With the continuous appearance and expansion of high-strength hydrogels in emerging fields such as industry, medicine, and green development, the synthesis and application of high-strength hydrogels have developed dramatically and achieved remarkable results from the aspects of raw materials, preparation methods, and reinforcement mechanisms. However, there is still a lack of systematic reviews on high-strength hydrogels. Herein, we first discuss the advantages of natural and synthetic materials, and the characteristics of high-strength hydrogels prepared from different raw materials; we then expound on the influence mechanism of physical interactions or chemical bonds on the strength of the hydrogel from three aspects: physical cross-linking, chemical cross-linking, and dynamic chemical cross-linking; at last, we systematically expound the strengthening strategies, including double network/multi-network, nanocomposite, topology, supramolecular polymerization, and characteristics and strengthening mechanisms of such high-strength hydrogels. In addition, based on the development status of high-strength hydrogels, we combined the application requirements and the existing drawbacks of high-strength hydrogels to propose their possible development directions in the future.

Modeling the random fiber distribution of a fiber-reinforced composite is of great importance for studying the progressive failure behavior of the material on the micro scale. In this paper, we develop a new algorithm for generating random representative volume elements (RVEs) with statistical equivalent fiber distribution against the actual material microstructure. The realistic statistical data is utilized as inputs of the new method, which is archived through implementation of the probability equations. Extensive statistical analysis is conducted to examine the capability of the proposed method and to compare it with existing methods. It is found that the proposed method presents a good match with experimental results in all aspects including the nearest neighbor distance, nearest neighbor orientation, Ripley’s K function, and the radial distribution function. Finite element analysis is presented to predict the effective elastic properties of a carbon/epoxy composite, to validate the generated random representative volume elements, and to provide insights of the effect of fiber distribution on the elastic properties. The present algorithm is shown to be highly accurate and can be used to generate statistically equivalent RVEs for not only fiber-reinforced composites but also other materials such as foam materials and particle-reinforced composites.

In this paper, a plain-woven glass-fabric-reinforced polymer (GFRP) composite with embedded shape memory alloy (SMA) wires is investigated by means of experiments. The vacuum-assisted resin injection (VARI) method is utilized to fabricate the composite specimens. Quasi-static uniaxial tensile tests are then carried out to evaluate the influence of SMA reinforcement on the stress–strain behavior of the composite. Only the elastic behavior of the composite is considered in the present study. The tensile strain in all the experiments is kept below 2.5% to avoid debonding of the SMA-resin interface, which would lead to failure of the composite. Stress–strain curves are obtained and shown to present a bilinear behavior due to phase transformation taking place in the SMA wires beyond a certain stress threshold.

In this paper, a multiscale analysis method was proposed to simulate carbon nanoparticles (CNPs)−filled polymers which can be strain sensors applied in wearable electronic devices, flexible skin, and health monitoring fields. On the basis of the microstructure characteristics of the composite, a microscale representative volume element model of the CNPs−filled polymer was established using the improved nearest−neighbor algorithm. By finite element analysis, the variation of the junction widths of adjacent aggregates can be extracted from the simulation results. Then, according to the conductive mechanism of CNP−filled polymers, the composite was simplified as a circuit network composed of vast random resistors which were determined by the junction widths between adjacent aggregates. Hence, by taking junction widths as the link, the resistance variation of the CNPs−filled polymer with the strain can be obtained. To verify the proposed method, the electromechanical responses of silicone elastomer filled with different CNPs under different filling amounts were investigated numerically and experimentally, respectively, and the results were in good agreement. Therefore, the multiscale analysis method can not only reveal the strain−sensing mechanism of the composite from the microscale, but also effectively predict the electromechanical behavior of the CNPs−filled polymer with different material parameters.

Quadriaxial non-crimp fabric (QNCF) composites are increasingly being used as primary structural materials in aircraft and automotive applications. Predicting the mechanical properties of QNCF lamina is more complicated compared with that of unidirectional (UD) composites, because of the knitting connection of different plies. In this study, to analyze the stiffness and strength of the QNCF composites, a novel modeling strategy for the meso-scale features is presented based on the semi-laminar assumption. Following the view of the mechanical properties of single composite lamina, the complex QNCF layer is decomposed into individual plies. Three different representative unit cells along fiber direction are selected to predict the mechanical performance of QNCF, including in-plane stiffness, damage initiation, and stiffness degradation. To validate the developed modeling strategy, the predictions are compared with existing experimental results, where a good agreement is presented on the prediction of in-plane stiffness and strength. Furthermore, the effect of in-plane fiber distortion, induced by the stitching yarn on the mechanical properties, is studied.