The effect of clay dispersion on the properties of LLDPE/LLDPE‐<i>g</i>‐MAH/montmorillonite nanocomposites
Bibliographic record
Abstract
Abstract In this work, three coupling agents presenting different grafting contents and molecular weights were used to prepare linear low density polyethylene (LLDPE)/linear low density polyethylene grafted with maleic anhydride (LLDPE‐g‐MAH)/montmorillonite nanocomposites with various morphologies. The clay dispersion was analyzed at the micrometric level by scanning electron microscopy and at the nanometric level by X‐ray diffraction (XRD) and transmission electron microscopy (TEM). It was found that coupling agents having intermediate molecular weights led to the highest exfoliation extents, whereas the coupling agent presenting the highest molecular weight led to a poor delamination of the clay platelets. The properties of the nanocomposites produced and of their LLDPE/LLDPE‐g‐MAH reference blends were analyzed. It was shown that the best improvements in mechanical and barrier properties are not necessarily achieved for the nanocomposites, exhibiting the highest exfoliation extents. The length of the tactoids also plays a crucial role on the macroscopic properties. In addition, a high level of delamination could result in a loss of reinforcement effect, due to the inherent flexibility of the individual clay platelets. Finally, the strength of the clay/polymer interface, which was evaluated through surface tension measurements, seems to play a significant role on the properties of the nanocomposites. POLYM. ENG. SCI., 2008. © 2008 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) | 0.001 | 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".