Elucidating Electrostatic Self-Assembly: Molecular Parameters as Key to Thermodynamics and Nanoparticle Shape
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- 作者：Giacomo Mariani ; Daniel Moldenhauer ; Ralf Schweins ; Franziska Gr?hn
- 刊名：Journal of the American Chemical Society
- 出版年：2016
- 出版时间：February 3, 2016
- 年：2016
- 卷：138
- 期：4
- 页码：1280-1293
- 全文大小：905K
- ISSN：1520-5126
文摘
The rational design of supramolecular nanoparticles by self-assembly is a crucial field of research due to the wide applications and the possibility of control through external triggers. Understanding the shape-determining factors is the key for tailoring nanoparticles with desired properties. Here, we show how the thermodynamics of the interaction control the shape of the nanoparticle. We highlight the connection between the molecular structure of building blocks, the interaction strength, and the nanoassembly shape. Nanoparticles are prepared by electrostatic self-assembly of cationic polyelectrolyte dendrimers of different generations and oppositely charged multivalent organic dyes relying on the combination of electrostatic and π–π interactions. Different building blocks have been used to vary interaction strength, geometric constraints, and charge ratio, providing insights into the assembly process. The nanoassembly structure has been characterized using atomic force microscopy, static light scattering, small angle neutron scattering, and UV–vis spectroscopy. We show that the isotropy/anisotropy of the nanoassemblies is related to the dye valency. Isothermal titration calorimetry has been used to investigate both dye–dye and dye–dendrimer interaction. The existence of a threshold value in entropy and enthalpy change separating isotropic and anisotropic shapes for both interactions has been demonstrated. The effects of the dye molecular structure on the interaction thermodynamics and therefore on the nanoparticle structure have been revealed using molecular modeling. The polar surface area of the dye molecule takes a key role in the dye self-interaction. This study opens the possibility for a priori shape determination knowing the building blocks structure and their interactions.