Investigation of Hybrid Conjugated/Nonconjugated Polymers for Sorting of Single-Walled Carbon Nanotubes
Bibliographic record
Abstract
Structure–selectivity relationships between conjugated polymer backbone structure and enriched semiconducting carbon nanotube dispersions remain unclear. A significant focus has been on the structure of the aromatic monomer incorporated into the conjugated polymer backbone, particularly with respect to derivatives of fluorene, thiophene, and carbazole. Less attention has been given to challenging the necessity of complete backbone conjugation in preparing samples of enriched semiconducting carbon nanotubes. Here, we synthesize and study a series of polymer backbone structures containing nonconjugated flexible linkers with lengths of 3–12 carbon atoms and incorporation percentages of 5–50%. We prepare nanotube dispersions with HiPCO starting material and characterize the dispersions using UV–vis–NIR, Raman, and fluorescence spectroscopy. We find that at an optimized alkyl spacer length (six carbon atoms) and incorporation percentage (25%) an enriched sample of semiconducting carbon nanotubes can be prepared in THF, showing that complete polymer conjugation is not necessary for nanotube sorting. Furthermore, we demonstrate that these polymers are efficient at dispersing higher average diameter plasma torch carbon nanotubes in both THF and toluene and that enriched, concentrated dispersions of semiconducting tubes can be achieved in toluene.
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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) | 0.000 | 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 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".