The topology and electrical properties of nanoparticle networks
Doctoral Thesis
2014
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University of Cape Town
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Abstract
The bulk and surface network topologies of milled silicon nanoparticle aggregates in layers deposited on porous and non-porous substrates have been quantitatively characterised using laboratory and synchrotron based small angle X-ray scattering and ultra-small angle X-ray scattering, as well as with a new surface scattering technique developed for this research, which can be described as wide angle low q scattering. A new scaling model applied to the small angle and ultra-small angle X-ray scattering data which was originally developed to describe branched polymers was shown to be applicable to the description of the networks of silicon particles. The milled particles which have a highly polydisperse size distribution, form agglomerates, which in turn cluster to form larger structures with a very high degree of aggregation. Results from the new scattering technique showed the rough surface of the printed layers to have a fractal structure with step heights of 10% to 20% between adjacent particles. This value is consistent with the topology of the particle aggregates in the layer inferred from ultra-small angle X-ray scattering. Flow properties of the inks on different substrates lead to quantitative differences in the mean aggregate separation, with slowly curing systems on materials which allow good capillary flow resulting in denser networks with smaller aggregates and better contact between particles. The electrical conductance of the layers was shown to be linearly related to parallel connections of the minimum paths of particles through the aggregates as determined from the analysis of ultra-small angle X-ray scattering data. The capacitance of the layers was shown to have a linear dependence on both the separation between primary particles and series connection of the minimum paths.
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Includes bibliographical references.
Reference:
Jonah, E. 2014. The topology and electrical properties of nanoparticle networks. University of Cape Town.