Brownian dynamics simulations of associating diblock copolymers

Area of Science:

• Polymer Science
• Computational Chemistry
• Soft Matter Physics

Background:

• Associating polymers form micelles through hydrophobic interactions.
• Understanding polymer aggregation is crucial for materials science and nanotechnology.
• Existing models may not efficiently capture aggregation behavior across various conditions.

Purpose of the Study:

• To develop a novel coarse-grained computational model for associating polymers.
• To simulate polymer micelle formation and properties at moderate computational cost.
• To investigate the relationship between packing fraction and aggregation behavior.

Main Methods:

• Gaussian "blob" representation for polymer chains.
• Brownian dynamics (BD) simulations.
• Attractive potential for hydrophobic interactions towards a central nodal point.

Main Results:

• Model accurately predicts aggregation numbers (Nagg ≈ 8 for AB diblock copolymers) and their increase with packing fraction.
• Observed extremely low critical micelle concentration (cmc) with Nagg ≈ 5 at low packing fractions (≈ 10⁻⁴).
• Long-time self-diffusion coefficient decreases logarithmically with packing fraction; viscosity follows the Huggins equation.

Conclusions:

• The proposed coarse-grained model effectively simulates associating polymers and micelle formation.
• Model predictions align well with experimental findings for aggregation numbers and cmc.
• The model provides insights into polymer diffusion and viscosity in aggregating systems.