<scp>UV‐light mediated decomposition of a polyester for enrichment and release of semiconducting carbon nanotubes</scp>
Bibliographic record
Abstract
Abstract Purification of single‐walled carbon nanotubes using conjugated polymers to selectively disperse either semiconducting or metallic nanotubes is effective and has received significant attention. However, the interaction between the conjugated polymer and the nanotube surface is very strong, making it difficult to remove the adsorbed polymer. Here, we report a poly(carbazole‐co‐terephthalate) polymer that is not only selective for semiconducting carbon nanotubes but can also be largely removed from the nanotube surface via irradiation with UV light. Irradiation of the polymer‐nanotube dispersion causes degradation of ester linkages in the polymer backbone, effectively cutting the polymer into fragments that no longer bind strongly to the nanotube surface. Characterization of the electronic nature of the samples was carried out via the combination of absorption, Raman, and fluorescence spectroscopy. In addition, thermogravimetric analysis allowed determination of the amount of polymer left on the nanotube surface after irradiation and indicated that a large proportion of the polymer is removed. The reported methodology opens new possibilities for purification of semiconducting single‐walled carbon nanotubes and their isolation from the polymeric dispersant.
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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) | 0.002 | 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".