Messy or Ordered? Multi-scale Mechanics DictatesShape-Morphing of Hierarchical 2D Fiber-Networks

TL;DR

This study reveals how the shape-morphing behavior of 2D fiber-networks depends on fiber diameter and network density, showing two distinct thermally driven morphing modes with potential for controlled shape transformations.

Contribution

It demonstrates experimentally and computationally that mesoscale fiber attributes dictate the morphing behavior of hierarchical 2D networks, introducing a hierarchical principle for shape control.

Findings

Networks exhibit either ordered or disordered morphing based on fiber diameter and density.

Networks can recover original shape after thermal shrinking.

Minute mesoscale changes cause significant macroscopic morphing differences.

Abstract

Shape-morphing networks of mesoscale filaments are a common hierarchical feature in biology and hold significant potential for a range of technological applications, from micro-muscles to shape-morphing optical devices. Here, we demonstrate both experimentally and computationally that the shape-morphing of highly-ordered 2D networks constructed of thermoresponsive mesoscale polymeric fibers strongly depends on the physical attributes of the single fiber, in particular on its diameter, as well as on the network's density. We show that based on these parameters, such fiber-networks exhibit one of two thermally driven morphing behaviors: (i) the fibers stay straight, and the network preserves its ordered morphology, exhibiting a bulk-like behavior; or (ii) the fibers buckle and the network becomes messy and highly disordered. Notably, in both cases, the networks display memory and regain their original ordered morphology upon shrinking. This hierarchical principle, demonstrated here on a range of networks, offers a new way for controlling the shape morphing of materials with mesoscale resolutions and elucidates that minute changes in the mesoscale structural attributes can translate to a dramatic change in the morphing behaviors at the macroscale.