Literature DB >> 26462923

Detection of λ-cyhalothrin by a core-shell spherical SiO2-based surface thin fluorescent molecularly imprinted polymer film.

Lin Gao1,2, Wenjuan Han1, Xiuying Li1, Jixiang Wang2, Yongsheng Yan3, Chunxiang Li2, Jiangdong Dai4.   

Abstract

A fluorescent core-shell molecularly imprinted polymer based on the surface of SiO2 beads was synthesized and its application in the fluorescence detection of ultra-trace λ-cyhalothrin (LC) was investigated. The shell was prepared by copolymerization of acrylamide with allyl fluorescein in the presence of LC to form recognition sites. The experimental results showed that the thin fluorescent molecularly imprinted polymer (FMIP) film exhibited better selective recognition ability than fluorescent molecularly non-imprinted polymer (FNIP). A new nonlinear relationship between quenching rate and concentration was found in this work. In addition, the nonlinear relationship allowed a lower concentration range of 0-5.0 nM to be described by the Stern-Volmer equation with a correlation coefficient of 0.9929. The experiment results revealed that the SiO2@FMIP was satisfactory as a recognition element for determination of LC in soda water samples. Therefore this study demonstrated the potential of MIP for the recognition and detection of LC in food.

Entities:  

Keywords:  Fluorescence detection; Nonlinear equation; Surface molecularly imprinted polymer; Ultra-trace; λ-Cyhalothrin

Mesh:

Substances:

Year:  2015        PMID: 26462923     DOI: 10.1007/s00216-015-9083-6

Source DB:  PubMed          Journal:  Anal Bioanal Chem        ISSN: 1618-2642            Impact factor:   4.142


  2 in total

1.  A novel fluorescent functional monomer as the recognition element in core-shell imprinted sensors responding to concentration of 2,4,6-trichlorophenol.

Authors:  Baixiang Ren; Huan Qi; Xiuying Li; Lihui Liu; Lin Gao; Guangbo Che; Bo Hu; Liang Wang; Xue Lin
Journal:  RSC Adv       Date:  2018-02-06       Impact factor: 3.361

Review 2.  Affinity Sensing Strategies for the Detection of Pesticides in Food.

Authors:  Denise Capoferri; Flavio Della Pelle; Michele Del Carlo; Dario Compagnone
Journal:  Foods       Date:  2018-09-05
  2 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.