SUMMARY

This work aims to study the isolation and sensing of cells and particles via their mechanical and surface properties in microfluidic channels. We study the size based sorting of rigid and deformable particles in power law fluids using three-dimensional numerical simulations. We then examine the stiffness based isolation of viable cells both experimentally and numerically. For that, we use a microfluidic channel decorated with diagonal ridges hanging from the top of the channel. We probe the effect of flow rate, ridge angle, and ridge gap on cells motion in the device. Furthermore, we numerically examine the adhesion based sorting of cells in the microfluidic device using two different adhesion models. Finally, we use machine learning methods for sensing and identification of different cells sub-populations. The results of this work have applications in particle and cell sorting, viability monitoring, and measuring adhesion cell signature to enable downstream purification and analysis.