The Role of Random Texture Scattering on the Absorptance Enhancement in Halide Perovskite Layers
Bibliographic record
Abstract
Hybrid perovskites are a class of thin-film semiconductors with remarkably steep absorption edges and high absorption coefficient. In the case of solar cells, a film thickness of less than a micrometer is usually sufficient to absorb most of the light when combined with a back reflector. Otherwise, an efficient light trapping strategy may be desired, e.g., in the case of tandem or semitransparent cells. Traditionally, light trapping is accomplished by employing randomly nanotextured substrates. In this contribution, absorption enhancements due to not only nanorough but also microrough substrates and with or without additional gold coating are evaluated from the point of gains in photocurrent and from the point of view of valid optical models. We find that light trapping from nanotextured substrates follows mainly the Yablonovitch model, leading to an apparent shift of absorption edge. This contrasts with microrough substrates and also the remarkable efficient light trapping capabilities of bare layers due to their native surface roughness, where the path enhancement in this case is almost uniform, making the layer optically thicker by factor two or more. Light trapping optical models as well as analytical techniques are reviewed, and new insights are presented.
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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) | 0.001 | 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".