Department of Mathematics, Payame Noor University, Tehran, Iran.
Mechanical Engineering Department, University of Tehran, Iran.
Chemosphere. 2024 May;356:141770. doi: 10.1016/j.chemosphere.2024.141770. Epub 2024 Mar 28.
The objective of the present study was to employ a green synthesis method to produce a sustainable ZnFeO/BiOI nanocomposite and evaluate its efficacy in the photocatalytic degradation of metronidazole (MNZ) from aqueous media. An artificial neural network (ANN) model was developed to predict the performance of the photocatalytic degradation process using experimental data. More importantly, sensitivity analysis was conducted to explore the relationship between MNZ degradation and various experimental parameters. The elimination of MNZ was assessed under different operational parameters, including pH, contaminant concentration, nanocomposite dosage, and retention time. The outcomes exhibited high a desirability performance of the ANN model with a coefficient correlation (R) of 0.99. Under optimized circumstances, the MNZ elimination efficiency, as well as the reduction in chemical oxygen demand (COD) and total organic carbon (TOC), reached 92.71%, 70.23%, and 55.08%, respectively. The catalyst showed the ability to be regenerated 8 times with only a slight decrease in its photocatalytic activity. Furthermore, the experimental data obtained demonstrated a good agreement with the predictions of the ANN model. As a result, this study fabricated the ZnFeO/BiOI nanocomposite, which gave potential implication value in the effective decontamination of pharmaceutical compounds.
本研究的目的是采用绿色合成方法制备可持续的 ZnFeO/BiOI 纳米复合材料,并评估其在光催化降解水介质中甲硝唑(MNZ)的效果。利用实验数据,开发了人工神经网络(ANN)模型来预测光催化降解过程的性能。更重要的是,进行了敏感性分析以探讨 MNZ 降解与各种实验参数之间的关系。在不同的操作参数下评估了 MNZ 的消除情况,包括 pH 值、污染物浓度、纳米复合材料用量和保留时间。结果表明,ANN 模型具有很高的理想性能,系数相关性(R)为 0.99。在优化条件下,MNZ 的消除效率以及化学需氧量(COD)和总有机碳(TOC)的减少分别达到 92.71%、70.23%和 55.08%。该催化剂具有再生 8 次的能力,其光催化活性仅略有下降。此外,实验数据与 ANN 模型的预测结果吻合良好。因此,本研究制备了 ZnFeO/BiOI 纳米复合材料,为有效去除药物化合物提供了潜在的应用价值。