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An artificial intelligence-assisted digital microfluidic system for multistate droplet control.

Kunlun Guo¹, Zerui Song¹, Jiale Zhou¹

¹Key Laboratory of Smart Manufacturing in Energy Chemical Process Ministry of Education, East China University of Science and Technology, Shanghai, China.

Microsystems & Nanoengineering

|September 26, 2024

Summary

This study introduces μDropAI, an AI-driven digital microfluidics (DMF) system for precise droplet control. It uses AI to recognize droplet states, enabling automated, accurate manipulation and splitting with improved volume precision.

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Area of Science:

Microfluidics
Artificial Intelligence
Biotechnology

Background:

Digital microfluidics (DMF) offers parallel and programmable droplet control.
Current DMF systems lack intelligent control for variable droplet states and interactions.
Existing research primarily focuses on droplet localization and shape recognition, not dynamic control.

Purpose of the Study:

To develop an AI-assisted DMF framework (μDropAI) for multistate droplet control based on droplet morphology.
To enable self-adaptive and intelligent control informed by real-time droplet states and interactions.
To improve the precision of volume control in droplet splitting operations.

Main Methods:

Integration of a semantic segmentation model into a custom-designed DMF system.
Utilizing a state machine for feedback control based on recognized droplet states and interactions.
Developing an AI framework (μDropAI) for droplet morphology-based control.

Main Results:

The μDropAI system achieved high accuracy (<0.63% error rate) in recognizing droplets of various colors and shapes.
Enabled user-independent control of droplets through automated recognition and feedback.
Reduced the coefficient of variation (CV) for split droplet volumes to 2.74%, surpassing traditional methods.

Conclusions:

The developed AI-assisted DMF framework (μDropAI) provides robust and precise multistate droplet control.
The system demonstrates significant improvements in volume control precision for droplet splitting.
This work paves the way for semantic-driven DMF systems and future integration with large language models for fully automated control.