From static micrographs to particle aggregation dynamics in three dimensions.

Area of Science:

• Colloid and surface science
• Materials science
• Statistical physics

Background:

• Colloidal aggregation theories and models exist, but a direct link to experimental observations is often missing.
• Understanding aggregation dynamics is crucial for controlling the properties of colloidal gels.

Purpose of the Study:

• To quantitatively compare diffusion limited cluster aggregation (DLCA) and reaction limited cluster aggregation (RLCA) models with experimental data of silica nanoparticle aggregates.
• To develop and apply novel topological summary functions for analyzing colloidal aggregate structures.

Main Methods:

• Analysis of scanning transmission electron micrographs of silica nanoparticle gels (9 wt%).
• Application of spatial statistics (empty space function, Ripley's K-function) and new graph theory-based cluster analysis functions.
• Quantitative comparison of experimental data with DLCA and RLCA simulations using a least-square approach.

Main Results:

• The study demonstrates a method to determine the probability of aggregation from static micrographs.
• Four topological summary statistics were used to characterize colloidal silica aggregates and simulated structures.
• The reaction limited cluster aggregation (RLCA) model provided a better fit to the experimental data compared to the diffusion limited cluster aggregation (DLCA) model.

Conclusions:

• The developed methods allow for a quantitative evaluation of different cluster aggregation scenarios.
• The findings suggest that RLCA is a more appropriate model for describing the formation of the studied silica nanoparticle gels.
• This work bridges the gap between theoretical aggregation models and experimental observations in colloidal systems.