Investigating the influence of the counter Si-cell on the optoelectronic performance of high-efficiency monolithically perovskites/silicon tandem cells under various optical sources

Nowadays,tandem structures have become a valuable competitor to conventional silicon solar cells,especially for perovskite over silicon,as metal halides surpassed Si with tunable bandgaps,high absorption coefficient,low deposition,and preparation costs.This led to a remarkable enhancement in the overall efficiency of the whole cell and its characteristics.Consequently,this expands the usage of photovoltaic technology in various fields of applications not only under conventional light source spectrum in outdoor areas,i.e.,AM1.5G,but also under artificial light sources found indoors with broadband intensity values,such as Internet of things(Io Ts)applications to name a few.We introduce a numerical model to analyze perovskite/Si tandem cells(PSSTCs)using both crystalline silicon(c-Si)and hydrogenated amorphous silicon(a-Si:H)experimentally validated as base cells.All proposed layers have been studied with J-V characteristics and energy band diagrams under AM1.5G by using SCAPS-1D software version_(3).7.7.Thereupon,the proposed architectures were tested under various artificial lighting spectra.The proposed structures of Li_(4)Ti_(5)O_(12)/CsPbCl_(3)/MAPbBr_(3)/CH_(3)NH_(3)PbI_(3)/Si recorded a maximum power conversion efficiency(PCE)of 25.25%for c-Si and 17.02%for a-Si:H,with nearly 7%enhancement concerning the Si bare cell in both cases.

Assessing the optoelectronic behavior of soiled silicon photovoltaic cell under harsh environmental conditions

In the current study,a monocrystalline Si photovoltaic(PV) cell was modeled using solar cell capacitance simulator(SCAPS) to demonstrate the optoelectronic performance of the cell under harsh environmental conditions.Harsh conditions are simulated in terms of wind speed and temperature fluctuations within the presence of a dust layer.All models are evaluated with respect to a bare model with no dust layer accumulated and operating under standard test conditions(STC).Accordingly,the PV under-test characteristics have been estimated under continuous wind speed and temperature variations.An interesting behavior for the cell operation under relatively high temperatures with an accumulated dust layer was observed.The short circuit current increased by 61.5% with decreasing open-circuit voltage by 47.3%,showing an overall positive trend for the power harvested.Such behavior contradicts the average temperature performance of cells without dust layer accumulation.A detailed justification is illustrated,where the heat transfer rate with dust accumulation highlighted an incremental increase concerning the bare cell by 14.57%.

Optoelectronic devices informatics: optimizing DSSC performance using random-forest machine learning algorithm

This paper provides an attempt to utilize machine learning algorithm,explicitly random-forest algorithm,to optimize the performance of dye sensitized solar cells(DSSCs)in terms of conversion efficiency.The optimization is implemented with respect to both the mesoporous TiO_(2) active layer thickness and porosity.Herein,the porosity impact is reflected to the model as a variation in the effective refractive index and dye absorption.Database set has been established using our data in the literature as well as numerical data extracted from our numerical model.The random-forest model is used for model regression,prediction,and optimization,reaching 99.87%accuracy.Perfect agreement with experimental data was observed,with 4.17%conversion efficiency.