Department of Civil Engineering, Faculty of Civil Engineering and Build Environment, Universiti Tun Hussein Onn Malaysia, 86400, Parit Raja, Batu Pahat, Johor, Malaysia; Micropollutant Research Centre (MPRC), Institute of Integrated Engineering, Universiti Tun Hussein Onn Malaysia, 86400, Parit Raja, Batu Pahat, Johor, Malaysia; Camborne School of Mines, College of Engineering, Mathematics and Physical Sciences, University of Exeter, Penryn, Cornwall, TR10 9FE, UK.
Department of Civil Engineering, Faculty of Civil Engineering and Build Environment, Universiti Tun Hussein Onn Malaysia, 86400, Parit Raja, Batu Pahat, Johor, Malaysia.
Environ Res. 2022 Sep;212(Pt E):113537. doi: 10.1016/j.envres.2022.113537. Epub 2022 Jun 6.
Antibiotics in water systems and wastewater are among the greatest major public health problem and it is global environmental issues. Herein a novel approach for the photocatalytic degradation of metronidazole (MTZ) by using eco-green zinc oxide nanoparticles (EG-ZnO NPs) which biosynthesised using watermelon peels extracts has been investigated. Mathematical prediction models using an adaptive neuro-fuzzy inference system (ANFIS), artificial neural networks (ANN) and response surface methodology (RSM) were used to determine the optimal conditions for the degradation process. The FESEM analysis revealed that EG-ZnO NPs was white with a spherical shape and size between 40 and 88 nm. The simulation process for the mathematical prediction model revealed that the best validation performance was 55.35 recorded at epoch 2, the coefficient (R) was 0.9967 for training data, as detected using ANN analysis. The best operating parameters for MTZ degradation was predicted using RSM to be: 170 mg L of EG-ZnO NPs, 20.61 mg 100 mL of MTZ, 10 min exposure time, and a pH of 5, with 77.48 vs 78.14% corresponding to the predicted and empirically measured respectively. The photocatalytic degradation of MTZ was fitted with pseudo-first-order kinetic (R > 0.90). MTZ lost the antimicrobial activity against Bacillus cereus (B. cereus) and Escherichia coli (E. coli) after degradation with EG-ZnO NPs at the optimal conditions as determined in the optimization process. These findings reflect the important role ANFIS and ANN in predicting and optimising the efficacy of engineered nanomaterials, including EG-ZnO NPs, for antibiotic degradation.
水系统和废水中的抗生素是最大的主要公共卫生问题之一，也是全球性的环境问题。在此，研究了一种利用西瓜皮提取物生物合成的新型环保氧化锌纳米粒子（EG-ZnO NPs）光催化降解甲硝唑（MTZ）的方法。采用自适应神经模糊推理系统（ANFIS）、人工神经网络（ANN）和响应面法（RSM）的数学预测模型来确定降解过程的最佳条件。FESEM 分析表明，EG-ZnO NPs 呈白色，球形，尺寸在 40 到 88nm 之间。数学预测模型的模拟过程表明，在第 2 个时期记录到的最佳验证性能为 55.35，在使用 ANN 分析时，训练数据的系数（R）为 0.9967。使用 RSM 预测 MTZ 降解的最佳操作参数为：EG-ZnO NPs 为 170mg·L、MTZ 为 20.61mg·100mL、暴露时间为 10min，pH 值为 5，预测值为 77.48%，实测值为 78.14%。MTZ 的光催化降解符合准一级动力学（R>0.90）。在优化过程中确定的最佳条件下，MTZ 用光催化降解 EG-ZnO NPs 后，失去了对蜡状芽孢杆菌（B. cereus）和大肠杆菌（E. coli）的抗菌活性。这些发现反映了 ANFIS 和 ANN 在预测和优化工程纳米材料（包括 EG-ZnO NPs）对抗生素降解的功效方面的重要作用。
