当前位置: X-MOL 学术Solar RRL › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
Nonfullerene Acceptors for Organic Photovoltaics: From Conformation Effect to Power Conversion Efficiencies Prediction
Solar RRL ( IF 6.0 ) Pub Date : 2019-07-31 , DOI: 10.1002/solr.201900258
Ming-Yue Sui 1 , Zi-Rui Yang 2 , Yun Geng 3 , Guang-Yan Sun 1, 4 , LiHong Hu 2 , Zhong-Min Su 3
Affiliation  

Theoretical predictions of macroscopic performance (power conversion efficiencies [PCEs]) and experimental analyses for microscopic material (conformation) have always urged for organic photovoltaics. A series of acceptors based on multi‐conformation bistricyclic aromatic enes core have been designed. The results suggested that A4‐2, A5‐2, and T4‐2 show the full folded conformation, fitting, and exhibiting advantageous properties of various parts for acceptors effectively, thus getting high VOC and JSC (kCS/kCR exceeds 1012) as well. Their PCEs of devices matching different donors were predicted through machine learning (ML). In traditional device structures and crude environments, a maximum PCE is about seven times higher than original. Herein, a comprehensive investigation, ranging for conformations → donor/acceptor interfaces → morphology → PCEs, is carried out by pure theoretical methods. Therefore, this quantitative micro‐analysis combined with the ML intelligent prediction leads to a new approach in the development of the next generation of nonfullerene acceptors.

中文翻译:

有机光伏非富勒烯受体:从构象效应到功率转换效率的预测

对于有机光伏,一直要求对宏观性能(功率转换效率[PCE])进行理论预测,并对微观材料(构象)进行实验分析。设计了一系列基于多构型双三环芳香烯核的受体。结果表明,A4-2A5-2T4-2表现出完全折叠的构象,拟合并有效地展示了各个部分对受体的有利特性,因此获得了较高的V OCJ SCk CS / k CR超过10 12)。通过机器学习(ML)预测了与不同供体匹配的设备的PCE。在传统的设备结构和原始环境中,最大PCE约为原始PCE的七倍。在此,通过纯理论方法对构象→供体/受体界面→形态→PCE进行了广泛的研究。因此,这种定量微分析与ML智能预测相结合,为下一代非富勒烯受体的开发提供了一种新方法。
更新日期:2019-07-31
down
wechat
bug