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A thin-film SnO2 sensor system for simultaneous detection of CO and NO2 with neural signal evaluation

: Endres, Hanns-Erik; Göttler, Wolfgang; Hartinger, Ralf; Drost, Stepahn; Hellmich, Wolfgang; Müller, Gerhard; Bosch von Braunmühl, Christa; Krenkow, Angelika; Perego, Cesare; Sberveglieri, Giorgio


Weetall, H.H. ; National Institute of Standards and Technology -NIST-:
6th International Meeting on Chemical Sensors 1996. Proceedings
Amsterdam: Elsevier, 1996 (Sensors and actuators. B Chemical)
S.353-357 (Part 2)
International Meeting on Chemical Sensors (IMCS) <6, 1996, Gaithersburg/Md.>
European Commission EC
Konferenzbeitrag, Zeitschriftenaufsatz
Fraunhofer IFT; 2000 dem IZM eingegliedert
gas sensor; micromachined semiconductor sensor; NO2; signal evaluation; simultaneous measurement; transient method; SnO2 sensor system; simultaneous detection; CO; neural signal evaluation

Simultaneous CO and NO2 measurements are of importance for the ventilation control of automobiles and other applications. For this purpose often semiconducting SnO2 sensors were used. A well known disadvantage of SnO2 sensors is the concurrent reaction of the oxidizing NO2 and the reducing CO on the sensor surface, which causes a near zero sensor signal in presence of both gases in a certain range of mixtures. A second disadvantage of SnO2 sensors are the long rise and decay times of the sensor signal. The combination of different SnO2 sensors, operated at different temperatures and combined with a signal evaluation system based on a specially trained neural forward network (Artificial Neural Net - ANN) solves this problem. The runtime version of the neural net is a small program, compatible to micro controllers. These signal evaluation techniques are applicable to similar problems using sensor arrays or single sensors in a non stationary operationg mode.