4.6 Article

Modified surface-active ionic liquid-coated magnetic graphene oxide as a new magnetic solid phase extraction sorbent for preconcentration of trace nickel

Journal

RSC ADVANCES
Volume 6, Issue 69, Pages 64193-64202

Publisher

ROYAL SOC CHEMISTRY
DOI: 10.1039/c6ra04163a

Keywords

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Funding

  1. Research Council of University of Tehran

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A new selective and high-capacity magnetic solid phase extraction sorbent was developed for preconcentration of trace amounts of nickel ions prior to their determination by flame atomic absorption spectrometry (FAAS). The sorbent was prepared by immobilization of the dimethylglyoxime ligand (DMG) onto magnetic graphene oxide nanoparticles (MGO) coated with the surface-active ionic liquid-based surfactant, 1-hexadecyl-3-methylimidazolium chloride ([C16MIM][Cl]). The prepared adsorbent (DMG-C16MIM/MGO) was characterized by FESEM, TEM, BET, XRD, VSM and FT-IR spectroscopy. The fabricated adsorbent combines the advantages of the superior adsorption capability of modified graphene oxide and magnetic separability of magnetic nanoparticles to provide high adsorption capacity, and easy isolation from sample solutions. Several important parameters influencing the extraction efficiency, such as pH, amount of adsorbent, extraction time, elution conditions, sample volume, interfering ions and adsorption capacity were studied and optimized. Applying all optimum conditions in the process, a high preconcentration factor of 100, linear range of 0.56-200 mg L-1, detection limit of 0.16 mg L-1, and precision (RSD%, n = 6) of 1.21%, were obtained for nickel. Following modification with 1-hexadecyl-3-methylimidazolium chloride and DMG, the modified adsorbent selectivity toward nickel ions was raised more than nine-fold compared to the unmodified magnetic graphene oxide. The adsorption capacity within a 15 min interaction time based on the Langmuir model was 129.87 and 26.59 mg g(-1) for modified and unmodified adsorbents, respectively. The recoveries in the case of real samples varied within the range of 96.8-99.2% confirming good performance of the method in various real samples.

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