Literature DB >> 29503989

Tailoring the mechanical properties by molecular integration of flexible and stiff polymer networks.

Haixiao Wan1, Jianxiang Shen2, Naishen Gao1, Jun Liu3, Yangyang Gao1, Liqun Zhang4.   

Abstract

Designing a multiple-network structure at the molecular level to tailor the mechanical properties of polymeric materials is of great scientific and technological importance. Through the coarse-grained molecular dynamics simulation, we successfully construct an interpenetrating polymer network (IPN) composed of a flexible polymer network and a stiff polymer network. First, we find that there is an optimal chain stiffness for a single network (SN) to achieve the best stress-strain behavior. Then we turn to study the mechanical behaviors of IPNs. The result shows that the stress-strain behaviors of the IPNs appreciably exceed the sum of that of the corresponding single flexible and stiff network, which highlights the advantage of the IPN structure. By systematically varying the stiffness of the stiff polymer network of the IPNs, optimal stiffness also exists to achieve the best performance. We attribute this to a much larger contribution of the non-bonded interaction energy. Last, the effect of the component concentration ratio is probed. With the increase of the concentration of the flexible network, the stress-strain behavior of the IPNs is gradually enhanced, while an optimized concentration (around 60% molar ration) of the stiff network occurs, which could result from the dominant role of the enthalpy rather than the entropy. In general, our work is expected to provide some guidelines to better tailor the mechanical properties of the IPNs made of a flexible network and a stiff network, by manipulating the stiffness of the stiff polymer network and the component concentration ratio.

Entities:  

Year:  2018        PMID: 29503989     DOI: 10.1039/c7sm02282d

Source DB:  PubMed          Journal:  Soft Matter        ISSN: 1744-683X            Impact factor:   3.679


  1 in total

1.  A Machine Learning Framework to Predict the Tensile Stress of Natural Rubber: Based on Molecular Dynamics Simulation Data.

Authors:  Yongdi Huang; Qionghai Chen; Zhiyu Zhang; Ke Gao; Anwen Hu; Yining Dong; Jun Liu; Lihong Cui
Journal:  Polymers (Basel)       Date:  2022-05-06       Impact factor: 4.967

  1 in total

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