The orientation of carbon nanotubes in poly(ethylene‐<i>co</i>‐octene) microcellular foaming and its suppression effect on cell coalescence
Bibliographic record
Abstract
Abstract Poly(ethylene‐co‐octene)/multiwall carbon nanotube (PEOc/MWNT) nanocomposites were prepared by a melt blending process. The MWNT's solubility and the transmission electron microscopy (TEM) observation indicated that the MWNT bonded well with a PEOc matrix. This facilitated the orientation of the MWNT when shear and extensional forces were applied to the nanocomposite melts. Microcellular PEOc/MWNT nanocomposite foams were prepared by a rising temperature process using supercritical CO2 as the blowing agent. Various foaming times were selected to reveal the cell‐structure evolution during the cell growth stage. The obvious cell opening, resulting from cell coalescence, was observed in the cell wall in the neat PEOc foams. When the MWNT was introduced, however, the MWNT tended to orient in the cell wall. Here, as a result of the strain hardening, it acted as a self‐reinforcing element, protecting the cells from destruction during cell growth. Consequently, a dramatic decrease in the open cell content and a still high cell density at long foaming times were obtained in the PEOc/MWNT nanocomposite foams. The present study provides experimental evidence of the vital effects of nanoparticle orientation on cell coalescence. POLYM. ENG. SCI., 2012. © 2012 Society of Plastics Engineers
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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) | 0.000 | 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 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".