Significance: Controlled assembly of nanoparticles at liquid crystal interfaces could lead to easily manufacturable building blocks for assembly of materials with tunable mechanical, optical, and electronic properties. Past work has examined nanoparticle assembly at planar liquid crystal interfaces. In this work, we show that nanoparticle assembly on curved interfaces is drastically different and arises for conditions under which assembly is too weak to occur on planar interfaces. We also demonstrate that liquid crystal-mediated nanoparticle interactions are strong, are remarkably sensitive to surface anchoring, and lead to hexagonal arrangements that do not arise in bulk systems. All of these elements form the basis for a highly tunable, predictable, and versatile platform for hierarchical materials assembly.

The morphology and through-film optical properties of nematic liquid crystals (LCs) confined between two surfaces may be engineered to create switches that respond to external electric fields, thereby enabling applications in optoelectronics that require fast responses and low power. Interfacial properties between the confining surfaces and the LC play a central role in device design and performance. Here we investigate the morphology of LCs confined in hybrid cells with a top surface that exhibits uniform homeotropic anchoring and a bottom surface that is chemically patterned with sub-micron and micron- wide planar anchoring stripes in a background of homeotropic anchoring. In a departure from past work, we first investigate isolated stripes, as opposed to dense periodic arrays of stripes, thereby allowing for an in-depth interpretation of the effects of patterning on LC morphology. We observe three LC morphologies and sharp transitions between them as a function of stripe width in the submicron and micron regimes. Numerical simulations and theory help explain the roles of anchoring energy, elastic deformation, entropy, pattern geometry, and coherence length of the LC in the experimentally observed behavior. The knowledge and models developed from an analysis of results generated on isolated features are then used to design dense patterned substrates for high-contrast and efficient orientational switching of LCs in response to applied fields.

The orientational and positional ordering of the two-dimensional system of hard zigzag particles has been investigated by means of Onsager theory. Analytical results are obtained for the transition densities of the isotropic-nematic and the nematic-smectic phase transitions. It is shown that the stability of the nematic and smectic phases is very sensitive to the molecular shape. In the hard needle limit, only the isotropic-nematic phase transition takes place, while increasing the tail length and the bent angle between the central core and the tails destabilizes the nematic phase. On the other hand the stability of the smectic phase is due to the increasing excluded area cost with bent angle and the tail length. The zigzag particles pack in a layered structure such that they are tilted and form semi-ideal gas in the layers to push the high cost excluded area regions into the interstitial regions. The predictions of Onsager theory are in good agreement with MC simulation data.

A theoretically informed coarse-grained Monte Carlo method is proposed for studying liquid crystals. The free energy functional of the system is described in the framework of the Landau-de Gennes formalism. The alignment field and its gradients are approximated by finite differences, and the free energy is minimized through a stochastic sampling technique. The validity of the proposed method is established by comparing the results of the proposed approach to those of traditional free energy minimization techniques. Its usefulness is illustrated in the context of three systems, namely, a nematic liquid crystal confined in a slit channel, a nematic liquid crystal droplet, and a chiral liquid crystal in the bulk. It is found that for systems that exhibit multiple metastable morphologies, the proposed Monte Carlo method is generally able to identify lower free energy states that are often missed by traditional approaches. Importantly, the Monte Carlo method identifies such states from random initial configurations, thereby obviating the need for educated initial guesses that can be difficult to formulate.