Spinodal decomposition in a binary polymer mixture: dynamic self-consistent-field theory and Monte Carlo simulations

Area of Science:

• Polymer Physics
• Materials Science
• Statistical Mechanics

Background:

• Phase separation is crucial in polymer mixture applications.
• Understanding early-stage kinetics is key to controlling material properties.
• Single chain dynamics play an often-overlooked role in mixture behavior.

Purpose of the Study:

• To investigate the influence of single polymer chain dynamics on phase separation kinetics.
• To compare theoretical models with simulation results for early-stage spinodal decomposition.
• To analyze the effect of different kinetic coefficient dependencies and fluctuations.

Main Methods:

• Dynamical self-consistent-field theory (SCFT) for Gaussian chains.
• External potential dynamics method.
• Monte Carlo simulations using the bond fluctuation model (64-segment chains).
• Mapping of time, length, and temperature scales between theory and simulation.

Main Results:

• The Rouse model for kinetic coefficients shows better agreement with simulations than local (wave vector-independent) factors.
• Including fluctuations in SCFT shortens the spinodal behavior timescale.
• Fluctuations reduce relaxation rates for small wave vectors and prevent negative rates for large wave vectors, matching simulation observations.

Conclusions:

• Single chain dynamics, particularly as described by the Rouse model, are essential for accurately predicting polymer mixture phase separation.
• Fluctuations significantly alter spinodal decomposition kinetics, improving theoretical predictions.
• A combination of advanced theoretical methods and simulations is vital for understanding complex polymer systems.