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Highly sensitive benzene detection with metal oxide semiconductor gas sensors – an inter-laboratory comparison

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For detection of benzene, a gas sensor system with metal oxide semiconductor (MOS) gas sensors using temperature-cycled operation (TCO) is presented. The system has been tested in two different laboratories at the concentration range from 0.5 ppb up to 10 ppb. The system is equipped with three gas sensors and advanced temperature control and read-out electronics for the extraction of features from the TCO signals. A sensor model is used to describe the sensor response in dependence of the gas concentration. It is based on a linear differential reduction of the surface (DSR) at a low temperature phase, which is linked to an exponential growth of the sensor conductance. To compensate cross interference to other gases the DSR is measured at three different temperatures (200 °C, 250 °C, 300 °C) and the calculated features are put into a multilinear regression (partial least square regression-PLSR) for the quantification of benzene at both laboratories. In the tests with the first setup, benzene was supplied in defined gas profiles in a continuous gas flow with variation of humidity and various interferents e.g. toluene and carbon monoxide (CO). Depending on the gas background and interferents, the quantification accuracy is between ± 0.2 ppb and ± 2 ppb. The second gas mixing system is based on a circulation of the carrier gas stream in a closed-loop control for the benzene concentration and other test gases based on continuously available reference measurements for benzene and other organic and inorganic compounds. In this system, a similar accuracy was achieved for low background contaminations and constant humidity; the benzene level could be quantified with an error of less than 0.5 ppb. The transfer of regression models for one laboratory to the other has been tested successfully.
2018-04-10
Copernicus Publications
JRC108193
2194-8771,   
https://publications.jrc.ec.europa.eu/repository/handle/JRC108193,   
10.5194/jsss-7-235-2018,   
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