The effect of molecular weight on the separation of semiconducting single‐walled carbon nanotubes using poly(2,7‐carbazole)s
Bibliographic record
Abstract
ABSTRACT The use of selective interactions between conjugated polymers and single‐walled carbon nanotubes has emerged as a promising method for the separation of nanotubes by electronic type. Although much attention has been devoted to investigating polyfluorenes and their ability to disperse semiconducting carbon nanotubes under specific conditions, other polymer families, such as poly(2,7‐carbazole)s, have been relatively overlooked. Poly(2,7‐carbazole)s have been shown to also preferentially interact with semiconducting carbon nanotubes, however a detailed investigation of polymer parameters, such as molecular weight, has not been performed. We have prepared seven different molecular weights of a poly(2,7‐carbazole), from short chain oligomers to high molecular weight polymers, and have investigated their effectiveness at dispersing semiconducting single‐walled carbon nanotubes. Although all polymer chain lengths were able to efficiently exfoliate carbon nanotube bundles using a mild dispersion protocol, only polymers above a certain threshold molecular weight ( M n ∼ 27 kDa) were found to exhibit complete selectivity for semiconducting nanotubes, with no observable signals from metallic species. Additionally, we found the quality of separation to be strongly dependent on the ratio of polymer to carbon nanotube. Contrary to previous reports, we have found that an excess of poly(2,7‐carbazole) leads to incomplete removal of metallic carbon nanotubes. © 2015 Wiley Periodicals, Inc. J. Polym. Sci., Part A: Polym. Chem. 2015 , 53 , 2510–2516
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 teacher head, 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".