Performance of machine learning algorithms in spectroscopic ellipsometry data analysis of ZnTiO3 nanocomposite
Abstract In this research, the optical properties of the PVP: ZnTiO3 nanocomposite are studied using the spectroscopic ellipsometry technique. The preparation procedure of the ZnTiO3 nanocomposite is explained in detail. The absorbance/transmittance, surface morphology, structural information, chemi...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2024-01-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-023-50620-4 |
| _version_ | 1827377331470073856 |
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| author | Ali Barkhordari Hamid Reza Mashayekhi Pari Amiri Süleyman Özçelik Ferhat Hanife Yashar Azizian-Kalandaragh |
| author_facet | Ali Barkhordari Hamid Reza Mashayekhi Pari Amiri Süleyman Özçelik Ferhat Hanife Yashar Azizian-Kalandaragh |
| author_sort | Ali Barkhordari |
| collection | DOAJ |
| description | Abstract In this research, the optical properties of the PVP: ZnTiO3 nanocomposite are studied using the spectroscopic ellipsometry technique. The preparation procedure of the ZnTiO3 nanocomposite is explained in detail. The absorbance/transmittance, surface morphology, structural information, chemical identification, and surface topography of the ZnTiO3 nanocomposite is studied using UV–Vis spectroscopy, field-emission scanning electron microscopy, Raman spectroscopy, Fourier transform infra-red, and atomic force microscopy, respectively. The ellipsometry method is used to obtain the spectra of the real and imaginary parts of the dielectric function and refractive index in the photon energy range of 0.59–4.59 eV. Moreover, using two machine learning algorithms, namely artificial neural network and support vector regression methods, the ellipsometric parameters ψ and Δ are analyzed and compared with non-linear regression. The error and accuracy of each three methods, as well as the time required for their execution, are calculated to compare their suitability in the ellipsometric data analysis. Also, the absorption coefficient was used to determine the band gap energy of the ZnTiO3 nanocomposite, which is found to be 3.83 eV. The second-energy derivative of the dielectric function is utilized to identify six critical point energies of the prepared sample. Finally, the spectral-dependent optical loss function and optical conductivity of the ZnTiO3 nanocomposite are investigated. |
| first_indexed | 2024-03-08T12:37:36Z |
| format | Article |
| id | doaj.art-8b042d488c4046aeb870fda4ce54e6e0 |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-03-08T12:37:36Z |
| publishDate | 2024-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-8b042d488c4046aeb870fda4ce54e6e02024-01-21T12:22:35ZengNature PortfolioScientific Reports2045-23222024-01-0114111410.1038/s41598-023-50620-4Performance of machine learning algorithms in spectroscopic ellipsometry data analysis of ZnTiO3 nanocompositeAli Barkhordari0Hamid Reza Mashayekhi1Pari Amiri2Süleyman Özçelik3Ferhat Hanife4Yashar Azizian-Kalandaragh5Faculty of Physics, Shahid Bahonar University of KermanFaculty of Physics, Shahid Bahonar University of KermanDepartment of Engineering Sciences, University of Mohaghegh ArdabiliDepartment of Photonics, Faculty of Applied Sciences, Gazi UniversityDepartment of Photonics, Faculty of Applied Sciences, Gazi UniversityDepartment of Physics, University of Mohaghegh ArdabiliAbstract In this research, the optical properties of the PVP: ZnTiO3 nanocomposite are studied using the spectroscopic ellipsometry technique. The preparation procedure of the ZnTiO3 nanocomposite is explained in detail. The absorbance/transmittance, surface morphology, structural information, chemical identification, and surface topography of the ZnTiO3 nanocomposite is studied using UV–Vis spectroscopy, field-emission scanning electron microscopy, Raman spectroscopy, Fourier transform infra-red, and atomic force microscopy, respectively. The ellipsometry method is used to obtain the spectra of the real and imaginary parts of the dielectric function and refractive index in the photon energy range of 0.59–4.59 eV. Moreover, using two machine learning algorithms, namely artificial neural network and support vector regression methods, the ellipsometric parameters ψ and Δ are analyzed and compared with non-linear regression. The error and accuracy of each three methods, as well as the time required for their execution, are calculated to compare their suitability in the ellipsometric data analysis. Also, the absorption coefficient was used to determine the band gap energy of the ZnTiO3 nanocomposite, which is found to be 3.83 eV. The second-energy derivative of the dielectric function is utilized to identify six critical point energies of the prepared sample. Finally, the spectral-dependent optical loss function and optical conductivity of the ZnTiO3 nanocomposite are investigated.https://doi.org/10.1038/s41598-023-50620-4 |
| spellingShingle | Ali Barkhordari Hamid Reza Mashayekhi Pari Amiri Süleyman Özçelik Ferhat Hanife Yashar Azizian-Kalandaragh Performance of machine learning algorithms in spectroscopic ellipsometry data analysis of ZnTiO3 nanocomposite Scientific Reports |
| title | Performance of machine learning algorithms in spectroscopic ellipsometry data analysis of ZnTiO3 nanocomposite |
| title_full | Performance of machine learning algorithms in spectroscopic ellipsometry data analysis of ZnTiO3 nanocomposite |
| title_fullStr | Performance of machine learning algorithms in spectroscopic ellipsometry data analysis of ZnTiO3 nanocomposite |
| title_full_unstemmed | Performance of machine learning algorithms in spectroscopic ellipsometry data analysis of ZnTiO3 nanocomposite |
| title_short | Performance of machine learning algorithms in spectroscopic ellipsometry data analysis of ZnTiO3 nanocomposite |
| title_sort | performance of machine learning algorithms in spectroscopic ellipsometry data analysis of zntio3 nanocomposite |
| url | https://doi.org/10.1038/s41598-023-50620-4 |
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