Campus Units

Chemistry

Document Type

Article

Publication Version

Accepted Manuscript

Publication Date

2019

Journal or Book Title

Analytica Chimica Acta

DOI

10.1016/j.aca.2019.02.013

Abstract

In this study, an innovative and high-throughput parallel-single-drop microextraction (Pa-SDME) using the [P6,6,6,14+]2[MnCl42−] magnetic ionic liquid (MIL) as extraction phase is demonstrated, for the first time, in the determination of methylparaben, ethylparaben, propylparaben, bisphenol A, butylparaben, benzophenone and triclocarban from environmental aqueous samples. This experimental setup comprised of a 96-well plate system containing a set of magnetic pins which aided in stabilizing the MIL drops and enabled the simultaneous extraction of up to 96 samples. Using this low-cost experimental apparatus, the sample throughput was lower than 1 min per sample. This novel approach exhibits a number of advantages over classical SDME approaches, particularly in maintaining a stable solvent microdrop and facilitating high-throughput analysis. Experimental conditions were carefully optimized using one-factor-at-a-time and multivariate designs. The optimal conditions employed 5.38 ± 0.55 mg (n = 10) of MIL, a sample volume of 1.5 mL at pH 6, and dilution in 20 μL of acetonitrile. The analytical parameters of merit were determined under the optimized conditions and highly satisfactory results were achieved, with LODs ranging from 1.5 to 3 μg L−1 and coefficients of determination higher than 0.994. Intraday and interday precision ranged from 0.6 to 21.3% (n = 3) and 10.4–20.2% (n = 9), respectively, with analyte relative recovery in three aqueous samples ranging between 63% and 126%.

Comments

This is a manuscript of an article published as Mafra, Gabriela, Augusto A. Vieira, Josias Merib, Jared L. Anderson, and Eduardo Carasek. "Single drop microextraction in a 96-well plate format: a step toward automated and high-throughput analysis." Analytica Chimica Acta (2019). DOI: 10.1016/j.aca.2019.02.013. Posted with permission.

Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

Copyright Owner

Elsevier B.V.

Language

en

File Format

application/pdf

Available for download on Friday, February 21, 2020

Published Version

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