Selective Detection of Hydrocarbons in Real Atmospheric Conditions by Single MOX Sensor in Temperature Modulation Mode

Selective Detection of Hydrocarbons in Real Atmospheric Conditions by Single MOX Sensor in Temperature Modulation Mode

Selective detection of hydrocarbons – methane and propane – in urban air for industrial safety properties by single metal oxide semiconductor gas sensor has been demonstrated. As sensors were fabricated on the basis of nanocrystalline SnO<sub>2 </sub>and alumina micro-hotplates. Sensor w...

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Main Authors: Valeriy Krivetskiy, Matvey Andreev, Alexander Efitorov
Format: Article
Language:English
Published: MDPI AG 2019-06-01
Series:Proceedings
Subjects:
metal oxide
semiconductor
gas sensor
temperature modulation
signal pre-processing
artificial neural networks
Online Access:https://www.mdpi.com/2504-3900/14/1/47
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spelling doaj-1313588df1724d748e3321395b7a1a892020-11-24T21:37:15ZengMDPI AGProceedings2504-39002019-06-011414710.3390/proceedings2019014047proceedings2019014047Selective Detection of Hydrocarbons in Real Atmospheric Conditions by Single MOX Sensor in Temperature Modulation ModeValeriy Krivetskiy0Matvey Andreev1Alexander Efitorov2Department of Chemistry, M.V. Lomonosov Moscow State University, Leninskie Gory 1/3, Moscow 119234, RussiaDepartment of Chemistry, M.V. Lomonosov Moscow State University, Leninskie Gory 1/3, Moscow 119234, RussiaSkobeltsyn Institute of Nuclear Physics (SINP MSU), M.V.Lomonosov Moscow State University, 1(2), Leninskie Gory, GSP-1, Moscow 119991, RussiaSelective detection of hydrocarbons – methane and propane – in urban air for industrial safety properties by single metal oxide semiconductor gas sensor has been demonstrated. As sensors were fabricated on the basis of nanocrystalline SnO<sub>2 </sub>and alumina micro-hotplates. Sensor working temperature modulation has been applied during raw sensor data collection. Pre-processing of acquired data – scaling, baseline extraction and exclusion of non-valid data points has been demonstrated to be critical procedures before application of machine learning algorithms. The achieved accuracy of 86% for correct gas identification in 40-200 ppm range has been demonstrated.https://www.mdpi.com/2504-3900/14/1/47metal oxidesemiconductorgas sensortemperature modulationsignal pre-processingartificial neural networks
collection DOAJ
language English
format Article
sources DOAJ
author Valeriy Krivetskiy
Matvey Andreev
Alexander Efitorov
spellingShingle Valeriy Krivetskiy
Matvey Andreev
Alexander Efitorov
Selective Detection of Hydrocarbons in Real Atmospheric Conditions by Single MOX Sensor in Temperature Modulation Mode
Proceedings
metal oxide
semiconductor
gas sensor
temperature modulation
signal pre-processing
artificial neural networks
author_facet Valeriy Krivetskiy
Matvey Andreev
Alexander Efitorov
author_sort Valeriy Krivetskiy
title Selective Detection of Hydrocarbons in Real Atmospheric Conditions by Single MOX Sensor in Temperature Modulation Mode
title_short Selective Detection of Hydrocarbons in Real Atmospheric Conditions by Single MOX Sensor in Temperature Modulation Mode
title_full Selective Detection of Hydrocarbons in Real Atmospheric Conditions by Single MOX Sensor in Temperature Modulation Mode
title_fullStr Selective Detection of Hydrocarbons in Real Atmospheric Conditions by Single MOX Sensor in Temperature Modulation Mode
title_full_unstemmed Selective Detection of Hydrocarbons in Real Atmospheric Conditions by Single MOX Sensor in Temperature Modulation Mode
title_sort selective detection of hydrocarbons in real atmospheric conditions by single mox sensor in temperature modulation mode
publisher MDPI AG
series Proceedings
issn 2504-3900
publishDate 2019-06-01
description Selective detection of hydrocarbons – methane and propane – in urban air for industrial safety properties by single metal oxide semiconductor gas sensor has been demonstrated. As sensors were fabricated on the basis of nanocrystalline SnO<sub>2 </sub>and alumina micro-hotplates. Sensor working temperature modulation has been applied during raw sensor data collection. Pre-processing of acquired data – scaling, baseline extraction and exclusion of non-valid data points has been demonstrated to be critical procedures before application of machine learning algorithms. The achieved accuracy of 86% for correct gas identification in 40-200 ppm range has been demonstrated.
topic metal oxide
semiconductor
gas sensor
temperature modulation
signal pre-processing
artificial neural networks
url https://www.mdpi.com/2504-3900/14/1/47
work_keys_str_mv AT valeriykrivetskiy selectivedetectionofhydrocarbonsinrealatmosphericconditionsbysinglemoxsensorintemperaturemodulationmode
AT matveyandreev selectivedetectionofhydrocarbonsinrealatmosphericconditionsbysinglemoxsensorintemperaturemodulationmode
AT alexanderefitorov selectivedetectionofhydrocarbonsinrealatmosphericconditionsbysinglemoxsensorintemperaturemodulationmode
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