THICK-FILM PELLISTOR ARRAY WITH A NEURAL-NETWORK POSTTREATMENT

In pellistor gas sensors, the heat exhaust produced by the catalytic combustion of reducing gases increases the temperature of the device. A typical pellistor consists of a platinum wire supported in an alumina bead impregnated with a finely dispersed noble metal like palladium. The platinum wire serves as heater of the bead to its operating temperature and as a thermometer. In reality, the temperature measured by the resistance of the Pt wire is compared to that of a reference element which has a similar structure but without any catalytic activity. No selectivity of such a device has to be expected since the catalytic combustion of any combustible gas will lead to a temperature increase of the device. In order to try to achieve selectivity to methane, we have in a first step exploited the differential activity of palladium and platinum by using two screen-printed pellistors, one based on Pd and the other on Pt. At around 400 degrees C, all reducing gases including methane are oxidized by Pd whereas Pt oxidized all gases except methane, In order to extend the recognition process to combustible gases other than methane, that is to propane, and ethanol vapour, a small array of four pellistors with various percentages of Pd and Pt has been elaborated with thick film technology, which is very valuable for realizing series of similar sensors, required in arrays. The four microcalorimetric sensors are exposed to various gases and various concentration values. A recognition of methane, propane, and ethanol is obtained by neural network techniques. The network consists of three layers: an input layer; a hidden layer; and an output layer which permits gas identification. Back-propagation is used as the learning algorithm. In this case, the selectivity of the system is demonstrated.