An in silico study to geometrically optimize microfluidic trap array for trapping efficiency

Abstract

Microfluidic Trap Arrays (MTAs) appear as very promising tools for several applications like cancer understanding and treatment or immune synapse research. The MTAs principle is the following: samples are diluted inside a fluid going through a microfluidic chamber composed of an array of a large number of traps. While following the fluid flow, samples can encounter traps that isolate them or very small group of them from the others. However, it often appears that many traps stay empty, even after a long time which can drastically reduce the number of exploitable samples. In this paper we built a Computational Fluid Dynamic (CFD) Finite Element Model with a specific streamlines analysis to investigate how the MTA geometry affects its trapping efficiency and how it is possible to optimize the MTA geometry in order to maximize the trapping efficiency. Our results shows that optimization of MTA trapping efficiency is a complex and non-intuitive process and is about finding a compromise between combinations of geometric parameters values that can not be achieved with a purely experimental approach. Streamlines analysis suggests that the important feature is to deviate the particle trajectories that can be done with: a diagonal pressure gradient, the use of low