CO<sub>2</sub> sorption and diffusion in polymethyl methacrylate–clay nanocomposites
Bibliographic record
Abstract
Abstract This study reports the glass transition temperature (Tg), and sorption and diffusion of subcritical CO2 gas in polymethyl methacrylate (PMMA) nanocomposites containing organically modified smectite clay, Cloisite 20A (C20A). A range of methods for preparing the PMMA‐clay nanocomposites was investigated and a solution coprecipitation method was selected as the most appropriate. Using this method, PMMA nanocomposite containing 2, 4, 6, and 10 wt% nanoclay loadings were prepared. Wide‐angle X‐ray diffraction (XRD) analysis and scanning electron microscopy (SEM) indicated that the 2 wt% nanocomposite materials had a well‐dispersed intercalated clay structure. The Tg for PMMA‐C20A nanocomposites, as measured by differential scanning calorimetry (DSC), was found to be independent of the clay loading. CO2 solubility studies from 0 to 65°C and pressures up to 5.5 MPa using an in situ gravimetric technique were performed on compression‐molded films. The organoclay was found to have no effect on the solubility of CO2 in PMMA, and therefore the solubility of CO2 in the nanocomposite can be determined from the solubility of CO2 in the matrix polymer alone. Diffusion coefficients were determined using the appropriate transport models for these test conditions and the diffusion coefficients for CO2 in PMMA‐C20A composites were found to increase with organoclay loading. It is believed that the processing path taken to prepare the nanocomposites may have resulted in the agglomeration of the C20A organoclay, thereby preventing the polymer chains from fully wetting and intercalating a large number of clay particles. These agglomerations are responsible for the formation of large‐scale holes within the glassy nanocomposite, which behave as low resistance pathways for gas transport within the PMMA matrix. POLYM. ENG. SCI., 45:904–914, 2005. © 2005 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".