Rapid and sensitive detection of the phenoxy acid herbicides in environmental water samples by magnetic solid‐phase extraction combined with liquid chromatography–tandem mass spectrometry

Phenoxy acid herbicides are widely used herbicides that play an important role in improving the yield and quality of crops. However, some research has shown that this kind of herbicide is poisonous to human and animals. In this study, a rapid and sensitive method was developed for the detection of s...

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Veröffentlicht in:Journal of separation science 2018-05, Vol.41 (10), p.2221-2228
Hauptverfasser: Peng, Mao‐Min, Han, Ya‐Quan, Xia, Hong, Hu, Xi‐Zhou, Zhou, You‐Xiang, Peng, Li‐Jun, Peng, Xi‐Tian
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Sprache:eng
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Zusammenfassung:Phenoxy acid herbicides are widely used herbicides that play an important role in improving the yield and quality of crops. However, some research has shown that this kind of herbicide is poisonous to human and animals. In this study, a rapid and sensitive method was developed for the detection of seven phenoxy acid herbicides in water samples based on magnetic solid‐phase extraction followed by liquid chromatography and tandem mass spectrometry. Magnetic amino‐functionalized multiwalled carbon nanotubes were prepared by mixing bare magnetic Fe3O4 nanoparticles with commercial amino‐functionalized multiwalled carbon nanotubes in water. Then the amino‐functionalized multiwalled carbon nanotubes were used to enrich phenoxy acid herbicides from water samples based on hydrophobic and ionic interactions. The effects of experimental variables on the extraction efficiency have been studied in detail. Under the optimized conditions, the method validation was performed. Good linearities for seven phenoxy acid herbicides were obtained with squared regression coefficients ranging from 0.9971 to 0.9989. The limits of detection ranged from 0.01 to 0.02 μg/L. The method recoveries of seven phenoxy acid herbicides spiked at three concentration levels in a blank sample were from 92.3 to 103.2%, with inter‐ and intraday relative standard deviations less than 12.6%.
ISSN:1615-9306
1615-9314
DOI:10.1002/jssc.201701325