About my thesis - Synthesis of new active materials for flow batteries

This project aims to develop a high-throughput approach for discovering and synthesizing novel organic redox-active materials for use in redox flow batteries. By integrating machine learning-driven predictions with sustainable flow chemistry, the research seeks to overcome the limitations of traditional vanadium-based systems, providing a scalable, cost-effective, and environmentally friendly alternative for energy storage.

The project combines AI-guided synthesis planning with a comprehensive literature review to identify promising candidate molecules. High-throughput droplet-based flow synthesis will be employed to optimize synthetic routes for organic electrolytes, reducing reaction volumes and enhancing the overall efficiency and sustainability of the process. Using automated flow experiments, we will synthesize 3–5 target molecules, while Bayesian optimization will be used to iteratively improve yields and incorporate environmental considerations such as material and energy use. In addition, real-time analytical protocols will be developed to continuously monitor and optimize the synthesis process.

The work will leverage both computational and experimental methods to accelerate the identification of stable, efficient organic electrolytes, reducing the time and resources needed for testing new materials. The research aims to significantly advance the development of organic RAMs with improved stability and redox properties, contributing to the broader goal of enhancing the scalability and sustainability of RFB technologies. This will ultimately support the transition to renewable energy by improving the efficiency and scalability of energy storage systems.