Abstract
Dedicated methods for quantification and identification of reducing gases based on model-based temperature-cycled operation (TCO) using a single commercial MOS gas sensor are presented. During high temperature phases the sensor surface is highly oxidized, yielding a significant sensitivity increase after switching to lower temperatures (differential surface reduction, DSR). For low concentrations, the slope of the logarithmic conductance during this low-temperature phase is evaluated and can directly be used for quantification. For higher concentrations, the time constant for reaching a stable conductance during the same low-temperature phase is evaluated. Both signals represent the reaction rate of the reducing gas on the strongly oxidized surface at this low temperature and provide a linear calibration curve, which is exceptional for MOS sensors. By determining these reaction rates on different low-temperature plateaus and applying pattern recognition, the resulting footprint can be used for identification of different gases. All methods are tested over a wide concentration range from 10 ppb to 100 ppm (4 orders of magnitude) for four different reducing gases (CO, H2, ammonia and benzene) using randomized gas exposures.
Highlights
Metal oxide semiconductor (MOS) gas sensors are widely used for the detection of reducing and oxidizing gases
Following the line of the rate equation approach by Ding et al [14], we described a concept for the optimization of temperature-cycled operation (TCO) and feature generation in earlier work [16,17], which gives the basis for the quantification technique presented in this paper
We demonstrate two facile methods allowing linear calibration of gas sensors that are derived from this model and demonstrate their suitability by measuring four gases (CO, H2, ammonia and benzene) over four decades of concentration (10 ppb to 100 ppm)
Summary
Metal oxide semiconductor (MOS) gas sensors are widely used for the detection of reducing and oxidizing gases. TCO dates back more than 40 years, utilized for the discrimination of carbon monoxide and hydrocarbons [12]. It used, as most of the early attempts, a heuristically defined temperature cycle and only very few virtual multi-sensors, e.g., on temperature plateaus, instead of using an optimized cycle and virtual multi-sensor generation. As most of the early attempts, a heuristically defined temperature cycle and only very few virtual multi-sensors, e.g., on temperature plateaus, instead of using an optimized cycle and virtual multi-sensor generation This is probably due to the fact that modelling the sensor response throughout a cycle is quite challenging. While the observation of the temperature-dependant rate constants is limited for the classical Taguchi-type sensors due to their high thermal time constants, micro-machined sensor
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