Advancing High-Throughput Combinatorial Aging Studies of Hybrid Perovskite Thin-Films via Precise Automated Characterization Methods and Machine Learning Assisted Analysis

• Published in: arXiv.org 2023-11
• Main Authors: Wieczorek, Alexander, Kuba, Austin G, Sommerhäuser, Jan, Luis Nicklaus Caceres, Wolff, Christian, Siol, Sebastian
• Format: Article
• Language: English
• Subjects: Aging; Automation; Combinatorial analysis; Control stability; Degradation; Machine learning; Perovskites; Stability analysis; Thin films; Workflow; X-ray diffraction; X-ray fluorescence
• ISSN: 2331-8422

To optimize materials' stability, automated high-throughput workflows are of increasing interest. However, many of those workflows use processes not suitable for large-area depositions which limits the transferability of results. While combinatorial approaches based on vapour-based depositions are inherently scalable, their potential for controlled stability assessments has yet to be exploited. Based on MAPbI3 thin-films as a prototypical system, we demonstrate a combinatorial inert-gas workflow to study materials degradation based on intrinsic factors only, closely resembling conditions in encapsulated de-vices. Through a comprehensive set of automated X-Ray fluorescence (XRF), X-Ray diffraction (XRD) and UV-Vis characterizations, we aim to obtain a holistic understanding of thin-film properties of pristine and aged thin-films. From phase changes derived from XRD characterizations before and after aging, we observe simi-lar aging behaviours for MAPbI3 thin-films with varying PbI2 residuals. Using a custom-designed in-situ UV-Vis aging setup, the combinatorial libraries are exposed to relevant aging conditions, such as heat or light-bias exposure. Simultaneously, UV-Vis photospectroscopy is performed to gain kinetic insights into the aging process which can be linked to intrinsic degradation processes such as autocatalytic decomposition. Despite scattering effects, which complicate the conventional interpretation of in-situ UV-Vis results, we demonstrate how a machine learning model trained on the comprehensive characterization data before and after the aging process can link optical changes to phase changes during aging. Consequently, this approach does not only enable semi-quantitative comparisons of materials' stability but also provides detailed insights into the underlying degradation processes which are otherwise mostly reported for investigations on single samples.