Modeling and predicting the photodecomposition of methylene blue via ZnO-SnO2 hybrids using design of experiments (DOE)

In this research, we synthesize two kinds of hybrids containing decorated ZnO nanoparticles with different amount of SnO2 nanoparticles. The photocatalytic activity of the prepared hybrids is evaluated by photodecomposition of methylene blue as an organic pollutant. The synthesized hybrids characterized using transmission electron microscopy (TEM). TEM images show that the increasing the SnO2 precursor leads the enhancement of the SnO2 nanoparticles content in the hybrids. Statistical analysis study based on the design of experiments reveals that both of the studied main factors (time and weight fraction) and their interactions have a reasonable effect on the removal efficiency of methylene blue. Meanwhile, the results show that the photocatalytic activity of the prepared hybrids increases with respect to the irradiation time and weight fraction. In addition, the photocatalytic activity of the both hybrids can predict using proposed statistical models. The adequacy study of the models reveals that all of the proposed models can estimate the removal efficiency of the pollutant with high accuracy.