Machine Learning-Inspired Molecular Design, Divergent Syntheses, and X-Ray Analyses of Dithienobenzothiazole-Based Semiconductors Controlled by Sâ
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Chemistry
; 30(54): e202401080, 2024 Sep 25.
Article
en En
| MEDLINE
| ID: mdl-39039606
ABSTRACT
Inspired by the previous machine-learning study that the number of hydrogen-bonding acceptor (NHBA) is important index for the hole mobility of organic semiconductors, seven dithienobenzothiazole (DBT) derivatives 1 a-g (NHBA=5) were designed and synthesized by one-step functionalization from a common precursor. X-ray single-crystal structural analyses confirmed that the molecular arrangements of 1b (the diethyl and ethylthienyl derivative) and 1c (the di(n-propyl) and n-propylthienyl derivative) in the crystal are classified into brickwork structures with multidirectional intermolecular charge-transfer integrals, as a result of incorporation of multiple hydrogen-bond acceptors. The solution-processed top-gate bottom-contact devices of 1b and 1c had hole mobilities of 0.16 and 0.029â
cm2 V-1s-1, respectively.
Texto completo:
1
Colección:
01-internacional
Banco de datos:
MEDLINE
Idioma:
En
Revista:
Chemistry
Asunto de la revista:
QUIMICA
Año:
2024
Tipo del documento:
Article
País de afiliación:
Japón