Self-Assembly of Mesophases from Nanoparticles

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.

Thermodynamics and ordering kinetics in asymmetric PS-b-PMMA block copolymer thin films

“…68 This balance at the kinetic GT mimics the balance between entropic and enthalpic contributions at the thermodynamic ODT. 4,5…”

“…The change in local free energy due to the interaction between the two distinct monomers is quantified by the Flory-Huggins parameter (). [1], [2], [3], [4], [5], [6], [7], [8], [9] The BCP free energy is composed by nearly specular entropic (÷1/N) and enthalpic (÷) contributions. [4] At the order-disorder transition (ODT), these contributions are balanced.…”

Unsupervised learning of atomic environments from simple features

“…I conclude with a case study on binary mesophases [28,29] to demonstrate extensibility to multiple chemical species. Binary systems in general pose a serious challenge due to their combined translational and compositional order [7].…”

Point Group Analysis in Particle Simulation Data

“…Computer power and our understanding of ordering processes are advancing rapidly. More and more simulations nucleate and grow crystals, even highly complex ones, directly from the melt [2][3][4][5][6][7][8] . Bond orientational order parameters 9 and related approaches [10][11][12][13][14][15][16] reliably detect the presence of global and local order and identify a few simple crystals, including face-centered cubic, hexagonal close-packed, and body-centered cubic [17][18][19][20][21] .…”