Simultaneous extraction and concentration of water pollution tracers using ionic-liquid-based systems
authors Dinis T, Passos H, Lima D, Sousa ACA, Coutinho J, Esteves V, Freire MG
nationality International
journal Journal of Chromatography A
abstract Human activities are responsible for the release of innumerous substances into the aquatic environment. Some of these substances can be used as pollution tracers to identify contamination sources and to prioritize monitoring and remediation actions. Thus, their identification and quantification are of high priority. However, due to their presence in complex matrices and at significantly low concentrations, a pre-treatment/concentration step is always required. As an alternative to the currently used pre-treatment methods, mainly based on solid-phase extractions, aqueous biphasic systems (ABS) composed of ionic liquids (ILs) and K3C6H5O7 are here proposed for the simultaneous extraction and concentration of mixtures of two important pollution tracers, caffeine (CAF) and carbamazepine (CBZ). An initial screening of the IL chemical structure was carried out, with extraction efficiencies of both tracers to the IL-rich phase ranging between 95 and 100%, obtained in a single-step. These systems were then optimized in order to simultaneously concentrate CAF and CBZ from water samples followed by HPLC-UV analysis, for which no interferences of the ABS phase-forming components and other interferents present in a wastewater effluent sample have been found. Based on the saturation solubility data of both pollution tracers in the IL-rich phase, the maximum estimated concentration factors of CAF and CBZ are 28595- and 8259-fold. IL-based ABS can be thus envisioned as effective pre-treatment techniques of environmentally-related aqueous samples for a more accurate monitoring of mixtures of pollution tracers.
publisher Elsevier
year published 2017
digital object identifier (doi) 10.1016/j.chroma.2017.07.084
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  impact metrics
journal impact factor (jcr 2016): 3.981
5 year journal impact factor (jcr 2016): 4.008
category normalized journal impact factor percentile (jcr 2016): 83.027
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