AbstractSelf-assembly refers to the process by which initially disordered molecules organise themselves into ordered, functional structures without external driving forces. This thesis investigates the self-assembly of soft matter, a class of molecules that are structurally deformed by thermal fluctuations at room temperature. In contemporary times, soft matter self-assembly finds use in a variety of important applications, from drug and vaccine formulation, to nanotechnology and materials engineering. Different experimental techniques, such as neutron scattering and spectroscopy, can be used to provide vital information about soft matter nanostructures. Classical molecular dynamics (MD) simulations yield complementary information on the nanoscale that is unobtainable by experiment, which further informs the understanding of experimental systems. Broadly, the motivation of this thesis is to better understand soft matter self-assembly, and in turn the structure, dynamics and hydration of resultant self-assembled soft matter nanostructures, using classical MD simulations and data-driven approaches.
In this thesis, MD simulations have been conducted on surfactant monolayers and polymer micelles, two different classes of self-assembled nanostructures. The underlying structure of these nanostructures on the nanoscale is complex and heterogeneous, and they exhibit interesting interfacial behaviour. In this thesis, the internal and interfacial structure of polymer micelles has been revealed using unsupervised machine learning techniques, showing that distinctly different conformations exist at preferred locations within the core of the micelles. It is also shown that conformation changes of a small four-arm block copolymer, Tetronic 304, underpin its pH-dependent self-assembly. The interfaces formed between soft matter nanostructures and water are irregular and directly confer changes in the properties of the interfacial water molecules with respect to bulk solution. It is shown in this thesis that subtle differences in the packing density in surfactant monolayers invokes wide-ranging structural and dynamical changes in a nanoconfined water layer.
Analysing both the internal and interfacial structure of surfactant monolayers and polymer micelles is a challenging endeavour, which requires the application of novel analysis tools to extract useful descriptions of their properties. To this end, new techniques have been developed to analyse the structure and dynamics of monolayers and micelles. Intrinsic surface algorithms have been developed and applied to understand the interfacial structure of these nanostructures. A routine combining dimensionality reduction and clustering has been employed to provide a predictive understanding of the different conformations adopted by individual polymers within micelles. Hidden Markov models have also been used to show that distinct physical states make up seemingly entirely disordered micellar coronae. Work towards a new implicit solvent model is also reported, motivated by the high computational cost required to conduct MD simulations of self-assembled soft matter structures at atomistic resolution.
|Date of Award
|1 May 2021
|Chris Lorenz (Supervisor) & Franca Fraternali (Supervisor)