Blends of polypropylene with solid silicone additive
Bibliographic record
Abstract
Abstract Blends of polypropylene (PP) and silicone masterbatch (SMBPP)—a commercial formulation consisting of an ultrahigh molecular weigh polydimethylsiloxane (PDMS) dispersed in PP—were prepared by melt mixing in an internal mixer. Four binary blends with different SMBPP content and two ternary blends containing silane‐grafted polypropylenes (PP‐VTES) of different VTES content as compatibilizer were produced. The blends were analyzed by melting flow rate, rotational rheometry, scanning electron microscopy, and differential scanning calorimetry (DSC). In all blends, the SMBPP remained as dispersed phase in the continuous PP matrix. The addition of PP‐VTES reduced significantly the size of the SMBPP domains. Rheological and morphological data strongly indicate that the PP‐VTES acts only by lowering the interfacial tension of the system without generating strong interaction between SMBPP domains and the PP matrix. The dimensions of the SMBPP domains and the interfacial energy were observed to determine the characteristics of the pseudoplastic behavior of the blends in the melt state. For both binary and ternary blends, the SMBPP domains showed nucleating effect leading to an increase of the degree of crystallinity. However, the decrease in the interfacial energy and viscosity promoted by the addition of PP‐VTES to the system led to a more intense nucleating effect and to an increase of crystallization, melting temperatures, and melting enthalpy. © 2007 Wiley Periodicals, Inc. J Appl Polym Sci 104: 226–233, 2007
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".