2篇
您的检索式：作者名="Daniel Prochowicz"
|
|
|
题名
|
作者
|
年代
|
出处
|
被引量
|
| 1 | 基于铕掺杂的CsPbI2Br的高效稳定无机钙钛矿太阳能电池显示文摘文章简介根据2018年的认证,有机无机杂化钙钛矿太阳能电池的最新光电转换效率已达23.3%,成为可与硅电池媲美的新型太阳能电池。但钙钛矿中有机组分对热不稳定,虽然全无机钙钛矿(CsPbX3)可以从根本上解决该问题,但是低带隙全无机钙钛矿材料在室温下会从立方相(钙钛矿相)变成正交相(非钙钛矿相),从而失去光学活性。这种相不稳定性成为制约无机钙钛矿太阳能电池发展的瓶颈。 | 向万春 Zaiwei Wang Dominik JKubicki Wolfgang Tress Jingshan Luo Daniel Prochowicz Seckin Akin Lyndon Emsley Jiangtao Zhou Giovanni Dietler Michael Grätzel Anders Hagfeldt | 2020 | 科学新闻2020，，2： | 0 |
| 2 | Is machine learning redefining the perovskite solar cells?显示文摘Development of novel materials with desirable properties remains at the forefront of modern scientific research.Machine learning(ML),a branch of artificial intelligence,has recently emerged as a powerful technology in optoelectronic devices for the prediction of various properties and rational design of materials.Metal halide perovskites(MHPs)have been at the centre of attraction owing to their outstanding photophysical properties and rapid development in solar cell application.Therefore,the application of ML in the field of MHPs is also getting much attention to optimize the fabrication process and reduce the cost of processing.Here,we comprehensively reviewed different applications of ML in the designing of both MHP absorber layers as well as complete perovskite solar cells(PSCs).At the end,the challenges of ML along with the possible future direction of research are discussed.We believe that this review becomes an indispensable roadmap for optimizing materials composition and predicting design strategies in the field of perovskite technology in the future. | Nishi Parikh Meera Karamta Neha Yadav Mohammad Mahdi Tavakoli Daniel Prochowicz Seckin Akin Abul Kalam Soumitra Satapathi Pankaj Yadav | 2022 | Journal of Energy Chemistry2022，31，3： | 0 |
您所在的机构暂未开通此部分的功能，请联系管理员