Enhancing the electronic and optical performance of dye-sensitized solar cells with alizarin-based dyes: DFT/TDDFT investigations
Bibliographic record
Abstract
Dye-sensitized solar cells (DSSCs) offer several advantages over traditional silicon-based solar cells, such as lower cost, versatility, and transparency. Titanium dioxide (TiO2) is widely used as a photocatalyst in DSSCs due to its chemical stability, high photocatalytic activity, photostability, and non-toxicity. This study provides a computational analysis of the geometric, electronic, optical, and photovoltaic properties of ten novel dyes using Density Functional Theory (DFT) and Time-Dependent DFT (TD-DFT). To our knowledge, these dyes have not been previously explored in the literature. Our findings indicate that structural modifications can significantly enhance the electronic, optical, and photovoltaic properties of these dyes. The B3LYP functional was identified as the most effective for predicting the geometric and electronic properties, while TD-DFT calculations with the CAM-B3LYP functional and the 6-31G(d,p) basis set accurately predicted the absorption properties. The absorption maxima of the dyes ranged from 427.82 nm to 755.93 nm, with strong UV-Vis absorption attributed to delocalized π-π* transitions. The calculated band gaps varied from 1.928 eV to 2.425 eV, showing that increased conjugation leads to reduced band gaps and improved dye performance. Open-circuit voltage (Voc) values for TiO₂ ranged from 0.893 eV to 1.38 eV, suggesting good potential for efficient electron injection into the TiO2 conduction band. In conclusion, the ten novel dyes studied exhibit significant potential for use in DSSCs, and the theoretical methods employed here offer a reliable framework for predicting the properties of other materials. This approach can guide the development of new materials designed to improve the performance of DSSCs.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".