Numerical Simulation of Lead-Free Cs₂AgBiBr₆ Double Perovskite Solar Cells: Performance Limits and Illumination Geometry Effects
Bibliographic record
Abstract
This study employs numerical simulation through SCAPS-1D to rigorously evaluate the performance potential of lead-free Cs₂AgBiBr₆ double perovskite solar cells, with three specific objectives: (1) determining the theoretical efficiency limit via systematic optimization, (2) identifying critical performance-limiting factors, particularly recombination losses, and (3) assessing the impact of illumination geometry as a secondary factor relevant to bifacial and tandem applications.By carefully changing the material properties and the device's design, a maximum power conversion efficiency (PCE) of 17.53% is reached under standard AM 1.5G lighting (open-circuit voltage (VOC) = 0.887 V, short-circuit current density (JSC) = 25.31mA/cm² , fill factor (FF) = 78.05%).When the light comes from the other side, the device's performance doesn't change much (< 0.4% in PCE).This shows that the direction of the light doesn't matter in structures that are symmetric and well-optimized.This is important for the designs of bifacial and tandem cells.A thorough analysis backs up the optical model and shows that the high performance is because of the effective suppression of bulk and interfacial recombination.This study sets a clear standard for performance and gives a detailed plan for how to make Cs₂AgBiBr₆ photovoltaics that are both efficient and good for the environment.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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".