Machine Learning Driven Optimization of Electrolyte for Highly Reversible Zn-Air Batteries with Superior Long-Term...

Area of Science:

• Electrochemistry
• Materials Science
• Energy Storage

Background:

• Aqueous alkaline zinc-air batteries (ZABs) offer high energy density and safety.
• Poor electrochemical reversibility of zinc and low round-trip efficiency hinder ZAB development.
• Electrolyte composition is critical for optimizing the anode/electrolyte/cathode interface in ZABs.

Purpose of the Study:

• To enhance the performance and electrochemical reversibility of aqueous alkaline ZABs.
• To design optimal multicomponent electrolytes for improved ZAB stability and efficiency.
• To explore the role of ethylene glycol in modifying ZAB reaction pathways and discharge product formation.

Main Methods:

• Bayesian optimization was employed for the rational design of multicomponent electrolytes.
• Ethylene glycol was utilized as both an electrolyte additive and a fuel component.
• Electrochemical performance, including reversibility, stability, and round-trip efficiency, was systematically evaluated.

Main Results:

• The optimized electrolytes significantly enhanced ZAB performance.
• Ethylene glycol altered reaction pathways, promoting Zn2+-based hybrid particle colloids instead of ZnO deposition.
• The novel ZABs demonstrated superior stability (1700 h at 2 mA cm-2, 1400 h at 20 mA cm-2) and a round-trip efficiency of 76.3%.

Conclusions:

• The study successfully demonstrated the potential of Bayesian optimization for electrolyte design in ZABs.
• Ethylene glycol incorporation offers a promising strategy to overcome key limitations in ZAB technology.
• These findings pave the way for advanced ZABs with improved long-term energy storage capabilities.