Computational Modeling and Optimization of Cell Focusing in Inertial and Elasto-Inertial Microfluidics

Inertial and elasto-inertial microfluidics offer promising platforms for high-throughput, label-free cell sorting by leveraging hydrodynamic forces to focus particles and cells within confined flows. This dissertation presents a computational investigation into the mechanisms governing cell migration and focusing in such systems, with an emphasis on enhancing performance and specificity in biomedical and agricultural applications. This work systematically explores how parameters such as channel geometry, flow conditions, particle size, and shape affect lateral migration and equilibrium positioning. Particular attention is given to biologically relevant cells, including sperm cells, whose asymmetric morphology and mechanical properties pose challenges for traditional sorting techniques. The numerical models developed in this work capture fluid–structure interactions and complex dynamics in both Newtonian and non-Newtonian (viscoelastic) flow regimes. Key contributions include the quantification of lift and drag forces acting on non-spherical sperm cells, the analysis of particle focusing trajectories, and the introduction of metrics for evaluating focusing quality and alignment. The study also highlights the role of viscoelasticity and Dean flow effects in enhancing separation resolution. Simulated results are validated against available experimental data, demonstrating strong agreement and predictive capability. The insights gained from this computational framework provide a basis for optimizing microfluidic device design to improve sorting and separation efficiency and selectivity. This is particularly valuable in applications such as cancer diagnostics, and sperm sex sorting, where precise cell focusing can lead to improved outcomes. By reducing dependence on trial-and-error experimental approaches, the findings support the development of robust and scalable sorting platforms.