The Effect of Clay Content on PMMA-Clay Nanocomposite Foams
Bibliographic record
Abstract
In this study the CO 2 sorption at 45 °C in PMMA nanocomposite films containing 2 wt.% of nanoclay has been measured using an in-situ gravimetric technique. The films examined were prepared by compression moulding material obtained by dry-blend and solvent co-precipitation techniques. The CO 2 diffusion coefficients were found to be higher for the dry-blended nanocomposite due to the larger agglomerations of the organoclay agglomerations, which prevented the polymer chains from fully wetting and intercalating the clay particles. The T g -p profile for PMMA nanocomposite containing 2 wt.% nanoclay in the presence of CO 2 was also measured using high-pressure DSC. The glass transition phase envelope was shifted vertically by approximately 10 °C when compared to the value reported in the literature for neat PMMA. This result suggests that the nanoclay affects the plasticization behaviour of PMMA under high-pressure CO 2 conditions. The cellular morphologies obtained for these PMMA nanocomposite foams produced by batch processing with subcritical CO 2 are strongly dependent upon the clay content and the dispersion of the nanoclay in the material. In the case of intercalated nanocomposites, most clay particles exist as agglomerated stacks of silicate sheets. On foaming the cells tend to form around the clay particles causing either irregular-shaped cells or layers to be produced. As a result, the cell density increases and the mean cell size decreases in the foamed nanocomposite on increasing the nanoclay content. Accordingly, the resulting cell structures are highly non-uniform and show large variations in cellular morphologies throughout the foam.
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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) | 0.001 | 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".