Issue 9, 2019
From the journal:

Analyst

Preparation of salbutamol imprinted magnetic nanoparticles via boronate affinity oriented surface imprinting for the selective analysis of trace salbutamol residues†

Zijun Bie,ab   Weiman Zhao,b   Zhongyuan Lv,a   Songlin Liua  and  Yang Chen ORCID logo *ab  

* Corresponding authors

a Department of Chemistry, Bengbu Medical University, 2600 Donghai Avenue, Bengbu 233000, China
E-mail: nbastuff@yeah.net

b School of Pharmacy, Bengbu Medical University, 2600 Donghai Avenue, Bengbu 233000, China

Abstract

Salbutamol (SAL) is one of the most widely abused feed additives in animal husbandry. Selective enrichment of SAL from complex biological samples is a key step for the analysis of SAL. Herein, we present SAL-imprinted magnetic nanoparticles (MNPs) as an ideal sorbent for selective enrichment of SAL. The SAL-imprinted MNPs were prepared according to the newly reported boronate affinity oriented surface imprinting. The prepared imprinted MNPs exhibited several significant advantages, such as excellent selectivity towards SAL, superb tolerance to interferences, fast binding equilibrium and easy manipulation, which made the obtained imprinted MNPs an ideal sorbent for selective enrichment. The feasibility of SAL-imprinted MNPs for real samples was well demonstrated by the analysis of trace SAL in pig liver. Because of their highly desirable properties, the SAL-imprinted MNPs could find more applications in the analysis of SAL.

Graphical abstract: Preparation of salbutamol imprinted magnetic nanoparticles via boronate affinity oriented surface imprinting for the selective analysis of trace salbutamol residues

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Article information

DOI
https://doi.org/10.1039/C9AN00198K
Article type
Paper
Submitted
28 Jan 2019
Accepted
07 Mar 2019
First published
14 Mar 2019

Analyst, 2019,144, 3128-3135

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Preparation of salbutamol imprinted magnetic nanoparticles via boronate affinity oriented surface imprinting for the selective analysis of trace salbutamol residues

Z. Bie, W. Zhao, Z. Lv, S. Liu and Y. Chen, Analyst, 2019, 144, 3128 DOI: 10.1039/C9AN00198K

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