Miniaturized on-chip electromembrane extraction with QR code-based red-green-blue analysis using a customized Android...

Area of Science:

• Analytical Chemistry
• Environmental Science
• Materials Science

Background:

• Copper is an essential element but toxic at elevated levels, necessitating accurate quantification in diverse matrices.
• Existing methods for copper determination can be time-consuming, require specialized equipment, or involve complex sample preparation.

Purpose of the Study:

• To develop a miniaturized on-chip electromembrane extraction device for rapid and sensitive copper determination.
• To integrate QR code-based colorimetric analysis with a customized Android application for qualitative and quantitative copper analysis.
• To validate the device's performance for copper detection in water, food, and soil samples.

Main Methods:

• Electromembrane extraction using a miniaturized on-chip device with ascorbic acid and bathocuproine.
• Formation of a yellowish-orange complex for copper detection.
• QR code-based red-green-blue (RGB) analysis of the dried acceptor droplet.
• Development of a customized Android application incorporating principal component analysis (PCA) for data reduction and analysis.
• Optimization of extraction parameters and validation of analytical performance.

Main Results:

• The device successfully determined copper through the formation of a yellowish-orange complex.
• The developed Android application with PCA enabled accurate qualitative and quantitative analysis.
• Achieved a limit of detection (LOD) of 0.1 µg mL⁻¹ and a limit of quantification (LOQ) of 0.1 µg mL⁻¹.
• Demonstrated high precision with intra-assay relative standard deviations (RSD) of 2.0-2.3% and inter-assay RSD of 3.1-3.7%.
• Established a linear calibration range from 0.1 to 25 µg mL⁻¹ with a high coefficient of determination (R² = 0.9814).

Conclusions:

• The miniaturized on-chip electromembrane extraction device coupled with QR code-based RGB analysis provides a sensitive, rapid, and cost-effective method for copper determination.
• The integration of PCA in the Android application effectively reduces RGB data for robust analysis.
• This innovative approach holds significant potential for on-site and field monitoring of copper contamination in environmental and food samples.