机器学习辅助优化大面积钙钛矿薄膜制备及其组件性能研究
Study on the optimization of large-area perovskite fabrication assisted by machine learning and their module performance
袁立 1马璐瑶 1王洪强 1陈睿豪1
作者信息
摘要
钙钛矿太阳能电池凭借其成本低、可溶液加工的明显优势已经成为前沿研究热点方向.然而,大面积钙钛矿薄膜的可扩展加工困难,制约了钙钛矿太阳能组件开发及其商业化进程.发展钙钛矿薄膜的高通量制备技术是一条行之有效的途径.本研究结合机器学习高通量算法,探索刮涂参数对于空气环境中刮涂法制备的大面积钙钛矿薄膜的影响,并根据机器学习结果选择出最优的刮涂参数,成功地制备了 36 cm2的钙钛矿太阳能组件.空气制备的组件获得了 18.89％的光电转换效率.为了进一步提升组件性能,在钙钛矿薄膜表面修饰一层碘化苯乙铵盐,减少钙钛矿薄膜界面缺陷,制备的组件效率可达19.53％.空气稳定性达480 h,仍保留初始效率的95％.
Abstract
Perovskite solar cells have become a frontier research topic due to their low cost and significant advantages in solution processing.However,the scalability of fabricating large-area perovskite films re-mains a challenge,which hinders the development and commercialization of perovskite solar modules.De-veloping high-throughput fabrication techniques for perovskite films is an effective approach.In this study,machine learning high-throughput algorithms were used to explored the influence of coating param-eters on the preparation of large-area perovskite films prepared by blade coating in ambient air condition.Based on the machine learning results,we selected the optimal coating parameters and successfully fabri-cated 36 cm2 perovskite solar modules.The modules prepared in ambient air condition achieved a photoe-lectric conversion efficiency of 18.89％.To further enhance the performance of the modules,a layer of phenylethylammonium iodide was decorated on the surface of the perovskite film to reduce interface de-fects,and the efficiency of the corresponding modules reached up to 19.53％.The module still retaining 95％of the initial efficiency for 480 h under air condition.
关键词
钙钛矿薄膜/空气刮涂/机器学习/大面积模组Key words
perovskite films/air-condition blade coating/machine learning/large-area modules引用本文复制引用
基金项目
国家自然科学基金面上项目(22375163)
青年项目(52203338)
出版年
2024
NETL
NSTL
万方数据