Predictive Theoretical Framework for Dynamic Control of Bioinspired Hybrid Nanoparticle Self-Assembly
| Autor: | Xin Qi, Yundi Zhao, Kacper Lachowski, Julia Boese, Yifeng Cai, Orion Dollar, Brittney Hellner, Lilo Pozzo, Jim Pfaendtner, Jaehun Chun, François Baneyx, Christopher J. Mundy |
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| Rok vydání: | 2022 |
| Předmět: | |
| Zdroj: | ACS Nano. 16:1919-1928 |
| ISSN: | 1936-086X 1936-0851 |
| DOI: | 10.1021/acsnano.1c04923 |
| Popis: | At-will tailoring of the formation and reconfiguration of hierarchical structures is a key goal of modern nanomaterial design. Bioinspired systems comprising biomacromolecules and inorganic nanoparticles have potential for new functional material structures. Yet, consequential challenges remain because we lack a detailed understanding of the temporal and spatial interplay between participants when it is mediated by fundamental physicochemical interactions over a wide range of scales. Motivated by a system in which silica nanoparticles are reversibly and repeatedly assembled using a homobifunctional solid-binding protein and single-unit pH changes under near-neutral solution conditions, we develop a theoretical framework where interactions at the molecular and macroscopic scales are rigorously coupled based on colloidal theory and atomistic molecular dynamics simulations. We integrate these interactions into a predictive coarse-grained model that captures the pH-dependent reversibility and accurately matches small-angle X-ray scattering experiments at collective scales. The framework lays a foundation to connect microscopic details with the macroscopic behavior of complex bioinspired material systems and to control their behavior through an understanding of both equilibrium and nonequilibrium characteristics. |
| Databáze: | OpenAIRE |
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