The aim of this project is to develop an automated system for electrolyte production and integrate it with measurement/spectroscopy units. This system will enable the synthesis and analysis of electrolytes with varying types and concentrations of conductive salts, additives, and acids, specifically tailored for redox flow battery systems. The result is an efficient, reproducible method that significantly improves productivity compared to conventional approaches, which are often challenged with working personnel and larger timeframes.

A key objective is to produce stable and suitable electrolyte for electrochemical measurements of novel redox-active materials. It will also support conductivity measurements to assess electrolyte performance. AI-based pattern recognition will enable comparable and quantifiable outcomes, while integration with a centralized database will allow self-optimization through ML. This method will enable the system to autonomously identify electrolytes with optimal properties. Synthesizing and characterizing electrolytes identified previously by the computational high-throughput screening thus generating experimental data to validate and refine the modelling and simulation efforts associated with the project.

Performing automated measurements and increasing throughput will accelerate and enhance our understanding of the behavior of electrolyte components, offering a range of possibilities to improve performance. This high-throughput approach offers a clean and efficient shift to renewable energy particularly with electrochemical storage technologies.