Assembly of Multi-flavored Two-Dimensional Colloidal Crystals
Nathan Mahynski, Hasan Zerze, Harold W. Hatch, Vincent K. Shen, Jeetain Mittal
We systematically investigate the assembly of binary multi-flavored colloidal mixtures in two dimensions. In these mixtures all pairwise interactions between species may be tuned independently. This introduces an additional degree of freedom over more traditional binary mixtures with fixed mixing rules, which is anticipated to open new avenues for directed self- assembly. At present, colloidal self-assembly into non-trivial lattices tends to require either high pressures for isotropically interacting particles, or the introduction of directionally anisotropic interactions. Here we demonstrate tunable assembly into a plethora of structures which requires neither of these conditions. We develop a minimal model that defines a three- dimensional phase space containing one dimension for each pairwise interaction, then employ various computational techniques to map out regions of this phase space in which the system self-assembles into these different morphologies. We then present a mean-field model that is capable of reproducing these results for size-symmetric mixtures, which reveals how to target different structures by tuning pairwise interactions, solution stoichiometry, or both. Concerning particle size asymmetry, we find that domains in this models phase space, corresponding to different morphologies, tend to undergo a continuous rotation whose magnitude is proportional to the size asymmetry. Such continuity enables one to estimate the relative stability of different lattices for arbitrary size asymmetries. Owing to its simplicity and accuracy, we expect this model to serve as a valuable design tool for engineering binary colloidal crystals from multi-flavored components.
, Zerze, H.
, Hatch, H.
, Shen, V.
and Mittal, J.
Assembly of Multi-flavored Two-Dimensional Colloidal Crystals, Soft Matter, [online], https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=922982
(Accessed November 29, 2023)