(Peer-Reviewed) Tailoring polymer acceptors by electron linkers for achieving efficient and stable all-polymer solar cells
Qiang Wu 吴强 ¹, Wei Wang 王伟 ¹, Yao Wu 吴瑶 ¹, Rui Sun 孙瑞 ¹, Jing Guo 郭靖 ¹, Mumin Shi 石沐民 ¹, Jie Min 闵杰 ¹ ² ³
¹ The Institute for Advanced Studies, Wuhan University, Wuhan 430072, China; 武汉大学 高等研究院
² Key Laboratory of Materials Processing and Mold (Zhengzhou University), Ministry of Education, Zhengzhou 450002, China; 郑州大学 材料成型及模具技术教育部重点实验室
³ Institute of Polymer Optoelectronic Materials and Devices, State Key Laboratory of Luminescent Materials and Devices, South China University of Technology, Guangzhou 510640, China 华南理工大学 发光材料与器件国家重点实验室 高分子光电材料及器件研究所
National Science Review, 2021-08-16
Abstract
The trade-off between efficiency and stability is a bit vague, and it is often tricky to precise control of the bulk morphology for simultaneously improving device efficiency and stability.
Herein, three fused-ring conducted polymer acceptors containing furan, thiophene, and selenophene as the electron linkers in their conjugated backbones, namely PY-O, PY-S, and PY-Se, were designed and synthesized. The electron linker engineering affects the intermolecular interactions of relative polymer acceptors and their charge transport properties.
Furthermore, excellent material compatibility was achieved when PY-Se was blended with polymer donor PBDB-T, resulting in nanoscale domains with favorable phase separation. The optimized PBDB-T: PY-Se blend not only exhibits maximum performance with a power conversion efficiency of 15.48%, which is much higher than those of PBDB-T: PY-O (9.80%) and PBDB-T: PY-Se (14.16%) devices, but also shows better storage and operational stabilities, and mechanical robustness.
This work demonstrates that the precise modification of electron linkers can be a practical way to simultaneously actualize molecular crystallinity and phase miscibility for improving the performance of all-polymer solar cells, showing practical significance.
Separation and identification of mixed signal for distributed acoustic sensor using deep learning
Huaxin Gu, Jingming Zhang, Xingwei Chen, Feihong Yu, Deyu Xu, Shuaiqi Liu, Weihao Lin, Xiaobing Shi, Zixing Huang, Xiongji Yang, Qingchang Hu, Liyang Shao
Opto-Electronic Advances
2025-11-25
