Enhancing the electronic and optical performance of dye-sensitized solar cells with alizarin-based dyes: DFT/TDDFT investigations
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 it