Abstract
Barium titanate (BaTiO3) is a class of ceramic multifunctional materials with unique thermal stability, prominent piezoelectricity constant, excellent dielectric constant, environmental friendliness, and excellent photocatalytic activities. These features have rendered barium titanate indispensable in many areas of applications such as electromechanical devices, thermistors, multilayer capacitors, and electrooptical devices. The photocatalytic activity of barium titanate semiconductor is hindered by its large band gap and high rate of charge recombination. Doping of the parent barium titanate compound for band gap tuning is challenging and consumes appreciable time and other valuable resources. This present work relates the influence of foreign material incorporation into the parent barium titanate with the corresponding energy band gap by developing extreme learning machine- (ELM-) based models and hybridization of support vector regression (SVR) with gravitational search algorithm (GSA) using the structural lattice distortion that emanated from doping as model descriptors. The developed gravitationally optimized SVR (GSVR) is characterized with a low value of mean absolute error (MAE), mean absolute percentage error (MAPE), and root mean square error (RMSE) of 0.036 ev, 1.145 ev, and 0.122 ev, respectively. The developed GSVR model outperforms ELM-Sine and ELM-Sig models using various performance evaluators. The developed GSVR model investigates the significance of iodine and samarium incorporation on the band gap of the parent barium titanate and the attained energy gaps conform excellently to the experimentally reported values. The demonstrated precision of the developed GSVR as measured from the closeness of its estimates with the measured values provides a quick and accurate method of energy gap characterization with circumvention of experimental stress and conservation of valuable time as well as other resources.
Highlights
Barium titanate represents a kind of ferroelectric ceramic multifunctional compound with promising potentials in lead-free devices such as thermistors, electromechanical devices, multilayer capacitors, and electrooptical devices [1, 2]. ermal stability, prominent piezoelectricity constant, excellent dielectric constant, and environmental friendliness are unique features that facilitate the application of barium titanate as an electronic component as well as photocatalyst [3]. e choice of barium titanate semiconductor becomes necessary since the most widely explored titanium-based semiconductors are of low quantum efficiency and resisted within UV illumination
After the doping, the incorporated dopants induce the following modifications on the barium titanate: (a) negative and positive shift of the conduction and valence band, respectively, which narrows the band gap; (b) induction of high stability when doping with nonmetallic heavy atom; (c) dopant localized states being established within semiconductor band gap; and (d) intramolecular energy levels being perturbed which mixes pure triplet and singlet states and (f ) distorts the crystal structure of the parent barium titanate semiconductor [6]
Extreme learning machine and combination of gravitational search algorithm (GSA) with support vector regression (SVR) computational methods are proposed in this work while the distortions as a result of the incorporated dopants serve as the predictors for band gap estimation
Summary
Received 19 March 2021; Revised 17 April 2021; Accepted 20 April 2021; Published 27 April 2021. Doping of the parent barium titanate compound for band gap tuning is challenging and consumes appreciable time and other valuable resources. Is present work relates the influence of foreign material incorporation into the parent barium titanate with the corresponding energy band gap by developing extreme learning machine(ELM-) based models and hybridization of support vector regression (SVR) with gravitational search algorithm (GSA) using the structural lattice distortion that emanated from doping as model descriptors. E developed GSVR model investigates the significance of iodine and samarium incorporation on the band gap of the parent barium titanate and the attained energy gaps conform excellently to the experimentally reported values. E demonstrated precision of the developed GSVR as measured from the closeness of its estimates with the measured values provides a quick and accurate method of energy gap characterization with circumvention of experimental stress and conservation of valuable time as well as other resources Doping of the parent barium titanate compound for band gap tuning is challenging and consumes appreciable time and other valuable resources. is present work relates the influence of foreign material incorporation into the parent barium titanate with the corresponding energy band gap by developing extreme learning machine(ELM-) based models and hybridization of support vector regression (SVR) with gravitational search algorithm (GSA) using the structural lattice distortion that emanated from doping as model descriptors. e developed gravitationally optimized SVR (GSVR) is characterized with a low value of mean absolute error (MAE), mean absolute percentage error (MAPE), and root mean square error (RMSE) of 0.036 ev, 1.145 ev, and 0.122 ev, respectively. e developed GSVR model outperforms ELM-Sine and ELM-Sig models using various performance evaluators. e developed GSVR model investigates the significance of iodine and samarium incorporation on the band gap of the parent barium titanate and the attained energy gaps conform excellently to the experimentally reported values. e demonstrated precision of the developed GSVR as measured from the closeness of its estimates with the measured values provides a quick and accurate method of energy gap characterization with circumvention of experimental stress and conservation of valuable time as well as other resources
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