Self-assembly of ellipsoidal particles at fluid-fluid interfaces with an empirical pair potential

Luo, A., Vermant, J., Ilg, P. ORCID: https://orcid.org/0000-0002-7518-5543, Zhang, Z. and Sagis, L. M. C. (2019) Self-assembly of ellipsoidal particles at fluid-fluid interfaces with an empirical pair potential. Journal of Colloid and Interface Science, 534. pp. 205-214. ISSN 0021-9797 doi: 10.1016/j.jcis.2018.08.114

Abstract/Summary

Colloidal particles adsorbed at fluid-fluid interfaces interact via mechanisms that can be specific to the presence of interfaces, for instance, lateral capillary interactions induced by nonspherical particles. Capillary interactions are highly relevant for self-assembly and the formation of surface microstructures, however, these are very challenging to model due to the multibody nature of capillary interactions. This work pursues a direct comparison between our computational modelling approach and experimental results on surface microstructures formed by ellipsoidal particles. We begin by investigating the accuracy of using pairwise interactions to describe the multibody capillary interaction by contrasting exact two- and three-particle interaction energies and we find that the pairwise approximation appears reasonable for the experimentally relevant configurations studied. We then develop an empirical pair potential and use it in Monte-Carlo type simulations to efficiently model the structure formation process for relevant particle properties such as aspect ratio, contact angle and surface coverage, and succeed in reproducing our experimental observations where we spread sterically-stabilised ellipsoidal particles onto an oil-air interface at high surface coverage. At lower surface coverages, we find that the self-assembly process falls into the diffusion-limited colloid aggregation universality class.

Item Type	Article
URI	https://reading-clone.eprints-hosting.org/id/eprint/78871
Identification Number/DOI	10.1016/j.jcis.2018.08.114
Refereed	Yes
Divisions	Science > School of Mathematical, Physical and Computational Sciences > Department of Mathematics and Statistics
Publisher	Elsevier
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