Dye-sensitized solar cells based on anatase- and brookite-TiO<sub>2</sub>: enhancing performance through optimization of phase composition, morphology and device architecture
Bibliographic record
Abstract
Abstract Herein, we demonstrate an optimization of dye-sensitized solar cells (DSSCs) through the development of single-layer and double-layer configurations. Focusing on the incorporation of brookite and anatase phases in varying ratios, the study aims to determine the optimal composition for enhanced photovoltaic performance. The active layer, composed of anatase- and brookite-TiO2 nanoparticles, is further modified with a scattering layer comprising a mixture of anatase nanoparticles and brookite-TiO2 in the form of nanocube or rice-like particles. The synthesis of TiO2 nanostructures with various morphologies and phase compositions and their subsequent application in single-layer and double-layer DSSCs are presented. The results highlight the superior light-harvesting capabilities achieved through the strategic incorporation of brookite phase into the anatase phase, emphasizing the importance of optimizing the anatase: brookite ratio. The single-layer DSSCs exhibit a peak efficiency of 8.73%, achieved with a composition of 30 wt.% brookite and 70 wt.% anatase at a thickness of 15 μms. In the context of double-layer DSSCs, the combined optimization of the active layer composition, scattering layer morphology, and utilization of anatase nanoparticles leads to a remarkable efficiency of 9.18%. These findings underscore the critical role of composition and morphology in enhancing the performance of DSSCs, showcasing the potential for brookite-based DSSCs in solar energy conversion.
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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".