Enhanced properties of polylactide by incorporating cellulose nanocrystals
Bibliographic record
Abstract
Polylactide (PLA)‐cellulose nanocrystal (CNC) bionanocomposites with different CNC loadings were prepared via a simple solvent casting preparation method. Scanning electron microscopy showed some very fine aggregates with a size of 1–3 μm whereas transmission electron microscopy revealed the existence of well‐dispersed structure of CNCs within the PLA matrix at a nanoscale. The loss and storage moduli of the nanocomposites increased significantly with CNC content, particularly at low frequencies, indicative of a solid‐like behavior. The total crystalline content of the PLA in the nanocomposites and the crystallization temperature increased, which were ascribed to the nucleation effect of the CNCs on the crystallization of PLA. The Young modulus of the nanocomposites increased up to 23%, for PLA containing 6 wt% CNC compared to the neat PLA; however, the strain at break slightly decreased. In dynamic mechanical thermal analysis, the storage modulus of the nanocomposites increased up to 74% in glassy region and 490% in the rubbery region. Moreover, using a percolation model, the strength of the percolating CNC network was found to depend on temperature. POLYM. COMPOS., 39:2685–2694, 2018. © 2016 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) | 0.001 | 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".