卤化物
钙钛矿(结构)
光伏
三碘化物
三溴
相对湿度
水分
材料科学
光致发光
化学物理
纳米技术
光电子学
化学
化学工程
物理
物理化学
气象学
无机化学
光伏系统
电气工程
工程类
电极
复合材料
色素敏化染料
电解质
作者
John M. Howard,Qiong Wang,Meghna Srivastava,Tao Gong,Erica Lee,Antonio Abate,Marina S. Leite
标识
DOI:10.1021/acs.jpclett.2c00131
摘要
Metal halide perovskite (MHP) photovoltaics may become a viable alternative to standard Si-based technologies, but the current lack of long-term stability precludes their commercial adoption. Exposure to standard operational stressors (light, temperature, bias, oxygen, and water) often instigate optical and electronic dynamics, calling for a systematic investigation into MHP photophysical processes and the development of quantitative models for their prediction. We resolve the moisture-driven light emission dynamics for both methylammonium lead tribromide and triiodide thin films as a function of relative humidity (rH). With the humidity and photoluminescence time series, we train recurrent neural networks and establish their ability to quantitatively predict the path of future light emission with 18% error over 4 h. Together, our in situ rH-PL measurements and machine learning forecasting models provide a framework for the rational design of future stable perovskite devices and, thus, a faster transition toward commercial applications.
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