Modification of n-type and p-type semiconductor sensor devices for security applications

Research output: Thesis (awarded by external institution)Doctoral Thesis

Abstract

This thesis investigates the modification of three metal oxide semiconductor gas sensors with zeolite materials for the purposes of detecting trace concentrations of gases that have an effect on health, security, safety and the environment. SnO2, Cr2O3 and Fe2O3 were chosen as the base materials of interest. Zeolites HZSM- 5, Na-A and H-Y were incorporated into the sensing system either as admixtures with the base material or as coatings on top of it. The aim of introducing zeolites into the sensing system was to improve the performance of the otherwise unmodified sensors. Twenty-two novel zeolite-modified sensor systems are presented for the detection of a range of hydrocarbons and inorganic gases. Whilst sensors based on SnO2 systems were more responsive to gases, some sensors were also found to provide a greater degree of variability among repeat tests, particularly at lower operating temperatures i.e. 300 °C. Cr2O3 sensors modified by admixture with zeolite H-ZSM- 5 were seen to be poorly sensitive to most analytes. Cr2O3 sensors modified by admixture with zeolite Na-A and by overlayer of zeolite H-Y provided very promising sensitive and selective results towards toluene gas. Sensors based on the zeolite modification of Fe2O3 were not found to be promising candidates as gas sensors at this stage. Sensors were purposely exposed to gases that had similar molecular structures or kinetic diameters to assess the true capability of the sensors to discriminate among analytes. An array of four sensors based on n-type and p-type systems was subsequently chosen to see whether machine learning classifiers could be used to accurately discriminate among nine analytes. Using an SVM SMO classifier with a polykernel function, the model was 94.1% accurate in correctly classifying nine analytes of interest just after five seconds into the gas injection. Using an RBF kernel function, the model was 90.2% accurate in correctly classifying the data into gas type. These are very encouraging results, which highlight the importance of furthering research in this field; a sensing array based on zeolite-modified metal oxide semiconductor sensors may benefit a number of research domains by providing accurate results in a very fast and inexpensive manner.
Original languageEnglish
QualificationDoctor of Philosophy
Awarding Institution
  • University College London
Supervisors/Advisors
  • Parkin, Ivan, Supervisor, External person
  • Hailes, Stephen, Supervisor, External person
Thesis sponsors
Award date17 Feb 2017
Publisher
Publication statusPublished - 2017

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