Discovery of temperature-induced stability reversal in perovskites using high-throughput robotic learning

利用高通量机器人学习发现钙钛矿中温度引起的稳定性逆转

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作者:Yicheng Zhao #, Jiyun Zhang #, Zhengwei Xu #, Shijing Sun, Stefan Langner, Noor Titan Putri Hartono, Thomas Heumueller, Yi Hou, Jack Elia, Ning Li, Gebhard J Matt, Xiaoyan Du, Wei Meng, Andres Osvet, Kaicheng Zhang, Tobias Stubhan, Yexin Feng, Jens Hauch, Edward H Sargent, Tonio Buonassisi, Christop

Abstract

Stability of perovskite-based photovoltaics remains a topic requiring further attention. Cation engineering influences perovskite stability, with the present-day understanding of the impact of cations based on accelerated ageing tests at higher-than-operating temperatures (e.g. 140°C). By coupling high-throughput experimentation with machine learning, we discover a weak correlation between high/low-temperature stability with a stability-reversal behavior. At high ageing temperatures, increasing organic cation (e.g. methylammonium) or decreasing inorganic cation (e.g. cesium) in multi-cation perovskites has detrimental impact on photo/thermal-stability; but below 100°C, the impact is reversed. The underlying mechanism is revealed by calculating the kinetic activation energy in perovskite decomposition. We further identify that incorporating at least 10 mol.% MA and up to 5 mol.% Cs/Rb to maximize the device stability at device-operating temperature (<100°C). We close by demonstrating the methylammonium-containing perovskite solar cells showing negligible efficiency loss compared to its initial efficiency after 1800 hours of working under illumination at 30°C.

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