Dissipative Particle Dynamics Simulations of a Protein-Directed Self-Assembly of Nanoparticles
Author(s) -
Chunhui Li,
Xuewei Fu,
WeiHong Zhong,
Jin Liu
Publication year - 2019
Publication title -
acs omega
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.779
H-Index - 40
ISSN - 2470-1343
DOI - 10.1021/acsomega.9b01078
Subject(s) - dissipative particle dynamics , dissipative system , nanoparticle , dynamics (music) , particle (ecology) , statistical physics , materials science , nanotechnology , chemical physics , physics , thermodynamics , biology , composite material , polymer , ecology , acoustics
Design and fabrication of multifunctional porous structures play key roles in the development of high-performance energy storage devices. Our experiments demonstrated that nanostructured porous components, such as electrodes and interlayers, generated from the protein-directed self-assembly of nanoparticles can significantly improve the battery performances. The protein-directed assembly of nanoparticles in solution is a complex process involving the complicated interactions among proteins, particles, and solvent molecules. In this paper, we investigate the effects of coating proteins and specific solvent environments on the assembled porous structures. Comprehensive dissipative particle dynamics (DPD) simulations have been implemented to explore the molecular interactions and uncover the fundamental mechanisms in a gelatin-directed self-assembly of carbon black particles under different solvent conditions. Our simulations show that compact triple-strand "rod-like" structures are formed in water while loose curved "sheet-like" structures are formed in an acetic acid/water mixture. The structural difference is mainly due to the redistribution of the charges on the gelatin side chains under specific acid-solvent conditions. The strong and flexible "sheet-like" structures lead to a homogenous porous structure with high porosity and with large functionalized surfaces. Our simulations results can reasonably explain the experimental observations; this work demonstrates the great potential of DPD as a powerful tool in guiding future experimental design and optimization.
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