
The Library
Computational simulation of metal nucleation on diamond electrodes
Tools
Chaudhuri, Shayantan (2022) Computational simulation of metal nucleation on diamond electrodes. PhD thesis, University of Warwick.
|
PDF
WRAP_Theses_Chaudhuri_2022(b).pdf - Submitted Version - Requires a PDF viewer. Download (42Mb) | Preview |
Official URL: http://webcat.warwick.ac.uk/record=b3928366
Abstract
Metal nanocluster deposition is an important fabrication process that can be used to grow nanoarchitectures for use in various applications such as electronics and electrocatalysis. Classical nucleation theory is a powerful theory that can be used to qualitatively capture nucleation thermodynamics for many systems, but has been found to be inappropriate to describe the initial stages of nucleation due to its inherent assumptions. Despite the development of atomistic nucleation theories, much still remains unclear about the early stages of metal deposition and the role of the atomic-scale structure on the surface. In this regard, atomistic simulations based on electronic structure methods can play an important role in the elucidation of the initial nucleation processes and mechanisms. This thesis will use density functional theory and its derived methods to characterise the adsorption of gold nanoclusters on polycrystalline boron-doped diamond surfaces. First, a detailed investigation into the most stable oxygenation state of diamond (110) surfaces is conducted, and the most stable surface phase is shown to comprise coexistent and adjacent carbonyl and ether functional groups. Afterwards, the structural stability of single gold atoms on oxygen-terminated diamond (110) surfaces is investigated, and defects and dopants within the diamond surface are shown to significantly increase the adsorption energy and diffusion barriers of single gold atoms. The atom-by-atom growth of gold dimers, trimers and tetramers on diamond surfaces is subsequently studied by analysing their stabilities and identifying preferred morphologies. Finally, machine learning-based interatomic potentials are developed to facilitate accurate and computationally efficient geometry optimisations, and are used to predict the structures and stabilities of larger gold nanoclusters ranging from 6 to 147 atoms. This thesis is part of a scientific effort to develop modern atomistic theories of atomby- atom particle growth, and will help guide the controlled design of nanostructured catalysts in the future.
Item Type: | Thesis (PhD) | ||||
---|---|---|---|---|---|
Subjects: | Q Science > QD Chemistry | ||||
Library of Congress Subject Headings (LCSH): | Diamonds -- Electric properties, Electrochemistry -- Materials, Nucleation, Electroplating, Metal nanoparticles, Nanostructured materials, Doped semiconductor nanocrystals -- Surface, Polycrystalline semiconductors | ||||
Official Date: | December 2022 | ||||
Dates: |
|
||||
Institution: | University of Warwick | ||||
Theses Department: | Department of Chemistry | ||||
Thesis Type: | PhD | ||||
Publication Status: | Unpublished | ||||
Supervisor(s)/Advisor: | Maurer, Reinhard | ||||
Sponsors: | Engineering and Physical Sciences Research Council | ||||
Format of File: | |||||
Extent: | xxiii, 255 pages : illustrations (some colour) | ||||
Language: | eng |
Request changes or add full text files to a record
Repository staff actions (login required)
![]() |
View Item |