Cobalt-based Polymerized Porphyrinic Network for Visible-light-driven CO 2 Reduction.
Guo-Wei GuanSu-Tao ZhengShuang NiShan-Shan WangHeping MaXiangyu Y LiuXiaomeng PengJian WangQing-Yuan YangPublished in: ACS applied materials & interfaces (2024)
Visible-light-driven conversion of carbon dioxide to valuable compounds and fuels is an important but challenging task due to the inherent stability of the CO 2 molecules. Herein, we report a series of cobalt-based polymerized porphyrinic network (PPN) photocatalysts for CO 2 reduction with high activity. The introduction of organic groups results in the addition of more conjugated electrons to the networks, thereby altering the molecular orbital levels within the networks. This integration of functional groups effectively adjusts the levels of the lowest unoccupied molecular orbital (LUMO) and the highest occupied molecular orbital (HOMO). The PPN(Co)-NO 2 exhibits outstanding performance, with a CO evolution rate of 12 268 μmol/g/h and 85.8% selectivity, surpassing most similar photocatalyst systems. The performance of PPN(Co)-NO 2 is also excellent in terms of apparent quantum yield (AQY) for CO production (5.7% at 420 nm). Density functional theory (DFT) calculations, time-resolved photoluminescence (TRPL), and electrochemical tests reveal that the introduction of methyl and nitro groups leads to a narrower energy gap, facilitating a faster charge transfer. The coupling reaction in this study enables the formation of stable C-C bonds, enhancing the structural regulation, active site diversity, and stability of the catalysts for photocatalytic CO 2 reduction. This work offers a facile strategy to develop reliable catalysts for efficient CO 2 conversion.
Keyphrases
- visible light
- density functional theory
- metal organic framework
- molecular dynamics
- carbon dioxide
- photodynamic therapy
- highly efficient
- single molecule
- gene expression
- single cell
- computed tomography
- mass spectrometry
- transition metal
- ionic liquid
- energy transfer
- reduced graphene oxide
- carbon nanotubes
- network analysis