Magnetic‐Nanoparticle‐Based Dispersive Micro‐Solid Phase Extraction for the Determination of Crystal Violet in Environmental Water Samples

In present study, a rapid and simple method based on ultrasonic‐assisted magnetic dispersive micro‐solid phase extraction (MD‐μ‐SPE) using magnetic ZnFe2O4‐ZnS nanocomposites has been proposed for the determination of crystal violet (CV) in environmental water samples. To increase the extraction eff...

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Bibliographic Details
Published inChemistrySelect (Weinheim) Vol. 6; no. 19; pp. 4782 - 4790
Main Authors Shojaei, Siroos, Rahmani, Mashaallah, Khajeh, Mostafa, Abbasian, Ahmad Reza
Format Journal Article
LanguageEnglish
Published 20.05.2021
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Summary:In present study, a rapid and simple method based on ultrasonic‐assisted magnetic dispersive micro‐solid phase extraction (MD‐μ‐SPE) using magnetic ZnFe2O4‐ZnS nanocomposites has been proposed for the determination of crystal violet (CV) in environmental water samples. To increase the extraction efficiency, the affecting factors on the ultrasonic‐assisted magnetic dispersive micro‐solid phase extraction method were investigated and optimized by Taguchi method, artificial neural network (ANN), and response surface methodology (RSM). For proposed method, the crystal violet was selected as target dye. Under optimal conditions, the limits of detection were obtained 25 ng mL−1 and the calibration curve showed linearity in the range of 100–600 ng mL−1. The good extraction efficiency obtained indicates that the proposed MD‐μ‐SPE possesses a good potential for the determination of CV in environmental water samples. A rapid and simple method based on ultrasonic‐assisted magnetic dispersive micro‐solid phase extraction using magnetic ZnFe2O4‐ZnS nanocomposites has been proposed for the determination of crystal violet in environmental water samples. To increase the extraction efficiency, the affecting factors on the ultrasonic‐assisted magnetic dispersive micro‐solid phase extraction method were investigated and optimized by Taguchi method, artificial neural network, and response surface methodology.
ISSN:2365-6549
2365-6549
DOI:10.1002/slct.202100288