Enhancement of optoelectronic properties via substitutional doping of Cu, in and Ag in SnS nanorods for thin film photovoltaics

Baby, Benjamin Hudson; Thomas, Alphi Maria; Amrutha, E G; Mohan, Devendra

doi:10.1016/j.solener.2020.05.076

Cited by 16 publications

(7 citation statements)

References 46 publications

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“…Hybridization of valence and conduction bands as a result of dopants incorporation also influences the value of energy gap. The estimates of the developed STRA and GA-SVR models have excellent agreement with the measured values [31,104,108]. The deviations observed in the estimates of STRA model as compared with that of GA-SVR model may be attributed to the failure of STRA-based to account for nonlinearity behavior existing between lattice distortion of doped…”

Section: Computation and Comparison Of Thementioning

confidence: 56%

“…The reason for reduced theoretical photoconversion efficiency can be attributed to short carrier lifetime, band alignment and offset, diffusion length, crystalline lattice defects, presence of other tin sulfide phases [10,25], reduced purity, and fabrication flaws [26,27]. The performance of SnS as a material for absorbing solar energy can be enhanced by doping with Bi [28,29], Ag [30,31], Al [32], Fe [33], Cu [34], Sb [35,36], Ge [37], In [30,31], and Pb [38]. Different techniques have been reportedly employed in synthesizing SnS thin films.…”

Section: Introductionmentioning

confidence: 99%

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Modeling Energy Gap of Doped Tin (II) Sulfide Metal Semiconductor Nanocatalyst Using Genetic Algorithm‐Based Support Vector Regression

Okoye

Azi

Owolabi

et al. 2022

Journal of Nanomaterials

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Tin (II) sulfide (SnS) is a metal chalcogenide semiconducting material with fascinating and admirable physical features for practical applications in solid-state batteries, photodetectors, gas sensors, optoelectronic devices, emission transistors, and photocatalysis among others. The energy gap of SnS semiconductor nanomaterial that facilitates its usefulness in many applications can be adjusted through dopant incorporation which results in crystal lattice distortion at various crystallite sizes of the semiconductor. This work employs lattice parameter descriptors to develop a hybrid genetic algorithm (GA) and support vector regression algorithm (SVR) intelligent model for determining the energy gap of doped SnS semiconductors. The predictive strength of the developed GA-SVR model is compared with the stepwise regression algorithm- (STRA-) based model using different performance evaluation parameters. The developed GA-SVR model performs better than STRA model based on root mean square error, mean absolute error, and correlation coefficient with performance improvement of 70.68%, 67.63%, and 20.98%, respectively, using the testing set of data. Influence of different dopants and experimental conditions on energy gap of SnS semiconductor were investigated using the developed model, while the obtained values for the energy gaps agree with the measured values. The developed models demonstrate high degree of potentials in terms of accuracy, precision, and ease of implementation that fosters their real-life applicability in estimating the energy gap of doped SnS semiconductor with experimental stress circumvention.

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Section: Computation and Comparison Of Thementioning

confidence: 56%

Section: Introductionmentioning

confidence: 99%

Modeling Energy Gap of Doped Tin (II) Sulfide Metal Semiconductor Nanocatalyst Using Genetic Algorithm‐Based Support Vector Regression

Okoye

Azi

Owolabi

et al. 2022

Journal of Nanomaterials

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“…In the O 1s spectrum ( Figure 3 c), a peak at 530.28 eV corresponded to lattice oxygen. In Figure 3 d, two peaks at 368 eV and 374 eV with a splitting energy of 6.0 eV belong to 3d 5/2 and 3d 3/2 of Ag(I) [ 26 , 27 ], respectively. The peak intensity of Ag 3d was enhanced with its doping concentration.…”

Section: Resultsmentioning

confidence: 99%

Visible-Light-Driven Ag-Doped BiOBr Nanoplates with an Enhanced Photocatalytic Performance for the Degradation of Bisphenol A

et al. 2022

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Based on the low utilization rate of visible light and the high-charge carriers-recombination efficiency of bismuth oxybromide (BiOBr), in this work, noble metal Ag was used to modify BiOBr, and Ag-doped BiOBr nanoplates (Ag-BiOBr) were obtained through a one-step hydrothermal method. Compared with BiOBr, the absorption edge of Ag-BiOBr showed a redshift from 453 nm to 510 nm, and the absorption efficiency of visible light was, obviously, improved. Bisphenol A (BPA) was chosen as the target pollutant, to evaluate the photocatalytic performance of the samples. Ag0.1-BiOBr showed the highest degradation efficiency. The intrinsic photocatalytic activity of Ag0.1-BiOBr, under visible light, was approximately twice as high as that of BiOBr. In this way, a new visible-light-driven photocatalyst was proposed, to fight against organic pollution, which provides a promising strategy for water and wastewater treatment.

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“…4(B) shows a rise in the reflection peak intensity for the (111) plane with increasing Cu concentration that clearly reveals the successful substitutional Cu atom doping on Sn lattice sites. The atomic radius of Cu (128 pm) is smaller than Sn (140 pm), then for substitutional doping, it should result in a decrease 54 in the lattice parameters of SnS. Furthermore, Fig.…”

Section: Structural Analysismentioning

confidence: 97%

Effect of copper doping on plasmonic nanofilms for high performance photovoltaic energy applications

Tariq,

Asghar,

Shifa

et al. 2023

Phys. Chem. Chem. Phys.

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Enhancement of optoelectronic properties via substitutional doping of Cu, in and Ag in SnS nanorods for thin film photovoltaics

Cited by 16 publications

References 46 publications

Modeling Energy Gap of Doped Tin (II) Sulfide Metal Semiconductor Nanocatalyst Using Genetic Algorithm‐Based Support Vector Regression

Modeling Energy Gap of Doped Tin (II) Sulfide Metal Semiconductor Nanocatalyst Using Genetic Algorithm‐Based Support Vector Regression

Visible-Light-Driven Ag-Doped BiOBr Nanoplates with an Enhanced Photocatalytic Performance for the Degradation of Bisphenol A

Effect of copper doping on plasmonic nanofilms for high performance photovoltaic energy applications

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