Enhanced performance of dye-sensitized solar cells using gold nanoparticles modified fluorine tin oxide electrodes
Bibliographic record
Abstract
We have investigated plasmon-assisted energy conversion in dye-sensitized solar cells (DSCs) applying gold nanoparticles (NPs) modified fluorine tin oxide (FTO) electrodes. A series of Au NPs with different sizes (15–80 nm) were synthesized and immobilized onto FTO glass slides. Photoanodes were prepared on these Au modified FTO substrates using P25 TiO 2 powders and by the screen-printing method. The size effects of Au NPs on the photovoltaic performance of the formed DSCs were investigated systematically. Structural and photoelectrochemical properties of the formed photoanodes were examined by field emission scanning electron microscopy and electrochemical impedance spectroscopy. It was found that the energy conversion efficiency of the DSC was highly dependent on the Au particle size. When the particle size was not greater than 60 nm, the DSC based on the Au NP-FTO composite electrode showed a higher short-circuit current density and better photovoltaic (PV) performance than the cell based on the bare FTO. The best cell was achieved using 25 nm sized Au NPs modified FTO. It exhibited a conversion efficiency of 6.69%, which was 15% higher than that of DSCs without Au NPs. The related PV performance enhancement mechanisms, photoelectrochemical processes and surface-plasmon resonances in DSCs with Au nanostructures are analysed and discussed.
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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 | 0.001 | 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".