Toughening Hydrogels with Fibrillar Connected Double Networks.

Biological tissues, such as tendons or cartilage, possess high strength and toughness with very low plastic deformations. In contrast, current strategies to prepare tough hydrogels commonly utilize energy dissipation mechanisms based on physical bonds that lead to irreversible large plastic deformations, thus limiting their load-bearing applications. This article reports a strategy to toughen hydrogels using fibrillar connected double networks (fc-DN), which consist of two distinct but chemically interconnected polymer networks, that is, a polyacrylamide network and an acrylated agarose fibril network. The fc-DN design allows efficient stress transfer between the two networks and high fibril alignment during deformation, both contributing to high strength and toughness, while the chemical crosslinking ensures low plastic deformations after undergoing high strains. The mechanical properties of the fc-DN network can be readily tuned to reach an ultimate tensile strength of 8 MPa and a toughness of above 55 MJ m-3, which is 3 and 3.5 times more than that of fibrillar double network hydrogels without chemical connections, respectively. The application potential of the fc-DN hydrogel is demonstrated as load-bearing damping material for a jointed robotic lander. The fc-DN design provides a new toughening mechanism for hydrogels that can be used for soft robotics or bioelectronic applications.

Thermally trainable dual network hydrogels.

Inspired by biological systems, trainable responsive materials have received burgeoning research interests for future adaptive and intelligent material systems. However, the trainable materials to date typically cannot perform active work, and the training allows only one direction of functionality change. Here, we demonstrate thermally trainable hydrogel systems consisting of two thermoresponsive polymers, where the volumetric response of the system upon phase transitions enhances or decreases through a training process above certain threshold temperature. Positive or negative training of the thermally induced deformations can be achieved, depending on the network design. Importantly, softening, stiffening, or toughening of the hydrogel can be achieved by the training process. We demonstrate trainable hydrogel actuators capable of performing increased active work or implementing an initially impossible task. The reported dual network hydrogels provide a new training strategy that can be leveraged for bio-inspired soft systems such as adaptive artificial muscles or soft robotics.