Programming strain-stiffening in soft composites via structural memory near jamming
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arXiv
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| Format: | Preprint |
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2026
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| author | Zhao, Yiqiu Pan, Deng Pang, Yiming Barés, Jonathan Xu, Chang Liu, Che Hu, Haitao Jin, Yuliang Xu, Qin |
| author_facet | Zhao, Yiqiu Pan, Deng Pang, Yiming Barés, Jonathan Xu, Chang Liu, Che Hu, Haitao Jin, Yuliang Xu, Qin |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_20437 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Programming strain-stiffening in soft composites via structural memory near jamming Zhao, Yiqiu Pan, Deng Pang, Yiming Barés, Jonathan Xu, Chang Liu, Che Hu, Haitao Jin, Yuliang Xu, Qin Soft Condensed Matter 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. |
| title | Programming strain-stiffening in soft composites via structural memory near jamming |
| topic | Soft Condensed Matter |
| url | https://arxiv.org/abs/2604.20437 |