Optoelectronic performance and artificial neural networks (ANNs) modeling of n-InSe/p-Si solar cell

Darwish, A.A.A.; Hanafy, T. A.; Attia, A. A.; Habashy, D. M.; El-Bakry, Mahmoud Y.; El-Nahass, M.M.

doi:10.1016/j.spmi.2015.03.033

Cited by 36 publications

(12 citation statements)

References 27 publications

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“…a and b allowed determination of the E g values as 1.44, 1.85, and 1.52 eV, respectively. The values of the energy bandgap for CdSe and InSe are consistent with the literature data . The difference between the energy bandgaps of the CdSe and InSe/CdSe interface Δ E g is 0.33 eV.…”

Section: Resultssupporting

confidence: 89%

“…Owing to the remarkable optical properties they exhibit , the cadmium selenide and InSe thin films are attractive materials in optoelectronic technology. The quantum dot‐sensitized solar cells (QDSCs) of CdSe displayed high power conversion efficiency of 3.26 ± 0.10% under one sun illumination .…”

Section: Introductionmentioning

confidence: 99%

“…On the other hand, the n‐InSe/p‐Si heterojunction is reported to exhibit a photovoltaic characteristic with a power conversion efficiency of 3.42% . In addition, InSe thin films are also observed to have featured characteristics in thin‐film transistor technology.…”

Section: Introductionmentioning

confidence: 99%

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Optical interactions in the InSe/CdSe interface

Qasrawi

Rabbaa

2015

Phys. Status Solidi B

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In this work, the structural and optical properties of the InSe/ CdSe heterojunction are investigated by means of X-ray diffraction and ultraviolet-visible light spectrophotometry techniques. The hexagonal CdSe films that were deposited onto amorphous InSe and onto glass substrates at a vacuum pressure of 10 À5 mbar, exhibited interesting optical characteristics. Namely, the absorption, transmission, and reflection spectra that were recorded in the incident light wavelength range of 300-1100 nm, for the InSe, CdSe, and InSe/CdSe interface revealed direct allowed transition energy bandgaps of 1.44, 1.85, and 1.52 eV, respectively. The valence-band offset for the interface is found to be 0.36 eV. On the other hand, the dielectric constant spectral analysis displayed a large increase in the real part of the dielectric constant associated with decreasing frequency below 500 THz. In addition, the optical conductivity spectra that were analyzed and modeled in accordance with the Drude theory displayed a free-carrier average scattering time of 0.4 fs and a drift mobility of 6.65 cm 2 V À1 s À1 for the InSe/CdSe interface. The features of this interface nominate it as a promising member for the production of optoelectronic Schottky channels and as thinfilm transistors. 1 Introduction Owing to the remarkable optical properties they exhibit [1][2][3][4][5][6][7], the cadmium selenide and InSe thin films are attractive materials in optoelectronic technology. The quantum dot-sensitized solar cells (QDSCs) of CdSe displayed high power conversion efficiency of 3.26 AE 0.10% under one sun illumination [2]. In addition, the ZnTe/CdSe QD-based QDSCs showed a champion power conversion efficiency of 7.17% and an efficiency of 6.82% under one full sun illumination [3]. Moreover, the CdTe/ CdSe nanocrystal (NC) solar cells with the inverted structure (ITO/ZnO/CdSe/CdTe/Au) that had been fabricated by the simple solution process coupled with the layer-by-layer sintering technique displayed an annealing strategydependent device performance [4]. The efficiency of these NC devices reached 5.81% at an annealing temperature of 340 8C. Furthermore, very small size (3-5 nm) particles of zinc-blende cadmium selenide nanocrystals modified with oleic acid showed an NC solar cell efficiency of $1.08% [5]. The most beneficial property of the last two types is the ability of preparation from solutions.On the other hand, the n-InSe/p-Si heterojunction is reported to exhibit a photovoltaic characteristic with a

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Section: Resultssupporting

confidence: 89%

Section: Introductionmentioning

confidence: 99%

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Optical interactions in the InSe/CdSe interface

Qasrawi

Rabbaa

2015

Phys. Status Solidi B

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“…However, these conditions are not always obvious, occurring seldom outside, because they are mainly carried out under conditions of the laboratory by using a solar simulator matirials. Consequently, to carry out a characterization appropriate to the behavior of electric modules regular minutes (obtaining curves I-V and P-V), recently, several authors [5][6] are used the artificial intelligence technics such as the fuzzy logic [5][6][7] and the artificial neuron networks (ANN) [2,[6][7][8][9][10][11][12][13] to modeling OPV cells. This approach is logical if one were to consider the dependence of the solar cell to any variations conditions of the environment [8].…”

Section: Introductionmentioning

confidence: 99%

Neural Network Modeling and Experimental Evaluation of Organic Solar Panel Performance in Algerian Sahara

Ghaitaoui¹,

Benatiallah²,

Khachab³

et al. 2019

EJEE

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In this paper, the characterization and modeling results of the electrical parameters of the tendem organic photovoltaic cells (infinityPV) are presented. The electrical performances of this organic cell's module (I-V and P-V) are characterized and analyzed in the weather condition of southwest Algeria (Adrar site). A program in MATLAB language is developed locally for modeling the organic cell, using artificial intelligence. The multilayer feed forward perception type ANN is used for extraction of the organic photovoltaic model performances, and the back-propagation algorithm is used for the program training. The ANN obtained results by the simulation showed a good conformity with the results obtained experimentally in the saharan climate (MAPE:0.68%, MBE:4.5018e-09 A and RMSE:1.8062e-04 A).

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“…Recently, a great deal of attention has been paid to use such fascinating networks in solving many problems that encounter physicists due to lack of facilities. Developments in ANNs and their applications to physics have made it feasible to model numerous relations in different areas in physics (Attia, El-Nahass, El-Bakry, & Habashy, 2013;Bourouis, Meddour, & Moussaoui, 2006;Darwish et al, 2015;El-Bakry, 2003, 2004El-Metwally, Haweel, & El-Bakry, 2000;Haweel, El-Bakry, & El-Metwally, 2003). The ANNs architecture is inspired by the structure of the human brain, which is not quite the same as the PCs utilized today (Tabet & McGahan, 2000).…”

Section: Introductionmentioning

confidence: 99%

Optical constants characterization of As 30 Se 70−x Sn x thin films using neural networks

Attia

El-Bana

Habashy

et al. 2017

Journal of Applied Research and Technology

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This paper uses an artificial neural network (ANN) and resilient back-propagation (Rprop) training algorithm to determine the optical constants of As 30 Se 70−x Sn x (0 ≤ x ≤ 3) thin films. The simulated values of the ANN are in good agreement with the experimental data. The ANN models performance was also examined to predict the simulated values for As 30 Se 67 Sn 3 which was not included in the training and was found to be in accordance with the experimental data. The high precision of the ANN models as well as a great guessing performance have been exhibited. Moreover, the energy gap E g of As 30 Se 70−x Sn x (0 ≤ x ≤ 9) thin films were calculated theoretically.

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Optoelectronic performance and artificial neural networks (ANNs) modeling of n-InSe/p-Si solar cell

Cited by 36 publications

References 27 publications

Optical interactions in the InSe/CdSe interface

Optical interactions in the InSe/CdSe interface

Neural Network Modeling and Experimental Evaluation of Organic Solar Panel Performance in Algerian Sahara

Optical constants characterization of As 30 Se 70−x Sn x thin films using neural networks

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