In-situ Visualization and Quantification of Polymer Crystallization under Complex Flows during Foam Extrusion Processing
Bibliographic record
Abstract
The crystallization behavior of polypropylene (PP) and poly(lactic acid) (PLA) during foam extrusion processing was investigated via visualization techniques. In the experiments, a tandem extrusion system equipped with a conical converging flow channel and a high temperature/pressure visualization chamber were employed. Using the developed system, effect of processing parameters on crystallization was elucidated. These parameters included the processing temperature, the carbon dioxide content, the flow rate, and the strain rate. The visualized crystallites were quantified based on their light intensities and by the definition of the Crystallinity Index (CI) and the Crystallite Size (CS), where the CI and CS corresponded to the relative crystallinity and the size of crystallites, respectively. Moreover, a correlation was made between the content of the induced crystallites inside the extruder and the cellular structure of the extruded foams. It was verified by visualization that both PP and PLA crystallize during the process even at a temperature above their nominal melting peak point (Tm). The formation of crystallites at a temperature above the Tm was asserted to the strain-induced crystallization. Moreover, a procedure was proposed in order to decouple the effect of the flow rate (corresponded to the strain rate) and the cooling history. A significant enhancement in crystallization kinetics was observed by increasing the strain rate. This crystallization enhancement caused by the increased flow rate became more pronounced at lower temperatures, most probably due to the higher degree of molecular orientation. Adding the CO2 shifted the crystallization temperature to a lower processing temperature due to its plasticization effect, but it accelerated the crystallization kinetics. This clearly explained the decrease in the optimal processing temperature for foaming with an increased CO2 content. It was also confirmed that the induced crystallites during the process had a great effect on the cellular morphology of the resultant foam product.
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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 | 0.001 |
| 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".