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dc.contributor.authorAçıkbaş, Yaser
dc.contributor.authorErdoğan, Matem
dc.contributor.authorÇapan, Rifat
dc.contributor.authorErdoğan, Cansu Özkaya
dc.contributor.authorBaygu, Yasemin
dc.contributor.authorKabay, Nilgün
dc.contributor.authorGök, Yaşar
dc.contributor.authorKüçükyıldız, Gürkan
dc.date.accessioned2023-07-12T13:02:47Z
dc.date.available2023-07-12T13:02:47Z
dc.date.issued2022en_US
dc.identifier.issn2190-5509 / 2190-5517
dc.identifier.urihttps://doi.org/10.1007/s13204-022-02749-3
dc.identifier.urihttps://hdl.handle.net/20.500.12462/13199
dc.descriptionErdoğan, Matem (Balikesir Author)en_US
dc.description.abstractPhthalocyanines (Pcs), among synthetic macrocyclic structures, have recently been preferred sensing materials for harmful vapor detection owing to their attractive properties, such as chemical stability and highly thermal, and good production as nano-thin flm layers and their ?-electron conjugated system. Herein, phthalocyanine–zinc(II)-based (ZnPc) Langmuir– Blodgett (LB) nanothin flms were produced and characterized by UV–Visible spectrophotometer and Surface Plasmon Resonance (SPR) technique. The gas-sensing and optical properties of these ZnPc nanothin flms were also investigated by SPR method. The optical properties of ZnPc LB flms with varied numbers of layers were also expressed with this study. The refracting index values of ZnPc LB flm layers were identifed between 1.43 and 1.73 for the thicknesses of 3.7 and 12.6 nm with linear regression of 0.9926 by ftting SPR experimental data. The basic host–guest interaction principle was used to investigate the response of phthalocyanine–zinc(II)-based optical sensors against to the selected alcohol and ketone vapors. These kinetic data were performed by employing Fick’s difusion equation to study the swelling dynamics’ ZnPC nanothin flms. To prove the efciency of the experiments, the experimental data were modeled with the well-known deep learning models. The experimental data set is split into two sections: the training and test part. The 83% amount of data is operated to train the deep learning models and the remaining 17% samples of data are operated to observe the developed models prediction performance.en_US
dc.language.isoengen_US
dc.publisherSpringer Heidelbergen_US
dc.relation.isversionof10.1007/s13204-022-02749-3en_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectZinc(II)–Phthalocyanineen_US
dc.subjectNanothin Filmen_US
dc.subjectOptical Sensoren_US
dc.subjectSwelling Dynamicsen_US
dc.subjectSPRen_US
dc.subjectNeural Networksen_US
dc.titlePreparation and characterization of the phthalocyanine–zinc(II) complex‑based nanothin flms: optical and gas‑sensing propertiesen_US
dc.typearticleen_US
dc.relation.journalApplied Nanoscienceen_US
dc.contributor.departmentFen Edebiyat Fakültesien_US
dc.contributor.authorID0000-0003-3416-1083en_US
dc.contributor.authorID0000-0003-4440-1896en_US
dc.contributor.authorID0000-0001-9099-5854en_US
dc.contributor.authorID0000-0003-3222-9056en_US
dc.identifier.volumeEarly Access DEC 2022en_US
dc.identifier.startpage1en_US
dc.identifier.endpage14en_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US


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