Effect of Nano-Particles on Flow and Recovery of Polymer Nano-Composites in the Melt State
Bibliographic record
Abstract
Abstract The effect of nano-particle geometry on flow and recovery of polymer melts based on ethylene vinyl acetate (EVA) was investigated. Two nano-particles, calcium carbonate (CaCO 3 ) and montmorillonite clay, were used with concentrations between 2.5 and 15% by weight. First, by using small amplitude oscillatory shear and transient step shear tests, the linear and non-linear response of the nano-composites was studied. Then, to examine the structure recovery, the same behavior was studied on pre-sheared samples. The linear and non-linear data revealed that the effect of nano-particles in material flow is more important for anisometric particles which can be attributed to their ability to form a fractal structure controlling the rheological properties while spherical particles are not able to form such networks. Recovery experiments revealed that while nano-composites containing spherical particles have relatively similar response in subsequent tests, the rheological properties decreased significantly for platelet particles. The recovery results were also interpreted based on a filler-network mechanism and the reduction of the nano effect for anisometric particles was related to the breakdown of the filler network. These observations were validated by morphological investigations.
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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 | 0.000 | 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 | 0.001 |
| 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".