Martin, Christopher Paul (2007) Pattern formation in self-organised nanoparticle assemblies. PhD thesis, University of Nottingham.
An extremely wide variety of self-organised nanostructured patterns can be produced by spin-casting solutions of colloidal nanoparticles onto solid substrates. This is an experimental regime that is extremely far from thermodynamic equilibrium, due to the rapidity with which the solvent evaporates. It is the dynamics of ﬂow and evaporation that lead to the formation of the complex structures that are observed by atomic force microscopy (AFM). The mechanisms involved in the formation of these patterns are not yet fully understood, largely because it is somewhat challenging to directly observe the evaporation dynamics during spin-casting.
Monte Carlo simulations based on a modiﬁed version of the model of Rabani et al.  have allowed the study of the processes that lead to the production of particular nanoparticle morphologies. Morphological image analysis (MIA) techniques are applied to compare simulated and experimental structures, revealing a high degree of correspondence. Furthermore, these tools provide an insight into the level of order in these systems, and improve understanding of how a pattern’s speciﬁc morphology arises from its formation mechanisms.
Modifying the properties of a substrate on the scale of a few hundred nanometres by AFM lithography has a profound eﬀect on the processes of nanoparticle pattern formation. The simulation model of Rabani et al. was successfully modiﬁed to account for the eﬀect of surface modiﬁcation. The simulations were further modiﬁed to reproduce cellular structures on two distinct length scales– a phenomenon that is commonly seen in experiments.
The dynamic behaviour of simulated nanoparticle structures is examined in the “scaling” regime in relation to recent experiments carried out by Blunt et al.  in an attempt to understand the coarsening mechanism. Finally, a genetic algorithm approach is applied to evolve the simulations to a target morphology. In this way, an experimental target image can be automatically analysed with MIA techniques and compared with an evolving population of simulations until a target “ﬁtness” is reached.
|Item Type:||Thesis (PhD)|
|Faculties/Schools:||UK Campuses > Faculty of Science > School of Physics and Astronomy|
|Deposited By:||Dr Christopher P Martin|
|Deposited On:||10 Jun 2009 09:10|
|Last Modified:||10 Jun 2009 09:10|
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