Experimental Analysis of Polymer Nanocomposite Foaming Using Carbon Dioxide
Bibliographic record
Abstract
Currently, the polymer foam industry is testing carbon dioxide (CO 2 ) for its applicability as a physical blowing agent (PBA) due to the phase-out of chlorofluorocarbons (CFCs) and hydrochlorofluorocarbons (HCFCs) according to the Montreal Protocol [1].CO 2 is one of most promising alternatives because it is environmentally safe, non-toxic, non-flammable and inexpensive.However, CO 2 has its drawbacks, such as low solubility and high diffusivity in comparison with other blowing agents.Therefore CO 2 sometimes leads to foams with higher density and/or poor surface quality, and almost always requires higher operating pressures than other agents.Currently, the concept of adding nanoparticles to a polymer is being investigated around the world as one possible way to overcome these problems.Polymer blends can be another approach.In general, generating foams using a PBA includes saturating the polymer with the PBA at a certain pressure and temperature via thorough mixing.Then the mixture is subjected to a sudden thermodynamic change (temperature increase or pressure drop), resulting in the escape of the PBA and formation of the cellular structure.The typical foaming process includes cell nucleation, cell growth, and cell stabilization, the first two being the focus of this study.In the foaming process, several operating variables, e.g.Carbon nanofibers (CNFs) and activated carbon (AC) were used as additive/nucleation agents in polystyrene extrusion foaming.Both fillers showed promising application in insulation foams based on foam density, thermal conductivity, IR transmission, thermal stability, and compressive modulus.In addition, water acted as a co-blowing agent in the PS/wet AC foaming process.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".