Introducing a new and rapid microextraction approach based on magnetic ionic liquids: Stir bar dispersive liquid microextraction

摘要

With the aim of contributing to the development and improvement of microextraction techniques, a novel approach combining the principles and advantages of stir bar sorptive extraction (SBSE) and dispersive liquid-liquid microextraction (DLLME) is presented. This new approach, termed stir bar dispersive liquid microextraction (SBDLME), involves the addition of a magnetic ionic liquid (MIL) and a neodymium-core magnetic stir bar into the sample allowing the MIL coat the stir bar due to physical forces (i.e., magnetism). As long as the stirring rate is maintained at low speed, the MIL resists rotational (centrifugal) forces and remains on the stir bar surface in a manner closely resembling SBSE. By increasing the stirring rate, the rotational forces surpass the magnetic field and the MIL disperses into the sample solution in a similar manner to DLLME. After extraction, the stirring is stopped and the MIL returns to the stir bar without the requirement of an additional external magnetic field. The MIL-coated stir bar containing the preconcentrated analytes is thermally desorbed directly into a gas chromatographic system coupled to a mass spectrometric detector (TD-GC-MS). This novel approach opens new insights into the microextraction field, by using the benefits provided by SBSE and DLLME simultaneously, such as automated thermal desorption and high surface contact area, respectively, but most importantly, it enables the use of tailor-made solvents (i.e., MILs). To prove its utility, SBDLME has been used in the extraction of lipophilic organic UV filters from environmental water samples as model analytical application with excellent analytical features in terms of linearity, enrichment factors (67-791), limits of detection (low ng L-1), intra- and inter-day repeatability (RSD<15%) and relative recoveries (87-113%, 910-117% and 89-115% for river, sea and swimming pool water samples, respectively). (C) 2017 Elsevier B.V. All rights reserved.