Iron nanoparticles decorated hierarchical carbon fiber forest for the magnetic solid-phase extraction of multi-pesticide residues from water samples

This study describes a versatile, robust and fast sample pre-concentration novel method based on chemical vapour deposition grown iron nanoparticles dispersed hierarchical carbon fiber forest (Fe-ACF/CNF) for the determination of multi-pesticide residue in water samples. This method was developed by the implementation of Fe-ACF/CNF to magnetic solid-phase extraction method (MSPE) for the adsorption of twenty-nine pesticides of various classes using gas chromatography equipped with an electron capture detector. Fe-ACF/CNF was grown via tip growth mechanism and Fe-nanoparticles are moved to the tip of CNF. The presence of Fe-nanoparticles is responsible for the magnetic property of proposed adsorbents. The Fe-ACF/CNF is competent enough to extract twenty-nine pesticides of different physico-chemical characteristics from water samples. All the predominant parameters including the amount of sorbent desorption time, temperature, sonication effect, regeneration, and reusability of Fe-ACF/CNF were thoroughly investigated. Acceptable linearity was obtained in the range of 20-500 mu g/L with a correlation coefficient value >= 0.990 for all pesticides. The accuracy of the developed method was evaluated and the obtained recovery of the spiked samples was within 70-120% (standard deviation <= 15%) and reusability up to the 4th cycle. The limit of detection and quantification values was in the range of 1.44-5.15 and 4.76-17.0 mu g/L, respectively. The obtained results are also cross verified with real water samples from the Gomti river (Lucknow, India) and shown the excellent extraction efficiency of Fe-ACF/CNF.

Automated summary

This study developed a versatile and robust method using Fe-ACF/CNF for the determination of multi-pesticide residue in water samples, showing excellent extraction efficiency and high accuracy. Fe-ACF/CNF was proven competent to extract pesticides of various classes with acceptable linearity.