Proceedings of MATSUS Spring 2024 Conference (MATSUS24)
DOI: https://doi.org/10.29363/nanoge.matsus.2024.218
Publication date: 18th December 2023
Perovskite solar cells (PSCs) are the most promising PV technology in recent years. Efficiency rocketed from 3.1% to 26% in the last decade. The highest-performing PSCs are lead-based, which increases concerns about the environmental impact of this type of solar cell. Consequently, interest is growing in tin-based PSCs as an environmentally friendly alternative to the lead counterpart. However, tin perovskites are hindered by the tin (II) oxidation to tin (IV), which leads to self-doping and devastating cell performance. DMSO, the universal solvent for tin perovskites, was found to oxidize tin [1]. In our previous work, we introduced a group of 15 different solvents that can form 1 Molar solution of FASnI3 [2]. However, the film formation dynamics were challenging due to unsuitable coordination between the solvent molecules and the metal, leading to bad micro-structured films. In this work, we introduce a high-throughput screening of 73 antisolvents against the previously introduced solvents to engineer the crystallization dynamics in tin perovskites. Then, we feed the resultant data into a machine learning algorithm with the solvents and the antisolvents parameters to conclude the most related parameters that control the solvent-antisolvent interaction in tin halide perovskites. In addition, the algorithm predicts the most effective solvent-antisolvent pairs that can form a perovskite film based on the film darkness prediction. Furthermore, we use Hansen parameters spheres to explain the relationship between the solvents and the antisolvents that make the highest-performing perovskite film. Finally, we test the most promising tin perovskite films for their efficiency and report a PCE of around 9%, the highest DMSO-free tin halide PSC as far as we know.
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