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Co-expression of multi-genes for polynary perovskite electrocatalysts for reversible solid oxide cells

Author

Listed:
  • Xiaoxin Zhang

    (Xiamen University)

  • Hongyuan He

    (University of Liverpool)

  • Yu Chen

    (Xiamen University)

  • Guangming Yang

    (Nanjing Tech University)

  • Xiao Xiao

    (Xiamen University)

  • Haiping Lv

    (Xiamen University)

  • Yongkang Xiang

    (Xiamen University)

  • Shuxiong Wang

    (Xiamen University)

  • Chang Jiang

    (Xiamen University)

  • Jianhui Li

    (Xiamen University)

  • Zhou Chen

    (Xiamen University)

  • Subiao Liu

    (Central South University)

  • Ning Yan

    (Wuhan University)

  • Xue Yong

    (University of Liverpool)

  • Abdullah N. Alodhayb

    (King Saud University)

  • Yuanming Pan

    (University of Saskatchewan)

  • Ning Chen

    (University of Saskatchewan)

  • Jinru Lin

    (Chinese Academy of Sciences)

  • Xin Tu

    (University of Liverpool)

  • Zongping Shao

    (Curtin University)

  • Yifei Sun

    (Xiamen University
    Xiamen University
    Shenzhen Research Institute of Xiamen University)

Abstract

High-entropy LnBaCo2O5+δ perovskites are explored as rSOC air electrodes, though high configuration entropy (Sconfig) alone poorly correlates with performance due to multifactorial interactions. We systematically engineer LnBaCo2O5+δ perovskites (Ln = lanthanides) with tunable Sconfig and 20 consistent parameters, employing Bayesian-optimized symbolic regression to decode activity descriptors. The model identifies synergistic contributions from Sconfig, ionic radius, and electronegativity, enabling screening of 177,100 compositions. Three validated oxides exhibit superior activity/durability, particularly (Pr0.05La0.4Nd0.2Sm0.1Y0.25)BaCo2O5+δ, showing enhanced oxygen vacancy concentration and disordered transport pathways. First-principles studies reveal optimized charge transfer kinetics via cobalt-oxygen bond modulation. Further, the interplay between first ionization energy, atomic mass, and ionic Lewis acidity dictates stability. This data-driven approach establishes a quantitative framework bridging entropy engineering and catalytic functionality in complex oxides.

Suggested Citation

  • Xiaoxin Zhang & Hongyuan He & Yu Chen & Guangming Yang & Xiao Xiao & Haiping Lv & Yongkang Xiang & Shuxiong Wang & Chang Jiang & Jianhui Li & Zhou Chen & Subiao Liu & Ning Yan & Xue Yong & Abdullah N., 2025. "Co-expression of multi-genes for polynary perovskite electrocatalysts for reversible solid oxide cells," Nature Communications, Nature, vol. 16(1), pages 1-14, December.
  • Handle: RePEc:nat:natcom:v:16:y:2025:i:1:d:10.1038_s41467-025-58178-7
    DOI: 10.1038/s41467-025-58178-7
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    1. Houfu Lv & Le Lin & Xiaomin Zhang & Rongtan Li & Yuefeng Song & Hiroaki Matsumoto & Na Ta & Chaobin Zeng & Qiang Fu & Guoxiong Wang & Xinhe Bao, 2021. "Promoting exsolution of RuFe alloy nanoparticles on Sr2Fe1.4Ru0.1Mo0.5O6−δ via repeated redox manipulations for CO2 electrolysis," Nature Communications, Nature, vol. 12(1), pages 1-11, December.
    2. Baicheng Weng & Zhilong Song & Rilong Zhu & Qingyu Yan & Qingde Sun & Corey G. Grice & Yanfa Yan & Wan-Jian Yin, 2020. "Simple descriptor derived from symbolic regression accelerating the discovery of new perovskite catalysts," Nature Communications, Nature, vol. 11(1), pages 1-8, December.
    3. Keith T. Butler & Daniel W. Davies & Hugh Cartwright & Olexandr Isayev & Aron Walsh, 2018. "Machine learning for molecular and materials science," Nature, Nature, vol. 559(7715), pages 547-555, July.
    4. Lu Li & Gengwei Zhang & Chenhui Zhou & Fan Lv & Yingjun Tan & Ying Han & Heng Luo & Dawei Wang & Youxing Liu & Changshuai Shang & Lingyou Zeng & Qizheng Huang & Ruijin Zeng & Na Ye & Mingchuan Luo & S, 2024. "Lanthanide-regulating Ru-O covalency optimizes acidic oxygen evolution electrocatalysis," Nature Communications, Nature, vol. 15(1), pages 1-9, December.
    5. Shuo Zhai & Heping Xie & Peng Cui & Daqin Guan & Jian Wang & Siyuan Zhao & Bin Chen & Yufei Song & Zongping Shao & Meng Ni, 2022. "A combined ionic Lewis acid descriptor and machine-learning approach to prediction of efficient oxygen reduction electrodes for ceramic fuel cells," Nature Energy, Nature, vol. 7(9), pages 866-875, September.
    6. Michael Morley & Cliona M. Molony & Teresa M. Weber & James L. Devlin & Kathryn G. Ewens & Richard S. Spielman & Vivian G. Cheung, 2004. "Genetic analysis of genome-wide variation in human gene expression," Nature, Nature, vol. 430(7001), pages 743-747, August.
    7. Zuoqing Liu & Yuesheng Bai & Hainan Sun & Daqin Guan & Wenhuai Li & Wei-Hsiang Huang & Chih-Wen Pao & Zhiwei Hu & Guangming Yang & Yinlong Zhu & Ran Ran & Wei Zhou & Zongping Shao, 2024. "Synergistic dual-phase air electrode enables high and durable performance of reversible proton ceramic electrochemical cells," Nature Communications, Nature, vol. 15(1), pages 1-15, December.
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