Abstract
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.
Highlights
INTRODUCTIONThis research employs the size of semiconducting nano-particles and distorted crystal lattice constant to model the corresponding energy gap (EG) of zinc sulfide semiconductors using the hybrid genetic algorithm (GA) based support vector regression (HGSVREG) model and stepwise regression (STR) based model for energy gap (EG) estimation
Nano-particle semiconductors have attracted significant attention recently due to their potential applications and interesting properties that differ from known bulk features.1 Zinc sulfide is known to be a II–V semiconducting material due to the position of its constituent elements in the Periodic Table.2 It is a wide bandgap semiconductor with two known distinct crystal structures and a bulk energy gap (EG) of 3.68 eV.3 This semiconductor is one of the most stable semiconductors among II–VI semiconducting materials due to its excellent electrical, physical, and optical features that premise on the effect of quantum confinement
The outcomes of the developed HGSVR-EG and stepwise regression based model for EG estimation (STR-EG) models are presented
Summary
This research employs the size of semiconducting nano-particles and distorted crystal lattice constant to model the corresponding energy gap (EG) of zinc sulfide semiconductors using the hybrid genetic algorithm (GA) based support vector regression (HGSVREG) model and stepwise regression (STR) based model for energy gap (EG) estimation. Hybridization of support vector regression (SVR) with the genetic algorithm in this work addresses the energy gap characterization of zinc sulfide with excellent precision. Support vector regression (SVR) is a modeling technique under the computational intelligent algorithm domain with characteristic non-linear modeling potentials in real life applications.. Support vector regression (SVR) is a modeling technique under the computational intelligent algorithm domain with characteristic non-linear modeling potentials in real life applications.11 It reduces training error through error bound generalization using the structural risk minimization principle.
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