Issue 5, 2023

Deep eutectic solvent-based dispersive liquid–liquid microextraction followed by smartphone digital image colorimetry for the determination of carbofuran in water samples

Abstract

A detection method of carbofuran (CBF) in water samples was reported using deep eutectic solvent (DES)-based dispersive liquid–liquid microextraction (DLLME) combined with digital image colorimetry (DIC), which was environmentally friendly, solvent-saving, rapid, and convenient. Under alkaline conditions, the green and multifunctional extractant DESs dissociated into linalool and heptanoic acid, and CBF was hydrolyzed to 2,3-dihydro-2,2-dimethyl-7-benzofuranol and further coupled with fast blue BB salt to form an azo derivative. Heptanoic acid led to the dispersion of linalool to extract the orange-red azo derivative; DIC was used for quantitative analysis using a smartphone with its associated ease of data-acquisition. This experiment optimized the types, molar ratios, and volumes of DESs and the amounts of sodium carbonate and sodium chloride. Under optimal conditions, the limits of detection (LOD) and quantitation (LOQ) were 0.024–0.032 mg L−1 and 0.081–0.108 mg L−1, respectively. The extraction recoveries in real samples (tap, pond, and river water) were 92.4–101.0% with a relative standard deviation below 4.6%. This method has successfully analyzed CBF in different water samples and shows prospects for the monitoring and control of CBF residues in other environmental samples.

Graphical abstract: Deep eutectic solvent-based dispersive liquid–liquid microextraction followed by smartphone digital image colorimetry for the determination of carbofuran in water samples

Supplementary files

Article information

Article type
Paper
Submitted
13 Nov 2022
Accepted
24 Dec 2022
First published
26 Dec 2022

Anal. Methods, 2023,15, 648-654

Deep eutectic solvent-based dispersive liquid–liquid microextraction followed by smartphone digital image colorimetry for the determination of carbofuran in water samples

L. Zhao, M. Wang, J. Wang, J. Wu, Z. Zhang, X. Jing and X. Wang, Anal. Methods, 2023, 15, 648 DOI: 10.1039/D2AY01861F

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