4.6 Article Proceedings Paper

Graphene deposited onto aligned zinc oxide nanorods as an efficient coating for headspace solid-phase microextraction of gasoline fractions from oil samples

期刊

JOURNAL OF CHROMATOGRAPHY A
卷 1530, 期 -, 页码 45-50

出版社

ELSEVIER SCIENCE BV
DOI: 10.1016/j.chroma.2017.11.016

关键词

Headspace solid-phase microextraction; Graphene/ZnO nanorods coated fiber; Gasoline fractions; Oil samples; Gas chromatography-flame ionization detection

资金

  1. Applied Fundamental Research Program of Qingdao [15-9-1-94-JCH, 17-1-1-79-jch]
  2. Natural Scientific Foundation of Shandong [ZR2016BQ23]
  3. Fundamental Research Funds for the Central Universities [17CX02055]

向作者/读者索取更多资源

The content of gasoline fraction in oil samples is not only an important indicator of oil quality, but also an indispensable fundamental data for oil refining and processing. Before its determination, efficient preconcentration and separation of gasoline fractions from complicated matrices is essential. In this work, a thin layer of graphene (G) was deposited onto oriented ZnO nanorods (ZNRs) as a SPME coating. By this approach, the surface area of G was greatly enhanced by the aligned ZNRs, and the surface polarity of ZNRs was changed from polar to less polar, which were both beneficial for the extraction of gasoline fractions. In addition, the ZNRs were well protected by the mechanically and chemically stable G, making the coating highly durable for use. With headspace SPME (HS-SPME) mode, the G/ZNRs coating can effectively extract gasoline fractions from various oil samples, whose extraction efficiency achieved 1.5-5.4 and 2.1-8.2 times higher than those of a G and commercial 7-mu m PDMS coating respectively. Coupled with GC-FID, the developed method is sensitive, simple, cost effective and easily accessible for the analysis of gasoline fractions. Moreover, the method is also feasible for the detection of gasoline markers in simulated oil-polluted water, which provides an option for the monitoring of oil spill accident. (C) 2017 Elsevier B.V. All rights reserved.

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