Literature DB >> 20334869

DC dielectrophoretic particle-particle interactions and their relative motions.

Ye Ai1, Shizhi Qian.   

Abstract

When particles in an electrolyte subjected to an external electric field get close to each other, the presence of particles could alter the local electric field and consequently induce mutual dielectrophoretic (DEP) forces on each other. In this paper, a transient, two-dimensional (2D) multiphysics model taking into account the particle-fluid-electric field interactions under a thin electrical double layer (EDL) assumption is performed to investigate the effects of the imposed electric field, the initial particle's orientation and distance on the DEP particle-particle interaction between a pair of micro-sized particles and their relative motions. Prior to the study of the DEP particle-particle interaction, the magnitude comparison between the DEP particle-particle interaction and the Brownian motion is analyzed. When the DEP particle-particle interaction dominates the random Brownian motion, it is expected to observe the particle chaining along the direction of the imposed electric field, independent of the initial particle orientation. The numerical predictions are in qualitative agreement with the experimental observations available from the literature. During the attraction motion of particles, their velocities tend to dramatically decrease due to the rapid increase in the repulsive hydrodynamic pressure force when the particle distance decreases to a certain value. One exclusive exception of the particle chaining occurs when the initial connecting line of the particles is perpendicular to the imposed electric field, which is extremely unstable owing to the inevitable Brownian motion. 2010 Elsevier Inc. All rights reserved.

Year:  2010        PMID: 20334869     DOI: 10.1016/j.jcis.2010.03.003

Source DB:  PubMed          Journal:  J Colloid Interface Sci        ISSN: 0021-9797            Impact factor:   8.128


  11 in total

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Journal:  J Biol Phys       Date:  2018-10-26       Impact factor: 1.365

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Journal:  Electrophoresis       Date:  2019-04-29       Impact factor: 3.535

5.  Comparing machine learning and deep learning regression frameworks for accurate prediction of dielectrophoretic force.

Authors:  Sunday Ajala; Harikrishnan Muraleedharan Jalajamony; Midhun Nair; Pradeep Marimuthu; Renny Edwin Fernandez
Journal:  Sci Rep       Date:  2022-07-13       Impact factor: 4.996

6.  Elucidating the Mechanisms of Two Unique Phenomena Governed by Particle-Particle Interaction under DEP: Tumbling Motion of Pearl Chains and Alignment of Ellipsoidal Particles.

Authors:  Yu Zhao; Jozef Brcka; Jacques Faguet; Guigen Zhang
Journal:  Micromachines (Basel)       Date:  2018-06-01       Impact factor: 2.891

7.  Numerical Investigation of DC Dielectrophoretic Deformable Particle⁻Particle Interactions and Assembly.

Authors:  Xiang Ji; Li Xu; Teng Zhou; Liuyong Shi; Yongbo Deng; Jie Li
Journal:  Micromachines (Basel)       Date:  2018-05-25       Impact factor: 2.891

8.  Highly efficient removal of ammonia nitrogen from wastewater by dielectrophoresis-enhanced adsorption.

Authors:  Dongyang Liu; Chenyang Cui; Yanhong Wu; Huiying Chen; Junfeng Geng; Jianxin Xia
Journal:  PeerJ       Date:  2018-06-15       Impact factor: 2.984

9.  Colloidal shuttles for programmable cargo transport.

Authors:  Ahmet F Demirörs; Fritz Eichenseher; Martin J Loessner; André R Studart
Journal:  Nat Commun       Date:  2017-11-30       Impact factor: 14.919

10.  Deep-Learning Based Estimation of Dielectrophoretic Force.

Authors:  Sunday Ajala; Harikrishnan Muraleedharan Jalajamony; Renny Edwin Fernandez
Journal:  Micromachines (Basel)       Date:  2021-12-28       Impact factor: 2.891

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