TY  - UNPB
TI  - Modification of n-type and p-type metal oxide semiconductor systems for gas sensing applications
Y1  - 2017/04/28/
AV  - public
EP  - 318
N1  - Unpublished
N2  - 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.
ID  - discovery1549841
PB  - UCL (University College London)
UR  - https://discovery.ucl.ac.uk/id/eprint/1549841/
M1  - Doctoral
A1  - Tarttelin Hernandez, P
ER  -