Anchorage of ZnO quantum dots and CuO on graphene for sonophotocatalytic treatment of pharmaceutical effluent: From experimental data and prediction by advanced machine learning algorithms

Quantum dots (QDs) have recently received much attention as photocatalysts due to their unique properties. Despite the advantages reported for QDs as photocatalysts in literature, their sonophotocatalytic/photocatalytic activity is still hindered by a series of barriers, which limit them to operate after several successive cycles efficiently. The main aim of this work is to fabricate an efficient and recyclable sonophotocatalyst/photocatalyst using ZnO quantum dots (ZQDs), tenorite (CuO), and graphene (G). In this regard, different amounts of ZQDs (ZQDs(x)/G, x= 10, 20, 30, 40, and 50 mg) were immobilized on graphene by hydrothermal method and various weight percent of CuO was impregnated on ZQDs(40)/G. The PXRD patterns and Raman spectra confirmed the wurtzite and monoclinic crystal structure for ZQDs and CuO, respectively. Also, FESEM, AFM, and PXRD showed that the mean crystal size of ZQDs increased after immobilization on graphene and impregnation by CuO. PL and Mott–Schottky analyses showed that the presence of GO and CuO in CuO(0.5)/ZQDs(40)/G reduced the recombination of excitons and formed p-n heterogeneous junctions between CuO and ZnO. The VB and CB potential and bandgap energy were obtained using DRS and Mott–Schottky analyses, and the mechanism of tetracycline (TC) degradation was proposed according to active species trapping and H₂O₂ paper testes. The apparent rate constant (kapp) was 0.030 and 0.060 min⁻¹ for photocatalytic and sonophotocatalytic degradation of TC in the presence of CuO(0.5)/ZQDs(40)/G, respectively. The treated effluent of TC showed acceptable performance in growing wheat seeds. The electricity cost estimation showed that (CuO(0.5)/ZQDs(40)/G) consumed less electricity in sonophotocatalytic/photocatalytic TC degradation and could also be used in several successive cycles. Finally, two RF and AdaBoost models based on ML algorithms were developed to predict photocatalytic/sonophotocatalytic degradation. The results showed that the values of statistical metrics, including SAE, MAE, MSE, and RMSE, for the AdaBoost model were relatively lower than those for the RF model, indicating better prediction performance in train and test datasets.