Thermal and Rheological Properties of L‐Polylactide/Polyethylene Glycol/Silicate Nanocomposites Films
Bibliographic record
Abstract
The melt rheology and thermal properties of polylactide (PLA)-based nanocomposite films that were prepared by solvent casting method with L-PLA, polyethylene glycol (PEG), and montmorillonite clay were studied. The neat PLA showed predominantly solid-like behavior (G' > G″) and the complex viscosity (η*) decreased systematically as the temperature increased from 184 to 196 °C. The elastic modulus (G') of PLA/clay blend showed a significant improvement in the magnitude in the melt, while clay concentration was at 6% wt or higher. At similar condition, PEG dramatically reduced dynamic modulii and complex viscosity of PLA/PEG blend as function of concentration. A nanocomposite blend of PLA/PEG/clay (74/20/6) when compared to the neat polymer and PLA/PEG blend exhibited intermediate values of elastic modulus (G') and complex viscosity (η*) with excellent flexibility. Thermal analysis of different clay loading blends indicated that the melting temperature (T(m)) and glass transition temperature (T(g)) remained unaffected irrespective of clay concentration due to immobilization of polymer chain in the clay nanocomposite. PEG incorporation reduced the T(g) and the T(m) of the blends (PLA/PEG and PLA/PEG/clay) significantly, however, crystallinity increased in the similar condition. The transmission electron microscopy (TEM) image of nanocomposite films indicated good compatibility between PLA and PEG, whereas clay was not thoroughly distributed in the PLA matrix and remained as clusters. The percent crystallinity obtained by X-ray was significantly higher than that of differential scanning calorimeter (DSC) data for PLA.
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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".