Review on Y6-Based Semiconductor Materials and Their Future Development via Machine Learning
Sijing Zhong, Boon Kar Yap, Zhiming Zhong, Lei Ying
South China University of Technology Universiti Tenaga Nasional South China Institute of Collaborative Innovation
阅读操作
确认中在文库中上传 PDF 后可生成中文音频讲解。
摘要与影响
Non-fullerene acceptors are promising to achieve high efficiency in organic solar cells (OSCs). Y6-based acceptors, one group of new n-type semiconductors, have triggered tremendous attention when they reported a power-conversion efficiency (PCE) of 15.7% in 2019. After that, scientists are trying to improve the efficiency in different aspects including choosing new donors, tuning Y6 structures, and device engineering. In this review, we first summarize the properties of Y6 materials and the seven critical methods modifying the Y6 structure to improve the PCEs developed in the latest three years as well as the basic principles and parameters of OSCs. Finally, the authors would share perspectives on possibilities, necessities, challenges, and potential applications for designing multifunctional organic device with desired performances via machine learning.
逐年被引趋势
关键指标
同类平均 = 1
同领域 · 同年份 · 同类型
Google Scholar 与 OpenAlex 的被引统计范围不同,数值存在差异属正常。
AI 辅助阅读
依据:摘要
可就本文提问;依据不足时会说明。
学术脉络
学科主题
工程Organic Electronics and Photovoltaics
Conducting polymers and applications · Machine Learning in Materials Science
参考文献 110
此处列出前 3 条
引用本文 37
按被引量排序,此处列出前 3 条