Use of supercritical CO<sub>2</sub> as a dispersing agent in the preparation of a polymer‐layered silicate nanocomposite
Bibliographic record
Abstract
The usefulness of supercritical CO2 (scCO2) in the processing of thermoplastic olefin (TPO) nanocomposites was investigated using a simple extensional flow mixing device. An organoclay (Cloisite® 20A), a maleated polypropylene (Polybond® 3200), and a TPO polymer were selected for this study. The three components were combined in different manners to evaluate how scCO2 influenced the microstructure of a final nanocomposite prepared by different mixing orders of the formulation components. The organoclay was examined in its as‐supplied state as well as after being conditioned under scCO2. Analyses of the nanocomposites by X‐ray diffraction, transmission electron microscopy, and parallel plate rheology techniques showed that inclusion of scCO2 during melt compounding significantly contributed to disrupting the crystalline order in the structure of the original organoclay. However, the order by which the formulation components are combined under scCO2 was important to clay dispersion. It was shown that making use of the proper combination order led to an improvement in the modulus of the resulting nanocomposites as measured by rheology. POLYM. COMPOS., 38:987–995, 2017. © 2015 Society of Plastics Engineers
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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 | 0.000 | 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 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".