Optimization of Nanofluidic Devices for Geometry-Induced Electrostatic Trapping

Nanoparticle trapping in a nanofluidic device utilizing geometry-induced electrostatic (GIE) potential trap is an efficient and robust way to perform nano?object confinement and single particle studies. The GIE?trapping is a passive method that solely depends on the device geometry and device-particle surface interaction. Therefore, optimization of a nanofluidic device based on experimental requirements, helps to achieve stiffer single-particle trapping. The efficiency of a GIE?trapping device is defined in terms of residence time and trapping stiffness of the nanoparticle inside a potential trap. The present study reveals all crucial parameters that affect the device efficiency, particle trapping stiffness, and particle residence time. Furthermore, the trends of particle trapping stiffness are presented as a function of crucial parameters and demonstrate two variants of simulations to estimate the particle trapping efficiency: (a) using charged particle, and (b) using point charge approximation. Simulations with charged particle give more realistic values related to particle trapping whereas simulations with point charge approximation is a faster approach which gives approximate values and a guideline for more rigorous simulations. The results demonstrate a good agreement with experimental observations and hold the key for future developments in this field, wherein a device geometry can be very precisely optimized.