Issue 25, 2018

Ionic liquid-based air-assisted liquid–liquid microextraction combined with dispersive micro-solid phase extraction for the preconcentration of copper in water samples

Abstract

Ionic liquid-based air-assisted liquid–liquid microextraction (IL-AALLME) combined with dispersive micro-solid phase extraction (D-μ-SPE) was applied for the extraction and preconcentration of Cu prior to analysis by graphite furnace atomic absorption spectrometry. IL [Hmim][PF6] was used as the extractant, non-ionic surfactant Triton X-100 as the emulsifier and Fe3O4 nanoparticles as the sorbent. The emulsion was rapidly formed by pulling in and pushing out the mixture of aqueous sample solution and extraction solvent repeatedly using a 10 mL glass syringe. Then, the extractant phase was mixed with Fe3O4 magnetic nanoparticles and separated using a magnet. As a result, the conventional tedious procedure associated with AALLME such as centrifugation was eliminated and the analytical process was accelerated. The experimental parameters affecting the extraction efficiency of Cu such as pH, IL volume, Triton X-100 concentration, Fe3O4 amount, number of extraction times and ultrasound time were investigated and optimized. Under the optimal conditions, the calibration curve was linear in the range of 1.5–120 ng mL−1 for Cu with a correlation coefficient (R) of 0.9992. The limit of detection was 0.5 ng mL−1. Finally, the applicability of this newly developed method was investigated for the analysis of Cu in real water samples.

Graphical abstract: Ionic liquid-based air-assisted liquid–liquid microextraction combined with dispersive micro-solid phase extraction for the preconcentration of copper in water samples

Article information

Article type
Paper
Submitted
10 Apr 2018
Accepted
20 May 2018
First published
24 May 2018

Anal. Methods, 2018,10, 3032-3038

Ionic liquid-based air-assisted liquid–liquid microextraction combined with dispersive micro-solid phase extraction for the preconcentration of copper in water samples

W. Du, L. Yao, J. Bian, Y. Liu, X. Wang, J. Zhang and L. Pang, Anal. Methods, 2018, 10, 3032 DOI: 10.1039/C8AY00800K

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