Department of Chemistry, New York University, New York, New York, 10003, USA.
Department of Physics, Cornell University, Ithaca, New York, 14853, USA.
Soft Matter. 2023 Jun 14;19(23):4223-4236. doi: 10.1039/d3sm00196b.
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, , 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 . 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 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.
具有可动结合分子的胶体颗粒构成了探测自组装物理的强大平台。结合分子可以在表面上自由扩散和重新排列，从而自发控制液滴-液滴键的数量、配位数，作为结合剂浓度的函数。这种类型的配位数控制已经通过调整 DNA 包覆乳液液滴之间的相互作用强度在实验中实现。通过优化配位数为 2，可以得到称为“胶体聚合物”的液滴聚合物链，最近它们被用于探测折叠物理。为了理解潜在的自组装机制，我们在这里提出了一个粗粒化分子动力学（CGMD）模型，使用 来研究这类系统的自组装。我们探索了如何通过在强结合极限中的动力学控制来调整组装结构的配位数。更具体地说，我们优化实验控制参数以获得最长线性胶体聚合物链的最高产量。随后调整结合和解吸的动力学，一个温度依赖的模型使我们能够观察到七聚体链折叠成所有可能的刚性结构，与最近的折叠实验很好地吻合。我们的 CGMD 平台和动态键合模型（作为 HOOMD-Blue 的开源自定义插件实现）揭示了控制结合斑块大小和配位数控制的分子特征，并开启了胶体聚合物折叠途径的研究。因此，该模型可以为实验中的可编程设计提供指导。
