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

Shanghai Jiao Tong University Journal Center

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

Caption

  • 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.

Credit

Fangyuan Zheng, Baoyin Yuan, Youfeng Cai, Huanxin Xiang, Chunmei Tang, Ling Meng, Lei Du, Xiting Zhang, Feng Jiao, Yoshitaka Aoki, Ning Wang, Siyu Ye.

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License

CC BY-NC-ND

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