Modelling energy conversion mechanisms in semiconductor solar cells using quantum physics
Abstract
The efficiency of semiconductor solar cells remains fundamentally constrained by classical models, particularly the Shockley–Queisser (SQ) limit. To overcome this bottleneck, this study proposes a quantum-physics-based modeling framework for analyzing energy conversion mechanisms in emerging photovoltaic materials. A synthetic dataset encompassing ten semiconductors ranging from traditional silicon to novel perovskites and metal oxides was constructed based on key parameters: bandgap energy, quantum efficiency, crystal structure, and conversion efficiency. Statistical analyses revealed that quantum efficiency is the strongest linear predictor of performance, with a Pearson correlation coefficient of +0.98. Conversely, bandgap energy showed a moderate negative correlation (−0.43), consistent with theoretical expectations that efficiency peaks near the 1.3–1.6 eV range. The results are validated against contemporary advances in charge transport optimization, tandem architectures, and quantum dot integration, as evidenced by recent literature. This study contributes a multivariable modeling approach that synthesizes quantum mechanical principles with empirical design, offering predictive insights for material selection and device optimization. The findings advocate for integrative strategies that span photon absorption, carrier transport, and band engineering. This work lays a foundation for further investigations that incorporate exciton dynamics and temperature effects,…
Read the paper
Related studies
- Chaos and quantum thermalization
- The Dalton quantum chemistry program system
- Quantum dots: synthesis, bioapplications, and toxicity
- Cord Blood Leptin and Adiponectin as Predictors of Adiposity in Children at 3 Years of Age: A Prospective Cohort Study
- A Guide to In Silico Drug Design
- Quantitative metabolomics by H-NMR and LC-MS/MS confirms altered metabolic pathways in diabetes
Go deeper in Evidence Hub
Evidence Hub adds a study data sheet (design, sample size, evidence level, key numbers), an AI summary and a chat grounded in the paper's text.