Thermal, Rheological, and Mechanical Behaviors of LLDPE/PEMA/Clay Nanocomposites: Effect of Interaction Between Polymer, Compatibilizer, and Nanofiller
Bibliographic record
Abstract
Abstract Summary: Linear low density polyethylene/maleic anhydride grafted polyethylene/montmorillonite clay (LLDPE/PEMA/clay) nanocomposites prepared using a co‐rotating twin screw extruder exhibit unique thermal, rheological, and mechanical behaviors. All the mechanical properties including ductility increase with clay loading. X‐ray diffraction analysis and TEM images reveal an intercalated clay structure for the LLDPE/PEMA/clay composite with 5% clay and an exfoliated structure for that with 2% clay. Differential scanning calorimetry shows that the addition of PEMA does not influence the melting temperature but favors the formation of more thin lamellas. Rheological characterization indicates that the LLDPE‐PEMA blend has similar rheological behavior to neat LLDPE, implying the two polymers are completely miscible. The composites exhibit significantly higher storage and loss modulus and complex viscosity at low frequencies, and the magnitude of all these properties increases with clay loading. Furthermore, the slopes of G′, G″, and complex viscosity versus frequency are similar for the composites of different phase morphologies, suggesting that the rheological behaviors of the composites depends more on clay loading than phase morphology. The enhanced miscibility between LLDPE and PEMA, and more importantly, interfacial interaction between clay, PEMA, and LLDPE, are responsible for the distinct improvement in all the mechanical properties of the composite, and in particular for the marked improvement in ductility. Stress‐strain diagram for LLDPE, LLDPE/PEMA, and LLDPE/PEME‐clay nanocomposites. magnified image Stress‐strain diagram for LLDPE, LLDPE/PEMA, and LLDPE/PEME‐clay nanocomposites.
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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".