Expand this Topic clickable element to expand a topic
Skip to content
Optica Publishing Group
  • Applied Spectroscopy
  • Vol. 70,
  • Issue 11,
  • pp. 1891-1899
  • (2016)

Preconcentration of Zn, Cu, and Ni Ions from Coffee Infusions via 8-Hydroxyquinoline Complexes on Graphene Prior to Energy Dispersive X-ray Fluorescence Spectrometry Determination

Not Accessible

Your library or personal account may give you access

Abstract

A simple and effective preconcentration procedure based on dispersive micro solid-phase extraction prior to energy dispersive X-ray fluorescence spectrometric (EDXRF) determination of trace amounts of Ni, Cu, and Zn in coffee infusions was proposed. The method is based on the adsorption of 8-hydroxyquinoline metal complexes on micro amounts of graphene nanoparticles. In order to optimize adsorption process, the influence of some parameters such as pH, graphene mass, concentration of 8-hydroxyquinoline (8-HQ) and Triton X-100, sample volume, and sorption time were examined. At optimal preconcentration conditions, calibration curves were linear from 1 to 150 ng mL−1 for Ni and Cu and from 1 to 200 ng mL−1 for Zn. The recoveries of the metal ions were in the 95–98% range with the precision lower than 4.6%. The obtained detection limits were 0.08 ng mL−1 for Ni and 0.09 ng mL−1 for Cu and Zn. The proposed method was successfully applied to determination of Ni, Cu, and Zn in coffee infusions. Accuracy and repeatability of the proposed procedure were confirmed by the standard addition method and compared to the results obtained by ICP-OES technique.

© 2016 The Author(s)

PDF Article
More Like This

Cited By

You do not have subscription access to this journal. Cited by links are available to subscribers only. You may subscribe either as an Optica member, or as an authorized user of your institution.

Contact your librarian or system administrator
or
Login to access Optica Member Subscription

Select as filters


Select Topics Cancel
© Copyright 2024 | Optica Publishing Group. All rights reserved, including rights for text and data mining and training of artificial technologies or similar technologies.