Tailoring the Energy Harvesting Capacity of Zinc Selenide Semiconductor Nanomaterial through Optical Band Gap Modeling Using Genetically Optimized Intelligent Method

Olusayo Olubosede,Miloud Souiyah,Mouftahou Latif,Wasiu Oke,Nahier Aldhafferi,Abdullah Alqahtani,Taoreed Owolabi,Mohd Abd Rahman

doi:10.3390/cryst12010036

Abstract

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.

Highlights

The synthesis and characterization of zinc selenide semiconductor nano-materials has attracted global attention lately due to the novel properties demonstrated by zinc selenide semiconductors compared to other members of chalcogenide groups [1,2,3]
Data samples from forty-three compounds of doped zinc selenide nanostructured semiconducting materials were employed in developing support vector regression (SVR)-genetic algorithm (GA) and stepwise regression (SPR) models for energy gap quantification
The energy gap of the zinc selenide (ZnSe) nanostructured semiconductors with incorporated foreign materials is modeled through the stepwise regression (SPR) algorithm and the hybridization genetic algorithm (GA) in the support vector regression (SVR) algorithm

Summary

Introduction

The synthesis and characterization of zinc selenide semiconductor nano-materials has attracted global attention lately due to the novel properties demonstrated by zinc selenide semiconductors compared to other members of chalcogenide groups [1,2,3]. The features contributing to the uniqueness of this class of semiconductors include the lower electrical resistivity, non-toxicity, high transmission, insignificant lattice mismatching, wide energy band gap, as well as tunable light harvesting capacity over a range of spectrums [4]. The factors influencing the physical properties of zinc selenide semiconductors include the deposition parameters (for thin-film samples), experimental preparation conditions, synthesis techniques, and incorporation of dopants [7,8]. These factors control the crystal lattice parameters and the size of the semiconducting nanomaterial. This work aims to estimate the energy gap of doped zinc selenide semiconductors through the hybridization of a computational machine learning support vector regression algorithm and a genetic algorithm, using the lattice constant of the semiconductor and the size of the material as model inputs

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Conclusion