Increasing the Efficiency of Organic Dye‐Sensitized Solar Cells over 10.3% Using Locally Ordered Inverse Opal Nanostructures in the Photoelectrode
Bibliographic record
Abstract
Abstract 3D inverse opal (3D‐IO) oxides are very appealing nanostructures to be integrated into the photoelectrodes of dye‐sensitized solar cells (DSSCs). Due to their periodic interconnected pore network with a high pore volume fraction, they facilitate electrolyte infiltration and enhance light scattering. Nonetheless, preparing 3D‐IO structures directly on nonflat DSSC electrodes is challenging. Herein, 3D‐IO TiO2 structures are prepared by templating with self‐assembled polymethyl methacrylate spheres on glass substrates, impregnation with a mixed TiO2:SiO2 precursor and calcination. The specific surface increases from 20.9 to 30.7 m2 g−1 after SiO2 removal via etching, which leads to the formation of mesopores. The obtained nanostructures are scraped from the substrate, processed as a paste, and deposited on photoelectrodes containing a mesoporous TiO2 layer. This procedure maintains locally the 3D‐IO order. When sensitized with the novel benzothiadiazole dye YKP‐88, DSSCs containing the modified photoelectrodes exhibit an efficiency of 10.35% versus 9.26% for the same devices with conventional photoelectrodes. Similarly, using the ruthenium dye N719 as sensitizer an efficiency increase from 5.31% to 6.23% is obtained. These improvements originate mainly from an increase in the photocurrent density, which is attributed to an enhanced dye loading obtained with the mesoporous 3D‐IO structures due to SiO2 removal.
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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 | 0.001 |
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".