Surface modification strategies for multicomponent polymer systems. III: Effect of diblock copolymer‐modified rutile on the morphology and mechanical properties of LLDPE/PVC blends
Bibliographic record
Abstract
Abstract Rutile pigment was surface‐modified by the adsorption of various diblock copolymers and used as a component in two‐ and three‐component polymer blends involving the incompatible pair of linear, low‐density polyethylene (LLDPE) and poly(vinyl chloride) (PVC). Stress–strain analyses and electron microscopy show that the copolymer tethered to the rutile surface affects both mechanical and morphological properties of the blends. Inverse gas chromatography was used to evaluate dispersion surface energies and acid–base interaction parameters of the various solids. The mechanical and morphological characteristics of the blends can be rationalized by the concepts of acid–base and dispersion–force interaction. Of the copolymer modifiers used, the diblock based on polyisoprene and poly(4‐vinyl pyridine) (PIP‐P4VP) was best suited for use in LLDPE/PVC blends, ostensibly because of strong acid–base interaction between PVC and P4VP and mechanical interlocking between LLDPE and the PIP moiety. The properties of ternary blends were shown to be dependent on the method used for mixing the components. All mixing procedures used here resulted in time‐dependent variations of mechanical properties, suggesting that none gave rise to equilibrium morphology in the compounds. © 2001 John Wiley & Sons, Inc. J Appl Polym Sci 81: 1891–1901, 2001
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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 | 0.000 | 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".