Preparation and Characterization of Multilayered Polymer Nanotube Dispersions
Bibliographic record
Abstract
Despite considerable efforts to synthesize nanotubes using porous alumina or polycarbonate membrane templates, few studies have addressed the resulting nanotube dispersion. We prepared dispersions of multilayered polyethylenimine/maleic anhydride alternating copolymer (PEI/MAAC) nanotubes synthesized with porous alumina templates. After mechanical polishing to remove the residual polymer surface layer from templates and subsequent template dissolution, the multilayered PEI/MAAC nanotubes were easily dispersed in water at neutral pH by polyelectrolyte adsorption, producing nanotube dispersions that were stable for at least 3 months. We characterized the dispersions using phase-contrast optical microscopy, electro-optics, electrophoresis, and viscometry to help understand their colloidal properties in the dilute and semidilute regimes. The dispersions were resistant to salt-induced aggregation up to at least 1 mM NaCl and were optically anisotropic when subjected to an electric field or flow. Interestingly, the electrophoretic mobility of polystyrene sulfonate (PSS)-stabilized nanotubes increases with increasing ionic strength, because of the high surface charge and softness of the adsorbed polyelectrolyte. Furthermore, unlike many rod-like colloid systems, the polymer nanotube dispersion has low viscosity because of weak rotary Brownian motions and strong tendency to shear thinning. At the high shear rates achieved in capillary viscometry experiments, however, we observed a slight shear thickening, which can be attributed to transient hydrocluster formation.
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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 | 0.001 | 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".