Plasticization effects on bubble growth during polymer foaming
Bibliographic record
Abstract
Abstract During polymer foaming with physical blowing agents, plasticization affects the melt viscosity, gas diffusivity in the melt, and the gas–melt interfacial tension. In this paper, we propose a model for plasticization during bubble growth, and estimate its effects under typical foaming conditions. The theoretical model incorporates well‐established mixture theories into a recent model for diffusion‐induced bubble growth. These include the free‐volume theories for the viscosity and diffusivity in polymer‐blowing agent mixtures and the density gradient theory for the interfacial tension. The viscoelasticity of the melt is represented by an Oldroyd‐B constitutive equation. We study the radial growth of a single bubble in an infinite expanse of melt, using parameter values based on experiments on polystyrene–CO 2 systems. Our results show that even at relatively low gas concentrations, plasticization increases the blowing‐agent diffusivity markedly and thus boosts the rate of bubble growth. In contrast, the reduction in melt viscosity and interfacial tension has little effect on bubble growth. Though not intended as quantitative guidelines for process design, these results are expected to apply qualitatively to typical foaming conditions and common polymer‐blowing agent combinations. POLYM. ENG. SCI., 46:97–107, 2006. © 2005 Society of Plastics Engineers
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".