Supramolecular nanosolvent-based hollow fiber liquid phase microextraction as a novel method for simultaneous preconcentration of acidic, basic and amphiprotic pollutants $(document).ready(function() { if (!$("html").hasClass("ie8")) { $.ajax({ url: "https://crossmark-cdn.crossref.org/widget/v2.0/widget.js", dataType: "script", cache: true }).done(function() { $(".crossmark-button").addClass("visible"); }); } });

Abstract

The coextraction of acidic, basic and amphiprotic pollutants from various matrixes is a significant and disputable concept in sample preparation strategies. In this study, for the first time, coextraction of acidic, basic and amphiprotic pollutants was performed using supramolecular nanosolvent-based hollow fiber liquid phase microextraction (SS-HF-LPME) as an efficient method followed by high performance liquid chromatography-photodiode array detection. The supramolecular solvent (SUPRAS) is formed through coacervation of decanoic acid aqueous vesicles in the presence of tetrabutylammonium hydroxide. The results revealed that 40% SUPRAS in 1-decanol has the best extraction efficiency for three selected model analytes (4-nitrophenol, 3-nitroaniline and 1-amino-2-naphthol). The extraction process was accomplished in two-phase mode and the unique interactions between the solvent and polar analytes (hydrophobic, electrostatic, hydrogen bonding and π–cation interactions) resulted in elevated coextraction efficiency. Central composite design methodology combined with the desirability function approach was applied to develop predictive models for simulation and optimization of the SS-HF-LPME procedure. The optimized conditions were: pH of the sample, 9.0; percentage of SUPRAS in 1-decanol, 40%; extraction time, 30 min; salt concentration, 20% w/v; stirring rate, 1250 rpm. Under the optimum conditions, detection limits and linear dynamic ranges were achieved in the range of 0.1–0.2 μg L⁻¹ and 0.5–400 μg L⁻¹, respectively. The percentage extraction recoveries and relative standard deviations (n = 5) were in the range of 56.1–71.1 and 4.1–6.9, respectively. Finally, the applicability of this method was successfully confirmed by analyzing rain, snow, river, dam and wastewater samples.