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Title: Optical sensing of organic vapours using Langmuir-Blodgett films
Author: Wilde, Jason N.
ISNI:       0000 0001 3568 1609
Awarding Body: Durham University
Current Institution: Durham University
Date of Award: 1998
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This thesis describes hydrocarbon vapour sensing using Langmuir-Blodgett films prepared from: a co-ordination polymer; substituted phthalocyanines containing copper and zinc as the central metal ions; and a polysiloxane. The physical and chemical properties of the co-ordination polymer, 5,5'-methylenebis (N- hexadecylsalicylidenamine), at the air water interface were investigated using Brewster angle microscopy and surface pressure versus area measurements. Langmuir-Blodgett films were built-up on a variety of substrates. The addition of copper acetate to the subphase caused a change in both the physical and optical properties of the Langmuir- Blodgett layers. Film thickness data suggest that a true monolayer (thickness ca 2 nm) is only formed under these conditions. The multilayer films were studied using X-ray diffraction, UV/Visible spectroscopy, ellipsometry, surface plasmon resonance, surface profiling and electron spin resonance. The response of each film when exposed to, benzene, toluene, ethanol and water vapours were recorded. Two optical systems were used, both based on surface plasmon resonance. The first incorporated a silicon photodiode to record the intensity of the reflected light. The second was similar to that of surface plasmon microscopy, using a charge coupled device camera to monitor the reflected light intensity from the Langmuir-Blodgett film/metal interface. The co-ordination polymer was found to be most sensitive to benzene and could reliably detect concentrations of this vapour down to 100 vapour parts per million. Data obtained when the co-ordination polymer was exposed to benzene and water vapour (using the latter system) were presented to a neural network for recognition.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available
Keywords: Gas sensing; Thin films; Neural network