1. Machine Learning Assisted Imprinted Ag@PANI/CoFe2O4/C Heterojunction with Simultaneous Improvement of Selectivity and Activity for Antibiotic Photodegradation.
- Author
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Zhang, Xiangyan, Wei, Binghui, Cheng, Yu, Xu, Yangrui, Song, Minshan, Tang, Liguang, Jin, Jie, Liu, Xinlin, and Lu, Ziyang
- Subjects
MACHINE learning ,PHOTOCATALYSTS ,PHOTODEGRADATION ,CORNCOBS ,FLUOROQUINOLONES ,HETEROJUNCTIONS ,IMPRINTED polymers - Abstract
Improvement of selectivity and activity of imprinted photocatalysis is a major challenge for antibiotic photodegradation due to the functional monomers of imprinting hindering the photogenerated carrier migration. Here, an organic imprinted Ag-polyaniline/CoFe
2 O4 /Carbon photocatalyst (IM-Ag-PANI/CoFe2 O4 /C) was successfully prepared by photo-initiated polymerization which achieved selective photodegradation of tetracycline (TC). The heterojunction formed by the functional monomer Ag@PANI and CoFe2 O4 /C not only facilitates the separation of photo-excited carriers and the exposure of active sites but also contributes to the selective adsorption capability by imprinted cavity on Ag@PANI, thereby improving the photocatalytic activity and selectivity simultaneously. In addition, the Corncob conversion carbon matrix method and iron-based magnetic character enable a more environmentally friendly and recyclable capability of IM-Ag-PANI/CoFe2 O4 /C. After using machine learning models to train and predict experimental parameters by changing experimental parameters, the IM-Ag-PANI/CoFe2 O4 /C can photodegrade 82.23% of TC within 2 h, and it has a selective degradation ability compared to enrofloxacin hydrochloride (EH). This research provides a new idea for the construction of imprinted photocatalytic materials that can improve photocatalytic activity. [ABSTRACT FROM AUTHOR]- Published
- 2024
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