journal article·2022-12

Oxygen-incorporated carbon nitride porous nanosheets for highly efficient photoelectrocatalytic CO2 reduction to formate

Hong-zhi Wang, Yue-zhu Zhao, Zhong-xue Yang, Xin-ze Bi, Zhao-liang Wang, Ming-bo Wu

New Carbon Materials· Volume 37· Issue 6
Publisher page

Abstract

Using CO2 as a renewable carbon source for the production of high-value-added fuels and chemicals has recently received global attention. The photoelectrocatalytic (PEC) CO2 reduction reaction (CO2RR) is one of the most realistic and attractive ways of achieving this, and can be realized effectively under sunlight illumination at a low overpotential. Oxygen-incorporated carbon nitride porous nanosheets (CNs) were synthesized from urea or melamine by annealing in nitrogen or N2/O2 gas mixtures. They were used as the photoanode with Bi2CuO4 as the photocathode to realize PEC CO2 reduction to the formate. The electrical conductivity and the photoelectric response of the CNs were modified by changing the oxygen source. Oxygen in CNs obtained from an oxygen-containing precursor improved the conductivity because of its greater electronegativity, whereas oxygen in CNs obtained from the calcination atmosphere had a lower photoelectric response due to a down shift of the energy band structure. The CN prepared by annealing urea, which served as the source of oxygen and nitrogen, at 550 °C for 2 h in nitrogen is the best. It has a photocurrent density of 587 μA cm−2 and an activity of PEC CO2 reduction to the formate of 273.56 μmol cm−2 h−1, which is nearly 19 times higher than a conventional sample. The CN sample shows excellent stability with the photocurrent remaining constant for 24 h. This work provides a new way to achieve efficient catalysts for PEC CO2 reduction to the formate, which may be expanded to different PEC reactions using different cathode catalysts.
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Research context

OpenAlex topics: Advanced Photocatalysis Techniques · CO2 Reduction Techniques and Catalysts · Covalent Organic Framework Applications

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

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