期刊
RSC ADVANCES
卷 5, 期 75, 页码 61161-61169出版社
ROYAL SOC CHEMISTRY
DOI: 10.1039/c5ra10367c
关键词
-
资金
- Open Project of Key Laboratory of Modern Toxicology of the Ministry of Education [NMUMT201404]
- Jiangsu Province Science Foundation for Youths [BK20130644]
- National Basic Science Personal Training Fund [J0630858]
A novel magnetic molecularly imprinted polymer (MMIP) has been designed using flexible docking and molecular dynamics in computer simulation. Six kinds of representative functional monomers have been screened to obtain the optimal one, as well as optimizing its ratio to the template amlodipine (AML). Two MMIPs have been prepared with the most suitable functional monomer methacrylic acid (MAA) along with acrylamide (AM) as a comparison, in the ratio of 4 to AML. Fourier transform infrared (FT-IR) spectroscopy, transmission electron microscopy (TEM), scanning electron microscopy (SEM), and vibrating sample magnetometery (VSM) were used to characterize the structure, morphology and magnetic properties of the MMIPs. A static adsorption study was carried out with the help of response surface methodology (RSM) and in addition, adsorption kinetics and isotherms were studied to further explain the adsorption behavior. Three dihydropyridine calcium channel blockers (DHP-CCBs) as structural analogues and non-structural analogue penicillin V potassium were chosen for the selectivity study. The results showed that MMIP using MAA as functional monomer had a large adsorption capacity (53.77 mu g mg(-1)) as well as a large imprinting factor (more than 2) and the adsorption behavior was in accordance with a pseudo-second-order model and Freundlich isotherm model. Due to the excellent properties of the polymer: good magnetic properties, large adsorption capacity and great selectivity for DHP-CCBs, a magnetic molecularly imprinted solid phase extraction with ultraviolet detection (M-MISPE-UV) method had been established, validated and applied to the analysis of AML in urine sample.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据