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<p>This work investigates and compares the performance of photovoltaic (PV) titanium dioxide (TiO<sub>2</sub>) nanowire/aluminum (Al)-doped cadmium sulfide (CdS) with TiO<sub>2</sub>/CdS quantum dots (QDs) non-doped based PV-cells, where the TiO<sub>2</sub> is used as a photoanode. The parameters (power and capacitance versus voltage, and conductance) of TiO<sub>2</sub> nanowire/Al-doped CdS were examined. The TiO<sub>2</sub> nanowire/Al-doped CdS enhanced the current density in the PV-cells. The TiO<sub>2</sub> nanowire promotes electron transfer to the CdS, while adding an aluminum dopant can generate more charge, increasing the electron–hole pair density by absorbing white light. The experimental relationships between current–power densities versus voltage for the PV-cells were developed using the Lambert function for modeling and improved by machine learning. This study presents the application of artificial neural networks (ANNs) for predicting the current–voltage (I–V) characteristics of photovoltaic (PV) cells. The series and shunt resistances of the TiO<sub>2</sub> nanowire/Al-doped CdS and TiO<sub>2</sub>/CdS quantum dots (QDs) based photovoltaic cells were determined using the Lambert equation. The results indicate that the TiO<sub>2</sub> nanowire/Al-doped CdS configuration reduced both series and shunt resistances, contributing to increased current output compared to the TiO<sub>2</sub>/CdS QDs-based cells.</p> Graphical Abstract <p></p>

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Machine learning-enhanced Lambert W modeling of TiO2 nanowire/Al-doped CdS QDs photovoltaic cells

  • 期刊论文

  • 发表日期：

    2025年8月9日

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  • Nejmeddine Yahyaoui,
  • Mokhtar Hjiri,
  • Salaheddine Mansouri,
  • Nazir Mustapha,
  • Wazirzada Aslam Farooq,
  • Fahrettin Yakuphanoglu

摘要

This work investigates and compares the performance of photovoltaic (PV) titanium dioxide (TiO2) nanowire/aluminum (Al)-doped cadmium sulfide (CdS) with TiO2/CdS quantum dots (QDs) non-doped based PV-cells, where the TiO2 is used as a photoanode. The parameters (power and capacitance versus voltage, and conductance) of TiO2 nanowire/Al-doped CdS were examined. The TiO2 nanowire/Al-doped CdS enhanced the current density in the PV-cells. The TiO2 nanowire promotes electron transfer to the CdS, while adding an aluminum dopant can generate more charge, increasing the electron–hole pair density by absorbing white light. The experimental relationships between current–power densities versus voltage for the PV-cells were developed using the Lambert function for modeling and improved by machine learning. This study presents the application of artificial neural networks (ANNs) for predicting the current–voltage (I–V) characteristics of photovoltaic (PV) cells. The series and shunt resistances of the TiO2 nanowire/Al-doped CdS and TiO2/CdS quantum dots (QDs) based photovoltaic cells were determined using the Lambert equation. The results indicate that the TiO2 nanowire/Al-doped CdS configuration reduced both series and shunt resistances, contributing to increased current output compared to the TiO2/CdS QDs-based cells.

Graphical Abstract