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Issue 6, 2018
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Determination of ultra-trace levels of cobalt in water and wheat flour samples using cloud point extraction coupled with laser thermal lens spectrometry

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Abstract

A highly sensitive method for the determination of ultra-trace levels of cobalt using cloud point extraction (CPE) coupled with thermal lens spectrometry was proposed. It is based on the complexation of cobalt with a suitable chelating agent, 2-(5-iodio-2-pyridylazo)-5-dimethylaminoaniline (5-I-PADMA), and subsequent micelle-mediated extraction of the product using non-ionic surfactant octylphenoxypolyethoxyethanol (Triton X-114) as an extracting agent. The effects of experimental conditions such as pH, concentration of 5-I-PADMA and Triton X-114, equilibration temperature and time on CPE were studied. After phase separation, the surfactant rich phase containing the complex was dissolved with HCl–acetone solution prior to its analysis by thermal lens spectrometry. A single mode He–Ne laser with λ = 632.8 nm was used to produce both excitation and probe beams. Under optimal conditions, the calibration plot was linear in the range of 0.2–6.0 ng mL−1 cobalt, with a correlation coefficient of 0.9975. The detection limit obtained under optimum conditions was 0.02 ng mL−1. The sensitivity was enhanced by 331 times compared to that of the conventional spectrophotometric method. The proposed method was successfully applied to the extraction and determination of cobalt in water and wheat flour samples.

Graphical abstract: Determination of ultra-trace levels of cobalt in water and wheat flour samples using cloud point extraction coupled with laser thermal lens spectrometry

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Publication details

The article was received on 30 Oct 2017, accepted on 22 Dec 2017 and first published on 26 Dec 2017


Article type: Paper
DOI: 10.1039/C7AY02522J
Citation: Anal. Methods, 2018,10, 634-640
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    Determination of ultra-trace levels of cobalt in water and wheat flour samples using cloud point extraction coupled with laser thermal lens spectrometry

    Q. Han, Y. Huo, X. Yang, Y. He, J. Wu and H. Cai, Anal. Methods, 2018, 10, 634
    DOI: 10.1039/C7AY02522J

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