Unknown

Dataset Information

0

Improving photosensitization for photochemical CO2-to-CO conversion.


ABSTRACT: Inspired by nature, improving photosensitization represents a vital direction for the development of artificial photosynthesis. The sensitization ability of photosensitizers (PSs) reflects in their electron-transfer ability, which highly depends on their excited-state lifetime and redox potential. Herein, for the first time, we put forward a facile strategy to improve sensitizing ability via finely tuning the excited state of Ru(II)-PSs (Ru-1-Ru-4) for efficient CO2 reduction. Remarkably, [Ru(Phen)2(3-pyrenylPhen)]2+ (Ru-3) exhibits the best sensitizing ability among Ru-1-Ru-4, over 17 times higher than that of typical Ru(Phen)3 2+. It can efficiently sensitize a dinuclear cobalt catalyst for CO2-to-CO conversion with a maximum turnover number of 66 480. Systematic investigations demonstrate that its long-lived excited state and suitable redox driving force greatly contributed to this superior sensitizing ability. This work provides a new insight into dramatically boosting photocatalytic CO2 reduction via improving photosensitization.

SUBMITTER: Wang P 

PROVIDER: S-EPMC8288749 | biostudies-literature | 2020 Sep

REPOSITORIES: biostudies-literature

altmetric image

Publications

Improving photosensitization for photochemical CO<sub>2</sub>-to-CO conversion.

Wang Ping P   Dong Ru R   Guo Song S   Zhao Jianzhang J   Zhang Zhi-Ming ZM   Lu Tong-Bu TB  

National science review 20200528 9


Inspired by nature, improving photosensitization represents a vital direction for the development of artificial photosynthesis. The sensitization ability of photosensitizers (PSs) reflects in their electron-transfer ability, which highly depends on their excited-state lifetime and redox potential. Herein, for the first time, we put forward a facile strategy to improve sensitizing ability via finely tuning the excited state of Ru(II)-PSs (<b>Ru-1</b>-<b>Ru-4</b>) for efficient CO<sub>2</sub> redu  ...[more]

Similar Datasets

| S-EPMC10064321 | biostudies-literature
| S-EPMC9044022 | biostudies-literature
| S-EPMC10614036 | biostudies-literature
| S-EPMC10325001 | biostudies-literature
| S-EPMC10020152 | biostudies-literature
| S-EPMC6110867 | biostudies-literature
| S-EPMC7335376 | biostudies-literature
| S-EPMC11374781 | biostudies-literature
| S-EPMC9764354 | biostudies-literature
| S-EPMC8649055 | biostudies-literature