Study on droplet splitting in single-plate OEW chips

Abstract

Precise droplet manipulation is fundamental to digital microfluidics, yet traditional electrowetting techniques are constrained by fixed electrode geometries, limiting their adaptability. Optoelectrowetting (OEW) offers a reconfigurable solution through light-induced virtual electrodes, but achieving controlled droplet splitting remains a key challenge. Here, we present an AI-driven single-plate OEW platform that autonomously generates optical virtual electrodes, enabling unassisted droplet splitting in an oil environment across a volume range of 4–20 μL. We introduce a dimensionless parameter $\Gamma$ to optimize droplet splitting performance of split droplets, revealing a linear relationship between $\Gamma$ and the lateral displacement of the optical virtual electrode axis relative to the droplet axis. Additionally, our study demonstrates that increasing droplet volume necessitates a higher minimum splitting field strength, while greater medium viscosity further elevates this threshold. These findings establish a quantitative framework for optimizing OEW-based droplet manipulation, advancing its potential for scalable, high-precision microfluidic applications.