Login / Signup

Facile Removal of Phytochromes and Efficient Recovery of Pesticides Using Heteropore Covalent Organic Framework-Based Magnetic Nanospheres and Electrospun Films.

Wei LiHong-Xin JiangYue GengXiao-Han WangRong-Zhi GaoAn-Na TangDe-Ming Kong
Published in: ACS applied materials & interfaces (2020)
Nontargeted analysis of food safety requires selective removal of interference matrices and highly efficient recovery of chemical hazards. Porous materials such as covalent organic frameworks (COFs) show great promise in selective adsorption of matrix molecules via size selectivity. Considering the complexity of interference matrices, we prepared crystalline heteropore COFs whose two kinds of pores have comparable sizes to those of several common phytochromes, main interference matrices in vegetable sample analysis. By controlling the growth of COFs on the surface of Fe3O4 nanoparticles or by utilizing a facile co-electrospinning method, heteropore COF-based magnetic nanospheres or electrospun nanofiber films were prepared, respectively. Both the nanospheres and the films maintain the dual-pore structures of COFs and show good stability and excellent reusability. Via simple magnetic separation or immersion operation, respectively, they were successfully used for the complete removal of phytochromes and highly efficient recovery of 15 pesticides from the extracts of four vegetable samples, and the recoveries are in the range of 83.10-114.00 and 60.52-107.35%, respectively. Film-based immersion operation gives better sample pretreatment performance than the film-based filtration one. This work highlights the great application potentials of heteropore COFs in sample pretreatment for nontargeted analysis, thus opening up a new way to achieve high-performance sample preparation in many fields such as food safety analysis, environment monitoring, and so on.
Keyphrases
  • highly efficient
  • room temperature
  • risk assessment
  • reduced graphene oxide
  • high resolution
  • tissue engineering
  • gold nanoparticles
  • water soluble
  • human health
  • artificial intelligence
  • deep learning