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Soft fiber‐reinforced polymers (FRPs), consisting of rubbery matrices and rigid fabrics, are widely utilized in industry because they possess high specific strength in tension while allowing flexural deformation under bending or twisting. Nevertheless, existing soft FRPs are relatively weak against crack propagation due to interfacial delamination, which substantially increases their risk of failure during use. In this work, a class of soft FRPs that possess high specific strength while simultaneously showing extraordinary crack resistance are developed. The strategy is to synthesize tough viscoelastic matrices from acrylate monomers in the presence of woven fabrics, which generates soft composites with a strong interface and interlocking structure. Such composites exhibit fracture energy,Γ, of up to 2500 kJ m⁻², exceeding the toughest existing materials. Experimental elucidation shows that the fracture energy obeys a simple relation,Γ = W · l_T, whereWis the volume‐weighted average of work of extension at fracture of the two components andl_Tis the force transfer length that scales with the square root of fiber/matrix modulus ratio. SuperiorΓis achieved through a combination of extraordinarily largel_T(10–100 mm), resulting from the extremely high fiber/matrix modulus ratios (10⁴–10⁵), and the maximized energy dissipation density,W. The elucidated quantitative relationship provides guidance toward the design of extremelymore »tough soft composites.

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Unlike traditional structural materials, soft solids can often sustain very large deformation before failure, and many exhibit nonlinear viscoelastic behavior. Modeling nonlinear viscoelasticity is a challenging problem for a number of reasons. In particular, a large number of material parameters are needed to capture material response and validation of models can be hindered by limited amounts of experimental data available. We have developed a Gaussian Process (GP) approach to determine the material parameters of a constitutive model describing the mechanical behavior of a soft, viscoelastic PVA hydrogel. A large number of stress histories generated by the constitutive model constitute the training sets. The low-rank representations of stress histories by Singular Value Decomposition (SVD) are taken to be random variables which can be modeled via Gaussian Processes with respect to the material parameters of the constitutive model. We obtain optimal material parameters by minimizing an objective function over the input set. We find that there are many good sets of parameters. Further the process reveals relationships between the model parameters. Results so far show that GP has great potential in fitting constitutive models.

Double-network gels are a class of tough soft materials comprising two elastic networks with contrasting structures. The formation of a large internal damage zone ahead of the crack tip by the rupturing of the brittle network accounts for the large crack resistance of the materials. Understanding what determines the damage zone is the central question of the fracture mechanics of double-network gels. In this work, we found that at the onset of crack propagation, the size of necking zone, in which the brittle network breaks into fragments and the stretchable network is highly stretched, distinctly decreases with the increase of the solvent viscosity, resulting in a reduction in the fracture toughness of the material. This is in sharp contrast to the tensile behavior of the material that does not change with the solvent viscosity. This result suggests that the dynamics of stretchable network strands, triggered by the rupture of the brittle network, plays a role. To account for this solvent viscosity effect on the crack initiation, a delayed blunting mechanism regarding the polymer dynamics effect is proposed. The discovery on the role of the polymer dynamic adds an important missing piece to the fracture mechanism of this unique material.