CV


FA
Mohammad Ali Nasseri

Mohammad Ali Nasseri

Professor

Faculty: Science

Department: Chemistry

Degree: Ph.D

CV
FA
Mohammad Ali Nasseri

Professor Mohammad Ali Nasseri

Faculty: Science - Department: Chemistry Degree: Ph.D |

A novel ternary heterojunction BiFeO3/UiO-66-NH2/BiOI photocatalyst for efficient visible-light-induced photodecomposition of metronidazole: Artificial intelligence-assisted modeling and optimization

AuthorsMohammad ali Nasseri,farzaneh esmaili,Morteza Ghadirian,Fatemeh Ghadirian,Negin Nasseh
JournalJournal of Environmental Chemical Engineering
Page number1-27
Serial number14
Volume number5
Paper TypeFull Paper
Published At2026
Journal TypeTypographic
Journal CountryIran, Islamic Republic Of
Journal IndexISI،JCR،Scopus
KeywordsPlant, mediated synthesis, Magnetic photocatalyst, Pharmaceutical wastewater, Machine learning, Heterojunction mechanism

Abstract

The persistence of antibiotic contaminants in aquatic environments necessitates the development of efficient, reusable, and visible-light-responsive photocatalysts for wastewater remediation. In this study, a magnetically recoverable ternary heterostructure, BiFeO3/UiO-66-NH2/BiOI, was synthesized for the visible-light-driven photocatalytic degradation of metronidazole (MTZ). The incorporation of UiO-66-NH2 and BiOI significantly enhanced the physicochemical and optoelectronic properties of the heterostructure, increasing the specific surface area from 1.12 to 11.6 m2/g and promoting broader visible-light absorption. Reduced Nyquist impedance arc radius, photoluminescence quenching, and enhanced transient photocurrent response collectively confirmed improved separation and migration of photogenerated charge carriers within the ternary heterojunction. Radical trapping experiments further demonstrated the important role of reactive species and photogenerated holes in the degradation process. Under optimized conditions, the BiFeO3/UiO-66-NH2/BiOI nanocomposite achieved 88.69% MTZ degradation, accompanied by 71.40% chemical oxygen demand (COD) and 68.30% total organic carbon (TOC) removal, indicating substantial pollutant mineralization. The photocatalyst retained high catalytic activity after eight consecutive cycles, demonstrating excellent magnetic recoverability and structural stability. To complement the experimental findings, machine learning models including linear regression, Ridge, Lasso, and artificial neural network (ANN) approaches were employed to predict MTZ removal efficiency. Among them, the ANN model exhibited the highest prediction accuracy (R2 = 0.98), effectively capturing nonlinear relationships between operational variables and photocatalytic performance. The enhanced activity of the ternary heterostructure was mainly attributed to synergistic visible-light harvesting, increased surface area, efficient interfacial charge transfer, and reactive species generation, highlighting its strong potential for antibioticcontaminated wastewater treatment

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