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Dual-template molecularly imprinted polymers for dispersive solid-phase extraction of fluoroquinolones in water samples coupled with high performance liquid chromatography

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Abstract

Novel dual-template molecularly imprinted polymers (dt-MIPs) were prepared by simple and facile precipitation polymerization using norfloxacin (NOR) and enrofloxacin (ENR) as templates for simultaneous selective recognition and extraction of the two fluoroquinolones (FQs). The as-prepared dt-MIPs exhibited high adsorption capacity and excellent selectivity towards NOR and ENR. Several main parameters affecting the efficiency of dt-MIP based dispersive solid-phase extraction (DSPE) were systematically investigated, coupled with high performance liquid chromatography determination. Consequently, high enrichment factors of 71 and 61 were obtained for NOR and ENR respectively, and good linearity in the range of 1–200 μg L−1 was observed, with correlation coefficients (r) above 0.9977. The limits of detection and quantification for NOR were 0.22 and 0.67 μg L−1, respectively, and 0.36 and 0.98 μg L−1 for ENR. Satisfactory recoveries of the two FQs from spiked lake, sea and tap water samples at three concentration levels were attained in the range of 80.9–101.0% with relative standard deviations of 0.9–6.9%. The present study not only has great potential for applications in FQ determination, but will also enrich research into dual/multi-template imprinting.

Graphical abstract: Dual-template molecularly imprinted polymers for dispersive solid-phase extraction of fluoroquinolones in water samples coupled with high performance liquid chromatography

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Publication details

The article was received on 05 Nov 2018, accepted on 04 Dec 2018 and first published on 04 Dec 2018


Article type: Paper
DOI: 10.1039/C8AN02133C
Citation: Analyst, 2019, Advance Article
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    Dual-template molecularly imprinted polymers for dispersive solid-phase extraction of fluoroquinolones in water samples coupled with high performance liquid chromatography

    W. Lu, J. Liu, J. Li, X. Wang, M. Lv, R. Cui and L. Chen, Analyst, 2019, Advance Article , DOI: 10.1039/C8AN02133C

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