Gelation of poly(vinyl chloride) inside a single screw extruder and its effect on product properties
Bibliographic record
Abstract
Two resin formulations were extruded through a single screw extruder, equipped with a single flighted screw, rotating at 10 rpm, at different barrel temperature settings, which resulted in different melt temperatures measured in the adapter zone. The extrudates were subsequently subjected to assessments of the gelation level by three different methods: differential scanning calorimetry (DSC), capillary rheometry (CR), and solvent absorption (SA). The pros and cons of these methods are discussed. It appears that DSC is the most accurate technique for the quantitative determination of the gelation degree (GD), with the drawback that the specimen size is very small. The CR technique, based on measurement of entrance pressure, is also capable of providing reasonable estimates of GD, with some potential inaccuracies as a result of difficulties associated with measurement or assessment of temperature rise. The SA technique provided qualitative assessments of GD in agreement with the other two methods. Ultimate tensile strength, elongation at break, solid density, and color change were also measured and were correlated to melt temperatures and the corresponding GD. Extruded specimens having GDs in the range of 60%–90% possessed satisfactory properties for both resin formulations. J. VINYL ADDIT. TECHNOL., 25:E174–E180, 2019. © 2018 Society of Plastics Engineers
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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) | 0.001 | 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".