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

doi:10.1016/j.cjche.2024.05.028

中国化学工程学报 ›› 2024, Vol. 74 ›› Issue (10): 13-21.DOI: 10.1016/j.cjche.2024.05.028

• Full Length Article • 上一篇下一篇

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

Shanlong Tao^1,2, Xiaoyong Yang¹, Wei Yin¹, Yong Zhu¹

1 School of Mechanical and Power Engineering, East China University of Science and Technology, Shanghai 200237, China;
2 Research Center for Combustion and Environmental Technology, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China

收稿日期:2024-03-19 修回日期:2024-05-13 接受日期:2024-05-26 出版日期:2024-10-28 发布日期:2024-07-10
通讯作者: Xiaoyong Yang,E-mail:xyyang@ecust.edu.cn;Yong Zhu,E-mail:zyong@ecust.edu.cn
基金资助:
This work was supported by the National Natural Science Foundation of China (52200130, 22308100).

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

Shanlong Tao^1,2, Xiaoyong Yang¹, Wei Yin¹, Yong Zhu¹

1 School of Mechanical and Power Engineering, East China University of Science and Technology, Shanghai 200237, China;
2 Research Center for Combustion and Environmental Technology, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China

Received:2024-03-19 Revised:2024-05-13 Accepted:2024-05-26 Online:2024-10-28 Published:2024-07-10
Contact: Xiaoyong Yang,E-mail:xyyang@ecust.edu.cn;Yong Zhu,E-mail:zyong@ecust.edu.cn
Supported by:
This work was supported by the National Natural Science Foundation of China (52200130, 22308100).

摘要/Abstract

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 θ. And there is a negative correlation between the average particle chain length and electric field strength at the collection electrode surface (4.2×10⁵ to 1.6×10⁶ 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.

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

Abstract: 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 θ. And there is a negative correlation between the average particle chain length and electric field strength at the collection electrode surface (4.2×10⁵ to 1.6×10⁶ 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.

Key words: Artificial intelligence, In-situ imaging, Submicron particles, Lab-on-a-chip, Deposition

Shanlong Tao, Xiaoyong Yang, Wei Yin, Yong Zhu. Artificial intelligence-motivated in-situ imaging for visualization investigation of submicron particles deposition in electric-flow coupled fields[J]. 中国化学工程学报, 2024, 74(10): 13-21.

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

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

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