Low density of gold nanorods in the anodic layer for enhancing the efficiency of organic solar cells
Bibliographic record
Abstract
The effect of using an anodic layer with low density (~ 6x108 cm-2) of gold nanorods (GNR) in organic bulk heterojunction poly(3-hexylthiophene) (P3HT) and phenyl-C61-butyric acid methyl ester (PCBM) solar cells was studied. GNRs were deposited using several techniques, which produced various densities of GNRs on the anode layer. The anodic layers were characterized microscopically and spectroscopically. The power conversion efficiency and the short-circuit current for experimental devices incorporating GNR anodic layer showed an enhancement of up to 18% as compared to the control device. The results suggest that the electric field in the P3HT:PCBM active layer was increased by the localized surface plasmon resonances in GNRs. The increase in the electric field enhanced the photo-generation of excitons in the active layer near the plasmon peak, which improved the short-circuit current and the overall power conversion efficiency. Interestingly, photovoltaic devices with a low density of GNRs in the anodic layer showed an increase in the power conversion efficiency that was superior to that of devices with a higher density of GNRs in the anodic layer. This suggests that although the anodic layer with a higher density of GNRs absorbed more light, part of this light was confined in the anodic layer itself, and prevented from reaching the active layer of the bulk heterojunction device. In such cases, the power conversion efficiency was even found to be decreased with respect to the value for the control device.
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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) | 0.001 | 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 | 0.001 | 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 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".