Issue 13, 2023

Photochemically driven one-step triple dynamic network formation in printable tough hydrogel for self-healing tubular sensors

Abstract

Self-healing hydrogels have attracted widespread attention due to their excellent biocompatibility, mechanical properties, and reparability. However, it is still a challenge to construct in one step printable self-healing tough hydrogels (PSHTHs), which limits their advanced application. Here, we report a photochemically driven dynamic multinetwork formation (PDDMF) strategy for the one-step design of high-performance multinetwork PSHTHs. Orthogonal ruthenium photochemistry helps in the simultaneous formation of multiple covalent and dynamic networks in ∼10 s. Triple dynamic networks of PSHTHs improve their mechanical performance and enable them to have excellent self-healing ability. These reversible hydrogen-bonded and ionically crosslinked networks could be healed in around 3 s after being destroyed at large shear strains (1000%). Also, PSHTHs exhibit 90% self-healing efficiency after multiple instances of mechanical damage and have mechanical performances similar to those of fresh samples. More importantly, this PDDMF strategy is controlled by simply adjusting the irradiation procedures and is compatible with standard extrusion printing technology to make complex 3D structures. As a proof-of-concept, highly sensitive tubular sensors are designed to detect environmental pressures anisotropically. It is anticipated that this PDDMF strategy and the as-prepared PSHTHs have potential applications in fabricating high-performance self-healing wearable devices, sensors, and bioelectronics.

Graphical abstract: Photochemically driven one-step triple dynamic network formation in printable tough hydrogel for self-healing tubular sensors

Supplementary files

Article information

Article type
Paper
Submitted
29 Janv. 2023
Accepted
06 Marts 2023
First published
06 Marts 2023

Polym. Chem., 2023,14, 1536-1542

Photochemically driven one-step triple dynamic network formation in printable tough hydrogel for self-healing tubular sensors

L. Sun, Z. Lu, P. Zhang, H. Wei and Y. Yu, Polym. Chem., 2023, 14, 1536 DOI: 10.1039/D3PY00093A

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