Towards High-Efficiency Photon Trapping in Thin-Film Perovskite Solar Cells Using Etched Fractal Metadevices
Bibliographic record
Abstract
Reflective loss is one of the main factors contributing to power conversion efficiency limitation in thin-film perovskite solar cells. This issue has been tackled through several approaches, such as anti-reflective coatings, surface texturing, or superficial light-trapping metastructures. We report detailed simulation-based investigations on the photon trapping capabilities of a standard Methylammonium Lead Iodide (MAPbI3) solar cell, with its top layer conveniently designed as a fractal metadevice, to reach a reflection value R<0.1 in the visible domain. Our results show that, under certain architecture configurations, reflection values below 0.1 are obtained throughout the visible domain. This represents a net improvement when compared to the 0.25 reflection yielded by a reference MAPbI3 having a plane surface, under identical simulation conditions. We also present the minimum architectural requirements of the metadevice by comparing it to simpler structures of the same family and performing a comparative study. Furthermore, the designed metadevice presents low power dissipation and exhibits approximately similar behavior regardless of the incident polarization angle. As a result, the proposed system is a viable candidate for being a standard requirement in obtaining high-efficiency perovskite solar cells.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".