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Coarse grained model of entangled polymer melts.

A Rakshit1, R C Picu

  • 1Department of Mechanical, Aerospace and Nuclear Engineering, Rensselaer Polytechnic Institute, Troy, New York 12180, USA.

The Journal of Chemical Physics
|November 10, 2006
PubMed
Summary

This study introduces a coarse-graining method for polymer melts, accurately capturing chain structure and dynamics. The model ensures key properties like distribution functions and relaxation rates are well-represented.

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Area of Science:

  • Polymer Physics
  • Computational Materials Science
  • Statistical Mechanics

Background:

  • Polymer melts exhibit complex chain dynamics and structures.
  • Accurate simulation of these systems requires computationally intensive models.
  • Coarse-graining offers a computationally efficient alternative.

Purpose of the Study:

  • To develop a coarse-graining procedure for polymer melts.
  • To reproduce both the chain structure and dynamics of linear monodisperse polymers.
  • To establish a coarse model based on the entanglement segment length (Ne).

Main Methods:

  • A bead-spring model represents the fine-scale polymer melt.
  • Coarse-graining maps Ne consecutive beads into single blobs.
  • Mapping conditions ensure probability, dissipation, and phase-space constraints are conserved.
  • Inner blobs move along the chain backbone; end blobs move in 3D space.
  • Input parameters are calibrated using fine-scale model behavior.

Main Results:

  • The coarse model accurately represents pair and end-to-end distribution functions.
  • Segmental and end-to-end vector relaxation rates are well-reproduced.
  • Rouse modes and diffusion dynamics are properly captured.
  • While not all thermodynamics are reproduced, key dynamic and structural features are.

Conclusions:

  • The presented coarse-graining procedure effectively captures essential polymer melt dynamics and structure.
  • This method provides a computationally efficient route to study polymer melts.
  • The model's ability to reproduce distribution functions and relaxation dynamics is a significant advantage.

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