Phase separation of polymer‐functionalized SWNTs within a PMMA/polystyrene blend
Bibliographic record
Abstract
Abstract Phase separation of polystyrene (PS) and poly(methyl methacrylate) (PMMA) blends was used as a means to segregate PS‐ or PMMA‐functionalized single‐walled carbon nanotubes (SWNTs) in thin films. Dilute solutions (5 wt % in THF) of 1:1 PS/PMMA blends containing the functionalized nanotubes were spin cast and annealed at 180 °C for 12 h. Two different polymer molecular weights were used (Mn = 8000 or Mn = 22,000), and were of approximately equivalent molecular weight to those attached to the surface of the nanotubes. Nanotube functionalization was accomplished using the Cu(I)‐catalyzed [3 + 2] Huisgen cycloaddition, in which alkyne‐decorated nanotubes were coupled with azide‐terminated polymers, resulting in polymer‐SWNT conjugates that were soluble in THF. Characterization of the annealed films by scanning Raman spectroscopy, which utilized the unique Raman fingerprint of carbon nanotubes, enabled accurate mapping of the functionalized SWNTs within the films relative to the two phase‐separated polymers. It was found that nanotube localization within the phase‐separated polymer films was influenced by the type of polymer attached to the nanotube surface, as well as its molecular weight. © 2008 Wiley Periodicals, Inc. J Polym Sci Part A: Polym Chem 47: 450–458, 2009
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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".