Development of Polylactic Acid Films with Alkali- and Acetylation-Treated Flax and Hemp Fillers via Solution Casting Technique
Bibliographic record
Abstract
This study aims to enhance value addition to agricultural byproducts to produce composites by the solution casting technique. It is well known that PLA is moisture-sensitive and deforms at high temperatures, which limits its use in some applications. When blending with plant-based fibers, the weak point is the poor filler–matrix interface. For this reason, surface modification was carried out on hemp and flax fibers via acetylation and alkaline treatments. The fibers were milled to obtain two particle sizes of <75 μm and 149–210 μm and were blended with poly (lactic) acid at different loadings (0, 2.5%, 5%, 10%, 20%, and 30%) to form a composite film The films were characterized for their spectroscopy, physical, and mechanical properties. All the film specimens showed C–O/O–H groups and the π–π interaction in untreated flax fillers showed lignin phenolic rings in the films. It was noticed that the maximum degradation temperature occurred at 362.5 °C. The highest WVPs for untreated, alkali-treated, and acetylation-treated composites were 20 × 10−7 g·m/m2 Pa·s (PLA/hemp30), 7.0 × 10−7 g·m/m2 Pa·s (PLA/hemp30), and 22 × 10−7 g·m/m2 Pa·s (PLA/hemp30), respectively. Increasing the filler content caused an increase in the color difference of the composite film compared with that of the neat PLA. Alkali-treated PLA/flax composites showed significant improvement in their tensile strength, elongation at break, and Young’s modulus at a 2.5 or 5% filler loading. An increase in the filler loadings caused a significant increase in the moisture absorbed, whereas the water contact angle decreased with an increasing filler concentration. Flax- and hemp-induced PLA-based composite films with 5 wt.% loadings showed a more stable compromise in all the examined properties and are expected to provide unique industrial applications with satisfactory performance.
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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 | 0.001 |
| 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".