The main objective of the project is to understand and quantify how electrode surface properties, chemical reaction mechanisms and electrode morphology influence charge storage and transport phenomena in the context of redox flow batteries.

This involves coupling physics-based electrochemical models of the electrode-electrolyte interface describing double-layer formation, ion dynamics and adsorption phenomena with meso-scale simulations of porous electrode structures.

By integrating these models, the project aims to provide a predictive understanding of electrochemical behavior under various operating conditions. The expected outcome is a modelling tool capable of linking the fundamental interfacial physics with performance-relevant properties such as energy efficiency and power density.

The project will also involve close collaboration with the PREDICTOR network. Experimental results will be used to calibrate and validate the models, ensuring their physical consistency and generality. In turn, the models will support experimental partners in optimizing and interpreting chemical processes. In parallel, the developed models will be designed to be modular and integrable into larger-scale frameworks, such as full-cell and full-battery models, enabling interoperability across different scales.

The research workflow can be seen as a connecting line between the microscopic phenomena occurring at the interface and the macroscopic behavior, so the overall performance, of electrochemical cells in RFBs.

This will support the design of high-performance and sustainable materials for next-generation RFB systems. The work aligns with the broader goals of the network, promoting green and efficient energy storage through scientific collaboration and digital innovation.