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Abstract
Chemically patterned surfaces have been successfully employed to direct the kinetics of self-assembly of block copolymers into dense, periodic morphologies (”chemoepitaxy”). Significant efforts have been directed towards understanding the kinetics of structure formation and, particularly, the formation and annihilation of defects. In the present manuscript we use computer simulations of a soft, coarse-grained polymer model to study the kinetics of structure formation of lamellar-forming block copolymer thin films on a chemical pattern of lines and spaces. The case where the copolymer material replicates the surface pattern and the more subtle scenario of sparse guiding patterns are considered. Our simulation results highlight (1) the importance of the early stages of pattern-directed self-assembly that template the subsequent morphology and (2) the dependence of the free-energy landscape on the incompatibility between the two blocks of the copolymer.
Highlights
Block copolymers are comprised of two or multiple blocks of chemically distinct repeat units joined by covalent bonds into a flexible macromolecule
The structure formation after this quench can be divided into qualitatively distinct stages: (i) initial surface-directed spinodal self-assembly: The initial kinetics of collective density fluctuations can be qualitatively described by the dynamic Random-Phase Approximation (RPA) [34]
Summary and discussion Using a soft, coarse-grained model of block copolymer melts in conjunction with efficient, parallel simulation techniques we have studied the kinetics of structure formation of lamella-forming copolymer films on chemical guiding patterns
Summary
Block copolymers are comprised of two or multiple blocks of chemically distinct repeat units joined by covalent bonds into a flexible macromolecule. The structure formation after this quench can be divided into qualitatively distinct stages: (i) initial surface-directed spinodal self-assembly: The initial kinetics of collective density fluctuations can be qualitatively described by the dynamic Random-Phase Approximation (RPA) [34].
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