Colloidal particles are defined as small solid particles that are suspended in fluid phases by thermal energy fluctuations. The surface of colloidal particles can be decorated with anisotropic patches, creating a subclass of particles known as colloidal patchy particles. The self-assembly of patchy particles into structures with desired morphology and/or porosity for applications in several fields, such as tissue engineering, adsorption, and catalysis, can be controlled by carefully designing and tuning characteristics of these patches, including their size, location, number, and nature of interactions. We investigated the self-assembly behavior of colloidal particles where patches emerge as lobes (protrusions) by performing molecular dynamics simulations. Five ideal shapes for the colloidal particles were designed according to the location and number of lobes at the surface: trigonal planar, square planar, tetrahedral, trigonal bipyramidal, and octahedral, where the number of lobes varied from three to six. We evaluated the effect of incorporating charges on the lobes of colloidal particles in the morphology and porosity of the self-assembled structures across a range of temperatures. The simulations highlighted a direct correlation between the magnitude of the charges on the lobes and self-assembly phenomena, since an increase in the charges on the lobes allowed the colloidal particles to self-assemble over a wider range of temperatures. We also observed that the porosities of the self-assembled structures are enhanced when charges are incorporated to the lobes, when compared to colloidal particles with uncharged lobes. The evolving understanding of how to rationally design colloidal particles with desired properties gained via computational simulations studies can serve as a guide to further improve the manufacture of tissue engineering scaffolds that better mimic biological systems.

Evaluation of the self-assembly behavior of colloidal particles with charged lobes via molecular dynamics simulations

ACS Spring 2022

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