Selection of Electrolyte Additives for Enhanced Mg-Air Battery Performance Supported By Data-Driven Approach
Authors: Snihirova Darya , Wang Linqian , Deng Min , Vaghefinazari Bahram , Wu Yulong , Würger Tim , Meißner Robert , Winkler David , Feiler Christian , Höche Daniel , Lamaka Sviatlana , Zheludkevich Mikhail 
Published: February 2024

A Mg-air battery is a primary aqueous battery with high theoretical voltage and specific energy density. They have attracted much attention due to their high theoretical energy density, long shelf life, and safety. They consist of magnesium anode coupled to an air electrode. However, the performance of aqueous Mg-air batteries is relatively poor due to: a large overvoltage caused by formation of insoluble discharge products (MgO and Mg(OH)2); and fast self-corrosion of Mg anodes in aqueous electrolytes. Two main strategies have been developed to address these shortcomings – alloy development [1, 2] and electrolyte modification [3, 4]. Electrolyte additives are an effective way to control interfacial processes. They are inexpensive and straightforward methods for controlling self-corrosion of Mg. As was previously shown electrolyte additives can also improve the discharge performance of aqueous primary Mg-air batteries by changing the surface of Mg anode.
Here, we investigated the mechanism of action of several Mg2+ complexing agents as electrolyte additives for aqueous Mg-air batteries [5, 6]. Electrochemical impedance spectroscopy (EIS) measurements during discharge and real-time hydrogen evolution measurements were used to elucidate the mechanisms of action. We found that Mg2+ complexing agents at optimal concentration greatly slowed the formation of insoluble discharge products and decreased the voltage drop in Mg-air batteries. Additionally, anodic hydrogen evolution experiments quantified the effect of Mg2+ complexing agents on self-corrosion of the Mg anode during discharge. These results suggest that Mg2+ complexing agents increase the battery utilization efficiency by inhibiting self-corrosion and decreasing the non-uniform dissolution of the Mg anode. This work showcases the promise of computer-assisted methods for rapidly and efficiently screen large numbers of organic compounds as potential electrolyte additives. Two data-driven quantitative structure-property relationship (QSPR) machine learning models were trained and used for in silico searches for promising battery performance-boosting candidates [7].
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