Prevention of surface defects in calendered poly(vinyl chloride) sheets using a succinate‐capped poly(caprolactone) additive
Bibliographic record
Abstract
Abstract Surface defects known as “gas checks” often mar the surfaces of poly(vinyl chloride) (PVC) calendered films. These defects are typically prevented through changes in the calender operating parameters, a costly exercise which also limits the sheet thickness and the production rate. Adding a low concentration of poly(caprolactone) (PCL)‐based star‐shaped compound can eliminate gas check defects in PVC calendering. The effects of a triheptylsuccinate‐terminated PCL with a PCL triol core and number average molecular weight of 540 g/mol (i.e., PCL 540 ‐[(succ)‐C 7 ] 3 ) has been investigated on the material, thermal, and processing properties of PVC blends containing diisononyl phthalate (DINP) as a primary plasticizer and PCL 540 ‐[(succ)‐C 7 ] 3 in low quantities (i.e., 0, 5, or 10 parts per hundred rubber (phr)) as a secondary plasticizer and processing aid. The most significant differences between PVC blends containing PCL 540 ‐[(succ)‐C 7 ] 3 and those without are in the rheological properties of the PVC blends at higher temperatures and lower angular frequencies. Under these conditions, PVC blends containing 10 phr of PCL 540 ‐[(succ)‐C 7 ] 3 have a complex viscosity nearly three times higher than those containing only DINP. PVC/PCL 540 ‐[(succ)‐C 7 ] 3 blends had comparable tensile properties to those containing only DINP, with no significant change in maximum elongation and a small but significant increase of 28% in maximum stress. The addition of PCL 540 ‐[(succ)‐C 7 ] 3 made it possible to produce calendered films without gas checks that were twice as thick as those produced in its absence. In addition to reduced wastage of marred films, the increased calender operating range for PVC films containing PCL 540 ‐[(succ)‐C 7 ] 3 has the potential to significantly reduce energy costs for the calendering of thick PVC films.
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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".