Optical properties of NiO films: Effect of nitrogen-doping, substrate temperature and band gap estimation using machine learning

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dc.contributor.authorKaya, Dogan
dc.contributor.authorHopoğlu, Hicret
dc.contributor.authorÇelik, Ali
dc.contributor.authorAkyol, Mustafa
dc.contributor.authorKaradag, Faruk
dc.contributor.authorŞenadım Tüzemen, Ebru
dc.contributor.authorEkicibil, Ahmet
dc.date.accessioned2025-01-06T17:30:10Z
dc.date.available2025-01-06T17:30:10Z
dc.date.issued2024
dc.description.abstractIn this study, nickel oxide (NiO) thin films were synthesized on glass substrates using RF magnetron sputtering with varying nitrogen (N) doping ratio and substrate temperatures to explore modifications in their structural, morphological, and optical properties. The films were prepared using a high-purity NiO target under controlled sputtering conditions. Structural analysis by X-ray diffraction revealed an improvement in crystallinity in the (2 0 0) direction with increasing N ratio. In contrast, higher N ratio led to the suppression of (1 1 1) and (2 2 0) peaks, indicating a significant influence of N on the crystal structure and orientation. The films’ thickness and morphology, examined using scanning electron microscopy and energy-dispersive X-ray spectroscopy, showed uniform and homogeneous growth with smooth surface topologies. Optical properties, assessed by UV–Vis-NIR spectrophotometry, demonstrated a decrease in transmittance and a redshift in the absorption edge with increased N doping, corresponding to a narrowing of the energy bandgap from 3.7 eV to 3.45 eV. This bandgap reduction is attributed to N incorporation substituting oxygen sites, introducing defect states within the band structure. Additionally, the impact of substrate temperature on film growth enhanced crystallinity and orientation along the (1 1 1) plane at higher temperatures, with a simultaneous reduction in film thickness due to increased adatom mobility and potential thermal decomposition. The evaluation of Kernel Ridge Regression (KRR) and Ridge Regression (RR) models revealed their effectiveness in predicting band gap values for thin films at varying substrate temperatures and thicknesses. While RR excelled in predicting a band gap of 3.6 eV for a film with a substrate temperature of 24 °C and a thickness of 112.7 nm, KRR outperformed in predicting a band gap of 3.65 eV for a film with a substrate temperature of 24 °C and a thickness of 107 nm. These findings elucidate the dual influence of N doping and substrate temperature on enhancing the functional properties of NiO thin films, promising for applications in optoelectronic devices and gas sensors. © 2024 Elsevier B.V.
dc.description.sponsorshipNanophotonics Research and Application Center at Sivas Cumhuriyet University; Sivas Cumhuriyet University R&D Center; CUNAM; CUTAM; Department of Physics at Cukurova University; Scientific Research Project Fund of Sivas Cumhuriyet University, (F-2021-640)
dc.identifier.doi10.1016/j.mseb.2024.117507
dc.identifier.issn0921-5107
dc.identifier.scopus2-s2.0-85196354900
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.1016/j.mseb.2024.117507
dc.identifier.urihttps://hdl.handle.net/20.500.14669/1488
dc.identifier.volume307
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherElsevier Ltd
dc.relation.ispartofMaterials Science and Engineering: B
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_20241211
dc.subjectEnergy band gap
dc.subjectMachine learning
dc.subjectN-doping
dc.subjectNiO thin film
dc.subjectRF magnetron sputtering
dc.titleOptical properties of NiO films: Effect of nitrogen-doping, substrate temperature and band gap estimation using machine learning
dc.typeArticle

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