Suppr超能文献

用于检测番茄和山竹中丙溴磷的光响应性表面分子印迹聚合物

Photoresponsive Surface Molecularly Imprinted Polymers for the Detection of Profenofos in Tomato and Mangosteen.

作者信息

Chen Mei-Jun, Yang Hai-Lin, Si Ya-Min, Tang Qian, Chow Cheuk-Fai, Gong Cheng-Bin

机构信息

The Key Laboratory of Applied Chemistry of Chongqing Municipality, College of Chemistry and Chemical Engineering, Southwest University, Chongqing, China.

Department of Science and Environmental Studies, The Education University of Hong Kong, Tai Po, Hong Kong.

出版信息

Front Chem. 2020 Oct 9;8:583036. doi: 10.3389/fchem.2020.583036. eCollection 2020.

Abstract

As a moderately toxic organophosphorus pesticide, profenofos (PFF) is widely used in agricultural practice, resulting in the accumulation of a high amount of PFF in agricultural products and the environment. This will inevitably damage our health. Therefore, it is important to establish a convenient and sensitive method for the detection of PFF. This paper reports a photoresponsive surface-imprinted polymer based on poly(styrene--methyl acrylic acid) (PS--PMAA@PSMIPs) for the detection of PFF by using carboxyl-capped polystyrene microspheres (PS--PMAA), PFF, 4-((4-(methacryloyloxy)phenyl)diazenyl) benzoic acid, and triethanolamine trimethacrylate as the substrate, template, functional monomer, and cross-linker, respectively. PS--PMAA@PSMIP shows good photoresponsive properties in DMSO/HO (3:1, v/v). Its photoisomerization rate constant exhibits a good linear relationship with PFF concentration in the range of 0~15 μmol/L. PS--PMAA@PSMIP was applied for the determination of PFF in spiked tomato and mangosteen with good recoveries ranging in 94.4-102.4%.

摘要

丙溴磷(PFF)作为一种中等毒性的有机磷农药,在农业生产中被广泛使用,导致大量丙溴磷在农产品和环境中积累。这将不可避免地损害我们的健康。因此,建立一种方便、灵敏的丙溴磷检测方法具有重要意义。本文报道了一种基于聚(苯乙烯 - 甲基丙烯酸)(PS - PMAA@PSMIPs)的光响应表面印迹聚合物,分别以羧基封端的聚苯乙烯微球(PS - PMAA)、丙溴磷、4 - ((4 - (甲基丙烯酰氧基)苯基)重氮基)苯甲酸和三乙醇胺三甲基丙烯酸酯作为底物、模板、功能单体和交联剂来检测丙溴磷。PS - PMAA@PSMIP在二甲基亚砜/水(3:1,v/v)中表现出良好的光响应性能。其光异构化速率常数与丙溴磷浓度在0~15 μmol/L范围内呈现良好的线性关系。PS - PMAA@PSMIP用于加标番茄和山竹中丙溴磷的测定,回收率良好,在94.4% - 102.4%之间。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3c35/7581910/6a2dbdc59736/fchem-08-583036-g0001.jpg

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验