Gas check prevention during calendering of poly(vinyl chloride) films using poly(caprolactone)‐based additives
Bibliographic record
Abstract
Abstract Gas checks on poly(vinyl chloride) (PVC) calendered films are a common but not well understood surface quality defect that causes delays and resource wastage during the manufacturing of plastic sheets and films. Three modified poly(caprolactone) (PCL)‐based additives of differing molecular weights were used as a secondary plasticizer in PVC with the goal of preventing the formation of gas checks without modifying the calendering process conditions. To better understand gas check prevention processes, the effects of the PCL‐based additives on the thermal, mechanical, rheological, and chemical properties of PVC blends were assessed. A high molecular weight (2000 g/mol) PCL‐based additive was unable to prevent gas checks in PVC blends, unlike the lower molecular weight (540 g/mol and 900 g/mol) additives. Chemical and physicochemical properties affect the prevention or reduction of gas checks showing that the acid values and molecular weights of the PCL‐based additives are related to the number of gas checks. In contrast, physical properties, including rheological ones like complex viscosity or the storage and loss moduli that were previously hypothesized to be important factors in preventing gas check formation, were not significantly impacted by the addition of the secondary plasticizer. Furthermore, the acid value was observed to be related to the reduction in gas checks, likely due to the interaction of the dispersed functional acid groups with the entrapped air. Consequently, the effectiveness of the additive in eliminating gas checks declines as the acid value decreases. Highlights Low concentrations of PCL‐based additives prevent gas checks on PVC films. They do not significantly affect the physical/mechanical properties of films. Higher molecular weight additives are beneficial, but only below 2000 g/mol. The effectiveness of PCL‐based additives increases with acid value.
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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 | 0.001 |
| 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".