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Abstract
This study presents a highly automated workflow integrating a high-throughput experimental platform with an active learning algorithm to accelerate the discovery of optimal electrolyte formulations for redox flow batteries. The platform identified multiple solvents exceeding a 6.20 M solubility threshold for 2,1,3-benzothiadiazole, requiring solubility assessments for less than 10% of the 2000+ candidate solvents. Binary solvent mixtures, especially those with 1,4-dioxane, significantly boosted solubility.
Publisher
Nature Communications
Published On
Mar 29, 2024
Authors
Juran Noh, Hieu A. Doan, Heather Job, Lily A. Robertson, Lu Zhang, Rajeev S. Assary, Karl Mueller, Vijayakumar Murugesan, Yangang Liang
Tags
redox flow batteries
electrolyte formulation
high-throughput experimentation
solubility enhancement
active learning algorithms
solvent mixtures
energy storage

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