Use this URL to cite or link to this record in EThOS: https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.747365
Title: Modified metal oxide gas sensors for the detection of clandestine chemistry locations
Author: Pugh, D. C.
Awarding Body: UCL (University College London)
Current Institution: University College London (University of London)
Date of Award: 2018
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Abstract:
Clandestine laboratories are locations where chemistry is carried out in secret, often with the intent to produce illegal drugs or other controlled substances. These laboratories are unregulated and not maintained to a good laboratory standard, presenting a risk to first responders, bystanders and the environment. Electronic noses based on metal oxide semiconducting (MOS) gas sensors present a potential technology to create devices for the detection of clandestine activity. A range of sensors based on zinc oxide, chromium titanate and vanadium pentoxide have been manufactured and modified using zeolite material and metal ion doping. Sensor fabrication took place using a commercially available screen printer, a 3 x 3 mm alumina substrate containing interdigitated electrodes and a platinum heater track. Allmaterials were modified with the protonated forms of zeolite beta, Y, mordenite and ZSM5, by incorporating these materials into the metal oxide to make up 30 % of the total ink. Zinc oxide was also modified by indium doping; doping levels were set at 0.2, 0.5, 1 and 3-mol % indium. These materials were synthesised using a co-precipitation method. Sensors were exposed to a range of gases at operating temperatures between 250 and 500°C and concentrations between 50 ppb and 80 ppm. All tests were conducted on an in house testing rig, consisting of a 12-port sensing chamber, four mass flow controllers, six solenoid vales and supplies of compressed air and analyte gas. Modification of sensors was found to improve their responsiveness, compared to the control sensors, in almost all cases. This is due to a combination of surface area enhancements, increased adsorption of material and a more accessible microstructure. Machine learning techniques were applied to the sensor data to correctly classify the class of gas observed and to assess the overall sensor performance of each material. A high level of accuracy was achieved in determining the class of gas observed.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.747365  DOI: Not available
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