Robotically assisted evolution of gold nanoparticles and their hybridation with POMs

Martin Marti, Sergio (2017) Robotically assisted evolution of gold nanoparticles and their hybridation with POMs. PhD thesis, University of Glasgow.

Due to Embargo and/or Third Party Copyright restrictions, this thesis is not available in this service.
Printed Thesis Information: https://eleanor.lib.gla.ac.uk/record=b3294535

Abstract

The work presented in this thesis focused on the synthesis of gold nanoparticles, exploring new ways to synthesise them and also using new tools to improve the study and discovery of new nanomaterials. Whilst there has been a special concern in understanding how they are organised and which are the best conditions to achieve specific shapes, there is gap in finding new approaches that can allow fast synthesis of nanoparticles and fast screening of the chemical space and real time observation of the reaction under different reaction conditions.
In the first chapter of this thesis we are going to present a new synthesis method to prepare gold nanoparticles-POM hybrids. Also, we will discuss how POMs can influence the aggregation of nanoparticles, depending on the size and charge of the POM. For example, gold nanoparticles will aggregate more easily if they are surrounded by small and less negatively charged POMs.
In the next chapters of the thesis, we will aim to demonstrate that an automated system is able to evolve gold nanomaterials, this means that an automated system will use raw materials (simple chemicals, in this specific case HAuCl4, CTAB and NaBH4) to synthesise very simple nanostructures, such as spheres, then reuse those spheres and other chemicals to produce even more complex structures.
In chapter 2 we will go through the process of building an automated system, in this case, the system will be designed to synthesise gold nanoparticles. We will start by designing the automated system and testing it, we will see the flaws that those different systems had and how we overcame them by doing some improvements on them, such as more control over the temperature of the reaction, keeping a constant temperature of the reagents, improving the cleaning process, trying different concentrations of the reagents, trying different algorithms and different ways to calculate the fitness factor, etc. until we found a system that was very reliable, which was able to provide reproducible results.
In the last chapter of this thesis, we will focus on the results obtained in the different versions of the automated system. First, we will explain our first objective, which was obtaining gold nanorods of very short aspect ratio. We will analyse the results we obtained for that objective, trying different fitness factor calculations and how the different ways to calculate the fitness factor affected the process to obtain the desired product. We will discuss why we changed the fitness factor calculation and how this helped to achieve our objective. Then, we will jump to the next level of the project, which was to synthesise very simple gold nanoparticles from raw chemicals and reuse these simple structures to obtain more complex structures. This demonstrates that we have an automated system able to evolve gold nanomaterials; the first of this kind. In this chapter, we will also talk about the different techniques used to characterise the product, where we used in-line analytical techniques such as UV-Vis or image analysis (which are the ones used to calculate the fitness factor that the algorithm is going to use) and other techniques to fully characterise the final product such as TEM.

Item Type: Thesis (PhD)
Qualification Level: Doctoral
Keywords: Nanoparticles, POMS, machine learning, genetic algorithm (GA), hybridisation, automation, evolution.
Subjects: Q Science > QD Chemistry
Colleges/Schools: College of Science and Engineering > School of Chemistry
Supervisor's Name: Cronin, Professor Leroy
Date of Award: 2017
Embargo Date: 13 December 2021
Depositing User: Doctor Sergio Martin Marti
Unique ID: glathesis:2017-8631
Copyright: Copyright of this thesis is held by the author.
Date Deposited: 15 Dec 2017 14:06
Last Modified: 19 Jan 2018 09:47
URI: http://theses.gla.ac.uk/id/eprint/8631

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