Functionalised tetrathiafulvalene- (TTF-) macrocycles: recent trends in applied supramolecular chemistry
Bibliographic record
Abstract
Tetrathiafulvalene (TTF) has been extensively explored as a π-electron donor in supramolecular systems. Over the last two decades substantial advances have been made in terms of constructing elaborate architectures based on TTF and in exploiting the resulting systems in the context of supramolecular host-guest recognition. The inherent electron-donating character of TTF derivatives has led to their use in the construction of highly efficient optoelectronic materials, optical sensors, and electron-transfer ensembles. TTFs are also promising candidates for the development of the so-called "functional materials" that might see use in a range of modern technological applications. Novel synthetic strategies, coupled with the versatility inherent within the TTF moiety, are now allowing the architecture of TTF-based systems to be tuned precisely and modified for use in specific purposes. In this critical review, we provide a "state-of-the-art" overview of research involving TTF-based macrocyclic systems with a focus on their use in supramolecular host-guest recognition, as components in non-covalent electron transfer systems, and in the construction of "molecular machines".
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".