文献类型：期刊文献

英文题名：Artificial intelligence-motivated in-situ imaging for visualization investigation of submicron particles deposition in electric-flow coupled fields

作者：Tao, Shanlong[1,2];Yang, Xiaoyong[1];Yin, Wei[1];Zhu, Yong[1]

机构：[1]East China Univ Sci & Technol, Sch Mech & Power Engn, Shanghai 200237, Peoples R China;[2]Shanghai Jiao Tong Univ, Res Ctr Combust & Environm Technol, Sch Mech Engn, Shanghai 200240, Peoples R China

年份：2024

卷号：74

起止页码：13

外文期刊名：CHINESE JOURNAL OF CHEMICAL ENGINEERING

收录：;EI(收录号：20243616991895);WOS:【SCI-EXPANDED(收录号:WOS:001316648100001)】；

基金：This work was supported by the National Natural Science Foundation of China (52200130, 22308100) .

语种：英文

外文关键词：Artificial intelligence; In-situ imaging; Submicron particles; Lab-on-a-chip; Deposition

摘要：This study delves into the intricate deposition dynamics of submicron particles within electric-flow coupled fields, underscoring the unique challenges posed by their minuscule size, aggregation tendencies, and biological reactivity. Employing an operando investigation system that synergizes microfluidic technology with advanced micro-visualization techniques within a lab-on-a-chip framework enables a meticulous examination of the dynamic deposition phenomena. The incorporation of object detection and deep learning methodologies in image processing streamlines the automatic identification and swift extraction of crucial data, effectively tackling the complexities associated with capturing and mitigating these hazardous particles. Combined with the analysis of the growth behavior of particle chain under different applied voltages, it established that a linear relationship exists between the applied voltage and theta. And there is a negative correlation between the average particle chain length and electric field strength at the collection electrode surface (4.2x10(5) to 1.6x10(6) V.m(-1)). The morphology of the deposited particle agglomerate at different electric field strengths is proposed: dendritic agglomerate, long chain agglomerate, and short chain agglomerate. (c) 2024 The Chemical Industry and Engineering Society of China, and Chemical Industry Press Co., Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.