Programming strain-stiffening in soft composites via structural memory near jamming

TL;DR

This paper demonstrates a novel method to program strain-stiffening in soft composites by leveraging structural memory effects near jamming, independent of matrix nonlinearity.

Contribution

It introduces a design strategy that uses non-equilibrium memory effects in contact networks to achieve programmable strain-stiffening in soft composites.

Findings

Structural memory drives a crossover from granular-like to biopolymer-like stiffening.

Simulations show enhanced non-affine reconfigurations of contact networks.

The approach does not rely on matrix nonlinearity for stiffening.

Abstract

Soft composite solids, comprising discrete inclusions embedded within a compliant matrix, are emerging candidates for engineering synthetic tissues and soft robotic materials. Current strategies for controlling their nonlinear mechanics, such as strain-stiffening, have primarily relied on the nonlinear elasticity of polymer matrices. Although direct contacts between inclusions may enhance stiffening responses at high densities, the role of the non-equilibrium and history-dependent nature of disordered contact networks in composite mechanics remains unexplored. In this work, by applying a mechanical training protocol near a shear-jamming phase boundary, we demonstrate that the structural memory encoded in contact networks drives a crossover from granular-like to biopolymer-like strain stiffening. Simulations of a coarse-grained composite model reveal that this biopolymer-like mechanical response emerges from enhanced non-affine reconfigurations of nearly-jammed contact networks. Without relying on matrix nonlinearity, we establish a design strategy that leverages non-equilibrium memory effects intrinsic to granular systems to achieve highly programmable strain-stiffening in soft composites.