Issue 28, 2015

Green sample clean-up based on magnetic multiwalled carbon nanotubes for the determination of lamivudine by high performance liquid chromatography

Abstract

A simple, green and efficient method for the determination of trace lamivudine (LMV) based on magnetic solid-phase extraction (MSPE) coupled with high performance liquid chromatography (HPLC) was developed. Magnetic multiwalled carbon nanotubes (MMWCNTs) were prepared by assembling Ni nanoparticles onto multiwall carbon nanotubes (Ni/MWCNTs). Due to their excellent adsorption capability, the Ni/MWCNTs were used as adsorbents for magnetic solid-phase extraction (MSPE). Various parameters affecting the extraction efficiency such as the amount of adsorbent, the pH, ionic strength and desorption conditions were optimized. The extracted LMV was desorbed using ionic liquid (IL) “Green solvents” as substitutes of volatile organic solvents. Under optimized conditions, a limit of detection (LOD) of 0.14 ng mL−1 and enrichment factor (EF) of 292 were obtained. The calibration curves showed good linearity (r2 = 0.9978–0.9993) over the concentration ranges of 0.5–500 ng mL−1. The proposed method was applied to the analysis of river water and human urine samples, and the recoveries of analytes obtained were in the range of 97.6–103.0 and 93.4–102.2%, respectively. The relative standard deviations (RSD) were less than 5.93% (n = 5). The high efficiency of the method ensures its successful application in extraction of trace LMV from large volumes of environmental water and biological fluid samples.

Graphical abstract: Green sample clean-up based on magnetic multiwalled carbon nanotubes for the determination of lamivudine by high performance liquid chromatography

Article information

Article type
Paper
Submitted
04 Jan 2015
Accepted
12 Feb 2015
First published
12 Feb 2015

RSC Adv., 2015,5, 22022-22030

Green sample clean-up based on magnetic multiwalled carbon nanotubes for the determination of lamivudine by high performance liquid chromatography

L. Wang, X. Xu, Z. Zhang, D. Zhang, X. Liu and L. Zhang, RSC Adv., 2015, 5, 22022 DOI: 10.1039/C5RA00102A

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