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Modeling Colloidal Particle Aggregation Using Cluster Aggregation with Multiple Particle Interactions.

Jakob Antonsson1, Charlotte Hamngren Blomqvist2, Eva Olsson2

  • 1Department of Mathematical Sciences, Chalmers University of Technology and University of Gothenburg, SE-412 96 Gothenburg, Sweden.

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|April 30, 2024
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Summary
This summary is machine-generated.

This study simulated colloidal silica aggregation using various models. A sticking probability dependent on multiple nearby particles best matched experimental scanning transmission electron microscopy (STEM) data.

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Area of Science:

  • Colloid and Surface Science
  • Materials Science
  • Computational Modeling

Background:

  • Colloidal silica aggregation is crucial in various industrial processes.
  • Understanding aggregation dynamics requires accurate modeling of particle interactions.
  • Experimental data, like scanning transmission electron microscopy (STEM), provides benchmarks for simulations.

Purpose of the Study:

  • To investigate colloidal silica aggregation dynamics.
  • To compare different aggregation models with experimental STEM data.
  • To identify the most accurate model for silica aggregation.

Main Methods:

  • Simulated diffusion-limited cluster aggregation (DLCA) and reaction-limited cluster aggregation (RLCA) models.
  • Implemented variable sticking probabilities based on cluster mass and proximity of other particles.
  • Evaluated models using spatial statistics, fractal dimension, and mass transport properties.

Main Results:

  • Models with constant sticking probability showed less agreement with experimental data.
  • Sticking probability dependent on cluster mass improved model accuracy.
  • The model incorporating interaction with multiple particles near the collision site yielded structures most similar to STEM data.

Conclusions:

  • The sticking probability's dependence on multi-particle interactions is key to accurately simulating colloidal silica aggregation.
  • This finding enhances the predictive power of computational models for colloidal systems.
  • The study provides a refined approach for understanding and predicting silica-based material formation.