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Manufacturing Of Robust Natural Fiber Preforms Utilizing Bacterial Cellulose as Binder
Published on: May 22, 2014
Strengthened self-healable natural rubber composites based on carboxylated cellulose nanofibers participated in ionic
Mingliang Wu1, Li Yang1, Zhongjie Zheng1
1Key Laboratory of Disaster Prevention and Structural Safety of Ministry of Education, Guangxi University, Nanning 530004, China; School of Chemistry and Chemical Engineering, Guangxi University, Nanning 530004, China; Guangxi Key Laboratory of Disaster Prevention and Engineering Safety, Guangxi University, Nanning 530004, China.
Abstract:
Cellulose, as a green reinforcing agent for rubber, has excellent improvement on the tensile strength but usually accompany with a deterioration of extensibility and self-healing property. Herein, we report an efficient method to prepare robust and self-healable natural rubber/zinc dimethacrylate/carboxylated cellulose nanofibers (NR/ZDMA/CNC) composites which are constructed by a CNC participated ionic supramolecular network. Ionic supramolecular network in NR is generated by the polymerization of ZDMA during a controlled peroxide-initiated vulcanization of NR. Interestingly, NR with massive ion clusters has strong affinity with CNC, which facilitates the uniform dispersion of CNC and the compatibility between CNC and NR. Meanwhile, CNC participates into the supramolecular network via non-covalent interaction with NR chains equipped with ionic crosslinks. This greatly reduces the adverse effect of CNC on the dynamic characteristics of supramolecular network. As a result, the tensile strength of NR/ZDMA composite with 20 phr CNC could reach 4.13 MPa, while its self-healing efficiency still maintains at >80 %. Thus, NR composites with non-covalent interaction between CNC and supramolecular network display improved strength, maintained extensibility, and excellent self-healing capability. This study thus demonstrates a feasible approach to reduce the negative effect of reinforcing fillers on a self-healing rubber based on supramolecular networks.

