{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,7]],"date-time":"2026-09-07T14:47:01Z","timestamp":1788792421207,"version":"build-2803163510"},"reference-count":18,"publisher":"MDPI AG","issue":"24","license":[{"start":{"date-parts":[[2024,12,13]],"date-time":"2024-12-13T00:00:00Z","timestamp":1734048000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The need for odor measurement and pollution source identification in various sectors (aeronautic, automobile, healthcare\u2026) 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.<\/jats:p>","DOI":"10.3390\/s24247964","type":"journal-article","created":{"date-parts":[[2024,12,13]],"date-time":"2024-12-13T05:55:45Z","timestamp":1734069345000},"page":"7964","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Selective Detection of Formaldehyde and Nitrogen Dioxide Using Innovative Modeling of SnO2 Surface Response to Pulsed Temperature Profile"],"prefix":"10.3390","volume":"24","author":[{"given":"Emilie","family":"Bialic","sequence":"first","affiliation":[{"name":"Capgemini Engineering Research and Development, 31000 Toulouse, France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jimmy","family":"Leblet","sequence":"additional","affiliation":[{"name":"UR Magellan, Iaelyon School of Management, University Jean Moulin Lyon 3, 1 Avenue des Fr\u00e8res Lumi\u00e8re, 69008 Lyon, France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Aymen","family":"Sendi","sequence":"additional","affiliation":[{"name":"Laboratoire d\u2019Analyse et d\u2019Architecture des Syst\u00e8mes (LAAS), Universit\u00e9 de Toulouse, CNRS, UPS, 7 Avenue du Colonel Roche, 31031 Toulouse, France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Paul","family":"Gersberg","sequence":"additional","affiliation":[{"name":"Capgemini Engineering Research and Development, 31000 Toulouse, France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Axel","family":"Maupoux","sequence":"additional","affiliation":[{"name":"Capgemini Engineering Research and Development, 31000 Toulouse, France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nicolas","family":"Lassabe","sequence":"additional","affiliation":[{"name":"Capgemini Engineering Research and Development, 31000 Toulouse, France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7532-9148","authenticated-orcid":false,"given":"Philippe","family":"Menini","sequence":"additional","affiliation":[{"name":"Laboratoire d\u2019Analyse et d\u2019Architecture des Syst\u00e8mes (LAAS), Universit\u00e9 de Toulouse, CNRS, UPS, 7 Avenue du Colonel Roche, 31031 Toulouse, France"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,12,13]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"446","DOI":"10.13168\/cs.2023.0044","article-title":"SnO2-based CO sensors: Recent advances and future directions","volume":"67","author":"He","year":"2023","journal-title":"Ceram.-Silik."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"131668","DOI":"10.1016\/j.snb.2022.131668","article-title":"Drift in a popular metal oxide sensor dataset reveals limitations for gas classification benchmarks","volume":"361","author":"Dennler","year":"2022","journal-title":"Sens. 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