Authors: Afshin Ebrahimi | Kun‐Yi Andrew Lin | Malihe Moazeni
Source: Environmental Technology & Innovation (article, 2025-07-03)
Field: Materials Science — Materials Chemistry

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 (SO 4 •− ) 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. • M(F)/GO successfully initiated PMS to generate SO 4 •− and HO • radicals efficiently. • The M(F)/GO/PMS system achieved a maximum TCS removal of 98.31% within 10 min. • The ANFIS-HHO proved higher performance than other EV-ANFIS models. • RSM is more effective than ANFIS-HHO for predicting TCS removal by the M(F)/GO/PMS. • M(F)/GO indicated remarkable stability under the optimum experimental conditions. Graphene ; Heterojunction ; Oxide ; Degradation (telecommunications) ; Catalysis ; Materials science ; Adaptive neuro fuzzy inference system ; Fuzzy logic ; Inference ; Computer science ; Response surface methodology ; Chemical engineering ; Nanotechnology ; Chemistry ; Artificial intelligence ; Optoelectronics ; Fuzzy control system ; Engineering ; Machine learning ; Metallurgy ; Organic chemistry ; Telecommunications Covalent Organic Framework Applications ; Electrochemical sensors and biosensors ; Advanced Photocatalysis Techniques