Temperature modulation and artificial neural network evaluation for improving the CO selectivity of SnO2 gas sensor

Stannic oxide sensors were developed to monitor CO of 10–250 ppm concentrations. Cross sensitivities of these sensors against 100–2000 ppm methane can be suppressed by evaluating the features extracted from the sensor signals. For this purpose, the working temperature of the sensors was modulated between 250 and 300 °° C, and the dynamic responses were measured to different concentrations of CO, CH4, and their mixtures were measured. The discrete wavelet transform (DWT) was used to extract important features from the sensor responses. These features were then input to the pattern recognition (neural) method. The species considered can be discriminated with a 100% success rate by using a back propagation network and the concentrations of the gases studied can also be accurately predicted.