一种用于三氯生降解的异质结构MIL-101(Fe)/氧化石墨烯过氧单硫酸催化剂：响应面方法和基于进化的自适应神经模糊推理系统模型

IF 6.7 2区环境科学与生态学 Q1 BIOTECHNOLOGY & APPLIED MICROBIOLOGY

Environmental Technology & Innovation Pub Date : 2025-07-03 DOI:10.1016/j.eti.2025.104360

Afshin Ebrahimi , Kun-Yi Andrew Lin , Malihe Moazeni

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引用次数: 0

摘要

三氯生（TCS）是一种广泛使用的抗菌剂，由于其在水生系统中的持久性和生物蓄积性，造成了重大的环境和健康风险。本研究采用溶剂热法合成了一种新型非均相催化剂MIL-101(Fe)/氧化石墨烯（M(F)/氧化石墨烯），用于激活过氧单硫酸盐（PMS）降解TCS。采用场发射扫描电镜（FESEM）、透射电镜（TEM）、粉末x射线衍射（XRD）等表征技术对M(F)/GO的结构和理化性质进行了表征。批量实验表明，在最佳条件下，M(F)/GO/PMS体系的TCS去除率可达98.31 %；pH为9，催化剂用量为0.17 g/L， TCS初始浓度为400 μg/L， PMS初始浓度为8 µM，仅需10 min。采用响应面法（RSM）和基于进化算法的自适应神经模糊推理系统（EV-ANFIS）两种方法对退化效率进行建模和优化。RSM模型具有较高的预测精度（R²= 0.99），而ANFIS- Harris hawk optimization （HHO）混合模型在测试的机器学习模型中具有较好的预测性能（R²= 0.94）。催化剂投加量是影响TCS去除率最大的参数。机理研究表明，硫酸盐（SO4•−）和羟基（HO•）自由基主导了降解途径。此外，最小的铁浸出证实了催化剂的稳定性和可重复使用的潜力。与现有的高级氧化工艺（AOPs）相比，该系统具有效率高、催化剂和氧化剂用量少、pH适用范围广等优点。本工作介绍了一种有效去除水环境中持久性有机污染物（如TCS）的有前途的策略。

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A heterostructure MIL-101(Fe)/graphene oxide peroxymonosulfate catalyst for triclosan degradation: Response surface methodology and evolutionary-based adaptive neuro-fuzzy inference system models

Triclosan (TCS), a widely used antimicrobial agent, poses significant environmental and health risks due to its persistence and bioaccumulation in aquatic systems. This study presents a novel heterogeneous catalyst, MIL-101(Fe)/graphene oxide (M(F)/GO), synthesized via a solvothermal method for activating peroxymonosulfate (PMS) to degrade TCS. The structural and physicochemical properties of M(F)/GO were characterized using field-emission scanning electron microscopy (FESEM), transmission electron microscopy (TEM), powder X-ray diffraction (XRD), and other characterization techniques. Batch experiments demonstrated that the M(F)/GO/PMS system achieved up to 98.31 % TCS removal under optimal conditions; pH 9, 0.17 g/L catalyst dosage, 400 μg/L initial TCS concentration, and 8 µM PMS concentration in only 10 min. To model and optimize the degradation efficiency, two approaches, response surface methodology (RSM) based on central composite design (CCD) and an evolutionary algorithm-based adaptive neuro-fuzzy inference system (EV-ANFIS), were employed and compared. The RSM model showed high accuracy (R² = 0.99), while the ANFIS- Harris hawk optimization (HHO) hybrid demonstrated robust predictive performance among the machine learning models tested (R² = 0.94). Catalyst dosage was identified as the most influential parameter affecting TCS removal. Mechanistic studies revealed that sulfate (SO₄^•−) and hydroxyl (HO^•) radicals dominated the degradation pathway. Moreover, minimal Fe leaching confirmed the catalyst's stability and reusability potential. Compared to existing advanced oxidation processes (AOPs), this system offers advantages including high efficiency, reduced catalyst and oxidant dosage, and broad pH applicability. This work introduces a promising strategy for efficiently removing persistent organic pollutants like TCS from water environments.

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来源期刊

Environmental Technology & Innovation Environmental Science-General Environmental Science

CiteScore

14.00

自引率

4.20%

发文量

435

审稿时长

74 days

期刊介绍： Environmental Technology & Innovation adopts a challenge-oriented approach to solutions by integrating natural sciences to promote a sustainable future. The journal aims to foster the creation and development of innovative products, technologies, and ideas that enhance the environment, with impacts across soil, air, water, and food in rural and urban areas. As a platform for disseminating scientific evidence for environmental protection and sustainable development, the journal emphasizes fundamental science, methodologies, tools, techniques, and policy considerations. It emphasizes the importance of science and technology in environmental benefits, including smarter, cleaner technologies for environmental protection, more efficient resource processing methods, and the evidence supporting their effectiveness.