Highly Sensitive and Selective Detection of Dimethyl Methyl Phosphonate with Copolymer-Based QCM Sensors

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dc.authoridKILINC, Necmettin/0000-0003-2123-2938
dc.authoridBayazit, Şahika Sena/0000-0001-9643-4855
dc.authoridÖZTÜRK, Sadullah/0000-0002-4851-3062
dc.authoridÖZTÜRK, Sadullah/0000-0002-4851-3062
dc.authoridTuna, Suha/0000-0002-9492-6896
dc.authorwosidKILINC, Necmettin/AAT-9845-2020
dc.authorwosidBayazit, Şahika Sena/C-6333-2009
dc.authorwosidÖZTÜRK, Sadullah/ABH-4355-2020
dc.authorwosidÖZTÜRK, Sadullah/E-5309-2016
dc.contributor.authorOzturk, Sadullah
dc.contributor.authorKosemen, Arif
dc.contributor.authorSen, Zafer
dc.contributor.authorTuna, Suha
dc.contributor.authorBayazit, Sahika Sena
dc.contributor.authorKilinc, Necmettin
dc.date.accessioned2024-08-04T20:54:58Z
dc.date.available2024-08-04T20:54:58Z
dc.date.issued2024
dc.departmentİnönü Üniversitesien_US
dc.description.abstractIn this work, the volatile organic compounds (VOCs) sensing properties of a quartz crystal microbalance (QCM) transducer coated with six different poly(3-methylthiophene) (P3MT) copolymerized with polypyrrole (PPy) are investigated. The sensor preparation involves the electrochemical deposition of P3MT, PPy, and P3MT-co-PPy on Au-coated QCM transducers by electrochemical deposition techniques with a three-electrode cell. The structural properties of the copolymer films are characterized using scanning electron microscopy, and their oxidation/reduction behavior is investigated through cyclic voltammetry. The copolymer-based QCM sensors exhibit high sensitivity and selectivity to dimethyl methyl phosphonate and benzonitrile, even at low concentrations (<1 ppm) at room temperature. Langmuir, Freundlich, Temkin, and Dubinin-Radushkevich adsorption isotherms are studied to understand the VOCs sensing mechanism machine learning classification algorithms including quadratic discriminant (QD), neural nets, K-nearest neighbors, linear discriminant, and support vector machines are applied to classify the sensor responses for the 12 different analytes. With the help of machine learning algorithms, tested analytes are successfully classified into their groups. The highest accuracy of 97.34% is achieved using the QD method. The developed sensor, combined with machine learning algorithms, shows promising potential for accurate and reliable detection and classification of VOCs.en_US
dc.identifier.doi10.1002/mame.202300346
dc.identifier.issn1438-7492
dc.identifier.issn1439-2054
dc.identifier.issue5en_US
dc.identifier.scopus2-s2.0-85181515075en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.urihttps://doi.org/10.1002/mame.202300346
dc.identifier.urihttps://hdl.handle.net/11616/101756
dc.identifier.volume309en_US
dc.identifier.wosWOS:001137268300001en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherWiley-V C H Verlag Gmbhen_US
dc.relation.ispartofMacromolecular Materials and Engineeringen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectadsorption kineticsen_US
dc.subjectcopolymersen_US
dc.subjectdimethyl methyl phosphonateen_US
dc.subjectmachine learningen_US
dc.subjectpoly(3-methylthiophene)en_US
dc.subjectpolypyrroleen_US
dc.subjectquartz crystal balanceen_US
dc.subjectVOCs sensingen_US
dc.titleHighly Sensitive and Selective Detection of Dimethyl Methyl Phosphonate with Copolymer-Based QCM Sensorsen_US
dc.typeArticleen_US

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