Crystallinity and Gas Permeability of Poly (Lactic Acid)/Starch Nanocrystal Nanocomposite
Bibliographic record
Abstract
The present work seeks to determine the impact of weight percentage (wt%) of grafted starch nanocrystals (g-SNCs) on the oxygen and water vapour permeability of poly (lactic acid), PLA. Changes in the oxygen and water vapour permeability of PLA due to changes in PLA's crystalline structures and lamellar thickness were quantified. To this end, 3, 5, and 7 wt% of g-SNC nanoparticles were blended with PLA using the solvent casting method in order to study impact of g-SNC nanoparticles on crystallization behaviour, long spacing period, melting behavior, and oxygen and water barrier properties of PLA nanocomposites. This was achieved by wide-angle X-ray diffraction (WAXD), small-angle X-ray diffraction (SAXD), differential scanning calorimetry (DSC), and oxygen and water vapour permeability machine. The results of the WAXD and SAXD analysis show that the addition of 5 wt% g-SNC in PLA induces α crystal structure at a lower crystallization time, while it significantly increases the α crystal thickness of PLA, in comparison to neat PLA. However, when g-SNC concentrations were altered (i.e., 3 or 7 wt%), the crystallization time was found to increase due to the thermodynamic barrier of crystallization. Finally, the oxygen and water vapour permeability of PLA/SNC-g-LA (5 wt%) nanocomposite film were found to be reduced by ∼70% and ~50%, respectively, when compared to the neat PLA film. This can lead to the development of PLA nanocomposites with high potential for applications in food packaging.
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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".