A Predictive Coarse-Grained Model for Semiflexible Polymers in Specific Solvents

A predictive CG model based on a conventional freely rotating chain was developed to describe semiflexible polymers on a relatively large length/time scale. Parameterization of the model requires only two material properties such as, the Kuhn length and coil density. The diameter of spherical “beads...

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Bibliographic Details
Published in:Macromolecular theory and simulations 2010-05, Vol.19 (4), p.179-189
Main Authors: Shie, Sheng C., Lee, Cheng K., Hua, Chi C., Chen, Show A.
Format: Article
Language:English
Subjects:
aggregation
Applied sciences
Beads
coarse-grained model
Coiling
conducting polymers
Density
Exact sciences and technology
Mathematical models
Organic polymers
Parametrization
Physicochemistry of polymers
Polymers
poor solvents
Properties and characterization
Segments
semiflexible chains
Solution and gel properties
Solvents
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Summary:A predictive CG model based on a conventional freely rotating chain was developed to describe semiflexible polymers on a relatively large length/time scale. Parameterization of the model requires only two material properties such as, the Kuhn length and coil density. The diameter of spherical “beads” employed in the model is used as an effective parameter that needs to be determined from preliminary data. Once determined for a particular solvent system, this parameter can then be used to model general solvent systems on a parameter‐free basis. Comparison with SANS data on dilute conjugated polymer solutions reveals that the CG polymer model can well describe material properties ranging from local rodlike segments to bulk interchain aggregates. A predictive coarse‐grained model based on conventional bead‐spring chains has been proposed to quantitatively simulate the single‐chain and aggregation properties of specific semiflexible polymers in specific (poor) solvents. The fair agreement found in theory/data comparisons for dilute solutions of a semiconducting polymer and the level of coarse‐graining achieved in the simulation promise capturing real large‐scale properties in future applications.
ISSN:1022-1344
1521-3919
1521-3919
DOI:10.1002/mats.200900075