A dynamically reprogrammable surface with self-evolving shape morphing

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 () and Xiaoyue Ni ()
Additional contact information
Yun Bai: Duke University
Heling Wang: Northwestern University
Yeguang Xue: 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
Yonggang Huang: Northwestern University
John A. Rogers: Northwestern University
Xiaoyue Ni: Duke University

Nature, 2022, vol. 609, issue 7928, 701-708

Abstract: 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.

Date: 2022
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DOI: 10.1038/s41586-022-05061-w

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