Minimization of the threshold voltage parameter of the co-doped ZnO doped liquid crystals by machine learning algorithms

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dc.contributor.authorOnsal, Guelnur
dc.contributor.authorUgurlu, Onur
dc.contributor.authorKaynar, Umit H.
dc.contributor.authorEliiyi, Deniz Tursel
dc.date.accessioned2024-03-09T18:48:29Z
dc.date.available2024-03-09T18:48:29Z
dc.date.issued2023
dc.departmentİzmir Bakırçay Üniversitesien_US
dc.description.abstractThis study aims to examine the influence of the co-doped semiconductor nanostructure (Al-Cu):ZnO on the electro-optical properties of the E7 coded pure nematic liquid crystal structures and minimize the threshold voltage of pure E7 liquid crystal. To determine the ideal concentration ratios of the materials for the minimum threshold voltage, we employed different machine learning algorithms. In this context, we first produced twelve composite structures through lab experimentation with different concentrations and created an experimental dataset for the machine learning algorithms. Next, the ideal concentration ratios were estimated using the AdaBoost algorithm, which has an R-2 of 96% on the experimental dataset. Finally, additional composite structures having the estimated concentration ratios were produced. The results show that, with the help of the employed machine learning algorithms, the threshold voltage of pure E7 liquid crystal was reduced by 19% via the (Al-Cu):ZnO doping.en_US
dc.description.sponsorshipScientific and Technological Research Council of Turkey (TUBITAK) [:121M185]en_US
dc.description.sponsorshipAcknowledgementsThis research was supported financially by the Scientific and Technological Research Council of Turkey (TUBITAK)(Project No:121M185).en_US
dc.identifier.doi10.1038/s41598-023-39923-8
dc.identifier.issn2045-2322
dc.identifier.issue1en_US
dc.identifier.pmid37550479en_US
dc.identifier.scopus2-s2.0-85166784141en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.urihttps://doi.org/10.1038/s41598-023-39923-8
dc.identifier.urihttps://hdl.handle.net/20.500.14034/1342
dc.identifier.volume13en_US
dc.identifier.wosWOS:001044365500058en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.indekslendigikaynakPubMeden_US
dc.language.isoenen_US
dc.publisherNature Portfolioen_US
dc.relation.ispartofScientific Reportsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.titleMinimization of the threshold voltage parameter of the co-doped ZnO doped liquid crystals by machine learning algorithmsen_US
dc.typeArticleen_US

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