Functional Nanocomposite Films of Poly(Lactic Acid) with Well-Dispersed Chitin Nanocrystals Achieved Using a Dispersing Agent and Liquid-Assisted Extrusion Process
Bibliographic record
Abstract
The development of bio-based nanocomposites is of high scientific and industrial interest, since they offer excellent advantages in creating functional materials. However, dispersion and distribution of the nanomaterials inside the polymer matrix is a key challenge to achieve high-performance functional nanocomposites. In this context, for better dispersion, biobased triethyl citrate (TEC) as a dispersing agent in a liquid-assisted extrusion process was used to prepare the nanocomposites of poly (lactic acid) (PLA) and chitin nanocrystals (ChNCs). The aim was to identify the effect of the TEC content on the dispersion of ChNCs in the PLA matrix and the manufacturing of a functional nanocomposite. The nanocomposite film's optical properties; microstructure; migration of the additive and nanocomposites' thermal, mechanical and rheological properties, all influenced by the ChNC dispersion, were studied. The microscopy study confirmed that the dispersion of the ChNCs was improved with the increasing TEC content, and the best dispersion was found in the nanocomposite prepared with 15 wt% TEC. Additionally, the nanocomposite with the highest TEC content (15 wt%) resembled the mechanical properties of commonly used polymers like polyethylene and polypropylene. The addition of ChNCs in PLA-TEC15 enhanced the melt viscosity, as well as melt strength, of the polymer and demonstrated antibacterial activity.
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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".