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A dynamically reprogrammable surface with self-evolving shape morphing

Author

Listed:
  • Yun Bai

    (Duke University)

  • Heling Wang

    (Northwestern University
    Northwestern University
    Northwestern University
    Tsinghua University)

  • Yeguang Xue

    (Northwestern University
    Northwestern University
    Northwestern University)

  • Yuxin Pan

    (Duke University)

  • Jin-Tae Kim

    (Northwestern University)

  • Xinchen Ni

    (Northwestern University)

  • Tzu-Li Liu

    (Northwestern University)

  • Yiyuan Yang

    (Northwestern University)

  • Mengdi Han

    (Northwestern University
    Peking University)

  • Yonggang Huang

    (Northwestern University
    Northwestern University
    Northwestern University
    Northwestern University)

  • John A. Rogers

    (Northwestern University
    Northwestern University
    Northwestern University
    Northwestern University)

  • Xiaoyue Ni

    (Duke University
    Northwestern University
    Duke University)

Abstract

Dynamic shape-morphing soft materials systems are ubiquitous in living organisms; they are also of rapidly increasing relevance to emerging technologies in soft machines1–3, flexible electronics4,5 and smart medicines6. Soft matter equipped with responsive components can switch between designed shapes or structures, but cannot support the types of dynamic morphing capabilities needed to reproduce natural, continuous processes of interest for many applications7–24. Challenges lie in the development of schemes to reprogram target shapes after fabrication, especially when complexities associated with the operating physics and disturbances from the environment can stop the use of deterministic theoretical models to guide inverse design and control strategies25–30. Here we present a mechanical metasurface constructed from a matrix of filamentary metal traces, driven by reprogrammable, distributed Lorentz forces that follow from the passage of electrical currents in the presence of a static magnetic field. The resulting system demonstrates complex, dynamic morphing capabilities with response times within 0.1 second. Implementing an in situ stereo-imaging feedback strategy with a digitally controlled actuation scheme guided by an optimization algorithm yields surfaces that can follow a self-evolving inverse design to morph into a wide range of three-dimensional target shapes with high precision, including an ability to morph against extrinsic or intrinsic perturbations. These concepts support a data-driven approach to the design of dynamic soft matter, with many unique characteristics.

Suggested Citation

  • Yun Bai & Heling Wang & Yeguang Xue & Yuxin Pan & Jin-Tae Kim & Xinchen Ni & Tzu-Li Liu & Yiyuan Yang & Mengdi Han & Yonggang Huang & John A. Rogers & Xiaoyue Ni, 2022. "A dynamically reprogrammable surface with self-evolving shape morphing," Nature, Nature, vol. 609(7928), pages 701-708, September.
  • Handle: RePEc:nat:nature:v:609:y:2022:i:7928:d:10.1038_s41586-022-05061-w
    DOI: 10.1038/s41586-022-05061-w
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    Citations

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    Cited by:

    1. Siqi An & Xiaowen Li & Zengrong Guo & Yi Huang & Yanlin Zhang & Hanqing Jiang, 2024. "Energy-efficient dynamic 3D metasurfaces via spatiotemporal jamming interleaved assemblies for tactile interfaces," Nature Communications, Nature, vol. 15(1), pages 1-14, December.
    2. Jiefeng Sun & Elisha Lerner & Brandon Tighe & Clint Middlemist & Jianguo Zhao, 2023. "Embedded shape morphing for morphologically adaptive robots," Nature Communications, Nature, vol. 14(1), pages 1-13, December.
    3. Yanbin Li & Antonio Lallo & Junxi Zhu & Yinding Chi & Hao Su & Jie Yin, 2024. "Adaptive hierarchical origami-based metastructures," Nature Communications, Nature, vol. 15(1), pages 1-15, December.
    4. B. K. Johnson & M. Naris & V. Sundaram & A. Volchko & K. Ly & S. K. Mitchell & E. Acome & N. Kellaris & C. Keplinger & N. Correll & J. S. Humbert & M. E. Rentschler, 2023. "A multifunctional soft robotic shape display with high-speed actuation, sensing, and control," Nature Communications, Nature, vol. 14(1), pages 1-11, December.
    5. Xiaohao Sun & Liang Yue & Luxia Yu & Connor T. Forte & Connor D. Armstrong & Kun Zhou & Frédéric Demoly & Ruike Renee Zhao & H. Jerry Qi, 2024. "Machine learning-enabled forward prediction and inverse design of 4D-printed active plates," Nature Communications, Nature, vol. 15(1), pages 1-15, December.
    6. Yuanxi Zhang & Chengfeng Pan & Pengfei Liu & Lelun Peng & Zhouming Liu & Yuanyuan Li & Qingyuan Wang & Tong Wu & Zhe Li & Carmel Majidi & Lelun Jiang, 2023. "Coaxially printed magnetic mechanical electrical hybrid structures with actuation and sensing functionalities," Nature Communications, Nature, vol. 14(1), pages 1-15, December.

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