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Area of Science:

  • Soft Matter Physics
  • Materials Science
  • Robotics and Soft Machines

Background:

  • Dynamic shape-morphing soft materials are crucial for living organisms and emerging technologies like soft machines, flexible electronics, and smart medicines.
  • Existing responsive soft matter can switch shapes but lacks the continuous morphing capabilities needed for natural processes, hindering applications.
  • Reprogramming shapes post-fabrication is challenging due to complex physics and environmental disturbances, limiting deterministic inverse design and control.

Purpose of the Study:

  • To introduce a novel mechanical metasurface with dynamic shape-morphing capabilities.
  • To demonstrate complex, continuous shape transformations in soft matter systems.
  • To enable data-driven design and precise control of soft materials for advanced applications.

Main Methods:

  • Fabrication of a mechanical metasurface using filamentary metal traces.
  • Actuation via reprogrammable, distributed Lorentz forces generated by electrical currents in a static magnetic field.
  • Implementation of an in situ stereo-imaging feedback strategy with a digitally controlled actuation scheme guided by an optimization algorithm.

Main Results:

  • The metasurface demonstrated complex, dynamic morphing capabilities with response times under 0.1 seconds.
  • The system achieved high-precision 3D shape morphing into a wide range of target shapes.
  • The metasurface successfully adapted to extrinsic and intrinsic perturbations, maintaining shape fidelity.

Conclusions:

  • The developed mechanical metasurface overcomes limitations in current soft matter morphing.
  • The data-driven, optimization-guided approach enables precise control and reprogramming of soft material shapes.
  • This work paves the way for advanced applications in soft machines, electronics, and medicine requiring dynamic shape-morphing.