Energy gap estimation of zinc sulfide metal chalcogenide nanostructure semiconductor using genetically hybridized support vector regression

Zinc sulfide is a metal chalcogenide semiconductor with promising potentials in environmental sensors, short wavelength light emitting diodes, biomedical imaging, display light sources, transistors, flat panel displays, optoelectronics, and photocatalysis. Adjusting the energy gap (EG) of zinc sulfi...

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Autor principal:	Nahier Aldhafferi
Formato:	article
Lenguaje:	EN
Publicado:	AIP Publishing LLC 2021
Materias:	Physics QC1-999
Acceso en línea:	https://doaj.org/article/f5f842214f724e2c83196255e59986a5
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spelling	oai:doaj.org-article:f5f842214f724e2c83196255e59986a52021-12-01T18:52:06ZEnergy gap estimation of zinc sulfide metal chalcogenide nanostructure semiconductor using genetically hybridized support vector regression2158-322610.1063/5.0069749https://doaj.org/article/f5f842214f724e2c83196255e59986a52021-11-01T00:00:00Zhttp://dx.doi.org/10.1063/5.0069749https://doaj.org/toc/2158-3226Zinc sulfide is a metal chalcogenide semiconductor with promising potentials in environmental sensors, short wavelength light emitting diodes, biomedical imaging, display light sources, transistors, flat panel displays, optoelectronics, and photocatalysis. Adjusting the energy gap (EG) of zinc sulfide for light response enhancement that is suitable for desired applications involves foreign material incorporation through chemical doping or co-doping mechanisms with structural distortion and host symmetry breaking. This work optimizes support vector regression (SVR) parameters with a genetic algorithm to develop a hybrid genetically optimized SVR (HGSVR-EG) model with the precise capacity to estimate the EG of a doped zinc sulfide semiconductor using the crystal lattice constant and the crystallite size as descriptors. The precision of the developed HGSVR-EG model is compared with that of the stepwise regression based model for EG estimation (STR-EG) using different error metrics. The developed HGSVR-EG model outperforms the STR-EG model with a performance improvement of 64.47%, 74.52%, and 49.52% on the basis of correlation coefficient, mean squared error, and root mean square error, respectively. The developed HGSVR-EG model explores and investigates the zinc sulfide bandgap reduction effect of manganese and chromium nano-particle incorporation in the host semiconductor, and the obtained EGs agree well with the measured values. The developed HGSVR-EG model was further validated with an external set of data, and an excellent agreement between the measured and estimated EGs was obtained. The outstanding performance of the developed predictive models in this work would ultimately facilitate EG characterization of zinc sulfide without experimental stress.Nahier AldhafferiAIP Publishing LLCarticlePhysicsQC1-999ENAIP Advances, Vol 11, Iss 11, Pp 115222-115222-11 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Physics QC1-999
spellingShingle	Physics QC1-999 Nahier Aldhafferi Energy gap estimation of zinc sulfide metal chalcogenide nanostructure semiconductor using genetically hybridized support vector regression
description	Zinc sulfide is a metal chalcogenide semiconductor with promising potentials in environmental sensors, short wavelength light emitting diodes, biomedical imaging, display light sources, transistors, flat panel displays, optoelectronics, and photocatalysis. Adjusting the energy gap (EG) of zinc sulfide for light response enhancement that is suitable for desired applications involves foreign material incorporation through chemical doping or co-doping mechanisms with structural distortion and host symmetry breaking. This work optimizes support vector regression (SVR) parameters with a genetic algorithm to develop a hybrid genetically optimized SVR (HGSVR-EG) model with the precise capacity to estimate the EG of a doped zinc sulfide semiconductor using the crystal lattice constant and the crystallite size as descriptors. The precision of the developed HGSVR-EG model is compared with that of the stepwise regression based model for EG estimation (STR-EG) using different error metrics. The developed HGSVR-EG model outperforms the STR-EG model with a performance improvement of 64.47%, 74.52%, and 49.52% on the basis of correlation coefficient, mean squared error, and root mean square error, respectively. The developed HGSVR-EG model explores and investigates the zinc sulfide bandgap reduction effect of manganese and chromium nano-particle incorporation in the host semiconductor, and the obtained EGs agree well with the measured values. The developed HGSVR-EG model was further validated with an external set of data, and an excellent agreement between the measured and estimated EGs was obtained. The outstanding performance of the developed predictive models in this work would ultimately facilitate EG characterization of zinc sulfide without experimental stress.
format	article
author	Nahier Aldhafferi
author_facet	Nahier Aldhafferi
author_sort	Nahier Aldhafferi
title	Energy gap estimation of zinc sulfide metal chalcogenide nanostructure semiconductor using genetically hybridized support vector regression
title_short	Energy gap estimation of zinc sulfide metal chalcogenide nanostructure semiconductor using genetically hybridized support vector regression
title_full	Energy gap estimation of zinc sulfide metal chalcogenide nanostructure semiconductor using genetically hybridized support vector regression
title_fullStr	Energy gap estimation of zinc sulfide metal chalcogenide nanostructure semiconductor using genetically hybridized support vector regression
title_full_unstemmed	Energy gap estimation of zinc sulfide metal chalcogenide nanostructure semiconductor using genetically hybridized support vector regression
title_sort	energy gap estimation of zinc sulfide metal chalcogenide nanostructure semiconductor using genetically hybridized support vector regression
publisher	AIP Publishing LLC
publishDate	2021
url	https://doaj.org/article/f5f842214f724e2c83196255e59986a5
work_keys_str_mv	AT nahieraldhafferi energygapestimationofzincsulfidemetalchalcogenidenanostructuresemiconductorusinggeneticallyhybridizedsupportvectorregression
_version_	1718404667661090816

Energy gap estimation of zinc sulfide metal chalcogenide nanostructure semiconductor using genetically hybridized support vector regression

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