Application Of Zinc-based Nanocomposites In Photocatalytic Degradation Of Dyes(Xylenol Orange,Acid Fuchsin) In Simulated Wastewater

Dye wastewater has deep color,complex components,fluctuation of water quality and high biological toxicity,and contains a variety of biological toxicity or organic substances that cause"three causes"（carcinogenic,teratogenic,and mutagenic）properties,which have brought serious harm to the environment.Photocatalytic technology has attracted much attention due to its simplicity,low cost and green technology,which is an effective water purification strategy.Their principle is to use radicals to directly degrade organic pollutant molecules into harmless molecules（e.g.,H2O and CO2）.The photocatalytic materials studied include oxides（e.g.,zinc oxide and titanium dioxide）,sulfides（e.g.,cadmium sulfide）,graphite phase carbon nitride,perovskite and metal organic frameworks.Zinc oxide（ZnO）semiconductor is an ideal choice for degradation of dye wastewater（e.g.,xylenol orange,acid fuchsin）due to its large storage capacity,low cost and environmental protection.A facile synthesis of rGO/Fe₃O₄/Mg-doped ZnO and rGO/Fe₃O₄/La-doped ZnO nanocomposites was performed in this study by the impregnation of graphene oxide support.RGO/Fe₃O₄/Mg-doped ZnO nanocomposites were used as a photocatalyst for degrading xylenol orange with the assistance of artificial intelligence modeling.The prepared nanocomposites were characterized by X-ray diffraction,Raman spectroscopy,scanning electron microscopy,N2-absorption,photoluminescence spectra,and UV-vis diffuse reflectance spectra.The X-ray diffraction results show that the nanocomposites comprise Fe₃O₄ of cubic structure and ZnO of hexagonal wurtzite structure.Based on the analysis of the scanning electron microscopy,the average size of particles loaded on reduced graphene oxide platelets was 12.34 nm.The Brunauer–Emmett–Teller specific surface area of the nanocomposites was 91.49m²/g and Barrett–Joyner–Halenda pore-size distribution showed the average mesoporous pore size of 2.52 nm.The saturation magnetisation of the nanocomposites（23.2 emu/g）was observed,which can be utilized for magnetic separation.The UV-visible diffuse reflectance spectroscopy revealed that the presence of GO/Fe₃O₄ and Mg in ZnO can increase the light absorption intensity and reduce the band gap.Through photoluminescence spectra analysis,the electron-hole pair recombination rate of the doped ZnO composite material was decreased and the separation rate thus became higher.The effects of concentration（20-60 mg/L）,initial p H（4-8）,dosage（0.07-0.11 g）,and contact time（55-75 min）on the degree of degradation of xylenol orange by the material were investigated with the aid of response surface methodology and artificial intelligence（i.e.,gradient boosting regression tree,random forest,artificial neural network-particle swarm,artificial neural network-genetic algorithm）.Among these artificial intelligence models,artificial neural network-genetic algorithm was the best model for predicting the degradation efficiency of xylenol orange by the nanocomposites.The evaluation of variables shows that the dye concentration gives the maximum importance to the degradation of xylenol orange.The experimental data were analyzed by using Langmuir-Hinshelwood and pseudo second order kinetics models.The results indicated that the process of xylenol orange degradation by the nanocomposites conformed to the Langmuir-Hinshelwood model with R² of 0.991.RGO/Fe₃O₄/La-doped ZnO nanocomposites were used as a photocatalyst for degrading acid fuchsin with the assistance of artificial intelligence modeling.The prepared nanocomposites were characterized by X-ray diffraction,Raman spectroscopy,scanning electron microscopy,N2-absorption,photoluminescence spectra,and UV-vis diffuse reflectance spectra.The X-ray diffraction results show that the nanocomposites comprise Fe₃O₄ of cubic structure and ZnO of hexagonal wurtzite structure.Based on the analysis of the scanning electron microscopy,the average size of particles loaded on reduced graphene oxide platelets was 28.25 nm.The Brunauer–Emmett–Teller specific surface area of the nanocomposites was144.748 m²/g and Barrett–Joyner–Halenda pore-size distribution showed the mesoporous pore size of 3.708 nm.The saturation magnetisation of the nanocomposites（24.55 emu/g）was observed,which can be utilized for magnetic separation.The UV-visible diffuse reflectance spectroscopy revealed that the presence of GO/Fe₃O₄ and La in ZnO can increase the light absorption intensity and reduce the band gap.Through photoluminescence spectra analysis,the electron-hole pair recombination rate of the doped ZnO composite material was decreased and the separation rate thus became higher.The effects of concentration（30-70 mg/L）,initial p H（4-8）,dosage（0.07-0.11 g）,and contact time（40-80 min）on the degree of degradation of acid fuchsin by the material were investigated with the aid of response surface methodology and artificial intelligence（i.e.,gradient boosting regression tree,random forest,artificial neural network-particle swarm,artificial neural network-genetic algorithm）.Among these artificial intelligence models,artificial neural network-genetic algorithm was the best model for predicting the degradation efficiency of acid fuchsin by the nanocomposites.The evaluation of variables shows that the dye concentration gives the maximum importance to the degradation of acid fuchsin.The experimental data were analyzed by using Langmuir-Hinshelwood and pseudo second order kinetics models.The results indicated that the process of acid fuchsin degradation by the nanocomposites conformed to the pseudo second order model with R² of 0.84.