A novel method of improving the selectivity of metal oxide gas sensors has been developed by using catalytically active molecular sieve materials. They have been successfully introduced into a proprietary sensor array and a commercially available electronic nose. The cracking patterns of various organic compound groups including alkanes, aromatics and flavours over transition metal exchanged zeolites (ZSM-5, zeolite Y, and zeolite p) have been measured using a zeolite bed/GC/MS experimental set-up within a temperature range of 200 C to 400 C. The findings have been successfully translated for the use of the zeolites as filter technology on chromium titanium oxide (CTO) sensors for the purposes of selective gas sensing. Studies have been carried out regarding the effects of metal loading, zeolite type, material fabrication techniques and operating temperature with regards to catalytic activity and selectivity. Variations in activity due to alkane chain length have been related to the ability of the molecule to enter the zeolite cavity, the quantity of supported metal complexes and their oxidation state in the zeolite pores. The composite sensors utilising the novel zeolite materials have been used in a custom built sensor rig that houses 3 dual electrode sensors and can measure real time responses of these sensors to an introduced headspace generated from organic liquids. The response data have been utilised in a statistical software package (SPSS 12.0) to rationalise sensor discriminatory behaviour to various compound groups. The zeolite coated CTO sensors have also been tested in a commercial electronic nose array which has provided enhanced discrimination as compared to a standard CTO sensor array for a number of active and potential commercial applications mainly involving complex flavour compound mixtures.
Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:429334 |
Date | January 2005 |
Creators | Mann, Dominic Peter |
Publisher | University College London (University of London) |
Source Sets | Ethos UK |
Detected Language | English |
Type | Electronic Thesis or Dissertation |
Source | http://discovery.ucl.ac.uk/1445742/ |
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