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A multiscale polymerization framework towards network structure and fracture of double-network hydrogels 被引量:1

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摘要 Double-network(DN)hydrogels,consisting of two contrasting and interpenetrating polymer networks,are considered as perhaps the toughest soft-wet materials.Current knowledge of DN gels from synthesis methods to toughening mechanisms almost exclusively comes from chemically-linked DN hydrogels by experiments.Molecular modeling and simulations of inhomogeneous DN structure in hydrogels have proved to be extremely challenging.Herein,we developed a new multiscale simulation platform to computationally investigate the early fracture of physically-chemically linked agar/polyacrylamide(agar/PAM)DN hydrogels at a long timescale.A“random walk reactive polymerization”(RWRP)was developed to mimic a radical polymerization process,which enables to construct a physically-chemically linked agar/PAM DN hydrogel from monomers,while conventional and steered MD simulations were conducted to examine the structural-dependent energy dissipation and fracture behaviors at the relax and deformation states.Collective simulation results revealed that energy dissipation of agar/PAM hydrogels was attributed to a combination of the pulling out of agar chains from the DNs,the disruption of massive hydrogen bonds between and within DN structures,and the strong association of water molecules with both networks,thus explaining a different mechanical enhancement of agar/PAM hydrogels.This computational work provided atomic details of network structure,dynamics,solvation,and interactions of a hybrid DN hydrogel,and a different structural-dependent energy dissipation mode and fracture behavior of a hybrid DN hydrogel,which help to design tough hydrogels with new network structures and efficient energy dissipation modes.Additionally,the RWRP algorithm can be generally applied to construct the radical polymerization-produced hydrogels,elastomers,and polymers.
出处 《npj Computational Materials》 SCIE EI CSCD 2021年第1期371-379,共9页 计算材料学(英文)
基金 J.Z.thanksfinancial supports from NSF grants of 1607475 and 1825122.
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