Achieving 19% efficiency in nonfused ring electron acceptor solar cells via solubility control of donor and acceptor crystallisation

Nonfused ring electron acceptors (NFREAs) are interesting n-type near infrared (NIR) photoactive semiconductors with strong molecular absorption and easy synthetic route. However, the low backbone planarity and bulky substitution make NFREA less crystalline, which significantly retards charge transport and the formation of bicontinuous morphology in organic photovoltaic device. Donor and acceptor solubility in different solvents is studied, and the created solubility hysteresis can induce the formation of the highly crystalline donor polymer fibril to purify the NFREA phase, thus a better bicontinuous morphology with improved crystallinity. Based on these results, a general solubility hysteresis sequential condensation (SHSC) thin film fabrication methodology is established to produce highly uniform and smooth photoactive layer. The well-defined interpenetrating network morphology afforded a record efficiency of 19.02%, which is ~22% improvement comparing to conventional device fabrication. A high efficiency retention (Pr) value of 92.3% is achieved in 1 cm² device (17.28% efficiency).

Identifier
Source https://archive.materialscloud.org/record/2024.67
Metadata Access https://archive.materialscloud.org/xml?verb=GetRecord&metadataPrefix=oai_dc&identifier=oai:materialscloud.org:2163
Provenance
Creator Zeng, Rui; Zhang, Ming; Wang, Xiaodong; Zhu, Lei; Hao, Bonan; Zhong, Wenkai; Zhou, Guanqing; Deng, Jiawei; Tan, Senke; Zhuang, Jiaxing; Han, Fei; Zhang, Anyang; Zhou, Zichun; Xue, Xiaonan; Xu, Shengjie; Xu, Jinqiu; Liu, Yahui; Lu, Hao; Wu, Xuefei; Wang, Cheng; Fink, Zachary; Russell, Thomas P.; Jing, Hao; Zhang, Yongming; Bo, Zhishan; Liu, Feng
Publisher Materials Cloud
Publication Year 2024
Rights info:eu-repo/semantics/openAccess; Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode
OpenAccess true
Contact archive(at)materialscloud.org
Representation
Language English
Resource Type Dataset
Discipline Materials Science and Engineering