Biopolymer Blends of Poly(lactic acid) and Poly(hydroxybutyrate) and Their Functionalization with Glycerol Triacetate and Chitin Nanocrystals for Food Packaging Applications
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Polylactic acid (PLA) is a biopolymer that has potential for use in food packaging applications; however, its low crystallinity and poor gas barrier properties limit its use. This study aimed to increase the understanding of the structure property relation of biopolymer blends and their nanocomposites. The crystallinity of the final materials and their effect on barrier properties was studied. Two strategies were performed: first, different concentrations of poly(hydroxybutyrate) (PHB; 10, 25, and 50 wt %) were compounded with PLA to facilitate the PHB spherulite development, and then, for further increase of the overall crystallinity, glycerol triacetate (GTA) functionalized chitin nanocrystals (ChNCs) were added. The PLA:PHB blend with 25 wt % PHB showed the formation of many very small PHB spherulites with the highest PHB crystallinity among the examined compositions and was selected as the matrix for the ChNC nanocomposites. Then, ChNCs with different concentrations (0.5, 1, and 2 wt %) were added to the 75:25 PLA:PHB blend using the liquid-assisted extrusion process in the presence of GTA. The addition of the ChNCs resulted in an improvement in the crystallization rate and degree of PHB crystallinity as well as mechanical properties. The nanocomposite with the highest crystallinity resulted in greatly decreased oxygen (O) and carbon dioxide (CO2) permeability and increased the overall mechanical properties compared to the blend with GTA. This study shows that the addition ChNCs in PLA:PHB can be a possible way to reach suitable gas barrier properties for food packaging films.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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 it