Issue 1, 2018

An ionic liquid-magnetic graphene composite for magnet dispersive solid-phase extraction of triazine herbicides in surface water followed by high performance liquid chromatography

Abstract

A new ionic liquid-magnetic graphene (IL-MG) composite was used as the adsorbent in magnetic dispersive solid-phase extraction to rapidly extract and isolate triazine herbicides from surface water. IL-MG was synthesized by a simple and time-saving one-pot strategy where the synthesis of magnetic Fe3O4, the modification with an IL, and the reduction of graphene oxide to graphene were conducted at the same time. An IL was applied to enrich the interaction mechanism between IL-MG and analytes (π–π, hydrophobic interaction, and electrostatic interaction). Moreover, the IL and Fe3O4 nanoparticles acted as spacers, inserting between the layers of graphene to prevent the aggregation of graphene, which improved the adsorption ability because of the large specific surface area of IL-MG. The resultant IL-MG had hierarchical flake structures and showed a high adsorption capacity (8266.0–12 324.1 μg g−1) toward triazine herbicides. Under suitable conditions, the linearity for triazine herbicides was achieved in the range of 0.55–500 ng mL−1 with a detection limit of 0.09–0.15 ng mL−1 and a quantitation limit of 0.31–0.51 ng mL−1, and the enrichment factor was 83-fold, which indicated that the proposed method could be successfully applied for the determination of triazine herbicides in surface water.

Graphical abstract: An ionic liquid-magnetic graphene composite for magnet dispersive solid-phase extraction of triazine herbicides in surface water followed by high performance liquid chromatography

Article information

Article type
Paper
Submitted
03 ส.ค. 2560
Accepted
01 พ.ย. 2560
First published
03 พ.ย. 2560

Analyst, 2018,143, 175-181

An ionic liquid-magnetic graphene composite for magnet dispersive solid-phase extraction of triazine herbicides in surface water followed by high performance liquid chromatography

H. Zhang, Y. Yuan, Y. Sun, C. Niu, F. Qiao and H. Yan, Analyst, 2018, 143, 175 DOI: 10.1039/C7AN01290J

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