Discrete state model of a self-aggregating colloidal system with directional interactions

Area of Science:

• Colloid and Surface Science
• Statistical Mechanics
• Computational Biophysics

Background:

• Coarse-grained models are crucial for understanding complex system kinetics, particularly in biophysics (e.g., Markov state models for protein folding).
• Colloidal self-assembly driven by external fields presents a complex dynamic system that benefits from simplified modeling approaches.

Purpose of the Study:

• To develop a coarse-grained, discrete state model for field-responsive colloidal particle self-aggregation.
• To describe the kinetics of simultaneous aggregate formation and evolution from single particles.
• To investigate how transition dynamics change with aggregate size and explore model validity.

Main Methods:

• Developed a discrete state model based on local particle structure, analogous to Markov state modeling.
• Utilized particle-resolved Brownian dynamics simulations as the basis for model development.
• Defined states by particle local structure and modeled kinetics as stochastic, memoryless jumps.
• Validated the model by comparing predicted population fractions with simulation data.

Main Results:

• The coarse-grained model successfully describes the simultaneous formation and evolution of multiple colloidal aggregates.
• The model accounts for changes in transition dynamics as the largest cluster size increases.
• Validation confirmed good agreement between predicted and simulated population fractions across different aggregation stages.

Conclusions:

• A novel coarse-grained discrete state model effectively captures the complex kinetics of colloidal self-aggregation in orthogonal electric and magnetic fields.
• The model provides a computationally efficient approach to study self-assembly dynamics, adaptable to varying conditions and parameter changes.
• Further exploration of detailed balance conditions in different aggregation stages is warranted.