Quantitative Correlation of Droplets on Galvanic-Coupled Arrays with Response Current by Image Processing

Area of Science:

• Materials Science
• Sensor Technology
• Microfluidics

Background:

• Accurate detection of trace moisture is vital for applications like dew condensation monitoring and biological fluid analysis.
• Existing methods for micro-volume water detection can be complex or time-consuming.

Purpose of the Study:

• To develop and validate a microgalvanic cell-based moisture sensor for quantifying slight amounts of water.
• To investigate the relationship between sensor response current and water droplet geometry.

Main Methods:

• Fabrication of a moisture sensor using narrow metal arrays in a microgalvanic cell configuration.
• Simultaneous recording of sensor output current and microscopic imaging of water droplets.
• Analysis of droplet parameters using manual methods, ImageJ, and a deep learning approach for image processing.

Main Results:

• The sensor's response current positively correlates with the total projected area of water droplets bridging the sensor's metal arrays.
• A strong linear correlation was observed between the response current and the total volume of bridging water droplets.
• Deep learning image analysis significantly accelerated droplet quantification (1/1000th of manual time) with high accuracy (90-100%).

Conclusions:

• The developed microgalvanic moisture sensor effectively estimates the presence and volume of trace water.
• The sensor's response is reliably linked to droplet geometry, offering a practical solution for micro-water detection.