Effectiveness study of Cellulose Nanocrystal (CNC) filler usage on polylactic acid (PLA) properties through plasticizer addition optimization: Application in paper-coated tableware
Bibliographic record
Abstract
Polylactic acid (PLA) represents a bioplastic imbued with biocompatibility, biodegradability , and potential as a synthetic plastic substitute. However, PLA's brittleness and inferior toughness present limitations to its broader utility. To combat this, plasticizers are incorporated to lower the glass transition temperature (Tg), enhance ductility, and refine processability . Yet, while enhancing film elongation, plasticizer use may reduce tensile strength . An alternative solution involves incorporating reinforcements or fillers such as Cellulose Nanocrystal (CNC). This study aims to scrutinize the impact of additive incorporation on resulting film characteristics. The methodology involves solvent blending via stirring at 250 rpm for 6 hours at ambient temperature, followed by 24-h film evaporation under enclosed conditions. Optimization utilizing Response Surface Methodology (RSM) via the Face Centered Composite Design (FCCD) method is also conducted. Evaluation of additive influence on the film includes X-ray diffraction (XRD) analysis to ascertain crystallinity values through characteristic peaks at 2θ ≈ 16.7°, 19.2°, and 30°, Fourier Transform Infrared (FTIR) for identifying organic and inorganic chemical compounds, and Dynamic Mechanical Analysis (DMA) to analyze tensile strength , Young's modulus , and elongation at break . While higher crystallinity is traditionally associated with increased rigidity and brittleness in polymeric materials, the results from this study reveal an interesting relationship between crystallinity and elongation in the context of PLA-based films. The addition of CNC as a filler and PEG200 as a plasticizer facilitates a unique interplay between crystalline and amorphous regions within the composite structure. CNC acts as a nucleating agent , promoting uniform crystallization and creating a finer microstructure that enhances stress distribution, while PEG200 improves the flexibility of the matrix by increasing chain mobility in the amorphous regions. This balance allows the films to stretch further before breaking, resulting in higher elongation values without significant brittleness. Furthermore, the homogeneity of CNC dispersion within the PLA matrix, as confirmed by SEM and FTIR analyses, reduces the formation of stress concentrators that typically lead to brittle failure . Through elongation at break optimization, optimal outcomes are achieved with a composition of 90 % PLA: 10 % Additive – 66 % CNC: 34 % PEG200, yielding 14.49 % elongation at break, tensile strength of 4.26 MPa, and Young's modulus of 22.95. Similar testing at the optimal point using glycerol and sorbitol plasticizers results in elongation at break values of 10 % and 91.25 %, respectively, with tensile strength values of 4.71 and 5.12 MPa, and Young's modulus values of 46.74 and 36.44 MPa, respectively. Additive inclusion in the composite also increases the crystallinity value, and morphological analysis reveals increased porosity compared to unmodified samples, indicating dispersion within the composite. The crystallinity index values exhibit linearity with elongation at break test outcomes, suggesting that higher elongation at break leads to higher crystallinity index values. This highlights how controlled crystallinity, paired with effective plasticization , can yield films that defy conventional expectations by achieving both higher crystallinity and improved elongation.
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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".