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New method for selectivity enhancement of SiC field effect gas sensors for quantification of NO.

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Title: New method for selectivity enhancement of SiC field effect gas sensors for quantification of NO.
Authors: Bur, Christian c.bur@LMT.uni-saarland.de, Reimann, Peter1, Andersson, Mike2, Lloyd Spetz, Anita2, Schütze, Andreas1
Source: Microsystem Technologies. Aug2012, Vol. 18 Issue 7/8, p1015-1025. 11p.
Subjects: Silicon carbide, Field-effect transistors, Detectors, Signal processing, Impulse response
Abstract: A silicon carbide based enhancement type metal insulator field effect transistor with porous gate metallization has been investigated as a total NO sensor operated in a temperature cycling mode. This operating mode is quite new for gas sensors based on the field effect but promising results have been reported earlier. Based on static investigations we have developed a suitable T-cycle optimized for NO detection and quantification in a mixture of typical exhaust gases (CO, CH, and NH). Significant features describing the shape of the sensor response have been extracted and evaluated with multivariate statistics (e.g. linear discriminant analysis) allowing quantification of NO. Additional cleaning-cycles every 30 min improve the stability of the sensor further. With this kind of advanced signal processing the influence of sensor drift and cross sensitivity to ambient gases can be reduced effectively. Measurements have proven that different concentrations of NO can be detected even in a changing mixture of other typical exhaust gases under dry and humid conditions. In addition to that, unknown concentrations of NO can be detected based on a small set of training data. It can be concluded that the performance of GasFETs for NO determination can be enhanced considerably with temperature cycling and appropriate signal processing. [ABSTRACT FROM AUTHOR]
Copyright of Microsystem Technologies is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: A silicon carbide based enhancement type metal insulator field effect transistor with porous gate metallization has been investigated as a total NO sensor operated in a temperature cycling mode. This operating mode is quite new for gas sensors based on the field effect but promising results have been reported earlier. Based on static investigations we have developed a suitable T-cycle optimized for NO detection and quantification in a mixture of typical exhaust gases (CO, CH, and NH). Significant features describing the shape of the sensor response have been extracted and evaluated with multivariate statistics (e.g. linear discriminant analysis) allowing quantification of NO. Additional cleaning-cycles every 30 min improve the stability of the sensor further. With this kind of advanced signal processing the influence of sensor drift and cross sensitivity to ambient gases can be reduced effectively. Measurements have proven that different concentrations of NO can be detected even in a changing mixture of other typical exhaust gases under dry and humid conditions. In addition to that, unknown concentrations of NO can be detected based on a small set of training data. It can be concluded that the performance of GasFETs for NO determination can be enhanced considerably with temperature cycling and appropriate signal processing. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Microsystem Technologies is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1007/s00542-012-1434-z
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      – Code: eng
        Text: English
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        PageCount: 11
        StartPage: 1015
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      – SubjectFull: Silicon carbide
        Type: general
      – SubjectFull: Field-effect transistors
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      – SubjectFull: Detectors
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      – SubjectFull: Signal processing
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      – SubjectFull: Impulse response
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              Text: Aug2012
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