Automation of Fluorescence-Activated Droplet Release by Deep-Learning-Based Droplet Detector.

Area of Science:

• Microfluidics
• Biotechnology
• Artificial Intelligence

Background:

• Droplet microfluidics facilitates high-throughput biochemical assays but struggles with selective droplet retrieval.
• Existing light-induced droplet release methods are complex or slow.
• Previous work developed a light-responsive fluorosurfactant for rapid droplet release.

Purpose of the Study:

• To develop a fully automated system for droplet microfluidics.
• To integrate light-triggered release with AI for autonomous droplet selection.
• To enable precise, high-throughput screening workflows.

Main Methods:

• Developed a Fluorescence-Activated Droplet Release (FADR) system.
• Integrated a deep-learning-based droplet detector for real-time identification and localization.
• Utilized a previously developed light-responsive fluorosurfactant and 532 nm laser for droplet release.

Main Results:

• Achieved fully automated, AI-driven identification and selective release of droplets.
• Demonstrated real-time triggering of droplet release based on fluorescence intensity.
• Enabled precise droplet manipulation without human intervention.

Conclusions:

• The FADR system offers a scalable and intelligent solution for droplet microfluidics.
• This closed-loop platform enhances high-throughput screening capabilities.
• The system provides robust and efficient droplet manipulation with reduced hardware complexity.