Sumer, Zeynep; (2021) Investigating self-assembly and anchoring of surfactants and nanoparticles on liquid crystal nano-droplets by multi-scale simulations. Doctoral thesis (Ph.D), UCL (University College London).

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Abstract

Liquid crystals (LCs) continue to receive significant attention due to their tunable order-disorder transitions. They are the foundation of many technological advancements such as displays, sensors, and other devices. Applications in which LCs are utilized build on the ability of controlling the orientation of mesogens with respect to a direction vector. To advance applications such as sensing and displays, it is required to detect and control, respectively, changes in LCs order due to external stimuli. To control LCs’ order, it is possible to add amphiphiles to systems in which LC – solvent interfaces are present. One example of the latter approach is provided by the interface between thermotropic LCs and immiscible aqueous phases. Many researchers investigated how to tune the LC phases via external stimuli which take advantage of said interfaces, for example via the introduction of block copolymers or nanoparticles (NP) to LC-containing systems. Although the results are promising, many molecular-level mechanisms remain to be completely understood, including the anchoring strength, and effects due to confinement and temperature. The aim of this thesis is to computationally investigate how adsorption of different compounds such as surfactants and nanoparticles can be used to control LCs droplets’ orientational order. For that purpose, coarse-grained molecular simulations were used. Firstly, in part for computational reasons, cylindrical LC assemblies were investigated. Then, to secure a stronger connection with practical devices, spherical LC droplets were considered. To overcome the limitations of coarse-grained simulation approaches, and to validate the results obtained at the nanometre-scale, the continuum-level Q-tensor approach was used. The latter has been shown to reproduce many experimental observables for bulk systems, and it allowed us to quantitatively analyse the defect structures inside LC droplets. The multiscale approach developed and implemented here, which allows us to investigate the properties of molecular aggregates at the shorter length scale, to macroscopic assemblies at the larger length scale, could provide a computational platform for future LC-based applications such as advanced biosensors that detect e.g., traces of viruses, bacteria, or air contaminants.

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