Synthesis of Sulfur-Doped 2D Graphitic Carbon Nitride Nanosheets for Efficient Photocatalytic Degradation of Phenol and Hydrogen Evolution.
Haiqin LvYing HuangRanjit T KoodaliGuimei LiuYubin ZengQingguo MengMingzhe YuanPublished in: ACS applied materials & interfaces (2020)
Sulfur-doped two-dimensional (2D) graphitic carbon nitride nanosheets (2D-SCN) with efficient photocatalytic activity were synthesized via (1) polycondensation of thiourea to form bulk sulfur-doped graphitic carbon nitride (SCN) and (2) followed by thermal oxidative treatment of the prepared SCN via an etching strategy to form 2D-SCN. Sulfur was doped in situ into SCN by using thiourea as the precursor, and the 2D nanosheet structure was obtained during the thermal oxidative etching process. The structural, morphological, and optical properties of the 2D-SCN sample were investigated in detail. Herein, it is shown that the thermal oxidative etching treatment and sulfur doping induced a 2D nanosheet structure (2D-SCN-3h) with a thickness of about 4.0 nm and exposure of more sulfur elements on the surface. The surface area increased from 16.6 m2/g for SCN to 226.9 m2/g. Compared to bulk SCN, a blue shift of the absorption peaks was observed for the obtained 2D-SCN-3h photocatalyst, and the absorption intensity was higher than that of the sulfur-free counterpart (2D-CN). The successful in situ doping of S element into SCN or 2D-SCN-3h samples is beneficial to the introduction of surface N defects and O species. 2D-SCN-3h indicated higher efficiency in photogenerated charge carrier separation and showed the highest reductive activity in photocatalytic splitting of water at a rate of 127.4 μmol/h under simulated solar light irradiation, which was 250 times and 3 times higher than that of SCN and 2D-CN photocatalysts, respectively. The apparent quantum efficiency was estimated to be 8.35% at 420 nm irradiation. The S-C-N bond formed by sulfur doping was beneficial to the charge-transfer process, and this led to higher photocatalytic activity according to partial density of state analysis computed by first-principles methods.