中国物理B ›› 2023, Vol. 32 ›› Issue (3): 36801-036801.doi: 10.1088/1674-1056/ac834b

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Atomistic insights into early stage corrosion of bcc Fe surfaces in oxygen dissolved liquid lead-bismuth eutectic (LBE-O)

Ting Zhou(周婷)1,2, Xing Gao(高星)1,2,†, Zhiwei Ma(马志伟)1,2, Hailong Chang(常海龙)1,2, Tielong Shen(申铁龙)1,2, Minghuan Cui(崔明焕)1,2, and Zhiguang Wang(王志光)1,2,†   

  1. 1 Institute of Modern Physics, Chinese Academy of Sciences, Lanzhou 730000, China;
    2 School of Nuclear Science and Technology, University of Chinese Academy of Sciences, Beijing 100049, China
  • 收稿日期:2022-06-06 修回日期:2022-07-12 接受日期:2022-07-22 出版日期:2023-02-14 发布日期:2023-02-14
  • 通讯作者: Xing Gao, Zhiguang Wang E-mail:xinggao@impcas.ac.cn;zhgwang@impcas.ac.cn
  • 基金资助:
    We deeply appreciate Professor Zhipan Liu and Professor Cheng Shang at Fudan University for their help in training the quaternary G-NN potential used in our MD simulations. This work was supported by the National Natural Science Foundation of China (Grant No. U1832206).

Atomistic insights into early stage corrosion of bcc Fe surfaces in oxygen dissolved liquid lead-bismuth eutectic (LBE-O)

Ting Zhou(周婷)1,2, Xing Gao(高星)1,2,†, Zhiwei Ma(马志伟)1,2, Hailong Chang(常海龙)1,2, Tielong Shen(申铁龙)1,2, Minghuan Cui(崔明焕)1,2, and Zhiguang Wang(王志光)1,2,†   

  1. 1 Institute of Modern Physics, Chinese Academy of Sciences, Lanzhou 730000, China;
    2 School of Nuclear Science and Technology, University of Chinese Academy of Sciences, Beijing 100049, China
  • Received:2022-06-06 Revised:2022-07-12 Accepted:2022-07-22 Online:2023-02-14 Published:2023-02-14
  • Contact: Xing Gao, Zhiguang Wang E-mail:xinggao@impcas.ac.cn;zhgwang@impcas.ac.cn
  • Supported by:
    We deeply appreciate Professor Zhipan Liu and Professor Cheng Shang at Fudan University for their help in training the quaternary G-NN potential used in our MD simulations. This work was supported by the National Natural Science Foundation of China (Grant No. U1832206).

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摘要： Classical molecular dynamics simulations with global neural network machine learning potential are used to study early stage oxidation and dissolution behaviors of bcc Fe surfaces contacting with stagnant oxygen dissolved liquid lead-bismuth eutectic (LBE-O). Both static and dynamic simulation results indicate that the early stage oxidation and dissolution behaviors of bcc Fe show strong orientation dependence under the liquid LBE environments, which may explain the experimental observations of uneven interface between iron-based materials and liquid LBE. Our investigations show that it is the delicate balance between the oxide growth and metal dissolution that leads to the observed corrosion anisotropy for bcc Fe contacting with liquid LBE-O.

关键词: liquid lead-bismuth eutectic (LBE), global neural network (G-NN) potential, dissolution

Abstract: Classical molecular dynamics simulations with global neural network machine learning potential are used to study early stage oxidation and dissolution behaviors of bcc Fe surfaces contacting with stagnant oxygen dissolved liquid lead-bismuth eutectic (LBE-O). Both static and dynamic simulation results indicate that the early stage oxidation and dissolution behaviors of bcc Fe show strong orientation dependence under the liquid LBE environments, which may explain the experimental observations of uneven interface between iron-based materials and liquid LBE. Our investigations show that it is the delicate balance between the oxide growth and metal dissolution that leads to the observed corrosion anisotropy for bcc Fe contacting with liquid LBE-O.

Key words: liquid lead-bismuth eutectic (LBE), global neural network (G-NN) potential, dissolution

中图分类号:  (Liquid-solid interfaces)

  • 68.08.-p
28.41.Qb (Structural and shielding materials) 28.52.Fa (Materials) 28.52.Av (Theory, design, and computerized simulation)