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Preparation and photocatalytic study of CoFe2O4/TiO2/Au nanocomposites and their applications in organic pollutant degradation and modeling by an artificial neural network (ANN)

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Abstract

Due to the increase of environmental pollution by various industries in recent decades, preparing drinking water has become one of the most vital issues for many countries. The organic pollutant, such as different azo dyes, is one of the most important issues. Using photocatalyst materials is considered to be an optimal solution to prevent environmental pollution. In this work, novel ternary catalysts of CoFe2O4/TiO2/Au were synthesized for the photocatalytic reduction of methyl orange (MO) under UV light illumination. The localized surface plasmon resonance (LSPR) property of Au nanoparticles is widely exploited for their photocatalytic activities. In this research, both CoFe2O4 and TiO2 nanoparticles (NPs) were prepared by sol–gel method. Hydrothermal treatment was also used to synthesize the nanocomposite. Au nanoparticles were successfully loaded on the CoFe2O4/TiO2 surface to get CoFe2O4/TiO2/Au magnetic nanocomposites. To characterize the shape of the structure, morphology, purity, and particle size of the nanocomposite, scanning and transmission electron microscopy (SEM and TEM), X-ray diffraction (XRD), X-ray photoelectron spectroscopy (XPS), Zeta potential analysis, dynamic light scattering (DLS), photoluminescence spectroscopy (PL), Brunauer–Emmett–Teller (BET), and Fourier transform infrared (FT-IR) spectroscopy were employed. Alternating gradient field magnetometer (AGFM) studies show the superparamagnetic properties of the CoFe2O4 nanostructures. Finally, we investigated the catalytic performance and recyclability in reducing MO of synthesized nanocomposites by monitoring a UV–visible spectrophotometer. The composite catalysts can then be easily separated from the reaction solution using a magnet bar and ultimately reused. We used artificial neural network (ANN) to remove expensive experimental research and tried solving and predicting the novel phenomena with huge factors. Initially, information about the degradation of MO was gathered by experimental analyses. We then tried shaping and calculating the special algorithm that could find the best relation and high percentage of accuracy between input variables. The genetic algorithm as one of the most popular algorithms in an artificial world was selected to predict and train the model. In conclusion, the experimental results determined that the CoFe2O4/TiO2/Au magnetic nanocomposites were successfully synthesized and it exhibited a useful effect on the removal of azo dyes from the contaminated solutions. According to the prediction of removal efficiency of pollution by artificial neural network, the results show that using this algorithm has a high percentage of accuracy to investigate the experimental results of the current research.

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  1. Department of Physics, Faculty of Science, Arak University, 38156-88349, Arak, Iran

    Ali Shabani & Gholamreza Nabiyouni

  2. Institute of Nanoscience and Nanotechnology, Arak University, Arak, Iran

    Ali Shabani & Gholamreza Nabiyouni

  3. Department of Science, Arak University of Technology, Arak, Iran

    Davood Ghanbari

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All authors contributed to the study conception and design. Material preparation, data collection, and analysis were performed by AS, GN, and DG. All authors read and approved the final manuscript.

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Shabani, A., Nabiyouni, G. & Ghanbari, D. Preparation and photocatalytic study of CoFe2O4/TiO2/Au nanocomposites and their applications in organic pollutant degradation and modeling by an artificial neural network (ANN). J Mater Sci: Mater Electron 33, 9885–9904 (2022). https://doi.org/10.1007/s10854-022-07978-4

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