Optimization of donor units in push–pull thieno[3,2-b]thiophene-based organic dyes: A dft/td-dft study of nineteen candidates for enhanced photovoltaic performance
Bibliographic record
Abstract
Designing high-efficiency sensitizers remains a central challenge for dye-sensitized solar cells (DSSCs). In this work, nineteen donor motifs commonly employed in the literature were screened to identify optimal donors and to assess the impact of donor substitution on the reference dye D0 (2-cyano-3-[2′-(2″-(4″-(dimethylamino)phenyl)ethynyl)thieno[3,2-b]thiophen-5′-yl]acrylic acid) with the goal of improving device performance. Using DFT/TD-DFT with the CAM-B3LYP functional, we characterized nineteen literature-derived donor motifs via their electronic absorption spectra and key photovoltaic descriptors. Donor substitution, most notably with D1, D3, D5, D6, and D15, substantially improves the electronic and optical properties, yielding lower HOMO energies, a reduced band gap (Egap), enhanced absorption intensity, and a bathochromic shift of the main band. Performance indicators, notably the electron-injection free energy (ΔGinj) and open-circuit voltage (Voc), indicate spontaneous, thermodynamically favorable electron injection from the excited dye into the TiO₂ conduction band.
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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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".