| As a class of organic pollutants in aquatic media, phenols and their substitutes are of great environmental concern owing to their high toxicity. The decomposition of phenols is difficult due to, principally, their stability and their solubility in water. Because of their high toxicity, as well as their unpleasant organoleptic properties, some phenols have been included in the priority pollution list of the European Union (EU) and US Environmental Protection Agency (EPA). Therefore, the determination of trace phenols is very important for evaluating the total toxicity of an environmental water sample. Various methods, such as spectrophotometry, electrochemical methods, gas chromatography (GC), GC–mass spectrometry (GC–MS), liquid chromatography (LC), LC–MS, and capillary electrophoresis (CE) have been described in the literatures for the detection of phenolic compounds in water samples. Among the various methods, GC and HPLC have been commonly used for the determination of individual phenols. As a powerful complementary new technique to GC and HPLC, CE has been shown to be a rapid, powerful and efficient technique for separating phenols due to its major merit of high separation efficiency, no need of organic solvent for separation, extremely low solvent consumption, small sample volume requirement, ease of automation, and low running cost. This technique avoids the derivatization procedure and allows the accomplishment of well–resolved separations in less time than that usually taken by chromatographic methods. Nevertheless, CE techniques usually suffer from poor detection sensitivity, especially in the conventional UV absorbance detection because the system has the intrinsic drawbacks of short optical path length and low volume load ability. In order to improve the limited sensitivity in CE, coupling of CE to sample preconcentration techniques such as extraction, evaporation, and dialysis is an effective approach to determine analytes present at low levels in environmental samples.Conventional extraction methods, such as liquid–liquid extraction (LLE) and solid–phase extraction (SPE) are the most commonly used techniques for preconcentration and clean up of phenols. However, these methods involving multistep procedures are complex, laborious and time–consuming. Besides, LLE requires large volumes of organic solvent, making it environmentally unfriendly and a potential dange to human health. With respect to sample preconcentration, miniaturization of the pretreatment system has become the recent trend in consideration of the environmental compatibility and the high capability of the analytical systems.As an alternative to SPME, a methodology for ploymer monolith microextraction (PMME) based on the use of a capillary monolithic column was introduced by Prof Yuqi FENG of Wuhan University. Compared with conventional SPME methods, the monolithic structure has a larger surface area and emerges as a more popular alternative to packed columns due to the simplicity of its preparation. The in–tube configuration is better at protecting the extraction material from physical damage. In addition, the covective mass transfer procedure and low pressure–drop offered by the porous structure can facilitate the extraction process.The work presented here is to study the combination of poly–(MAA–EGDMA) monolith microextraction with CE for the determination of four phenols, catechol, resorcinol, 2,6–dimethylphenol, and 2,4,6–trinitrophenol in environmental water samples. Variable parameters including sample pH, sample flow rate, sample volume, and eluent flow rate are studied. Good linearity was achieved in the range of 0.03 to 26μg/mL with a linear coefficient r value above 0.9990. Under the experimental conditions, the intra–day and inter–day relative standard deviations are determined as lower than 6% and the recoveries for them in water samples are higher than 80%. Results show that the method combining polymer monolith microextraction with capillary electrophoresis is simple, rapid, sensitive and competent in monitoring phenols in water samples.
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