High-throughput solubility determination for data-driven materials design and discovery in redox flow battery research

摘要

Solubility is crucial for redox flow batteries as it affects their energy density. A data-driven approach based on AI/ML models can speed up the development of highly soluble redox active materials, but accurate solubility prediction remains elusive because of the lack of relevant databases. To overcome this deficiency, we developed a high-throughput experimentation process that combines a robotically controlled platform with high-throughput methodology to collect large-scale and high-quality solubility data. We demonstrate the potential utility and applicability of this high-throughput process by measuring the aqueous and non-aqueous solubilities of redox active materials and studying the effect of additives on their solubilities for both aqueous and non-aqueous redox flow battery applications. A redox flow battery based on our optimized negative electrolyte formulation and ferrocyanide positive electrolyte offers highly stable performance over 18 days (>100 cycles) with consistent capacity and a 24% boost in energy density.