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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".