Foam Extrusion of PS Blown with a Mixture of HFC-134a and Isopropanol
Bibliographic record
Abstract
Use of mixtures of blowing agents in foam extrusion has gained interest in the past few years. Reasons for blending physical foaming agents (PFA) are numerous. The incentives may be economical, environmental or technical. With respect to that latter factor, blending suitable PFAs is often regarded as providing a better control of processing conditions. For example, a specific PFA could be selected for its inflation performance and blended with other co-blowing agents chosen for their stabilizing role. Although considerable amount of work has been done in that area, very little information has been disclosed in open literature. 1,1,1,2-tetrafluoroethane (HFC-134a) has been reported as an interesting candidate for polystyrene (PS) foaming, although low densities cannot be easily obtained and stable processing conditions are difficult to achieve. This work studies the effect of blending HFC-134a with isopropanol as a co-blowing agent for PS. Off-line solubility data, on-line rheology measurements and extrusion foaming performance of this mixture will be discussed. The function of each blowing agent during the process will be analyzed with respect to the plasticization, nucleation, expansion and stabilization phases. Attention will also be paid to interaction involving the two PFA components.
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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 | 0.001 | 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".