Magnetic quadrupole assemblies with arbitrary shapes and magnetizations.


Journal

Science robotics
ISSN: 2470-9476
Titre abrégé: Sci Robot
Pays: United States
ID NLM: 101733136

Informations de publication

Date de publication:
30 Oct 2019
Historique:
received: 02 05 2019
accepted: 09 10 2019
entrez: 2 11 2020
pubmed: 3 11 2020
medline: 3 11 2020
Statut: ppublish

Résumé

Magnetic dipole-dipole interactions govern the behavior of magnetic matter across scales from micrometer colloidal particles to centimeter magnetic soft robots. This pairwise long-range interaction creates rich emergent phenomena under both static and dynamic magnetic fields. However, magnetic dipole particles, from either ferromagnetic or paramagnetic materials, tend to form chain-like structures as low-energy configurations due to dipole symmetry. The repulsion force between two magnetic dipoles raises challenges for creating stable magnetic assemblies with complex two-dimensional (2D) shapes. In this work, we propose a magnetic quadrupole module that is able to form stable and frustration-free magnetic assemblies with arbitrary 2D shapes. The quadrupole structure changes the magnetic particle-particle interaction in terms of both symmetry and strength. Each module has a tunable dipole moment that allows the magnetization of overall assemblies to be programmed at the single module level. We provide a simple combinatorial design method to reach both arbitrary shapes and arbitrary magnetizations concurrently. Last, by combining modules with soft segments, we demonstrate programmable actuation of magnetic metamaterials that could be used in applications for soft robots and electromagnetic metasurfaces.

Identifiants

pubmed: 33137733
pii: 4/35/eaax8977
doi: 10.1126/scirobotics.aax8977
pii:
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Informations de copyright

Copyright © 2019 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works.

Auteurs

Hongri Gu (H)

Institute of Robotics and Intelligent Systems, ETH Zürich, Zürich CH-8092, Switzerland.

Quentin Boehler (Q)

Institute of Robotics and Intelligent Systems, ETH Zürich, Zürich CH-8092, Switzerland.

Daniel Ahmed (D)

Institute of Robotics and Intelligent Systems, ETH Zürich, Zürich CH-8092, Switzerland.

Bradley J Nelson (BJ)

Institute of Robotics and Intelligent Systems, ETH Zürich, Zürich CH-8092, Switzerland. bnelson@ethz.ch.

Classifications MeSH