Automatic signal decoding and sensor stability of a 3-electrode mixed-potential sensor for NOx/NH3 quantification

Abstract

Sensors to detect mixtures of NOx/NH3 are needed to monitor emissions of diesel automobiles where a selective catalytic reduction system uses an NH3 mediated reaction to reduce NOx. We report on the application of a three electrode La0.8Sr0.2CrO3, Au0.5Pd0.5, Pt mixed potential sensor using yttria-stabilized-zirconia (YSZ) as a solid electrolyte to NOx/NH3 sensing. Artificial neural networks were used to automatically decode the concentrations of NOx/NH3 and errors of less than 15% are achieved. The optimal architecture for ANN decoding and the maximum density of training data points are also determined. The stability of the sensor was monitored by electrochemical impedance spectroscopy. The impedance associated with YSZ oxygen ion conduction and the electrochemical reactions at the three-phase interface are tracked over a period of over 100 days.