Discrete bivariate population balance modelling of heteroaggregation processes

Area of Science:

• Colloid and Surface Science
• Computational Chemistry
• Chemical Engineering

Background:

• Understanding heteroaggregation in binary particle mixtures is crucial for controlling particle system behavior.
• Existing models often simplify particle properties, limiting accuracy for complex interactions.

Purpose of the Study:

• To simulate heteroaggregation in binary particle mixtures using a discrete population balance model.
• To investigate the influence of particle size, zeta-potential, and mixing ratios on aggregation dynamics.
• To develop and validate a charge-balance kernel for predicting aggregation rates.

Main Methods:

• Employed a discrete population balance model with two internal coordinates for particle properties.
• Reduced property space using a semi-heuristic approach for computational efficiency.
• Utilized deterministic models for Brownian motion and stability, incorporating DLVO potentials and a novel charge-balance kernel.

Main Results:

• Electrostatic phenomena were identified as the dominant factor in the aggregation process.
• Particle mixing ratio critically influenced aggregation: impeded aggregation at 1:1, restabilization at 100:1, and complex clusters at 10:1.
• Simulation results demonstrated excellent agreement with experimental flow cytometric data.

Conclusions:

• The developed charge-balance kernel effectively models heteroaggregation influenced by electrostatic interactions.
• Particle mixing ratio is a key parameter for controlling heteroaggregation and aggregate structure.
• The simulation approach provides a reliable tool for predicting and understanding particle aggregation in complex mixtures.