Supercritical Fluids in Thermoplastics Foaming: Facts or Fallacies?
Bibliographic record
Abstract
The past two decades have seen extensive interests and efforts for developing processes based on supercritical fluids (SCF). Microcellular foaming is one of these processes that take advantage of the unique properties of supercritical fluids when they are used as physical foaming agents (PFA). In this technology, the emphasis has been mostly focused on inert gases such as carbon dioxide and nitrogen that both inherently provides very high cell densities and very small cell sizes. Incidentally the benign carbon dioxide is frequently considered as the panacea of PFA, in response notably to the environmental pressures related to the destruction of the ozone layer. Hydrofluorocarbons (HFCs) have also been identified as potential alternative agents for extruded polystyrene foam. Unfortunately, HFCs remain difficult to process at the high concentrations required to yield low-density foams. Surprisingly, the processing difficulties occur when the pressures required for dissolving high HFCs concentrations reach the range located immediately above the critical pressure of the PFA used. PS/HFC systems have been well documented in terms of abnormal behaviors occurring as the foaming agent gets into the supercritical conditions, and similar observations have also been made for other SCF used for thermoplastic foaming. These observations are reported here, and attempts are made to link the supercritical nature of the fluid to the PFA heterogeneities suspected under these conditions.
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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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.013 |
| Scholarly communication | 0.004 | 0.013 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".