Ag nanoparticle-decorated single wall carbon nanotube films for photovoltaic applications
Bibliographic record
Abstract
We report on the use of pulsed laser deposition to decorate single wall carbon nanotube (SWCNTs) films with Ag nanoparticles (NPs) in hybrid SWCNTs/n-Si photovoltaic (PV) devices. PV devices are built by coating n-Si surfaces with a controlled density of a SWCNTs’ suspension via an air-brush method. By adjusting the number of laser pulses (N Lp) from the KrF laser, we are able to control the size and density of the Ag NPs covering the SWCNTs films. Through adjustment of N Lp, we are able to improve the power conversion efficiency of the SWCNTs/n-Si devices from 3.5 to over 6 % at N Lp = 1250 and the corresponding fill factor (FF) from 35 to 60 %. This increase is shown to be correlated with Raman, electrical and optical properties of the Ag NPs-coated SWCNTs films. UV–Vis spectroscopy measurements show the presence of optical scattering that is directly attributed to the presence of plasmon in the range of 450–600 nm and internal quantum efficiency measurement shows significant improvement over this range. In addition to this direct increase of the generate photocurrent, the overall Ag NPs film resistance is sufficiently lowered to ensure higher FF and thus a higher PCE. Beyond the optimal value of N Lp > 1250, we show that the decreasing PCE is caused by low optical transmission of the Ag NPs films and poorer rectification.
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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".