Modeling plasmonic scattering combined with thin-film optics
Bibliographic record
Abstract
Plasmonic scattering from metal nanostructures presents a promising concept for improving the conversion efficiency of solar cells. The determination of optimal nanostructures and their position within the solar cell is crucial to boost the efficiency. Therefore we established a one-dimensional optical model combining plasmonic scattering and thin-film optics to simulate optical properties of thin-film solar cells including metal nanoparticles. Scattering models based on dipole oscillations and Mie theory are presented and their integration in thin-film semi-coherent optical descriptions is explained. A plasmonic layer is introduced in the thin-film structure to simulate scattering properties as well as parasitic absorption in the metal nanoparticles. A proof of modeling concept is given for the case of metal-island grown silver nanoparticles on glass and ZnO:Al/glass substrates. Using simulations a promising application of the nanoparticle integration is shown for the case of CuGaSe(2) 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".