A new approach to NO2 Gas sensing based on Pulsed UV light and FFT analysis using MOX sensors

Metal Oxide Semiconductor gas sensors have been recently temperature modulated, and UV light activated to improve their sensitivity and selectivity. In this work, we present the first known development of calibration models, using pulsed UV light modulation for WO3 based gas sensing. Partial Least Squares Regression (PLSR) and Principal Component Regression (PCR) methods have been developed using components from the FFT analysis of the DC resistance signal of the sensor. The use of pulsed UV light, combined with low-temperature activation allowed a significant reduction in power consumption as compared to the high operating temperature traditionally used with Metal Oxide non-MEMs-based sensors. The methodology proposed in this study allows diminishing the time necessary to determine the concentration, with the reduction of the pulsed UV light period, and the number of pulses used for this purpose, in respect to the use of resistance rate analysis, as proposed by other authors. The FFT analysis made before performing the linear regression methods allows the diminution of the prediction error from the models, as compared to the rate analysis. These advantages present a progress over the analysis of the rates from the resistance signal, recently presented by other authors. The correct performance of the presented procedure, working with NO2 concentrations under harmful exposure limits, opens the opportunity of using this methodology in real air quality applications.