Abstract
Metal-oxide (MOX) gas sensors are widely used for gas concentration estimation and gas identification due to their low cost, high sensitivity, and stability. However, MOX sensors have low selectivity to different gases, which leads to the problem of classification for mixtures and pure gases. In this study, a square wave was applied as the heater waveform to generate a dynamic response on the sensor. The information of the dynamic response, which includes different characteristics for different gases due to temperature changes, enhanced the selectivity of the MOX sensor. Moreover, a polynomial interaction term mixture model with a dynamic response is proposed to predict the concentration of the binary mixtures and pure gases. The proposed method improved the classification accuracy to 100%. Moreover, the relative error of quantification decreased to 1.4% for pure gases and 13.0% for mixtures.
Highlights
Estimating the gas concentration is essential to obtain detailed information for analysis
In a previous study [14], a linear-quadratic model was proposed to indicate the relationship between the gas concentration and the sensor response
The aforementioned formula assumes that the characteristics of a gas sensor and the gas concentration exhibit a quadratic relationship
Summary
Estimating the gas concentration is essential to obtain detailed information for analysis. A standard method of distinguishing components in a gas mixture is gas chromatography-mass spectrometry (GC-MS); GC-MS is expensive and time-consuming. Gas selectivity can be improved by modulating the temperature of the sensor film to change its chemical reaction rate and obtain additional features [8,9,10,11,12]. The response of the sensor to different gases can reflect the chemical characteristics and reveal additional information for achieving increased selectivity [13]. The features with increased selectivity obtained through temperature modulation may contribute to the gas prediction model. In 2018, researchers reported to use a single sensor to predict the mixture concentration with the peak response under temperature modulation and proposed a linear-quadratic mixture model suitable for the features [14]. IInn aa pprreevviioouuss ssttuuddyy [[1144]],, aa lliinneeaarr--qquuaaddrraattiicc mmooddeell wwaass pprrooppoosseedd ttoo iinnddiiccaattee tthhee rreellaattiioonnsshhiipp bbeettwweeeenn tthhee ggaass ccoonncceennttrraattiioonn aanndd tthhee sseennssoorr rreessppoonnssee
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
