文献类型：期刊文献

英文题名：Topology-based coordination control for multi-droplet tasks in autonomous digital microfluidics

作者：Guo, Kunlun[1];Song, Zerui[1];Feng, Boyi[1];Qiu, Tiaofen[1];Zhou, Jiale[1];Shen, Bin[1];Yan, Bingyong[1];Gu, Zhen[1,2];Wang, Huifeng[1,2]

机构：[1]East China Univ Sci & Technol, Key Lab Smart Mfg Energy Chem Proc, Minist Educ, Shanghai 200237, Peoples R China;[2]East China Univ Sci & Technol, State Key Lab Bioreactor Engn, Shanghai 200237, Peoples R China

年份：2026

外文期刊名：LAB ON A CHIP

收录：;EI(收录号：20261720564820);WOS:【SCI-EXPANDED(收录号:WOS:001745876800001)】；

基金：This research was funded by the National Key R&D Program of China (2023YFA1802000), the National Natural Science Foundation of China (62103148) and the Shanghai Science and Technology Commission (contract number: 23141900900).

语种：英文

外文关键词：Adaptive control systems - Closed loop control systems - Control theory - Coordination reactions - Drop formation - Dynamics - Electrodes - Feedback control - Large scale systems - Optimization - Screening - Semantic Segmentation - Semantics - Topology

摘要：Digital microfluidics (DMF) is a versatile technique for parallel and field-programmable control of individual droplets. The challenge of large-scale parallel droplet manipulation in DMF is essentially a cross-scale complex system control problem that combines multi-droplet coordination optimization and feedback control. Here, we develop an unmanned topology-based digital microfluidics control (TDMC) system that integrates adaptive path planning with semantic segmentation feedback for autonomous multi-droplet operations. The core innovation lies in a dynamic droplet-electrode topological graph that both unifies the representation of droplets with arbitrary sizes and morphologies and resolves inter-droplet conflicts. Building upon this representation, the adaptive-topology path planning algorithm implements a leading-vertex guidance mechanism to efficiently coordinate the movements of droplets covering multiple electrodes while preserving morphological integrity. By fusing an encoder-decoder semantic segmentation model with event-driven feedback control, the system achieves closed-loop autonomy for dynamic path reconfiguration and real-time task adaptation. Experimental validation demonstrates that the TDMC system successfully handles complex multi-droplet scenarios including morphological adaptations, obstacle avoidance, and dynamic droplet operations, achieving complete on-chip automation of biological assay workflows. Thus, this unmanned TDMC system provides an adaptive, flexible, and robust microfluidic manipulation for point-of-care testing, high-throughput drug screening, and synthetic chemistry.