An integrated microchannel with continuous electrodynamic anti-adhesion capability for particle loss reduction in air-based microfluidic chips
Source abstract
Abstract
In air-based microfluidic chips, particle adhesion to the walls of a microchannel deteriorates its performance and reliability due to structural changes such as channel clogging. In this study, an integrated anti-adhesion microchannel with a continuous electrodynamic anti-adhesion technique, used to detach particles adhered to the wall of a microchannel, was developed for applications using air-based microfluidic chips. Theoretical analysis of particle adhesion and electrodynamic anti-adhesion in a microfluidic channel was addressed based on computational fluid dynamics and electric simulation results. Particle loss due to adhesion to the wall of a microchannel was evaluated by the supply of airborne particles to the I-shaped microchannel and the serpentine microchannel. The reduction of particle loss using the electrodynamic anti-adhesion technique was then analyzed. In the I-shaped microchannel for the evaluation of penetration efficiency, most of the upstream particles penetrated the channel, making particle loss rare. The measured average penetration efficiency was 93.2%. On the other hand, heavy particle loss occurred in the curved section of the serpentine microchannel. The proposed technique was adopted to decrease particle loss. For the 1.28 μm polystyrene latex, the initial particle loss (0 V) was 94 ± 0.6%, while the particle loss with the proposed technique (3 kV at 1 kHz) was 78 ± 1.3%. The maximum reduction in particle loss was 17.9%. According to the experimental results, the proposed technique could enhance the sampling performance of air-based microfluidic chips.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Aerosol Filtration and Electrostatic Precipitation · Surface Modification and Superhydrophobicity · Particle Dynamics in Fluid Flows
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (20)
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