Study On The Performances And Thermodynamic Behaviors Of Shape Memory Polymers And Their Composites

In this thesis,trans-1,4 polyisoprene?TPI?shape memory polymer was used as the research object.By introducing a series of inorganic functional fillers such as graphene oxide?GO?into TPI matrix,TPI shape memory polymer composites were prepared,and their physical and micro characteristics,as well as thermal,mechanical,thermodynamic cycle and shape memory performances were characterized and analyzed from micro scale to macro scale.Based on the thermodynamic properties of TPI SMP,a new micro physical structure of polymer was constructed,and a new thermodynamic viscoelastic phase transition model was proposed by combining the structure with viscoelastic constitutive theory.First,GO was introduced into the TPI matrix to prepare the GO/TPI shape memory polymer composites based on a new preparation process.Through experiments and theoretical analysis,the influence laws and related mechanisms of the thermodynamic properties of composite systems were systematically studied.In addition,a new test method was used to characterize the deformation recovery ability of the polymer composites.While ensuring the deformation recovery ability of the shape memory polymer composites,the relevant thermal and mechanical properties of the composite systems were greatly enhanced,and the optimal ratio of GO added to the GO/TPI composites was determined.Second,the GO-Al₂O₃/TPI shape memory polymer hybrid composites were prepared by introducing GO and Al₂O₃ nanoparticles with different mass fractions into the TPI matrix by physical blending.The microstructure,thermal properties,mechanical properties,thermodynamic cycle properties and shape memory properties of the composite systems were systematically studied,and the influence mechanism of the hybrid nanofiller content and temperature change on the properties of the composite systems were analyzed.Third,a new preparation process was developed based on charge attraction method.Under the guidance of this process,GO and amino silica?Si O₂-NH₂?were hybridized by charge attraction,and a new kind of Si O₂@GO nano core-shell structure functional hybrid filler was prepared,which was introduced into TPI matrix as a new filler,and then the Si O₂@GO/TPI hybrid composite systems were obtained.According to a series of characterization from microcosmic scale to macroscopic scale,the interface interaction between the new core-shell hybrid filler and TPI matrix was studied,and the thermodynamic properties and shape memory properties of the composite systems were analyzed.The successful preparation of the core-shell structure provides a new interface design strategy for the development of high-performance polymer composite.Fourth,a green,simple and effective layer by layer self-assembly?LBL?method was proposed.Based on electrostatic interaction,GO/Si O₂ multilayer films are deposited on the surface of carbon fiber?CFs?layer by layer.The surface microstructure,wettability and mechanical strength of single fiber were systematically characterized and analyzed.In addition,the modified carbon fiber obtained by LBL self-assembly was introduced into TPI matrix as a new type of filler,and the modified CFs reinforced polymer composite was developed.The interfacial adhesion between TPI matrix and modified CFs,as well as the related thermodynamic properties and deformation recovery ability of the composite systems were systematically characterized and analyzed.The green and effective Lb L self-assembly method has great potential in manufacturing high-performance CFs reinforced polymer composites.Finally,a new type of shape memory polymer microstructure was proposed,the corresponding phase transformation constitutive model was constructed,and the thermomechanical viscoelastic constitutive equation was given.The accuracy and rationality of the thermomechanical viscoelastic phase transformation constitutive model were verified by comparing the theoretical prediction results of the phase transformation model with the thermodynamic test data.