Exploiting Localized Transition Waves to Tune Sound Propagation in Soft Materials

TL;DR

This study demonstrates how instabilities, geometric frustration, and mechanical deformations in soft materials can be harnessed to control sound wave propagation, enabling advanced applications in sound management and soft robotics.

Contribution

It introduces a novel approach to tune sound propagation in soft materials by exploiting localized transition waves generated through instabilities and geometric frustration.

Findings

Localized transition waves can be induced and controlled in soft materials.

Deformation-induced order can be achieved in disordered assemblies.

Patterns formed by defects enable tuning of sound propagation.

Abstract

Programmable materials hold great potential for many applications such as deployable structures, soft robotics, and wave control, however, the presence of instability and disorder might hinder their utilization. Through a combination of analytical, numerical, and experimental analyses, we harness the interplay between instabilities, geometric frustration, and mechanical deformations to control the propagation of sound waves within self-assembled soft materials. We consider levitated magnetic disks confined by a magnetic boundary in-plane. The assemblies can be either ordered or disordered depending on the intrinsic disk symmetry. By applying an external load to the assembly, we observe the nucleation and propagation of different topological defects within the lattices. In the presence of instabilities, the defect propagation gives rise to time-independent localized transition waves. Surprisingly, in the presence of frustration, the applied load briefly introduces deformation-induced order to the material. By further deforming the lattices, new patterns emerge across all disk symmetries. We utilize these patterns to tune sound propagation through the material. Our findings could open new possibilities for designing exotic materials with potential applications ranging from sound control to soft robotics.