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Colloidal particles with mobile binding molecules constitute a powerful platform for probing the physics of self-assembly. Binding molecules are free to diffuse and rearrange on the surface, giving rise to spontaneous control over the number of droplet–droplet bonds, i.e. , valence, as a function of the concentration of binders. This type of valence control has been realized experimentally by tuning the interaction strength between DNA-coated emulsion droplets. Optimizing for valence two yields droplet polymer chains, termed ‘colloidomers’, which have recently been used to probe the physics of folding. To understand the underlying self-assembly mechanisms, here we present a coarse-grained molecular dynamics (CGMD) model to study the self-assembly of this class of systems using explicit representations of mobile binding sites . We explore how valence of assembled structures can be tuned through kinetic control in the strong binding limit. More specifically, we optimize experimental control parameters to obtain the highest yield of long linear colloidomer chains. Subsequently tuning the dynamics of binding and unbinding via a temperature-dependent model allows us to observe a heptamer chain collapse into all possible rigid structures, in good agreement with recent folding experiments. Our CGMD platform and dynamic bonding model (implemented as an open-source custom plugin to HOOMD-Blue) reveal the molecular features governing the binding patch size and valence control, and opens the study of pathways in colloidomer folding. This model can therefore guide programmable design in experiments.
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A mean-field model of linker-mediated colloidal interactions
Programmable self-assembly is one of the most promising strategies for making ensembles of nanostructures from synthetic components. Yet, predicting the phase behavior that emerges from a complex mixture of many interacting species is difficult, and designing such a system to exhibit a prescribed behavior is even more challenging. In this article, I develop a mean-field model for predicting linker-mediated interactions between DNA-coated colloids, in which the interactions are encoded in DNA molecules dispersed in solution instead of in molecules grafted to particles’ surfaces. As I show, encoding interactions in the sequences of free DNA oligomers leads to new behavior, such as a re-entrant melting transition and a temperature-independent binding free energy per kBT. This unique phase behavior results from a per-bridge binding free energy that is a nonlinear function of the temperature and a nonmonotonic function of the linker concentration, owing to subtle entropic contributions. To facilitate the design of experiments, I also develop two scaling limits of the full model that can be used to select the DNA sequences and linker concentrations needed to program a specific behavior or favor the formation of a prescribed target structure. These results could ultimately enable the programming and tuning of hundreds of mutual interactions by designing cocktails of linker sequences, thus pushing the field toward the original goal of programmable self-assembly: these user-prescribed structures can be assembled from complex mixtures of building blocks through the rational design of their interactions.
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- Award ID(s):
- 1710112
- PAR ID:
- 10593501
- Publisher / Repository:
- American Institute of Physics
- Date Published:
- Journal Name:
- The Journal of Chemical Physics
- Volume:
- 153
- Issue:
- 12
- ISSN:
- 0021-9606
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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