A Systematic Study of Solubility of Physical Blowing Agents and their Blends in Polymers and their Nanocomposites
Bibliographic record
Abstract
The solubility of a blowing agent (BA) in polymer melts is a critically important parameter affecting the plastic foams fabrication. Theoretically, cell nucleation occurs when the pressure of the polymer/gas mixture drops below the solubility pressure. For effective process design, accurate solubility data for blowing agents in polymers is necessary. However, getting more accurate solubility data is a big scarce. Through this research, it was possible to generate more accurate solubility and pressure-volume-temperature (PVT) data and to verify various equations of states (EOS). During the last two decades, due to ozone-depleting-potential (ODP) and global warming issues of BAs, the plastic foam industry has experienced serious regulatory, environmental, and economical pressures. In response to this, researchers and industry have been exploring the uses of blowing agent blends. Nevertheless, very limited fundamental research on the foaming mechanism using blowing agent mixtures has been conducted. The end results of this research is expected to provide guidance to choose the optimal composition of environmental-friendly blowing agent blends and offer insights to develop sustainable foaming technology. This thesis also highlights a comprehensive research for the PVT and solubility behavior of polymer nanocomposites (PNCs). By using the magnetic suspension balance (MSB) and PVT apparatus, it was possible to determine the solubility behavior of PNC more accurately. Fully experimental results indicated that infusion of nanoparticles decreases the volume swelling as well as solubility and diffusivity. It was hypothesized that infusion of organoclay nanoparticles generates a significant amount of solidified (solid-like) polymer near its surface and consequently reduces total absorption capacity of the system. However, it is believed that the solubility behavior of polymeric composites fully depends on the interaction or affinity between fillers (micro or nano) and gas. In other words, if the nanoparticles (such as CaCO3, aluminum oxide, tin oxide) or fillers (such as carbon black, zeolites, silica gel) are highly polar and/or porous, overall sorption (absorption + adsorption) might increase due to adsorption phenomenon. Through the solubility, PVT and modeling of nanocomposites, this research has advanced the understanding of the effect of nanoparticles on solubility that governs different physical phenomena (such as cell nucleation and, cell growth) during plastic foaming.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 teacher head, 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".