Morphological aspects of injected polypropylene/clay nanocomposite materials
Bibliographic record
Abstract
Abstract Polypropylene (PP) clay nanocomposites were injection‐molded using two different coupling agents based on maleic anhydride‐grafted PP (MA‐g‐PP) and two clay loadings. The morphological aspects of these materials were studied by depth profiling. Molecular chain and clay orientations were characterized using attenuated total reflectance‐infrared analysis and transmission electron microscopy (TEM). Both clay platelets and PP molecular chain orientations were found to decrease from the surface toward the core of the injection–molded specimens. Clay intercalation, characterized by both complementary X‐ray diffraction and TEM, was found to be significantly influenced by both the characteristics of the coupling agent used and the type of residual stresses generated at each layer across the thickness of the injection‐molded parts. The use of low‐molecular weight (Mw) MA‐g‐PP led to a uniform intercalation but with no further exfoliation. The use of higher molecular weight MA‐g‐PP led to a heterogeneous intercalation with some signs of exfoliation. The crystallization behavior of PP clay nanocomposites studied by differential scanning calorimetry showed an increase in the level of crystallinity from the surface to the core of the specimens; these results were also confirmed by scanning electron microscopy. POLYM. ENG. SCI., 2013. © 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) | 0.001 | 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".