Supramolecular polymer networks (SPNs) based on host-guest recognition demonstrate great promise in the development of smart materials owing to their attractive dynamic properties. Nevertheless, the SPNs based on host-guest recognition, in most cases, suffer from poor mechanical performance probably due to the relatively weak binding ability of cross-links. Such shortage in mechanical properties would hinder their applications in some specific fields. Therefore, we pursue to explore novel strategies in order to effectively improve the mechanical performance of these SPNs. On one hand, we utilize the sequential supramolecular self-assemblies including the host-guest recognition and metal coordination to fabricate the polymer network with an interwoven topology. The dual supramolecular interactions together with the unique network topology provide effective pathways for materials to dissipate energy, thereby resulting in the remarkable improvement in mechanical properties of SPNs. Moreover, the topology of the SPN could be modulated by multiple stimuli including K<sup>+</sup> and PPh<sub>3</sub>, leading to tunable mechanical performance. On the other hand, we rely on the cryptand/paraquat host-guest recognition motifs as cross-links to prepare a class of highly strong and tough SPNs. Unlike the general supramolecular cross-links with relatively weak binding abilities, the cryptand-based host-guest interactions have high association constant and steady complexing structure, which could effectively stabilize the network and resist the mechanical deformation resulted from external force. Such favorable structural ability is able to furnish our SPNs with enhanced mechanical performance. Meanwhile, our SPNs also retain abundant dynamic properties including good abilities in energy dissipation, reprocessability and stimuli-responsiveness. We hope these studies would provide novel insights into the preparation of SPNs with excellent mechanical properties, and promote the development of high-performance intelligent supramolecular materials.

Highly strong and tough supramolecular polymer networks based on host-guest recognition

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