文献类型：期刊文献

英文题名：An artificial intelligence-assisted digital microfluidic system for multistate droplet control

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

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

年份：2024

卷号：10

期号：1

外文期刊名：MICROSYSTEMS & NANOENGINEERING

收录：;EI(收录号：20243917116951);WOS:【SCI-EXPANDED(收录号:WOS:001324825400005)】；

基金：This research was funded by the National Key R&D Program of China (2021YFC2101101) and the National Natural Science Foundation of China (No. 62103148).

语种：英文

外文关键词：Digital microfluidics - Drop breakup - Feedback control - Programmed control systems - Self adjusting control systems

摘要：Digital microfluidics (DMF) is a versatile technique for parallel and field-programmable control of individual droplets. Given the high level of variability in droplet manipulation, it is essential to establish self-adaptive and intelligent control methods for DMF systems that are informed by the transient state of droplets and their interactions. However, most related studies focus on droplet localization and shape recognition. In this study, we develop the AI-assisted DMF framework mu DropAI for multistate droplet control on the basis of droplet morphology. The semantic segmentation model is integrated into our custom-designed DMF system to recognize the droplet states and their interactions for feedback control with a state machine. The proposed model has strong flexibility and can recognize droplets of different colors and shapes with an error rate of less than 0.63%; it enables control of droplets without user intervention. The coefficient of variation (CV) of the volumes of split droplets can be limited to 2.74%, which is lower than the CV of traditional dispensed droplets, contributing to an improvement in the precision of volume control for droplet splitting. The proposed system inspires the development of semantic-driven DMF systems that can interface with multimodal large language models (MLLMs) for fully automatic control.