Multiscale Modeling of Bubble Evolution on Gas-Evolving Electrodes

Gas-evolving electrodes are critical components in electrochemical systems, with significant applications in hydrogen production through water electrolysis, where water is split into hydrogen and oxygen gases. A primary challenge in these systems is the accumulation of gas bubbles on the electrode surface, creating bubble coverage that acts as a major source of overpotential, drastically reducing hydrogen production efficiency. To address this, current research focuses on manipulating the surface properties and topology of electrodes to promote efficient bubble formation and detachment. This study aims to bridge existing gaps in the literature by providing a more realistic understanding of bubble distribution and formation rates, accounting for the combined effects of electrostatics, electrochemical reactions, surface properties, and gas/ion transport. To accelerate progress, machine learning techniques are vital, enabling the prediction of microscale bubble distributions and formation rates based on surface characteristics. By integrating multiscale computational fluid dynamics (CFD) simulations with machine learning, this project seeks to develop advanced electrode designs that maximize efficiency in hydrogen production systems.