Optical properties of NiO films: Effect of nitrogen-doping, substrate temperature and band gap estimation using machine learning
[ X ]
Tarih
2024
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Elsevier Ltd
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
In 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.
Açıklama
Anahtar Kelimeler
Energy band gap, Machine learning, N-doping, NiO thin film, RF magnetron sputtering
Kaynak
Materials Science and Engineering: B
WoS Q Değeri
Scopus Q Değeri
Q1
Cilt
307
