Robotic-assisted dynamic large drop microextraction.

Area of Science:

• Analytical Chemistry
• Environmental Science
• Separation Science

Background:

• Sulfonamides and fluoroquinolones are common contaminants in surface water.
• Accurate determination of these pollutants is crucial for environmental monitoring.
• Existing microextraction techniques can be labor-intensive and require significant optimization.

Purpose of the Study:

• To develop and validate an automated dynamic large drop microextraction method.
• To determine sulfonamides and fluoroquinolones in surface water samples.
• To assess the performance and reliability of the novel analytical strategy.

Main Methods:

• Utilized a lab-made multipurpose autosampler for automated dynamic large drop microextraction (DLM).
• Employed direct immersion single drop microextraction (SDME) coupled with liquid chromatography-tandem mass spectrometry (LC-MS/MS).
• Investigated critical parameters including extraction mode, stirring rate, salt addition, and drop size using univariate and multivariate optimization.

Main Results:

• The automated DLM method achieved high extraction efficiencies due to large, stable drops and dynamic mode.
• The method demonstrated excellent reliability with low intraday (4.0–7.6%) and interday (4.8–9.3%) relative standard deviations.
• Achieved low limits of detection (LOD) and quantification (LOQ) for sulfonamides and fluoroquinolones (15–50 ng/L and 35–100 ng/L, respectively).

Conclusions:

• The developed automated dynamic large drop microextraction is a sensitive, robust, and reliable tool for analyzing sulfonamides and fluoroquinolones in surface water.
• The innovative autosampler design simplifies complex microextraction procedures.
• This technique offers a valuable advancement for environmental water quality assessment.