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

Journal Article · · Cell Reports Physical Science

Solubility is crucial for redox flow batteries because it affects their energy density. A data-driven approach based on artificial intelligence/machine learning models can accelerate the development of highly soluble redox-active materials, but the lack of relevant, large-quantity data makes accurate solubility prediction difficult. 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 a ferrocyanide-positive electrolyte offers highly stable performance over 18 days (>100 cycles) with consistent capacity and a 24% boost in energy density.

Research Organization:
Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)
Sponsoring Organization:
USDOE Laboratory Directed Research and Development (LDRD) Program; USDOE Office of Electricity (OE)
Grant/Contract Number:
AC05-76RL01830
OSTI ID:
2204720
Report Number(s):
PNNL-SA--182963
Journal Information:
Cell Reports Physical Science, Journal Name: Cell Reports Physical Science Journal Issue: 10 Vol. 4; ISSN 2666-3864
Publisher:
ElsevierCopyright Statement
Country of Publication:
United States
Language:
English

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