Decontamination of Metronidazole Antibiotic: A Novel Nanocomposite-Based Strategy
Department of Environmental Technology, College of Environmental Science and Technology, University of Mosul, 41001, Mosul, Iraq
Department of Mining Engineering, College of Petroleum and Mining Engineering, University of Mosul, Mosul, 41001, Mosul, Iraq
Department of Environmental Engineering, College of Engineering, University of Mosul, 41001, Mosul, Iraq
Autor do korespondencji
Mohammed Salim Shihab   

Department of Environmental Engineering, College of Engineering, University of Mosul, 41001, Mosul, Iraq
J. Ecol. Eng. 2023; 24(9):246-259
In this study, the synthesis of magnetic nanoparticles (MNPs) employing leaf extract from Alocasia macrorrhiza was investigated as a reducing agent. CuFe2O4, CuFe2O4/CuO, and CuFe2O4@ CuO/CdS made constituted the core-shell of these MNPs, which were stabilized on naturally Ninevite rocks (NRs) to provide a more cost-effective support. Analytical techniques of various methods were used to characterize the MNPs/NR nanocomposite that was produced utilizing eco-friendly methods. Among the methods used were infrared spectroscopy, X-ray diffraction, scanning electron microscopy, and vibrating sample magnetometry (VSM). The antibiotic Metronidazole (MET) was broken down using a potent nanocatalyst made of MNPs in a solar-irradiated batch system. A solar-photocatalytic system was used to investigate the effects of the initial MET concentration, irradiation time, H2O2 concentration, catalyst nanocomposite concentration, and pH solution on MET photodegradation. Artificial neural networks (ANNs) were also used in data modeling to determine which oxidation technique performed the best in certain conditions. This investigation showed that the CuFe2O4@CuO-CdS magnetic catalyst had the greatest MET removal efficiency of 97% among all MNPs. Moreover, ANN were used to examine data from the photocatalytic oxidation of MET utilizing a CuFe2O4@CuO-CdS/NRs catalyst. The results revealed that the MNP dose had the highest influence on the photodegradation of MET. The correlation coefficients (R2) for the training regressions, validation, testing, and total data were all 0.999, 0.996, 0.993, and 0.998, respectively.
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