Hybrid Time-Dependent Ginzburg–Landau Simulations of Block Copolymer Nanocomposites: Nanoparticle Anisotropy

Polymers

Javier Diaz¹  Ignacio Pagonabarraga¹  Marco Pinna²  Andrei V. Zvelindovsky² 

[1] CECAM, Centre Européen de Calcul Atomique et Moléculaire, EPFL, École Polytechnique Fédérale de Lausanne, Batochime—Avenue Forel 2, 1015 Lausanne, Switzerland;Centre for Computational Physics, University of Lincoln, Brayford Pool, Lincoln LN6 7TS, UK;

关键词: block copolymer;    nanoparticle;    nanorod;    colloid;    nanocomposite;    computer simulation;   

DOI  :  10.3390/polym14091910

来源: DOAJ

【 摘 要 】

Block copolymer melts are perfect candidates to template the position of colloidal nanoparticles in the nanoscale, on top of their well-known suitability for lithography applications. This is due to their ability to self-assemble into periodic ordered structures, in which nanoparticles can segregate depending on the polymer–particle interactions, size and shape. The resulting coassembled structure can be highly ordered as a combination of both the polymeric and colloidal properties. The time-dependent Ginzburg–Landau model for the block copolymer was combined with Brownian dynamics for nanoparticles, resulting in an efficient mesoscopic model to study the complex behaviour of block copolymer nanocomposites. This review covers recent developments of the time-dependent Ginzburg–Landau/Brownian dynamics scheme. This includes efforts to parallelise the numerical scheme and applications of the model. The validity of the model is studied by comparing simulation and experimental results for isotropic nanoparticles. Extensions to simulate nonspherical and inhomogeneous nanoparticles are discussed and simulation results are discussed. The time-dependent Ginzburg–Landau/Brownian dynamics scheme is shown to be a flexible method which can account for the relatively large system sizes required to study block copolymer nanocomposite systems, while being easily extensible to simulate nonspherical nanoparticles.

【 授权许可】

Unknown