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Updated: Dec 22, 2025

Origami Inspired Self-assembly of Patterned and Reconfigurable Particles

Origami Inspired Self-assembly of Patterned and Reconfigurable Particles

Published on: February 4, 2013

Statistical reprogramming of macroscopic self-assembly with dynamic boundaries.

Utku Culha¹, Zoey S Davidson¹, Massimo Mastrangeli²

¹Physical Intelligence Department, Max Planck Institute for Intelligent Systems, 70569 Stuttgart, Germany.

Proceedings of the National Academy of Sciences of the United States of America

|May 10, 2020

Summary

This summary is machine-generated.

Scientists reprogrammed 2D self-assembled structures using dynamic confinement of magnetic particles. This method allows repeatable and reversible pattern selection, enabling tunable material properties and novel robotic assembly applications.

Keywords:

dynamic confinement control mechanism design programmable self-assembly

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Last Updated: Dec 22, 2025

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Area of Science:

Physics
Materials Science
Robotics

Background:

Self-assembly generates complex structures from simple components through local interactions.
Programming self-assembly pathways offers insights into fundamental physics and fabrication methods.
Reprogrammability of self-assembled patterns is challenging but crucial for tuning material properties.

Purpose of the Study:

To demonstrate statistical reprogramming of two-dimensional (2D) self-assembled structures.
To achieve repeatable and reversible selection of self-assembled patterns.
To explore implications for granular materials and robotic assembly.

Main Methods:

Utilized dynamic confinement of orbitally shaken, magnetically repulsive millimeter-scale particles.
Controlled the assembly arena radius via moving hard boundaries under a constant shaking regime.
Temporarily trapped particles in stable states to demonstrate reprogrammable stiffness and 3D magnetic clutching.

Main Results:

Achieved statistical reprogramming of 2D noncompact self-assembled structures.
Demonstrated repeatable and reversible selection among a finite set of self-assembled patterns.
Showcased 2D reprogrammable stiffness and 3D magnetic clutching capabilities.

Conclusions:

The developed system enables reprogrammable granular materials through out-of-equilibrium self-assembly.
Dynamic boundary regulation offers bottom-up control strategies for robotic assembly.
The approach has potential applications across various physical scales and robotic systems.