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Gas identification based on bias induced hysteresis of a gas-sensitive SiC field effect transistor
Saarland University, Saarbruecken, Germany. (Dept. of Mechatronics Lab for Measurement Technology)
Linköping University, Department of Physics, Chemistry and Biology, Applied Sensor Science. Linköping University, The Institute of Technology. Saarland University, Saarbruecken, Germany. (Applied Sensor Science & Dept. of Mechatronics Lab for Measurement Technology)
Linköping University, Department of Physics, Chemistry and Biology, Applied Sensor Science. Linköping University, The Institute of Technology. (Applied Sensor Science)ORCID iD: 0000-0002-2817-3574
Linköping University, Department of Physics, Chemistry and Biology, Applied Sensor Science. Linköping University, The Institute of Technology. (Applied Sensor Science)
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2014 (English)In: Journal of Sensors and Sensor Systems, Vol. 3, 9-19 p.Article in journal (Refereed) Published
Abstract [en]

In this work dynamic variation of gate bias is used on a gas-sensitive SiC field effect transistor ("GasFET") to optimize its sensitivity and increase its selectivity. Gate bias ramps introduce strong hysteresis in the sensor signal. The shape of this hysteresis is shown to be an appropriate feature both for the discrimination of various gases (ammonia, carbon monoxide, nitrogen monoxide and methane) as well as for different gas concentrations (250 and 500 ppm). The shape is very sensitive to ambient conditions as well as to the bias sweep rate. Thus, the influences of oxygen concentration, relative humidity, sensor temperature and cycle duration, i.e., sweep rate, are investigated and reasons for the observed signal changes, most importantly the existence of at least two different and competing processes taking place simultaneously, are discussed. Furthermore, it is shown that even for very fast cycles, in the range of seconds, the gas-induced shape change in the signal is strong enough to achieve a reliable separation of gases using gate bias cycled operation and linear discriminant analysis (LDA) making this approach suitable for practical application.

Place, publisher, year, edition, pages
2014. Vol. 3, 9-19 p.
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Mechanical Engineering Physical Sciences Medical Biotechnology
Identifiers
URN: urn:nbn:se:liu:diva-113290DOI: 10.5194/jsss-3-9-2014OAI: oai:DiVA.org:liu-113290DiVA: diva2:780757
Available from: 2015-01-15 Created: 2015-01-15 Last updated: 2015-01-22Bibliographically approved

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Bur, ChristianLloyd Spetz, AnitaAndersson, Mike

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