DFT investigations of phenyldithiafulvene dimers at different oxidation states
Bibliographic record
Abstract
Oxidative dimerization of aryl-substituted dithiafulvenes (Ar-DTFs) presents an efficient C-C bond forming method for the preparation of diverse redox-active π-conjugated molecules and conductive polymers. Previous experimental data indicated a reaction pathway in which direct combination of two Ar-DTF radical cations is a key step. However, mechanistic details about how Ar-DTF dimers are formed under different oxidation states have not yet been clearly established prior to this work. The assembly of two Ar-DTF molecules generates a vast conformational and configurational landscape, which is quite complex but fundamentally important for understanding the dimerization mechanism. To cast a deep insight into this aspect, we have performed density functional theory (DFT) calculations at the M06-2X/Def2-SVP level of theory to thoroughly investigate the potential energy surfaces (PESs) of various dimers of a phenyl-substituted dithiafulvene (Ph-DTF) in the mixed-valence radical cation and dication states. Key stationary points in these PESs, including minimum-energy conformers (π-dimers and σ-dimers) as well as the transition states connected to them, were examined and compared. We have also calculated the binding energies of these dimers to evaluate the energetic driving forces for their formation. Based on our computational results, the roles that various Ph-DTF dimers play in different pathways of oxidative dimerization have been clarified.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.003 | 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".