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Durham e-Theses
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Electrical Properties of Single-Walled Carbon Nanotube Networks Produced by Langmuir-Blodgett Deposition

MASSEY, MARK,KIERAN (2013) Electrical Properties of Single-Walled Carbon Nanotube Networks Produced by Langmuir-Blodgett Deposition. Doctoral thesis, Durham University.

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This thesis investigates the use of the Langmuir-Blodgett (LB) deposition technique as a means for building up ultra-thin networks of single-walled carbon nanotubes (SWCNTs) on various substrates. Transfer from a water subphase is successfully demonstrated for a range of SWCNTs and the electrical properties of the networks are discussed in detail. In contrast to the majority of literature on LB networks of SWCNTs, transfer is completed without the addition of surfactants to the nanotube material. However, and as expected, improved deposition is achieved when SWCNTs are functionalised (through thermal oxidation) with carboxylic acid groups, decreasing their hydrophobicity.
In-plane electrical data reveal preferential alignment of the nanotubes along the direction of dipping. Comprehensive studies of the current dependence on temperature and the field dependence of conductivity are presented for sorted
metallic and semiconducting nanotubes in an attempt to reveal the dominant conduction mechanisms. For metallic nanotubes, typical metallic conductivity is observed with an increasing resistance with increasing temperature. The metallic nanotube temperature coefficient of resistance is 0.001/K. At high electricfield strengths (>10^6 V/m), conduction in semiconducting SWCNT networks is dominated by the Poole-Frenkel effect.
Transistor structures are presented with SWCNTs as the active semiconducting layer. The best device shows p-type depletion mode behaviour with an on/offratio of around 8 and a carrier mobility of 0.3 cm^2/Vs.

Item Type:Thesis (Doctoral)
Award:Doctor of Philosophy
Keywords:Langmuir-Blodgett; carbon; nanotubes; electrical; conduction; mechanisms; networks
Faculty and Department:Faculty of Science > Engineering and Computing Science, School of (2008-2017)
Thesis Date:2013
Copyright:Copyright of this thesis is held by the author
Deposited On:03 Dec 2013 14:50

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