Design of plasmonic enhanced silicon-based solar cells
Bibliographic record
Abstract
We report a novel plasmonic solar cell design implemented on an amorphous silicon platform. The enhancement of the scattering and trapping of the light is achieved by embedding nano-metallic cubic particles within the cell’s junction. Amorphous silicon cell with a thickness of 1200nm is used. The spectral absorption of the silicon cell is limited to wavelengths larger than 1.1 u. Our proposed solar cell has a p-i-n configuration, with the amorphous silicon as the photo-active layer. Silver cubic nanoparticles are embedded at different locations within the photoactive layers of the solar cell. With the use of an FDTD simulator, we are able to characterize the optical performance of the solar cell. Our results show that the plasmonic properties of the cubic nanoparticles are more attractive for sensing applications compared to the traditional spherical configuration. The geometry of the cubic nanoparticles enables control over plasmon resonances both in the resonant wavelength and the degree of field enhancement. This is done by improving the refractive-index sensitivity on a thin silicon film, as well as increasing the scattering and trapping of light. Our simulations predict that the silver metallic nanoparticles will enhance the solar cell efficiency, by optimizing the plasmonic properties of the silver nanocube monolayer. We have achieved a 67% increase (from 7.5% to 12.5%) in the cell’s efficiency by adding plasmonics to traditional amorphous p-i-n solar cell.
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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 | 0.001 | 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".