A survey of the rheological properties, phase morphology, and crystallization behavior of <scp>PP‐POSS</scp> materials with weak phase separation
Bibliographic record
Abstract
Abstract Polypropylene (PP) compounds with varying amounts (0.4‐8.2 vol%) of tailored allyl‐isobutyl polyhedral oligomeric silsesquioxane (POSS) were prepared by melt‐blending. The dependence of the crystallization behavior and crystalline structure of PP on the melt‐state phase morphology of PP‐POSS materials is addressed. PP‐POSS systems were predicted to exhibit weak phase separation in both the molten and solid states based on Bagley plot using Hansen solubility parameters. Small‐amplitude shear rheometry analysis suggested that the highest loaded (8.2 vol%) PP‐POSS system behaves as a two‐liquid emulsion, whereas the low‐content POSS (0.4‐4.0 vol%) systems deviate from this model, resembling a polymer nanocomposite. Based on these findings we hypothesized the formation of a heterogeneous phase morphology in the molten state comprised of nano‐size “pseudo‐solid” POSS clusters and micrometer‐size “pseudo‐liquid” POSS droplets dispersed within the PP matrix, depending on the POSS content. Upon cooling, POSS droplets comingle, forming cube‐like micrometer‐sized crystal domains. The nonisothermal crystallization of PP is enhanced by the presence of POSS clusters. Small‐angle X‐ray scattering analysis revealed the formation of thinner and more heterogeneous folded chain PP lamellae in the PP‐POSS systems.
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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".