Use this URL to cite or link to this record in EThOS: https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.786094
Title: Quantitative structural and compositional studies of catalyst nanoparticles using imaging and spectroscopy in STEM
Author: Varambhia, Aakash Mahesh
ISNI:       0000 0004 7971 5630
Awarding Body: University of Oxford
Current Institution: University of Oxford
Date of Award: 2018
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Abstract:
The thesis aims to develop methods to explore how catalyst structure and composition affect catalytic performance of nanoparticles used in Proton Exchange Membrane Fuel Cell (PEMFC)applications. Using Scanning Transmission Electron Microscopy (STEM), the structure and composition of Pt and Pt-Co catalysts are explored with the intent of designing better catalysts. Catalysts such as pure Pt and alloyed Pt-Co ensembles are hugely popular in the PEMFC industry due to their catalytic activity and stability. Out of the two ensembles, the bimetallic Pt-Co catalysts exhibit better performance and are cheaper to manufacture. The addition of a secondary element reduces the overall Pt loading making the catalyst cheaper. Introducing a secondary alloy also results in enhanced catalytic activity for reasons not well explained. To study the effects of enhanced activity, a thorough characterisation at the atomic scale is required. This means studying a catalyst's size, shape, strain and composition simultaneously, at high throughput, and linking this to catalytic activity. Full characterisation of this detail is challenging, but not impossible. Using STEM, it is possible to obtain catalyst size, shape, strain and composition simultaneously. However due to the nature of the technique is it challenging to characterise industrially relevant materials at high throughput. This thesis develops upon current methods for characterising nanoparticles in STEM to obtain nanoparticle size, shape, strain and composition at high throughput. In STEM, the size, shape and strain information is obtained from the Annular Dark Field (ADF) signal, whereas the composition information is obtained from Energy Dispersive X-ray Spectroscopy (EDS) and Electron Energy Loss Spectroscopy (EELS). One of the challenges is combining the information from ADF, EDS and EELS signals into one quantifiable unit and building an analysis framework for catalyst nanoparticles. This can be achieved by converting the measured STEM signal into scattering cross-sections. The scattering cross-section describes the effective area corresponding to the probability of scattering from a sample. With careful microscope calibration, the scattering cross-section can be converted to number of atoms. Atom counts from the STEM signals provide the structure and composition information from a nanoparticle. The thesis will explore the minimum requirements needed to develop new automated methods to characterise structure and composition of nanoparticles using scattering cross-sections at high throughput. In addition, limitations and capabilities of current hardware and software are explored. The three-dimensional models obtained from the quantitative analysis can be used as inputs for Density Functional Theory (DFT) simulations to understand molecule adsorption mechanics better. Combining high throughput technique development, STEM quantification and high scale DFT simulation together unlocks a detailed insight in understanding catalyst activity. Such detailed description of a system is the stepping stone towards reducing cost and time for better catalyst design.
Supervisor: Nellist, Peter ; Lozano-Perez, Sergio ; Ozkaya, Dogan Sponsor: Engineering and Physical Sciences Research Council ; Johnson Matthey
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.786094  DOI: Not available
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