Selective Detection of Formaldehyde and Nitrogen Dioxide Using Innovative Modeling of SnO<sub>2</sub> Surface Response to Pulsed Temperature Profile
The need for odor measurement and pollution source identification in various sectors (aeronautic, automobile, healthcare…) has increased in the last decade. Multisensor modules, such as electronic noses, seem to be a promising and inexpensive alternative to traditional sensors that were only sensiti...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-12-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/24/24/7964 |
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| author | Emilie Bialic Jimmy Leblet Aymen Sendi Paul Gersberg Axel Maupoux Nicolas Lassabe Philippe Menini |
| author_facet | Emilie Bialic Jimmy Leblet Aymen Sendi Paul Gersberg Axel Maupoux Nicolas Lassabe Philippe Menini |
| author_sort | Emilie Bialic |
| collection | DOAJ |
| description | The need for odor measurement and pollution source identification in various sectors (aeronautic, automobile, healthcare…) has increased in the last decade. Multisensor modules, such as electronic noses, seem to be a promising and inexpensive alternative to traditional sensors that were only sensitive to one gas at a time. However, the selectivity, the non-repetitiveness of their manufacture, and their drift remain major obstacles to the use of electronic noses. In this first work, we show how the mathematical modeling of the sensor response can be used to find new selectivity characteristics, different from those classically used in the literature. We identified new specific characteristics that have no physical meaning that can be used to find criteria for the presence of formaldehyde and nitrogen dioxyde alone or in a mixture. We discuss the limitations of the methodology presented and suggest avenues for improvement, with more precise modeling techniques involving symbolic regression. |
| format | Article |
| id | doaj-art-e9f7e123e94d4ba9a0f72e5d87368727 |
| institution | Kabale University |
| issn | 1424-8220 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-e9f7e123e94d4ba9a0f72e5d873687272024-12-27T14:52:34ZengMDPI AGSensors1424-82202024-12-012424796410.3390/s24247964Selective Detection of Formaldehyde and Nitrogen Dioxide Using Innovative Modeling of SnO<sub>2</sub> Surface Response to Pulsed Temperature ProfileEmilie Bialic0Jimmy Leblet1Aymen Sendi2Paul Gersberg3Axel Maupoux4Nicolas Lassabe5Philippe Menini6Capgemini Engineering Research and Development, 31000 Toulouse, FranceUR Magellan, Iaelyon School of Management, University Jean Moulin Lyon 3, 1 Avenue des Frères Lumière, 69008 Lyon, FranceLaboratoire d’Analyse et d’Architecture des Systèmes (LAAS), Université de Toulouse, CNRS, UPS, 7 Avenue du Colonel Roche, 31031 Toulouse, FranceCapgemini Engineering Research and Development, 31000 Toulouse, FranceCapgemini Engineering Research and Development, 31000 Toulouse, FranceCapgemini Engineering Research and Development, 31000 Toulouse, FranceLaboratoire d’Analyse et d’Architecture des Systèmes (LAAS), Université de Toulouse, CNRS, UPS, 7 Avenue du Colonel Roche, 31031 Toulouse, FranceThe need for odor measurement and pollution source identification in various sectors (aeronautic, automobile, healthcare…) has increased in the last decade. Multisensor modules, such as electronic noses, seem to be a promising and inexpensive alternative to traditional sensors that were only sensitive to one gas at a time. However, the selectivity, the non-repetitiveness of their manufacture, and their drift remain major obstacles to the use of electronic noses. In this first work, we show how the mathematical modeling of the sensor response can be used to find new selectivity characteristics, different from those classically used in the literature. We identified new specific characteristics that have no physical meaning that can be used to find criteria for the presence of formaldehyde and nitrogen dioxyde alone or in a mixture. We discuss the limitations of the methodology presented and suggest avenues for improvement, with more precise modeling techniques involving symbolic regression.https://www.mdpi.com/1424-8220/24/24/7964metal oxide gas sensorsnanomaterialsselectivitytemperature modulationmathematical modelingdata analysis |
| spellingShingle | Emilie Bialic Jimmy Leblet Aymen Sendi Paul Gersberg Axel Maupoux Nicolas Lassabe Philippe Menini Selective Detection of Formaldehyde and Nitrogen Dioxide Using Innovative Modeling of SnO<sub>2</sub> Surface Response to Pulsed Temperature Profile Sensors metal oxide gas sensors nanomaterials selectivity temperature modulation mathematical modeling data analysis |
| title | Selective Detection of Formaldehyde and Nitrogen Dioxide Using Innovative Modeling of SnO<sub>2</sub> Surface Response to Pulsed Temperature Profile |
| title_full | Selective Detection of Formaldehyde and Nitrogen Dioxide Using Innovative Modeling of SnO<sub>2</sub> Surface Response to Pulsed Temperature Profile |
| title_fullStr | Selective Detection of Formaldehyde and Nitrogen Dioxide Using Innovative Modeling of SnO<sub>2</sub> Surface Response to Pulsed Temperature Profile |
| title_full_unstemmed | Selective Detection of Formaldehyde and Nitrogen Dioxide Using Innovative Modeling of SnO<sub>2</sub> Surface Response to Pulsed Temperature Profile |
| title_short | Selective Detection of Formaldehyde and Nitrogen Dioxide Using Innovative Modeling of SnO<sub>2</sub> Surface Response to Pulsed Temperature Profile |
| title_sort | selective detection of formaldehyde and nitrogen dioxide using innovative modeling of sno sub 2 sub surface response to pulsed temperature profile |
| topic | metal oxide gas sensors nanomaterials selectivity temperature modulation mathematical modeling data analysis |
| url | https://www.mdpi.com/1424-8220/24/24/7964 |
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