Issue 28, 2022, Issue in Progress

Boosting the singlet oxygen production from H2O2 activation with highly dispersed Co–N-graphene for pollutant removal

Abstract

Singlet oxygen (1O2) is a promising reactive species for the selective degradation of organic pollutants. However, it is difficult to generate 1O2 from H2O2 activation with high efficiency and selectivity. In this work, a graphene-supported highly dispersed cobalt catalyst with abundant Co–Nx active sites (Co–N-graphene) was synthesized for activating H2O2. The Co–N-graphene catalyzed H2O2 reaction system selectively catalyzed 1O2 production associated with the superoxide radical (O2˙) as the critical intermediate, as proven by scavenger experiments, electron spin resonance (ESR) spin trapping and a kinetic solvent isotope effect study. This resulted in excellent degradation efficiency towards the model organic pollutant methylene blue (MB), with an outstanding pseudo-first-order kinetic rate constant of 0.432 min−1 (g Lcatalyst−1)−1 under optimal reaction conditions (CH2O2 = 400 mM, initial pH = 9). Furthermore, this Co–N-graphene catalyst enabled strong synergy with HCO3 in accelerating MB degradation, whereas the scavenger experiment implied that the synergy herein differed significantly from the current Co2+–HCO3 reaction system, in which contribution of O2˙ was only validated with a Co–N-graphene catalyst. Therefore, this work developed a novel catalyst for boosting 1O2 production from H2O2 activation and will extend the inventory of catalysts for advanced oxidation processes.

Graphical abstract: Boosting the singlet oxygen production from H2O2 activation with highly dispersed Co–N-graphene for pollutant removal

Article information

Article type
Paper
Submitted
18 Apr 2022
Accepted
23 May 2022
First published
16 Jun 2022
This article is Open Access
Creative Commons BY-NC license

RSC Adv., 2022,12, 17864-17872

Boosting the singlet oxygen production from H2O2 activation with highly dispersed Co–N-graphene for pollutant removal

Y. Yu, W. Quan, Y. Cao, Q. Niu, Y. Lu, X. Xiao and L. Cheng, RSC Adv., 2022, 12, 17864 DOI: 10.1039/D2RA02491H

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