Tunneling Oxide Passivated Contact (TOPCon) solar cells have emerged as a dominant technology in the global photovoltaic market due to their superior efficiency and long-term stability. However, the metallization process continues to rely predominantly on costly silver (Ag) pastes, making cost reduction a prerequisite for their continued large-scale deployment. In this study, nickel (Ni) particles were strategically incorporated into conventional Ag pastes to reduce noble metal consumption without compromising device performance. To address the inherent challenges of Ni-namely its lower bulk conductivity and work function mismatch with the silicon emitter-the Ni/Ag weight ratio was systematically optimized. SEM and EDS analyses confirmed a highly uniform distribution of Ag and Ni within the printed features. Notably, the Ag-Ni hybrid paste exhibited enhanced rheological properties, yielding grid lines with a higher aspect ratio compared to benchmark pure-Ag pastes. High-resolution TEM images of the contact interface revealed a unique morphology where Ni particles were encapsulated by Ag colloids, preventing direct Ni-Si contact, whereas interfacial gaps were effectively filled by glass frit. At an optimized Ni content of 10 wt%, the solar cells demonstrated optimal performance, achieving an average contact resistivity of 2.79 m Omega.cm2. In a mass-production environment, the fill factor (FF) and efficiency (E ff) exhibited only minimal reductions of 0.36% and 0.1%, respectively, compared to the reference cell. The optimized cells maintained a high conversion efficiency of over 26.5%, with an open-circuit voltage of 0.744 V, a short-circuit current density of 41.47 mA/cm2, and a FF of 87.10%. Furthermore, the robust reliability in internal cell-level testing exhibited less than 5% performance degradation after a 48-h damp heat test under the conditions of 45 degrees C/95% RH. These results demonstrate a viable pathway toward cost-effective metallization for next-generation TOPCon solar cells.
The efficiency of photovoltaic (PV) panels, determined by irradiance and operating temperature, depends on thermal interaction with the environment. Solar radiation is the main thermal input, whereas energy conversion, convection, and radiation fluxes are the primary heat dissipation mechanisms. Radiation fluxes are based on boundary temperatures, such as ground and sky temperature, making accurate sky temperature estimation essential. However, despite its importance, there is limited work comparing the available models, which are often either very simplified or highly complex and adapted to specific geographic conditions. This article examines five sky temperature models and their impact on estimating the operating temperatures of PV panels. Experimental validation shows that variations in sky temperature estimates lead to significant differences in panel operating temperatures. Although the effect on energy generation estimates is smaller, the findings highlight the importance of accurate sky temperature models for better PV performance predictions. This study provides a basis for improving PV energy yield forecasts through enhanced sky temperature modeling.
ABSTRACT This article presents a benchmarking of indicators for automated defect detection in photovoltaic modules based on I‐V curves. The methodology is structured in three stages. First, an ideal defect‐free I‐V curve is generated through simulation to provide a reference for comparison with experimentally measured I‐V data under standard test conditions. Second, several electrical and curve‐shape indicators are computed to quantify the deviation between simulated and measured curves. Electrical parameters are expressed as ratios between experimental and simulated values, whereas curve‐shape descriptors integrate the comparison directly into their formulation. Finally, a comprehensive feature selection strategy is applied; filter (Pearson correlation and Mutual Information) and wrapper methods (Recursive Feature Elimination, Permutation Importance, and Boruta) are tested across several machine learning classifiers, including Decision Trees, Support Vector Classifiers, K‐nearest neighbors, Random Forests, Gradient Boosting, XGBoost, and LightGBM. The approach is evaluated using an experimental I‐V curve database generated by the GdS‐Optronlab research group. Results consistently discard indicators such as the open‐circuit voltage ratio and short‐circuit current ratio, while highlighting the current at the maximum power point ratio, the fill factor, and the mutual information of the maximum power point.
We have studied the potential of (GaAs)(Ge) alloys as a photovoltaic material. Solar cells and single crystal films have been grown by molecular beam epitaxy on GaAs (001) substrates. Even with a moderate Ge content (6.5%), a large reduction of the band gap is obtained (from 1.42 to 1 eV). Intense room temperature luminescence is found for n-type samples. Both n-type and p-type samples have high carrier densities, high compensation, and consequently low mobilities. X ray diffraction results indicate a large positive deviation from Vegard's law. Density functional modeling suggests that this deviation from Vegard's law is evidence of the formation of a true alloy, rather than a nanoscale mixture of GaAs and Ge domains. The latter possibility is also discarded by transmission electron microscopy. Energy dispersive x-ray spectroscopy indicates that films have As deficiency, even when using a 14 to 1 V/III flux ratio, presumably due to the presence of Ge at the surface inhibiting As incorporation. Cross-sectional scanning tunneling microscopy reveals the presence of vacancies and antisites. It is also found that Ge can substitute both Ga and As sites, with a prevalence of the former sites in n-type samples. Heterojunction solar cell devices were fabricated with 0.76-V open circuit voltage and an internal quantum efficiency above 40% at energies below the bandgap of the GaAs emitter.
