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Fully automatic transfer and measurement system for structural superlubric materials

Author

Listed:
  • Li Chen

    (Tsinghua University
    Tsinghua University)

  • Cong Lin

    (University of California)

  • Diwei Shi

    (Tsinghua University
    Tsinghua University)

  • Xuanyu Huang

    (Tsinghua University
    Tsinghua University)

  • Quanshui Zheng

    (Tsinghua University
    Tsinghua University
    Research Institute of Tsinghua University in Shenzhen
    Tsinghua University)

  • Jinhui Nie

    (Research Institute of Tsinghua University in Shenzhen)

  • Ming Ma

    (Tsinghua University
    Tsinghua University
    Research Institute of Tsinghua University in Shenzhen)

Abstract

Structural superlubricity, a state of nearly zero friction and no wear between two contact surfaces under relative sliding, holds immense potential for research and application prospects in micro-electro-mechanical systems devices, mechanical engineering, and energy resources. A critical step towards the practical application of structural superlubricity is the mass transfer and high throughput performance evaluation. Limited by the yield rate of material preparation, existing automated systems, such as roll printing or massive stamping, are inadequate for this task. In this paper, a machine learning-assisted system is proposed to realize fully automated selective transfer and tribological performance measurement for structural superlubricity materials. Specifically, the system has a judgment accuracy of over 98% for the selection of micro-scale graphite flakes with structural superlubricity properties and complete the 100 graphite flakes assembly array to form various pre-designed patterns within 100 mins, which is 15 times faster than manual operation. Besides, the system is capable of automatically measuring the tribological performance of over 100 selected flakes on Si3N4, delivering statistical results for new interface which is beyond the reach of traditional methods. With its high accuracy, efficiency, and robustness, this machine learning-assisted system promotes the fundamental research and practical application of structural superlubricity.

Suggested Citation

  • Li Chen & Cong Lin & Diwei Shi & Xuanyu Huang & Quanshui Zheng & Jinhui Nie & Ming Ma, 2023. "Fully automatic transfer and measurement system for structural superlubric materials," Nature Communications, Nature, vol. 14(1), pages 1-10, December.
    • Handle: RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-023-41859-6
    DOI: 10.1038/s41467-023-41859-6
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    References listed on IDEAS

    as
    1. Xuanyu Huang & Xiaojian Xiang & Jinhui Nie & Deli Peng & Fuwei Yang & Zhanghui Wu & Haiyang Jiang & Zhiping Xu & Quanshui Zheng, 2021. "Author Correction: Microscale Schottky superlubric generator with high direct-current density and ultralong life," Nature Communications, Nature, vol. 12(1), pages 1-1, December.
    2. Satoru Masubuchi & Masataka Morimoto & Sei Morikawa & Momoko Onodera & Yuta Asakawa & Kenji Watanabe & Takashi Taniguchi & Tomoki Machida, 2018. "Autonomous robotic searching and assembly of two-dimensional crystals to build van der Waals superlattices," Nature Communications, Nature, vol. 9(1), pages 1-12, December.
    3. Oded Hod & Ernst Meyer & Quanshui Zheng & Michael Urbakh, 2018. "Structural superlubricity and ultralow friction across the length scales," Nature, Nature, vol. 563(7732), pages 485-492, November.
    4. Yuan Cao & Valla Fatemi & Shiang Fang & Kenji Watanabe & Takashi Taniguchi & Efthimios Kaxiras & Pablo Jarillo-Herrero, 2018. "Unconventional superconductivity in magic-angle graphene superlattices," Nature, Nature, vol. 556(7699), pages 43-50, April.
    5. Xuanyu Huang & Xiaojian Xiang & Jinhui Nie & Deli Peng & Fuwei Yang & Zhanghui Wu & Haiyang Jiang & Zhiping Xu & Quanshui Zheng, 2021. "Microscale Schottky superlubric generator with high direct-current density and ultralong life," Nature Communications, Nature, vol. 12(1), pages 1-10, December.
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