Limited selectivity of amperometric gas sensors operating in multicomponent gas mixtures and methods of selectivity improvement

In recent years, smog and poor air quality have become a growing environmental problem. There is a need to continuously monitor the quality of the air. The lack of selectivity is one of the most important problems limiting the use of gas sensors for this purpose. In this study, the selectivity of si...

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Bibliographic Details
Published in:Bulletin of the Polish Academy of Sciences. Technical sciences 2020, Vol.68 (6), p.1275-1282
Main Authors: Dmitrzak, M., Jasinski, P., Jasinski, G.
Format: Article
Language:English
Subjects:
Air pollution
Air quality
Algorithms
amperometric sensor
Artificial neural networks
cross-sensitivity
Electrical measurement
Flow velocity
Gas flow
Gas mixtures
gas sensor
Gas sensors
Gases
multiple linear regression
Nitrogen dioxide
Relative humidity
Selectivity
Sensors
Smog
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Summary:In recent years, smog and poor air quality have become a growing environmental problem. There is a need to continuously monitor the quality of the air. The lack of selectivity is one of the most important problems limiting the use of gas sensors for this purpose. In this study, the selectivity of six amperometric gas sensors is investigated. First, the sensors were calibrated in order to find a correlation between the concentration level and sensor output. Afterwards, the responses of each sensor to single or multicomponent gas mixtures with concentrations from 50 ppb to 1 ppm were measured. The sensors were studied under controlled conditions, a constant gas flow rate of 100 mL/min and 50 % relative humidity. Single Gas Sensor Response Interpretation, Multiple Linear Regression, and Artificial Neural Network algorithms were used to predict the concentrations of SO2 and NO2. The main goal was to study different interactions between sensors and gases in multicomponent gas mixtures and show that it is insufficient to calibrate sensors in only a single gas.
ISSN:2300-1917
0239-7528
2300-1917
DOI:10.24425/bpasts.2020.134648