Effect of polymer chain length on the solubility of polystyrene grafted single‐walled carbon nanotubes in tetrahydrofuran
Bibliographic record
Abstract
Abstract BACKGROUND: While carbon nanotubes are highly interesting materials for a variety of applications, their inherent insolubility limits widespread applications and solution‐phase processing. It is known that chemical functionalization can overcome this insolubility problem, and covalent grafting of polymers to the nanotube surface has been shown to be effective. In this study, the effect of polymer molecular weight on the solubility of polymer–nanotube conjugates was investigated. RESULTS: A series of nitroxide‐capped polystyrene polymers ranging in molecular weight from 2900 to 105 000 g mol−1 were grafted to single‐walled carbon nanotubes (SWNTs). The resulting polystyrene–SWNT conjugates exhibited different degrees of solubility in tetrahydrofuran. Subsequent thermogravimetric and UV‐visible spectroscopy analyses indicated that carbon nanotube solubility reached a maximum when a polymer sample with a weight‐average molecular weight of 10 000 g mol−1 was used. Higher and lower molecular weights resulted in reduced solubilities. CONCLUSION: Polymer chains of intermediate length maximize SWNT solubility, while lengths that are too low or too high seem to diminish the ability of the polymer–SWNT conjugates to remain in solution. Copyright © 2008 Society of Chemical Industry
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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 | 0.001 |
| 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".