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Monte Carlo Simulation Of Striped Surface-initiated Polymerization

Posted on:2022-12-13Degree:MasterType:Thesis
Country:ChinaCandidate:S W LiuFull Text:PDF
GTID:2481306770971279Subject:Organic Chemical Industry
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Patterned polymer brushes have unique spatial distributions of structures and components,which are widely applied to synthesize materials with functional interfaces in the fields like surface antifouling,lubrication,novel micro-nano fluidic devices,etc.The applications of patterned polymer brushes require a thorough understanding of the properties of the brushes,such as composition,shape,and size.It has been proved that the properties of patterned polymer brushes are significantly affected by the preparation process,but the underlying mechanism is not well understood.Patterned polymer brushes are usually prepared via the top-down method,or bottom-up method.In this thesis,we are interested in the influence of the bottom-up method,i.e.,the patterned surface-initiated polymerization(SIP)on the structure and properties of the corresponding patterned polymer brush.We examined the polymerization kinetics of a SIP with stripe-distributed initiators.The influence of the polymerization kinetics on the properties,such as the polymer conformation,and structures of the patterned polymer brushes is examined.The results are helpful in precisely controlling the structure and properties of patterned polymer brushes.Today,computer simulations have become an important research method,which can be applied to study surface-initiated polymerization from different scales and provide information that cannot be obtained in experiments.A lattice Monte Carlo simulation with a stochastic reaction model is applied to examine the polymerization kinetics of striped surface-initiated polymerization and the structure and properties of the corresponding striped patterned polymer brushes.It has been proved that the heterogeneity of the reaction environment should be considered in the simulation of heterogeneous polymerizations like striped surface-initiated polymerization.In this study,a novel stochastic reaction model is applied,in which the reaction probability of an active center is determined by the local reaction environment.The effects of grafting density,size of the stripe and other factors on the kinetics of striped surface-initiated polymerization have been examined.Also the differences of striped patterned polymer brushes obtained by different preparation methods(the bottom-up method and the top-down method)were compared.Our research will provide theoretical guidance for the preparation and applications of patterned polymer brushes.The contents of each chapter are mainly arranged as follows:The basic knowledge of the Monte Carlo simulation method is summarized in Chapter?,including its basic principles,as well as the simulation details like the sampling methods,lattice model,off-lattice model and the bond fluctuation model.In chapter ?,we studied the properties of mono-dispersed polymer brushes such as density profiles of the polymer brush and polymer ends.Various methods are applied to character the thickness of the brushes.The results consist with literature well.In chapter IV,the striped pattern-initiated polymerization was examined with a novel stochastic reaction model,in which the heterogeneity of the reaction environment is considered,The effects like grafting density,striped pattern size on the properties like the density profile,molecular weight and the distribution,polydispersity are investigated.Furthermore,patterned brushes and mono-dispersed chains are also examined.This chapter can provide effective theoretical guidance for revealing the similarities and differences between surface-initiated polymerization and striped surface-initiated polymerization.In chapter V,the effects of the distribution of the grafting points on the assembly of linear and Y-shape mixed polymer brushes are examined.
Keywords/Search Tags:patterned polymer brushes, surface-initiated polymerization, striped surface-initiated polymerization, Monte Carlo simulation, stochastic reaction model
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