4.7 Article

Graphene grown on stainless steel mesh as a highly efficient sorbent for sorptive microextraction of polycyclic aromatic hydrocarbons from water samples

Journal

ANALYTICA CHIMICA ACTA
Volume 994, Issue -, Pages 29-37

Publisher

ELSEVIER SCIENCE BV
DOI: 10.1016/j.aca.2017.08.049

Keywords

Polycyclic aromatic hydrocarbons; Stainless steel mesh; Graphene; Gas chromatography; Water samples

Funding

  1. Research Council of Hakim Sabzevari University

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In this study, graphene grown on stainless steel mesh efficiently incorporates the advanced properties of graphene in the form of an ultra-thin coating with the open geometry of the substrate, resulting in a highly sensitive and fast sorptive device capable of extracting target analytes directly from sample matrices. The synthesis of graphene on the stainless steel mesh was confirmed by scanning electron microscopy (SEM) and transmission electron microscopy (TEM). The advantages of the new micro-extraction device have been investigated for the determination of polycyclic aromatic hydrocarbons (PAHs) in environmental water samples in combination with gas chromatographyeflame ionization detector (GC-FID). The factors affecting the extraction efficiency such as stirring speed, extraction time, ionic strength and desorption conditions, were investigated and the optimal extraction conditions were established. Under the optimal extraction condition, linearity was in the range of 0.003-100 ng mL(-1) with a correlation coefficient of 0.9923-0.9985. The limits of detection (LODs) of the developed method are obtained between 1 and 3 pg mL(-1). The repeatability was assessed at two concentration levels (0.01 and 10 ng mL(-1)) and the relative standard deviations (RSDs) obtained were 4.9-8.7%. The preparation reproducibility of sorbent device, evaluated with the RSDs, was in the range of 7.3-10.2% (n = 5) in one batch, and 8.3-11.0% (n = 5) among different batches. Finally, the proposed method is applied for the determination of the target PAHs in the real water samples. (C) 2017 Elsevier B.V. All rights reserved.

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