Thermoplastic silicone elastomer lubricant in extrusion of polypropylene wood flour composites
Bibliographic record
Abstract
Abstract A possibility of using a thermoplastic silicone elastomer (TPSE) for reduction of surface defects in the extrusion of wood‐filled metallocene polypropylene (mPP) has been investigated in this work. A capillary rheometer and a single‐screw extruder have been utilized to study the effect of the additive on the extrudate distortions. Maleated syndiotactic metallocene polypropylene was also used as an adhesion promoter in mPP/wood flour composites. At loadings of 50 wt% wood flour in the mPP, the extrudates come out of the die with significant tearing and surface roughness. The surface rupture mechanism is similar to that of sharkskin in neat polymers; however, the defect is much more exaggerated in the case of wood fiber‐filled composites. It was found that TPSE at low concentrations (1 wt%) was able to reduce or even completely eliminate extrudate surface tearing. Increasing the extrusion speed yielded better results. Addition of a coupling agent also provides improvement in the extrudate appearance. © 2007 Wiley Periodicals, Inc. Adv Polym Techn 26:100–108, 2007; Published online in Wiley InterScience ( www.interscience.wiley.com ). DOI 10.1002/adv.20090
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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".