Abstract
Water pollution is a significant concern that affects the environment and human health. Antibiotics are among the most dangerous types of pollutants spreading in our time because of their harmful effects, such as generating antibiotic-resistant bacteria. Therefore, it is necessary to attain an eco-friendly and efficient method for treating those hazardous compounds. This study proposes a clean approach for removing antibiotics from pharmaceutical wastewater, using an efficient photocatalyst Bi12TiO20 (BTO) synthesized by the sol-gel method. Several characterizations were carried out to identify the obtained catalyst, such as XRD, BET, Raman, FESEM, EDX with elemental mapping, TEM, UV–Vis, and PL, in which the space group of BTO crystals was discovered to be I23, with a bandgap of 2.9 eV. To evaluate the photocatalytic properties of the catalyst BTO, Cefixime (CFX) was chosen as a pollutant example. The photocatalytic efficiency was optimized using an artificial neural network (ANN) method with a deep learning technique. The ANN network was trained using experimental data with various BTO dosages and CFX initial concentrations at varying pH. The results have shown that the BTO catalyst can lead to 94.93% CFX degradation and 87.66% mineralization within only 3 h, this efficiency was very high compared to other catalysts used in previous studies. The relative importance of different photocatalytic parameters was estimated using ANN data, the highest effective parameter was the initial CFX concentration. Then, the by-products were analyzed using GC-MS, and a pathway for CFX degradation was suggested. The mechanism of the degradation was also investigated in the presence of scavenger agents. The results showed that the CFX molecules had been degraded totally into tiny molecules, proving the efficient performance of this catalyst. These findings make this sillenite an effective catalyst for removing antibiotics from the aquatic environment.
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