Preparation and evaluation of superparamagnetic core–shell dummy molecularly imprinted polymer for recognition and extraction of organophosphorus pesticide
Source abstract
Abstract
In this paper, we reported a simple and effective strategy to synthesize core–shell dummy template magnetic molecularly imprinted polymers (Fe3O4@DMIPs) using ethyl paraoxon as a template for the recognition and selective extraction of organophosphorus pesticide. Initially, monodisperse Fe3O4 nanoparticles were synthesized directly through a facile one-pot hydrothermal method. Then, the imprinted layer was synthesized directly on the surface of the magnetic core by means of a one-pot sol–gel copolymerization which avoided further modification of the external part of the magnetic core. The structure and morphology of the materials (Fe3O4@DMIPs) were characterized by SEM, TEM, FTIR, XRD, and VSM. It was observed that Fe3O4@DMIPs showed regular morphology, good dispersibility, and superparamagnetism. The synthesis conditions for the formation of Fe3O4@DMIPs were systematically investigated. It was found that the morphology and monodispersity of Fe3O4@DMIPs were highly influenced by the ratio of the mixture solvent of methanol and water and the volume ratio of functional monomer (APTES) and cross-linker (TEOS). The binding performance of the imprinted polymers was investigated through a series of adsorption experiments, which indicated that the Fe3O4@DMIPs had a fast adsorption rate (15 min) and high adsorption capacity (195.7 mg g−1) to methyl parathion and phoxim. Meanwhile, real wine sample tests demonstrated a good extraction effect. This study provides a possibility for the selective extraction of organophosphorus pesticide residue in a complex matrix.
Source: openaire · Quality: candidate unverified · Source record ↗
Research context
OpenAlex topics: Analytical chemistry methods development · Electrochemical sensors and biosensors · Pesticide Residue Analysis and Safety
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (48)
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