Our research group focuses on the manufacturing of porous materials for electrochemical devices, such as redox flow batteries, water electrolyzers, and fuel cells. Our research bridges the gap between experimental and computational approaches by combining imaging diagnostics, computational optimization, and additive manufacturing strategies. This enables us to design new porous materials tailored to specific reactor designs and operation conditions, ultimately improving the efficiency, durability, and cost-effectiveness of electrochemical devices.
Our research concentrates on three interconnected topics:
(1) Multiphase mass transport in porous media: Studying the structure-performance relationships in electrochemical devices through imaging diagnostics and computational modeling. Operando, in-situ, and ex-situ imaging diagnostics are utilized to visualize and characterize (multiphase) flow through electrochemical reactors, supplemented by macro- and mesoscale computational approaches, including continuum macroscale fluid dynamic simulations and pore-scale models.
(2) Computational optimization: Designing improved porous materials using strategies such as coupling pore-scale models with machine learning and heuristic algorithms to enhance the charge, mass, and heat transport in the porous media.
(3) Additive manufacturing: Developing enhanced porous materials with controlled structure and properties through various 3D printing approaches, resin engineering, and surface functionalization.
The principles and methodologies developed by our research group can be applied and adapted to a wide range of (electro)chemical devices and manufacturing processes.