Temperature and Spectral Effects on Perovskite Solar Cells: A SCAPS-1D Simulation Study
Bibliographic record
Abstract
This study indicates that SCAPS-1D software simulated perovskite solar cells to monitor their temperature and light wavelength reactions under monochromatic light at 400 nm, 450 nm, and 500 nm.The short-circuit current density (Jsc), open-circuit voltage (Voc), fill factor (FF %), and power conversion efficiency (PCE) were assessed across a temperature range of 290K to 340K.At 290K, the peak PCE values attained were 25.15% at 450 nm, 24.71% at 500 nm, and 22.30% at 400 nm; however, the open-circuit voltage (Voc) diminished by almost 20% on average across all wavelengths when the temperature rose to 340K.Longer wavelengths, particularly at 450 nm and 500 nm, enhanced performance marginally-by approximately 2-3% in power conversion efficiencyrelative to 400nm, owing to greater light penetration and less surface recombination.Nonetheless, elevated temperatures markedly diminished Voc, FF %, and PCE, chiefly due to heightened recombination and reverse saturation current.At 300K, the fill factor (FF %) reached a maximum of 85.3% for 450 nm, 85.3% for 500 nm, and 85.0% for 400 nm, thereafter decreasing to approximately 84.0-84.5%.The results validate that heat degradation and spectrum response are critical determinants influencing the performance of perovskite solar cells.This study underscores the necessity of enhancing thermal stability and spectrum absorption to get high-efficiency and stable perovskite solar cells.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".