Improving photovoltaic devices using silver nanocubes
Bibliographic record
Abstract
We present a design of implementing plasmonic nanoparticles made from silver onto the surface of amorphous silicon based solar cells. When adding these silver nanoparticles we expect to see enhancements to the solar cells due to the plasmonic effects induced by the metal nanoparticles. The nanoparticles are used as subwavelength scattering elements to couple and trap light within the cell. In addition, the excited surface plasmon-polaritons promote a strong localized field enhancement which increases the cells ability to absorb light. Our choice of geometry of the nanoparticle is cubic rather than the traditional spherical geometry. We expect to see the cell perform better with the cubic shape due to the larger surface area it spans. We investigate the effects of these particles on to the performance of the solar cells, as well as introduce an intrinsic layer between the active p and n region creating a p-i-n solar cell configuration. We report the use of an FDTD simulator to characterize the optical performance of the solar cell. Both cubical and spherical nanoparticles made from silver were studied. Our simulations predict an overall increase of 67% (from 7.5% to 12.5) based on the p-i-n configuration with inclusion of the plasmonic particles onto the surface of the cells. Experimentally we verified the results by first fabricating a crystalline silicon-based solar cell with a p-n configuration and then placing the silver nanocubes onto the surface of the cell. An overall increase of about 28% was experimentally demonstrated (from 3.97% to 5.081%). We anticipate further increases with the p-i-n configuration.
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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".