Issue 11, 2023

Sustainable mechanochemical growth of double-network hydrogels supported by vascular-like perfusion

Abstract

Double-network (DN) gels are unique mechanochemical materials owing to their structures that can be dynamically remodelled during use. The mechanical energy applied to DN gels is efficiently transferred to the chemical bonds of the brittle network, generating mechanoradicals that initiate the polymerisation of pre-loaded monomers, thereby remodelling the materials. To attain continuous remodelling or growth in response to repetitive mechanical stimuli, a sustainable supply of chemical reagents to such dynamic materials is essential. In this study, inspired by the vascular perfusion transporting nutrients to cells, we constructed a circulatory system for a continuous supply of chemicals to channel-containing DN hydrogels (c-DN gels). The perfusion of monomer solutions through the channel and permeability of the c-DN gels not only replenishes the monomers consumed by the polymerisation but also replenishes the water loss caused by the surface evaporation of hydrogel, thereby freeing the mechanochemical process of DN gels from the constraints of the underwater environment. The facile chemical supply enabled us to modulate the mechanical enhancement of the c-DN gel and attain muscle-like strengthening under repeated mechanical training in deoxygenated air. We also studied the kinetics of polymer growth and strengthening and deciphered unique features of mechanochemical reaction in DN gels including the extremely long-living radicals and delayed mechanical strengthening.

Graphical abstract: Sustainable mechanochemical growth of double-network hydrogels supported by vascular-like perfusion

Supplementary files

Article information

Article type
Communication
Submitted
05 7月 2023
Accepted
14 8月 2023
First published
15 8月 2023

Mater. Horiz., 2023,10, 4882-4891

Author version available

Sustainable mechanochemical growth of double-network hydrogels supported by vascular-like perfusion

G. Wei, Y. Kudo, T. Matsuda, Z. J. Wang, Q. F. Mu, D. R. King, T. Nakajima and J. P. Gong, Mater. Horiz., 2023, 10, 4882 DOI: 10.1039/D3MH01038D

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