Determination of parabens in wastewater samples via robot‐assisted dynamic single‐drop microextraction and liquid chromatography–tandem mass spectrometry
Dynamic single‐drop microextraction (SDME) was automatized employing an Arduino‐based lab‐made Cartesian robot and implemented to determine parabens in wastewater samples in combination with liquid chromatography–tandem mass spectrometry. A dedicated Arduino sketch controls the auto‐performance of a...
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Published in | Electrophoresis Vol. 43; no. 15; pp. 1567 - 1576 |
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Main Authors | , , , , |
Format | Journal Article |
Language | English |
Published |
Weinheim
Wiley Subscription Services, Inc
01.08.2022
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Subjects | |
Online Access | Get full text |
ISSN | 0173-0835 1522-2683 1522-2683 |
DOI | 10.1002/elps.202100390 |
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Summary: | Dynamic single‐drop microextraction (SDME) was automatized employing an Arduino‐based lab‐made Cartesian robot and implemented to determine parabens in wastewater samples in combination with liquid chromatography–tandem mass spectrometry. A dedicated Arduino sketch controls the auto‐performance of all the stages of the SDME process, including syringe filling, drop exposition, solvent recycling, and extract collection. Univariate and multivariate experiments investigated the main variables affecting the SDME performance, including robot‐dependent and additional operational parameters. Under selected conditions, limit of detections were established at 0.3 µg/L for all the analytes, and the method provided linear responses in the range between 0.6 and 10 µg/L, with adequate reproducibility, measured as intraday relative standard deviations (RSDs) between 5.54% and 17.94%, (n = 6), and inter‐days RSDs between 8.97% and 16.49% (n = 9). The robot‐assisted technique eased the control of dynamic SDME, making the process more feasible, robust, and reliable so that the developed setup demonstrated to be a competitive strategy for the automated extraction of organic pollutants from water samples. |
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Bibliography: | Color online See the article online to view Figures 1–5 in color. ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 |
ISSN: | 0173-0835 1522-2683 1522-2683 |
DOI: | 10.1002/elps.202100390 |