Optimization of the dielectric layer parameters through coupled numerical analysis to enhance droplet and particle manipulation in digital microfluidic chips

Abstract

Digital microfluidic chips (DMCs) have shown the ability to flexibly manipulate droplets and particles, which is meaningful for biomedical applications in drug screening and clinical diagnostics. As a critical component of DMCs, the dielectric layer, with its key physical parameters (permittivity and thickness), directly determines the voltage distribution, thereby significantly affecting the manipulation performance. To optimize manipulation performance, simulation studies on dielectric layer parameters are essential during the DMC design. Existing simulation methods can evaluate the effect of dielectric layer parameters on droplet manipulation but encounter inherent challenges when analyzing the manipulation of particles within droplets. Here, we propose a versatile numerical analysis approach that can simultaneously analyze the effect of dielectric layer parameters on both droplet and particle manipulation, thereby optimizing the dielectric layer parameters to enhance the DMC manipulation performance. Initially, the voltage distributions corresponding to different sets of dielectric layer parameters are solved using electromagnetic field theory. Subsequently, the voltage distribution data are used to calculate the droplet and particle driving forces based on the principle of virtual work. Finally, by comparing the driving forces across different sets of dielectric layer parameters, the optimal dielectric layer parameters are determined to enhance the DMC manipulation performance. Experimental results demonstrate that the droplet and particle accelerations align with the simulated driving force trends, thereby validating our numerical analysis method. We anticipate that our method will be able to provide theoretical guidance for the optimization of dielectric layer parameters to obtain a desirable manipulation performance in more complex DMC designs.