Additives to prevent the formation of surface defects during poly(vinyl chloride) calendering
Bibliographic record
Abstract
Abstract Gas checks are visible fleck‐shaped defects that occur on the surface of poly(vinyl chloride) (PVC) films during industrial calendering. Films containing these surface defects often do not meet minimum product specifications and therefore must be disposed of or recycled, resulting in increased cost and material waste. Currently, gas checks are controlled by keeping film gauge low and through trial‐and‐error modifications of processing parameters by calender operators. In this work, our group developed a series of chemical additives that can be blended with PVC to prevent the formation of gas check defects. We found that a series of poly(caprolactone) (PCL)‐based compounds with diester linkers and alkyl chain cappers were all effective at preventing the formation of gas checks during calendering, with additive concentrations as low as 8 phr producing films with no gas checks. We found that the blends produced with our additives had higher melt viscosities than those produced with additives that do not remove gas checks, suggesting that viscosity plays an important role in preventing gas check defects.
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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".