Use this URL to cite or link to this record in EThOS: http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.712926
Title: Advanced 3D imaging and quantification of battery materials
Author: Biton, Moshiel
Awarding Body: Imperial College London
Current Institution: Imperial College London
Date of Award: 2016
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Abstract:
Rising global demand for energy supply, storage and portability in a sustainable manner needs significant improvements to be made in the next generation of batteries, as rechargeable batteries for electric vehicles and energy storage applications are considered to be an effective solution for lower carbon electric mobility and balancing intermittent renewables in low carbon energy systems In particular because to their potential to store relatively high amounts of energy per unit volume (energy density).The need to develop better (i.e. lower cost and longer lifetime) batteries for these emerging applications drives the need to better understand battery performance, and this in turn is linked to a better understanding of the role of battery electrode microstructure. This will be improved through the ability to directly image changes as they occur within the battery which can be linked to battery degradation. Although the irreversible microstructural transformations in battery electrode structures is one of the key processes associated with battery degradation and failure, their mechanisms are poorly investigated, and existing data is not sufficient to draw clear guidelines for better electrode design. The performance of the battery is dependent on nano/micro-structure while during processing or operation microstructural evolution may degrade electrochemical performance. Degradation and failure mechanisms in electrochemical systems lead to poor cycle life, in particular dendritic growth and volume expansion of anode materials. These are important failure mechanisms in various battery systems. Understanding their 3D microstructure is essential for developing high performance batteries for electric vehicles and energy storage applications. Tomographic techniques allow for the direct 3D imaging and characterisation of complex microstructures from millimetres down towards nanometres and in real time during operation. The performance of the battery is dependent on the nano and micro-structure achieved during manufacture. Furthermore microstructural evolution during operation may degrade electrochemical performance. Here in this thesis, results from in-situ, in-operando 3D x-ray and ex-situ FIB-SEM tomography, enabling analysis of complex micro-structures during battery operation, are presented. 3D imaging of Zn dendrites formation, down to resolutions of tens of nanometres facilitating analysis at different scales especially of nano structures can provide exciting opportunities to study dendritic growth. This approach was found to be effective in understanding how dendrites growth at high resolutions and consequently that tomography coupled with modelling/experiments can provide new insights into degradation mechanisms. The growth of dendrites represents a limiting failure mechanism in some battery systems; in particular this can be a challenge in zinc-air batteries. Furthermore volume expansion during lithiation is another major failure mechanism. Tomographic techniques allow the direct 3D imaging and characterisation of complex microstructures, including the observation and quantification of dendrite growth and volume expansion. Moreover, in this thesis, a new methodology of contrast enhancement for multi modal 3D imaging, including novel advanced quantification, on a commercial Lithium Iron Phosphate (LFP) LiFePO4 cathode is presented. This enables higher focused ion beam – scanning electron microscope (FIB-SEM) resolution (3D imaging), which is amongst the highest ever reported for carbon containing electrode materials (e.g. composite LFP cathodes) using FIB-SEM. In turn it means that the particles are well defined and the size distribution of each phase can be analysed accurately from the complex 3D electrode microstructure using advanced quantification algorithms.
Supervisor: Brandon, Nigel Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.712926  DOI: Not available
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