Machine Learning Tailored Anodes for Efficient Hydrogen Energy Generation in Proton-Conducting Solid Oxide Electrolysis Cells

Abstract

Highlights Machine learning technique was employed to develop anode for proton-conducting solid oxide electrolysis cells (P-SOEC). The screened high-performance La0.9Ba0.1Co0.7Ni0.3O3−δ (LBCN9173) and La0.9Ca0.1Co0.7Ni0.3O3−δ (LCCN9173) anodes achieved a synergistic enhancement of water oxidation reaction kinetics and proton-conducting ability. P-SOECs with LBCN9173 anode demonstrated a top-rank current density of 2.45 A cm−2 and an extremely low polarization resistance of 0.05 Ω cm2 at 650 °C.

Keywords

• Machine learning
• Proton-conducting solid oxide electrolysis cells
• Hydrogen energy
• Anode