Literature DB >> 28960988

Self-Assembly of Mesophases from Nanoparticles.

Abhinaw Kumar1, Valeria Molinero1.   

Abstract

A growing number of crystalline and quasi-crystalline structures have been formed by coating nanoparticles with ligands, polymers, and DNA. The design of nanoparticles that assemble into mesophases, such as those formed by block copolymers, would combine the order, mobility, and stimuli responsive properties of mesophases with the electronic, magnetic, and optical properties of nanoparticles. Here we use molecular simulations to demonstrate that binary mixtures of unbound particles with simple short-ranged pair interactions produce the same mesophases as block copolymers and surfactants, including lamellar, hexagonal, gyroid, body-centered cubic, face-centered cubic, perforated lamellar, and semicrystalline phases. The key to forming the mesophases is the frustrated attraction between particles of different types, achieved through control over interparticle size and over strength and softness of the interaction. Experimental design of nanoparticles with effective interactions described by the potentials of this work would provide a distinct, robust route to produce ordered tunable liquid crystalline mesophases from nanoparticles.

Entities:  

Year:  2017        PMID: 28960988     DOI: 10.1021/acs.jpclett.7b02237

Source DB:  PubMed          Journal:  J Phys Chem Lett        ISSN: 1948-7185            Impact factor:   6.475


  2 in total

1.  Optimizing Chain Topology of Bottle Brush Copolymer for Promoting the Disorder-to-Order Transition.

Authors:  Jihoon Park; Hyun-Woo Shin; Joona Bang; June Huh
Journal:  Int J Mol Sci       Date:  2022-05-11       Impact factor: 6.208

2.  A generalized deep learning approach for local structure identification in molecular simulations.

Authors:  Ryan S DeFever; Colin Targonski; Steven W Hall; Melissa C Smith; Sapna Sarupria
Journal:  Chem Sci       Date:  2019-07-11       Impact factor: 9.825

  2 in total

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