Optimizing Dye Adsorption in Graphene–TiO<sub>2</sub>Photoanodes for the Enhancement of Photoconversion Efficiency of DSSC Devices
Bibliographic record
Abstract
We report on the effect of graphene (G) incorporation in TiO2photoanodes (PAs) for improving their dye adsorption capacity, which in turn impacts the overall photoconversion efficiency of dye-sensitized solar cells (DSSCs). By varying the graphene content of TiO2PAs (over the 0.05–1 wt.% range), the power conversion efficiency (PCE) of the DSSCs was found to increase significantly from 3.0% for standard TiO2to a maximum value of 8.2% for PAs containing 0.1 wt. % of graphene. This corresponds to a PCE improvement of ∼173% in comparison with standard DSSCs made with TiO2alone (without graphene). On the other hand, by performing thermogravimetric analyses to quantify the dye adsorption capacity of the TiO2–G PAs, we were able to establish, for the first time, a direct correlation between the PCE of the DSSCs and the dye uptake of their G–TiO2-based PAs. Our results demonstrate that by coupling the optimal PA (with the optimal graphene content of 0.1 wt.% and optimal sensitization with a solution of 4 mM of N719) with Co–Ni nanoparticles decorated multi-walled carbon nanotubes (MWCNTs) based counter electrodes, we were able to achieve DSSCs exhibiting a PCE as high as 9.8%. This performance is quite impressive particularly considering that no platinum was used in the counter electrode.
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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 | 0.001 |
| 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".