Topology-based coordination control for multi-droplet tasks in autonomous digital microfluidics.

Area of Science:

• Microfluidics
• Control Systems Engineering
• Bioengineering

Background:

• Digital microfluidics (DMF) offers parallel droplet control but faces challenges in large-scale, coordinated manipulation.
• Complex control strategies are needed to manage multi-droplet interactions and optimize operations.

Purpose of the Study:

• To develop an unmanned, topology-based digital microfluidics control (TDMC) system for autonomous multi-droplet operations.
• To address challenges in coordinating droplet movements, adapting to dynamic environments, and ensuring morphological integrity.

Main Methods:

• A dynamic droplet-electrode topological graph was created to represent droplets of varying sizes and resolve conflicts.
• An adaptive-topology path planning algorithm with leading-vertex guidance was implemented for efficient multi-droplet coordination.
• An encoder-decoder semantic segmentation model fused with event-driven feedback control enabled closed-loop autonomy and real-time task adaptation.

Main Results:

• The TDMC system successfully managed complex multi-droplet scenarios, including morphological adaptations and obstacle avoidance.
• Experimental validation demonstrated complete on-chip automation of biological assay workflows.
• The system achieved adaptive, flexible, and robust microfluidic manipulation.

Conclusions:

• The unmanned TDMC system provides a novel solution for autonomous, large-scale droplet manipulation in digital microfluidics.
• This technology has significant potential for applications in point-of-care testing, high-throughput drug screening, and synthetic chemistry.