Abstract
Copper is a commonly preferred dopant for cadmium telluride based solar cells. Even though it is widely used as it enhances the electrical properties, it has the tendency to diffuse into the CdTe layer as well as the CdS/CdTe junction interface which adversely affects the performance of CdTe solar cells. In this experimental study copper doping of buffer cadmium sulfide layer has been performed to analyse its effect on structural, electrical, and optical properties of CdS and CdTe layers. While from the X-ray diffraction analysis it was observed that there was reduction in peak intensities and crystallite sizes of both the CdS and CdTe layers with the increase in amount of copper dopant, from the electrical properties it was found that there were improvements in carrier concentration, mobility, and conductivity of both the layers. To mitigate the losses due to Cu doping, enhance the efficiency and stability of CdTe solar cells an extensive numerical modelling approach was undertaken to employ electron transport layers (ETL) and hole transport layers (HTL) to the copper-doped CdS/CdTe solar cells. We obtained optimum results with titanium dioxide and copper barium thiostannate as ETL and HTL respectively. Finally, CdTe-based solar cells were modelled integrating copper-doped CdS as the buffer layers, TiO2 as ETL and CBTS as HTL respectively. The obtained experimental values of Cu-doped CdS and CdTe layers were implemented into this model. This superstrate configuration yielded impressive output parameters: open circuit voltage of 1.07 V, short-circuit current density of 29.32 mA cm−2, fill factor of 85.08 %, and efficiency of 26.67 %.
Acknowledgments
The authors express their gratitude to to Prof. Marc Burgelman from ELSI, University of Gent, Belgium for the SCAPS software.
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Research ethics: Not applicable.
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Informed consent: Not applicable.
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Author contributions: The authors have accepted responsibility for the entire content of this manuscript and approved its submission.
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Use of Large Language Models, AI and Machine Learning Tools: None declared.
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Conflict of interest: The authors state no conflict of interest.
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Research funding: The authors acknowledge financial support from DST, India (SR/FST/ET-1/2021/862).
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Data availability: The datasets generated and/or analyzed during the current study are available from the corresponding author on reasonable request.
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Supplementary Material
This article contains supplementary material (https://doi.org/10.1515/ijmr-2024-0050).
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