Issue 11, 2021

Application of core–satellite polydopamine-coated Fe3O4 nanoparticles–hollow porous molecularly imprinted polymer combined with HPLC-MS/MS for the quantification of macrolide antibiotics

Abstract

Core–satellite-structured magnetic nanosorbents (MNs) used for the selective extraction of macrolide antibiotics (MACs) were prepared in this study. The MNs (core–satellite polydopamine-coated Fe3O4 nanoparticles–hollow porous molecularly imprinted polymer) consisted of polydopamine-coated Fe3O4 nanoparticles (Fe3O4@PDA) “core” linked to numerous hollow porous molecularly imprinted polymer (HPMIP) “satellites” with bridging amine functional groups. It is worth mentioning that HPMIPs act as “anchors” for selectively capturing target molecules. Polymers were characterized using TEM, SEM, FT-IR, VSM, and TGA and applied as magnetic dispersive solid-phase extraction (MDSPE) sorbents for the enrichment of trace MACs from a complex food matrix prior to quantification by HPLC-MS/MS. Nanocomposites revealed outstanding magnetic properties (36.1 emu g−1), a high adsorption capacity (103.6 μmol g−1), selectivity (IF = 3.2), and fast kinetic binding (20 min) for MACs. The multiple advantages of the novel core–satellite-structured magnetic molecularly imprinted nanosorbents were confirmed, which makes us believe that the preparation method of the core–satellite MNs can be applied to other fields involving molecular imprinting technology.

Graphical abstract: Application of core–satellite polydopamine-coated Fe3O4 nanoparticles–hollow porous molecularly imprinted polymer combined with HPLC-MS/MS for the quantification of macrolide antibiotics

Supplementary files

Article information

Article type
Paper
Submitted
02 Nov 2020
Accepted
22 Feb 2021
First published
22 Feb 2021

Anal. Methods, 2021,13, 1412-1421

Application of core–satellite polydopamine-coated Fe3O4 nanoparticles–hollow porous molecularly imprinted polymer combined with HPLC-MS/MS for the quantification of macrolide antibiotics

W. Fan, D. Yang, N. Ding, P. Chen, L. Wang, G. Tao, F. Zheng and S. Ji, Anal. Methods, 2021, 13, 1412 DOI: 10.1039/D0AY02025G

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