Zr-doped TiO<sub>2</sub>nanoparticles synthesized via a sol–gel route and their application in dye-sensitized solar cells for thermo-stabilization
Bibliographic record
Abstract
Dye-sensitized solar cells (DSSCs) are regarded as one of the most promising solar cells amongst third-generation photovoltaic technologies, particularly due to their low cost, easy preparation, and minor environmental impact compared to earlier-generation devices. However, they have been challenged by thermal energy losses and low cell efficiencies. This work examined the incorporation of zirconium (Zr) into the crystal lattice of titania nanoparticles (nTiO 2 ) for subsequent fabrication into the photo anode of the DSSCs. The results showed that Zr doping of nTiO 2 inhibited the anatase-rutile phase transition. Higher calcination temperatures gave increased titania crystallinity, and stabilized the nTiO 2 aggregate pore structure and specific surface area, consequently improving the DSSC device performance. A doping concentration of 5 mol % Zr into the nTiO 2 demonstrated the best resistance against thermal degradation, achieving an optimized retention of specific surface area. Solar simulation confirmed the results, indicating better heat stability, although lowering the DSSC efficiencies.
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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".