Exploring the reactivity and interactions of a poly(fluorene‐<i>co</i>‐tetrazine)‐conjugated polymer with <scp>Single‐walled Carbon Nanotubes</scp>
Bibliographic record
Abstract
Abstract Conjugated tetrazine‐containing polymers that undergo inverse electron demand Diels‐Alder (IEDDA) reactions with trans ‐cyclooctenes are interesting not only for their intrinsic optoelectronic properties, but also their interactions with π‐conjugated surfaces. Here, we prepared a series of poly(fluorene‐ co ‐tetrazine) polymers and carried out IEDDA reactions to decorate them with hydroxyl, hexadecyl, or triethylene glycol side chains. The polymers were investigated pre‐ and post‐IEDDA coupling in terms of their ability to disperse single‐walled carbon nanotubes (SWNTs) in organic solvent. It was found that polymer molecular weight, side chain structure, and degree of conjugation all impacted the quality of SWNT dispersions. While the starting poly(fluorene‐ co ‐tetrazine) polymer produced concentrated dispersions, the post‐IEDDA polymer containing dihydropyridazine groups did not produce dispersions of equal concentration. However, upon oxidation to the fully aromatic pyridazines, the polymers regained their ability to form concentrated dispersions. Furthermore, the post‐IEDDA polymers exhibited increased selectivity toward metallic SWNTs relative to the starting polymer. Due to the efficiency of the IEDDA reaction, it was also possible to use this chemistry to derivatize the nanotube complex with poly(fluorene‐ co ‐tetrazine) post‐dispersion. Overall, this work demonstrates the first use of reactive polytetrazines to disperse SWNTs, allowing rapid modification of polymer‐nanotube complexes.
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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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".