Qualitative and quantitative differentiation of gases using ZnO thin film gas sensors and pattern recognition analysis

Abstract

In the present work we have grown highly textured, ultra-thin, nano-crystalline zinc oxide thin films using a metal organic chemical vapor deposition technique and addressed their selectivity towards hydrogen, carbon dioxide and methane gas sensing. Structural and microstructural characteristics of the synthesized films were investigated utilizing X-ray diffraction and electron microscopy techniques respectively. Using a dynamic flow gas sensing measurement set up, the sensing characteristics of these films were investigated as a function of gas concentration (10-1660 ppm) and operating temperature (250-380 °C). ZnO thin film sensing elements were found to be sensitive to all of these gases. Thus at a sensor operating temperature of ~300 °C, the response% of the ZnO thin films were ~68, 59, and 52% for hydrogen, carbon monoxide and methane gases respectively. The data matrices extracted from first Fourier transform analyses (FFT) of the conductance transients were used as input parameters in a linear unsupervised principal component analysis (PCA) pattern recognition technique. We have demonstrated that FFT combined with PCA is an excellent tool for the differentiation of these reducing gases.