Document Type : Research Article
Ph.D. student of Environmental pollution, Faculty of Environmental and Natural Resources, University of Birjand, Birjand, Iran
Associate Professor of Environmental Pollution, Environmental Sciences Research Institute, Shahid Beheshti University, 1983969411, Tehran, Iran
Associate Professor of Environmental, Faculty of Environmental and Natural Resources, University of Birjand, Birjand, Iran
The growth of industries has led to an increase in pollutant sources, especially organic dyes wastewater from the textile, pharmaceutical, etc. industries have caused an adverse anthropogenic effects in water resources. Photocatalysis is considered the most desirable solution to treat these pollutions. In this paper, nanophotocatalyst (CNMS) was synthesized by stabilization of graphitic carbon nitride nanoparticles (CNNP) during the hydrothermal process on periodic mesoporous organosilica (PMO) based and its efficiency studied in photocatalytic degradation of rhodamine B(RhB) under RGBW LED photoreactor. The similarity of structural and optical properties of CNMS with BCN and PMO in the results of XRD, SAXRD, FTIR, and DRS analysis showed that the structure and framework of CNMS have been preserved and have been affected by both precursors. Optimization of parameters affecting photocatalyst activity was performed in surface response analysis (RSM) and by Box-Benken (BBD) model in three variables of time, photocatalyst dosage and light wavelength. According to ANOVA results, accuracy and validity of the quadratic model were accepted with high F-value and all correlation coefficients, the significance of p-value parameter, small% C.V. And lack of autocorrelation on D-W test results. The wavelength variable (C) and the interaction wavelength and dosage (BC) variable have the most significant impact on photocatalytic degradation. Finally, by kinetic analysis under optimal conditions, the experimental degradation value was calculated to be 90.04, which corresponds to the predicted value of 92.20% and is within the predicted interval (PI 95%), thus confirming the model’s validity.