Effects of Graphene in Dye-Sensitized Solar Cells Based on Nitrogen-Doped TiO<sub>2</sub> Composite
Bibliographic record
Abstract
Graphene (GR) exhibits impressive photoelectric properties, including a large specific surface area, high charge-carrier mobility, high conductance, and fast electron transfer. In this study, the effect of GR on the performance of dye-sensitized solar cells (DSSCs) was investigated by mixing GR into N-doped TiO 2 photoelectrodes. GR/N-doped TiO 2 (GNT) nanoparticles were prepared using the sol–gel method. After preparation, the presence of GR in the photoelectrodes was confirmed using transmission electron microscopy (TEM), X-ray diffraction (XRD), and Raman spectroscopy analyses. After the addition of GR, the photoelectrodes displayed enhanced dye adsorption properties with lower internal resistances and faster transport times. Accordingly, DSSCs with these photoelectrodes generated high current density with a low electron-recombination rate. The maximum power conversion efficiency of DSSCs with GR/N-doped photoelectrodes was 9.32% with optimized DSSC parameters; this represents an enhancement of approximately 22% over that of DSSCs with N-doped photoelectrodes. The addition of excess GR weakened the crystallization of particles on the surface of photoelectrodes, which resulted in low dye adsorption and decreased efficiency of the DSSCs. In summary, the addition of GR promoted increased dye loading and enhanced DSSC efficiency. The optimal amount of GR for high-efficiency DSSCs was successfully determined in this study.
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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".