Manipulating Crystal Packing in Heterocycloarenes by an Atom Engineering Strategy for High‐Mobility Organic Field‐Effect Transistors
Abstract Developing (hetero)cycloarenes, an emerging class of ring‐shaped organic semiconductors (OSCs), for high‐performance optoelectronic applications remains significantly constrained due to their synthetic challenge and limited diversity of available materials. Herein, a series of coplanar chalcogen‐fused heterocycloarenes NO , NS , and NSe with long branched alkyl chains were synthesized in single‐crystal form, and chalcogen atom engineering on heterocycloarenes was investigated in detailed. Sulfur‐fused NS exhibits a closest herringbone crystal packing with π–π stacking distance as low as 3.11 Å, resulting in a record‐high hole mobility of 3.13 cm 2 V −1 s −1 among all reported ring‐shaped OSCs. Remarkably, selenium‐fused NSe without intermolecular π–π interactions in its crystalline state, also manifests the second highest mobility of up to 2.11 cm 2 V −1 s −1 , which is attributed to the presence of extensive short‐range edge‐to‐face Se…π and C−H…π interactions. Furthermore, these heterocycloarenes exhibit a selective supramolecular interaction with C 70 , with the trend in binding constants being: NS < NO < NSe . Overall, this work not only systematically elucidates the role of atom engineering on heterocycloarenes for the first time, but also paves the way for practical applications of the emerging ring‐shaped OSC materials.
Authors
- Rong Zhang (ORCID: https://orcid.org/0000-0002-7103-2996)
- Kai Chi (ORCID: https://orcid.org/0000-0003-3396-8061)
- Xuefeng Lu (ORCID: https://orcid.org/0000-0001-7647-1317)
- Jiaxi Zhang (ORCID: https://orcid.org/0000-0001-6177-8690)
- Teng Wang (ORCID: https://orcid.org/0000-0002-6262-2599)
- Wenhao Li (ORCID: https://orcid.org/0000-0003-2614-0768)
- Yan Jun Zhao (ORCID: https://orcid.org/0000-0002-4216-2150)
- Yunqi Liu (ORCID: https://orcid.org/0000-0001-5521-2316)
- Yuanhe Gu (ORCID: https://orcid.org/0009-0005-6265-0011)
Institutions
- Chinese Academy of Sciences (CN)
- Fudan University (CN)
- Beijing National Laboratory for Molecular Sciences (CN)
- Institute of Chemistry (CN)
Publication Details
- Journal
- Angewandte Chemie
- Published
- 2025-03-11
- DOI
- https://doi.org/10.1002/ange.202501686
- Primary Topic
- Machine Learning in Materials Science
- Type
- article
- Field-Weighted Citation Impact
- 0.00