Abstract
The assemblies of colloidal particles and their characterization are becoming more complex as new particle shapes, materials, and assembly methods are being introduced. Consequentially, there is a need for tools that can analyze videos and extract quantitative data from qualitative media to elucidate the dynamic processes of assembly formations. This contribution presents an algorithm that analyzes videos of the 2D assembly process of ellipsoidal particles. Particles are both classified using abstracted interparticle properties and tracked over time. Our algorithm is customized to quantiatively analyze the dynamic assembly of the ellipsoidal colloidal particles into chains, which break and reform over time. By analyzing the yielded data from our algorithm, we better elucidate the dynamic nature of particle chain formation. We foresee our algorithm being capable of analyzing videos in real time to study the dynamic assembly behavior of diverse matter such as biological samples and nanoparticles of various shapes.
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