Use this URL to cite or link to this record in EThOS: http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.731337
Title: Abstract surface growth modelling with application to graphene
Author: Enstone, Gwilym
ISNI:       0000 0004 6495 9085
Awarding Body: University of Warwick
Current Institution: University of Warwick
Date of Award: 2017
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Abstract:
Graphene is one of a number of layered materials which have tremendous applications in future devices due to their interesting electrical and structural properties. Key to material synthesis is understanding growth mechanisms for thin film formation, and the elimination of defects; consistently creating smooth layers. A number of interesting effects seen in graphene growth highlight the importance of understanding the interaction between the substrate and the growing layer, such as growth on liquid substrates, wrinkling and structural feedback. Chemical vapour deposition (CVD) on a copper substrate has been hailed as a promising scalable route to synthesis, however there are restrictions relating to in situ observation. Recent experimental advances have led to graphene grain production of increasing size and purity, yet the early stages of growth remain relatively unexplored. Current modelling techniques tend to either investigate small systems in a high level of detail, or neglect the substrate detail in a coarser grained model. In this thesis we present results from abstract surface growth modelling which incorporate substrate effects into larger scale simulations. Firstly, we present a lattice Monte Carlo (MC) model of graphene growth on a rough substrate, and show the introduction of a dynamic roughness energy leads to an increase in island size. Secondly, we introduce a dynamic field into the standard random field Ising model, and observe a domain size increase with a dynamic field, but also observe a lower temperature field ordering effect. Thirdly, we construct geometric effective potentials in a Molecular Dynamics model of graphene growth, reproducing experimental island orientation distributions and growth behaviour. Finally, we detail the construction of a cluster moving algorithm for lattice MC simulation, and demonstrate that its implementation leads to an enhancement of island size. Together, these results highlight the importance of substrate roughness and geometry in the early stages of growth.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.731337  DOI: Not available
Keywords: QD Chemistry
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