Optimization of Performance at Air Electrode Side for Protonic Solid Oxide Cells: Advances and Machine Learning...

Area of Science:

• Materials Science and Engineering
• Electrochemistry
• Energy Conversion

Background:

• Protonic solid oxide cells (P-SOCs) offer efficient hydrogen production and power generation without precious metals.
• Commercialization is limited by suboptimal air electrode performance, including catalytic activity and conductivity.
• Existing research focuses on triple-conducting oxides, exsolution, and interface optimization for air electrodes.

Purpose of the Study:

• To comprehensively review innovative strategies for P-SOC air electrode optimization.
• To highlight the role of machine learning (ML) in advancing P-SOC technology.
• To propose ML-guided perspectives and challenges for future P-SOC development.

Main Methods:

• Literature review of recent advancements in P-SOC air electrode design.
• Synthesis of strategies including triple-conducting oxides, exsolution, and interface engineering.
• Analysis of machine learning applications in optimizing P-SOC materials and performance.

Main Results:

• Identified key strategies to enhance electrochemical reactive area, kinetics, and durability of P-SOC air electrodes.
• Demonstrated the significant impact of ML in accelerating the design and optimization process.
• Highlighted the synergistic effects of material design and interface engineering.

Conclusions:

• Advanced strategies and ML are crucial for overcoming P-SOC air electrode limitations.
• ML offers a powerful roadmap for rational design of superior P-SOC air electrodes.
• This review provides insights for improving P-SOC performance and accelerating commercialization.