Artificial neural network modeling for potential performance enhancement of a planar perovskite solar cell with a novel TiO<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" id="d1e2458" altimg="si198.svg"><mml:msub><mml:mrow/><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msub></mml:math>/SnO<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" id="d1e2466" altimg="si198.svg"><mml:msub><mml:mrow/><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msub></mml:math> electro

Oboh, Innocent O.; Offor, Uchechukwu Herbert; Okon, Nsikakabasi D.

doi:10.1016/j.egyr.2021.12.010

Energy Reports

2022

DOI: 10.1016/j.egyr.2021.12.010

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Artificial neural network modeling for potential performance enhancement of a planar perovskite solar cell with a novel TiO2/SnO2 electro

Innocent O. Oboh

Uchechukwu Herbert Offor

Nsikakabasi D. Okon

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Cited by 9 publications

References 37 publications

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Investigating the Performance of FASnI₃‐Based Perovskite Solar Cells with Various Electron and Hole Transport Layers: Machine Learning Approach and SCAPS‐1D Analysis

Khan,

Ahmed

2024

Advcd Theory and Sims

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In this study, tungsten disulfide (WS2) as an electron transport layer (ETL) and zinc phosphide (Zn3P2) as a hole transport layer (HTL) are incorporated to improve the performance of the FASnI3‐based perovskite solar cell (PSC). The solar cell capacitance simulator in one dimension (SCAPS‐1D) is used to investigate the photovoltaic (PV) performances of the heterojunction Al/FTO/WS2/FASnI3/Zn3P2/Ni solar structure. The performance metrics of proposed device with numerous ETLs and HTLs are discussed. The suggested device provides appropriate band structures, which in turn potentially reduce minority electron recombination, thereby enhancing overall performances. Influences of various physical parameters such as thickness, doping concentration, bulk defect, interface defect states, work function, and back surface recombination velocity (BSRV) on the device performances have also been analyzed. An efficiency of 29.81% is achieved at the optimum thicknesses of 0.05 µm for WS2 ETL, 1.0 µm for FASnI3 absorber, and 0.1 µm for Zn3P2 HTL. Furthermore, a machine learning algorithm is used to assess the impact of multiple semiconductor parameters, and found that defect density influences the most. This model, which has an approximate correlation coefficient (R2) of 0.937, can predict the data with precision. Therefore, these numerical outcomes will help researchers further design and manufacture a low‐cost and highly efficient FASnI3‐based PSC.

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Investigating the Performance of FASnI₃‐Based Perovskite Solar Cells with Various Electron and Hole Transport Layers: Machine Learning Approach and SCAPS‐1D Analysis

Khan,

Ahmed

2024

Advcd Theory and Sims

View full text Add to dashboard Cite

show abstract

Cyanobacteria blue-green algae prediction enhancement using hybrid machine learning–based gamma test variable selection and empirical wavelet transform

Heddam

Yaseen‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬

Falah

et al. 2022

Environ Sci Pollut Res

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A review of recent developments in the application of machine learning in solar thermal collector modelling

Vakili

Salehi

2022

Environ Sci Pollut Res

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Artificial neural network modeling for potential performance enhancement of a planar perovskite solar cell with a novel TiO2/SnO2 electro

Cited by 9 publications

References 37 publications

Investigating the Performance of FASnI₃‐Based Perovskite Solar Cells with Various Electron and Hole Transport Layers: Machine Learning Approach and SCAPS‐1D Analysis

Investigating the Performance of FASnI₃‐Based Perovskite Solar Cells with Various Electron and Hole Transport Layers: Machine Learning Approach and SCAPS‐1D Analysis

Cyanobacteria blue-green algae prediction enhancement using hybrid machine learning–based gamma test variable selection and empirical wavelet transform

A review of recent developments in the application of machine learning in solar thermal collector modelling

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Resources

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Artificial neural network modeling for potential performance enhancement of a planar perovskite solar cell with a novel TiO2/SnO2 electro

Cited by 9 publications

References 37 publications

Investigating the Performance of FASnI3‐Based Perovskite Solar Cells with Various Electron and Hole Transport Layers: Machine Learning Approach and SCAPS‐1D Analysis

Investigating the Performance of FASnI3‐Based Perovskite Solar Cells with Various Electron and Hole Transport Layers: Machine Learning Approach and SCAPS‐1D Analysis

Cyanobacteria blue-green algae prediction enhancement using hybrid machine learning–based gamma test variable selection and empirical wavelet transform

A review of recent developments in the application of machine learning in solar thermal collector modelling

Contact Info

Product

Resources

About

Investigating the Performance of FASnI₃‐Based Perovskite Solar Cells with Various Electron and Hole Transport Layers: Machine Learning Approach and SCAPS‐1D Analysis

Investigating the Performance of FASnI₃‐Based Perovskite Solar Cells with Various Electron and Hole Transport Layers: Machine Learning Approach and SCAPS‐1D Analysis