表征(材料科学)
光伏
吞吐量
钙钛矿(结构)
太阳能电池
制作
过程(计算)
材料科学
沉积(地质)
组合综合
工艺工程
光伏系统
计算机科学
纳米技术
化学
组合化学
工程类
生物
化学工程
光电子学
电气工程
电信
无线
替代医学
病理
操作系统
古生物学
医学
沉积物
作者
Maciej Adam Surmiak,Tian Zhang,Jianfeng Lu,Kevin J. Rietwyk,Sonia R. Raga,David P. McMeekin,Udo Bach
出处
期刊:Solar RRL
[Wiley]
日期:2020-04-22
卷期号:4 (7)
被引量:30
标识
DOI:10.1002/solr.202000097
摘要
To discover the ideal perovskite material for solar cell application, a large parameter space (composition, surrounding condition, fabrication technique, etc.) must first be explored. Therefore, screening this parameter space using a rapid combinatorial screening approach can drastically speed up the rate of discovery. During the last decade, these discoveries and optimization processes of perovskite materials have been achieved using simple lab‐scale deposition techniques and characterization methods, resulting in a substantial time‐consuming process, slowing the rate of progress in the field of photovoltaics. Thus, the benefits of developing fully automated, high‐throughput characterization techniques become apparent. Herein, a high‐throughput solar cell testing system that enables parallel, real‐time, and comprehensive measurements is detailed, allowing for 16 solar cells to be characterized simultaneously. The importance of measurement reproducibility, condition verification, and structured data postprocessing is shown.
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