Optimizing Plasmonic Silicon Photovoltaics with Ag and Au Nanoparticle Mixtures
Bibliographic record
Abstract
The effects of size and surface coverage of gold nanoparticles (Au NPs) on the performance of modified silicon photovoltaic (Si PV) devices were investigated. An increase in external quantum efficiency (EQE) above 600 nm (relative to the unmodified Si PV) was observed for PV devices modified with Au NPs with an average diameter greater than 80 nm, and the maximum in enhanced photocurrent red-shifted as the Au NP size increased. A decrease in EQE was observed for wavelengths shorter than 600 nm, leading to minimum overall advantage in terms of electrical power generated by white light illumination relative to the unmodified standard. However, this negative effect was successfully minimized by adding silver (Ag) NPs to the surface of the modified PV device. A maximum ∼6% EQE enhancement was observed for PV devices modified with a mixture of metallic nanoparticles (Ag and Au) at the localized surface plasmon resonance (LSPR) wavelengths, and an overall increase in the white light power conversion efficiency of ∼5% was obtained.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".