Experimental and Numerical Study of Microcavity Filling Regimes for Lab-on-a-Chip Applications

TL;DR

This study combines experimental and 3D numerical simulations to analyze microcavity filling regimes, providing insights into the mechanisms and predicting filling states relevant for Lab-on-a-Chip applications.

Contribution

It introduces a comprehensive approach integrating experiments and simulations to understand microcavity filling, including a regime map and analysis of interface dynamics.

Findings

Simulations predict filling states accurately in most cases.

A regime map of filling states based on experimental parameters.

Quantitative analysis of interface progression and contact angles.

Abstract

The efficient and voidless filling of microcavities is of great importance for Lab-on-a-Chip applications. However, predicting whether microcavities will be filled or not under different circumstances is still difficult due to the local flow effects dominated by surface tension. In this work, a close-up study of the microcavity filling process is presented, shedding light on the mechanisms of the filling process using experimental insights accompanied by 3D numerical simulations. The movement of a fluid interface over a microcavity array is investigated optically under consideration of different fluids, capillary numbers, and cavity depths, revealing a regime map of different filling states. Moreover, the transient interface progression over the cavities is analyzed with attention to small-scale effects such as pinning. Besides the visual analysis of the image series, quantitative data of the dynamic contact angle and the interface progression is derived using an automated evaluation workflow. In addition to the experiments, 3D Volume-of-Fluid simulations are employed to further investigate the interface shape. It is shown that the simulations can not only predict the filling states in most cases, but also the transient movement and shape of the interface. The data and code associated with this work are publicly available at Bosch Research GitHub and at the TUDatalib data repository.