rather than individual elements. Employing methods minimising operator input leads to reproducible results and a high sensitivity to unexpected, spatially localised chemical compounds, such as phases with unpredicted stoichiometry, or elemental segregation. This approach is particularly valuable for hybrid perovskite-based films and devices, in which complex compounds can form, ionic species are prone to migration, and the electron dose during STEM analysis needs to be minimised to prevent local damage. Specifically, we tested and compared different computational methods - Principal Component Analysis (PCA) and Non-Negative Matrix Factorisation (NMF), demonstrating how they can be used to increase the SNR ratio of a dataset and provide new insights on the local chemistry [1]. The process flow is as follows: Initially, a cross-sectional sample is extracted from a solar cell (or a perovskite film) using conventional Focused Ion Beam (FIB) preparation and transferred to a (scanning) transmission electron microscope (STEM) [2]. STEM-EDX (Energy Dispersive X-ray spectroscopy) analysis is carried out using optimised electron illumination conditions to limit the electron dose on the sample, and the experimental data is processed using MVA algorithms. We use the implementation of MVA in Hyperspy, an open source python-based toolkit for scientific analysis.
This analytical approach has general applicability for novel complex compounds, with particular benefits for materials and devices that are sensitive to electron irradiation or contain a variety of intermixed compounds, such as metal-organic frameworks and organic-inorganic composites. Denoising and factorisation through MVA is also a powerful tool in the investigation of dynamic processes – for example, for in situ measurements in the TEM: denoising routines allow the extraction of information from data acquired quickly and with poor SNR, whereas factorisation can directly highlight the emergence of new phases.

S.C., C.D., and G.D. acknowledge funding from E.R.C. (25961976 PHOTO EM) and financial support from the E.U. (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 from the Research and Innovation programme (653296 (CHEOPS)). M.A.-J. thanks  Cambridge Materials Limited and EPSRC (EP/M005143/1) for their funding and technical support. S.D.S. acknowledges support from the Royal Society and Tata Group (UF150033). A.D.C. gratefully acknowledges the financial support of the Ministry of Education and Sciences of the Russian Federation in the framework of the Increased Competitiveness Program of NUST “MISiS” (Grant No. K2-2017-25), implemented by a governmental decree dated Mar. 16, 2013, N 211.

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