Optimizing amoxicillin photodegradation with GO/TiO₂ nanocomposites via RSM, ANN, and ANFIS

IF 3.9 Q2 MATERIALS SCIENCE, MULTIDISCIPLINARY

Carbon Trends Pub Date : 2025-09-14 DOI:10.1016/j.cartre.2025.100571

Seyed Ghasem Rezvannasab , Navid Safari , Abdol Mohammad Ghaedi

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Abstract

Visible-light photocatalysis has been reported to be one of the most effective means of wastewater treatment with high removal efficiency, process simplicity, and environmental friendliness. Photocatalytic degradation of Amoxicillin (AMX) was achieved successfully with GO/TiO₂ nanocomposites prepared via the hydrothermal process. The prepared nanocomposites were characterized by TEM, XRD, FE-SEM, EDS, and FTIR analysis. Three modeling approaches - adaptive neuro-fuzzy inference system (ANFIS), artificial neural network (ANN), and response surface methodology (RSM) - were employed to understand the relationships between input variables and photocatalytic degradation performance. R² values of 0.9876, 0.9159, and 0.7616 were obtained for RSM, ANN, and ANFIS, respectively, which indicates that the predictive capability of RSM and ANN models was better than ANFIS. The maximum degradation of amoxicillin of 91.01 % was realized within 105 min at 0.588 mg/mL GO/TiO₂ dosage, initial 36 mg/L AMX concentration, and pH 5.

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通过RSM、ANN和ANFIS优化氧化石墨烯/ tio2纳米复合材料对阿莫西林的光降解

可见光光催化具有去除率高、工艺简单、环境友好等优点，是目前处理废水最有效的方法之一。采用水热法制备了氧化石墨烯/TiO2纳米复合材料，成功地实现了对阿莫西林（AMX）的光催化降解。采用TEM、XRD、FE-SEM、EDS和FTIR对所制备的纳米复合材料进行了表征。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）三种建模方法来理解输入变量与光催化降解性能之间的关系。RSM、ANN和ANFIS模型的R²分别为0.9876、0.9159和0.7616，表明RSM和ANN模型的预测能力优于ANFIS模型。在GO/TiO2用量为0.588 mg/mL、AMX初始浓度为36 mg/L、pH为5的条件下，105 min内对阿莫西林的最大降解率为91.01%。

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