Utilizing machine learning to enhance performance of thin-film solar cells based on Sb 2 (S x Se 1- x ) 3 : investigating the influence of material properties.
Tanvir Mahtab KhanOkba SaidaniSheikh Rashel Al AhmedPublished in: RSC advances (2024)
Antimony chalcogenides (Sb 2 (S x Se 1- x ) 3 ) have drawn attention as a potential semiconducting substance for heterojunction photovoltaic (PV) devices due to the remarkable optoelectronic properties and wide range of bandgaps spanning from 1.1 to 1.7 eV. In this investigation, SCAPS-1D simulation software is employed to design an earth abundant, non-toxic, and cost-effective antimony sulfide-selenide (Sb 2 (S,Se) 3 )-based thin-film solar cell (TFSC), where tungsten disulfide (WS 2 ) and cuprous oxide (Cu 2 O) are used as an electron transport layer (ETL) and hole transport layer (HTL), respectively. The PV performance parameters such as power conversion efficiency, open-circuit voltage ( V oc ), short-circuit current ( J sc ), and fill factor (FF) are assessed through adjustments in material properties including thickness, acceptor concentration, bulk defect density of the absorber, defect state of absorber/ETL and HTL/absorber interfaces, operating temperature, work function of the rear electrode, and cell resistances. This analysis aims to validate their collective impact on the overall efficiency of the designed Ni/Cu 2 O/Sb 2 (S,Se) 3 /WS 2 /FTO/Al TFSC. The optimized physical parameters for the Sb 2 (S,Se) 3 TFSC lead to impressive PV outputs with an efficiency of 30.18%, V oc of 1.02 V, J sc of 33.65 mA cm -2 , and FF of 87.59%. Furthermore, an artificial neural network (ANN) machine learning (ML) algorithm predicts the optimal PCE by considering five semiconductor parameters: absorber layer thickness, bandgap, electron affinity, electron mobility, and hole mobility. This model, which has an approximate correlation coefficient ( R 2 ) of 0.999, is able to predict the data with precision. This numerical analysis provides valuable data for the fabrication of an environmentally friendly, economical, and incredibly non-toxic efficient heterojunction TFSC.
Keyphrases
- solar cells
- machine learning
- neural network
- big data
- single cell
- artificial intelligence
- deep learning
- electronic health record
- optical coherence tomography
- cell therapy
- mental health
- data analysis
- magnetic resonance imaging
- working memory
- minimally invasive
- computed tomography
- physical activity
- mass spectrometry
- mesenchymal stem cells
- room temperature
- quantum dots
- perovskite solar cells