Improving the Continuous Microcellular Extrusion Foaming Ability with Supercritical CO2 of Thermoplastic Polyether Ester Elastomer through In-Situ Fibrillation of Polytetrafluoroethylene
Bibliographic record
Abstract
In-situ fibrillated polytetrafluoroethylene (PTFE) enhanced nanocomposites were successfully prepared by mixing thermoplastic polyether ester elastomer (TPEE) and PTFE using a twin-screw extruder. Well-dispersed, long aspect ratio PTFE nanofibrils with a diameter of less than 200 nm were generated and interwoven into networks. Differential scanning calorimetry and in-situ polarized optical microscopy showed that the PTFE nanofibrils can greatly accelerate and promote crystallization of the TPEE matrix and the crystallization temperature can be increased by 6 °C. Both shearing and elongational rheometry results confirmed that the introduction of PTFE nanofibrils can significantly improve the rheological properties. The remarkable changes in the strain-hardening effect and the melt viscoelastic response, as well as the promoted crystallization, led to substantially improved foaming behavior in the continuous extrusion process using supercritical CO2 as the blowing agent. The existing PTFE nanofibrils dramatically decreased the cell diameter and increased cell density, together with a higher expansion ratio and more uniform cell structure. The sample with 5% PTFE fibrils showed the best foaming ability, with an average diameter of 10.4–14.7 μm, an expansion ratio of 9.5–12.3 and a cell density of 6.6 × 107–8.6 × 107 cells/cm3.
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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) | 0.000 | 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".