Tailoring the energy harvesting capacity of zinc selenide semiconductor nanomaterial through optical band gap modeling using genetically optimized intelligent method
Zinc selenide (ZnSe) nanomaterial is a binary semiconducting material with unique features, such as high chemical stability, high photosensitivity, low cost, great excitation binding energy, non-toxicity, and a tunable direct wide band gap. These characteristics contribute significantly to its wide...
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my.upm.eprints.952882023-01-09T02:00:44Z http://psasir.upm.edu.my/id/eprint/95288/ Tailoring the energy harvesting capacity of zinc selenide semiconductor nanomaterial through optical band gap modeling using genetically optimized intelligent method Olubosede, Olusayo Abd Rahman, Mohd Amiruddin Alqahtani, Abdullah Souiyah, Miloud Latif, Mouftahou B. Oke, Wasiu Adeyemi Aldhafferi, Nahier Owolabi, Taoreed O. Zinc selenide (ZnSe) nanomaterial is a binary semiconducting material with unique features, such as high chemical stability, high photosensitivity, low cost, great excitation binding energy, non-toxicity, and a tunable direct wide band gap. These characteristics contribute significantly to its wide usage as sensors, optical filters, photo-catalysts, optical recording materials, and photovoltaics, among others. The light energy harvesting capacity of this material can be enhanced and tailored to meet the required application demand through band gap tuning with compositional modulation, which influences the nano-structural size, as well as the crystal distortion of the semiconductor. This present work provides novel ways whereby the wide energy band gap of zinc selenide can be effectively modulated and tuned for light energy harvesting capacity enhancement by hybridizing a support vector regression algorithm (SVR) with a genetic algorithm (GA) for parameter combinatory optimization. The effectiveness of the SVR-GA model is compared with the stepwise regression (SPR)-based model using several performance evaluation metrics. The developed SVR-GA model outperforms the SPR model using the root mean square error metric, with a performance improvement of 33.68%, while a similar performance superiority is demonstrated by the SVR-GA model over the SPR using other performance metrics. The intelligent zinc selenide energy band gap modulation proposed in this work will facilitate the fabrication of zinc selenide-based sensors with enhanced light energy harvesting capacity at a reduced cost, with the circumvention of experimental stress. MDPI AG 2021-12-27 Article PeerReviewed Olubosede, Olusayo and Abd Rahman, Mohd Amiruddin and Alqahtani, Abdullah and Souiyah, Miloud and Latif, Mouftahou B. and Oke, Wasiu Adeyemi and Aldhafferi, Nahier and Owolabi, Taoreed O. (2021) Tailoring the energy harvesting capacity of zinc selenide semiconductor nanomaterial through optical band gap modeling using genetically optimized intelligent method. Crystals, 12 (1). art. no. 36. pp. 1-13. ISSN 2073-4352 https://www.mdpi.com/2073-4352/12/1/36 10.3390/cryst12010036 |
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Zinc selenide (ZnSe) nanomaterial is a binary semiconducting material with unique features, such as high chemical stability, high photosensitivity, low cost, great excitation binding energy, non-toxicity, and a tunable direct wide band gap. These characteristics contribute significantly to its wide usage as sensors, optical filters, photo-catalysts, optical recording materials, and photovoltaics, among others. The light energy harvesting capacity of this material can be enhanced and tailored to meet the required application demand through band gap tuning with compositional modulation, which influences the nano-structural size, as well as the crystal distortion of the semiconductor. This present work provides novel ways whereby the wide energy band gap of zinc selenide can be effectively modulated and tuned for light energy harvesting capacity enhancement by hybridizing a support vector regression algorithm (SVR) with a genetic algorithm (GA) for parameter combinatory optimization. The effectiveness of the SVR-GA model is compared with the stepwise regression (SPR)-based model using several performance evaluation metrics. The developed SVR-GA model outperforms the SPR model using the root mean square error metric, with a performance improvement of 33.68%, while a similar performance superiority is demonstrated by the SVR-GA model over the SPR using other performance metrics. The intelligent zinc selenide energy band gap modulation proposed in this work will facilitate the fabrication of zinc selenide-based sensors with enhanced light energy harvesting capacity at a reduced cost, with the circumvention of experimental stress. |
format |
Article |
author |
Olubosede, Olusayo Abd Rahman, Mohd Amiruddin Alqahtani, Abdullah Souiyah, Miloud Latif, Mouftahou B. Oke, Wasiu Adeyemi Aldhafferi, Nahier Owolabi, Taoreed O. |
spellingShingle |
Olubosede, Olusayo Abd Rahman, Mohd Amiruddin Alqahtani, Abdullah Souiyah, Miloud Latif, Mouftahou B. Oke, Wasiu Adeyemi Aldhafferi, Nahier Owolabi, Taoreed O. Tailoring the energy harvesting capacity of zinc selenide semiconductor nanomaterial through optical band gap modeling using genetically optimized intelligent method |
author_facet |
Olubosede, Olusayo Abd Rahman, Mohd Amiruddin Alqahtani, Abdullah Souiyah, Miloud Latif, Mouftahou B. Oke, Wasiu Adeyemi Aldhafferi, Nahier Owolabi, Taoreed O. |
author_sort |
Olubosede, Olusayo |
title |
Tailoring the energy harvesting capacity of zinc selenide semiconductor nanomaterial through optical band gap modeling using genetically optimized intelligent method |
title_short |
Tailoring the energy harvesting capacity of zinc selenide semiconductor nanomaterial through optical band gap modeling using genetically optimized intelligent method |
title_full |
Tailoring the energy harvesting capacity of zinc selenide semiconductor nanomaterial through optical band gap modeling using genetically optimized intelligent method |
title_fullStr |
Tailoring the energy harvesting capacity of zinc selenide semiconductor nanomaterial through optical band gap modeling using genetically optimized intelligent method |
title_full_unstemmed |
Tailoring the energy harvesting capacity of zinc selenide semiconductor nanomaterial through optical band gap modeling using genetically optimized intelligent method |
title_sort |
tailoring the energy harvesting capacity of zinc selenide semiconductor nanomaterial through optical band gap modeling using genetically optimized intelligent method |
publisher |
MDPI AG |
publishDate |
2021 |
url |
http://psasir.upm.edu.my/id/eprint/95288/ https://www.mdpi.com/2073-4352/12/1/36 |
_version_ |
1754531213188530176 |