Green Composites from Plasticized Cellulose Acetate and Microcrystalline Cellulose: Effect of Maleated Cellulose Acetate on the Biocomposite Performance
Bibliographic record
Abstract
Novel green composites were engineered from microcrystalline cellulose (MCC) and plasticized cellulose acetate (pCA). The influence of the MCC content and effect of maleic anhydride-grafted cellulose acetate (MA- g -CA) on the performance of green composites were also analyzed. Green composites were developed with up to 15 wt % MCC with different concentrations of MA- g -CA (2, 4, and 7 wt %). Green composites with 15 wt % MCC showed an increase of 21% and 5% in tensile and flexural moduli, respectively, compared to neat pCA, leading to enhanced stiffness and rigidity of the composite material. After the addition of MA- g -CA, the impact strength and elongation at break increased by 22% and 20%, respectively, than their counterparts without MA- g -CA, indicating its plasticizing effect. Scanning electron microscopy confirmed an adequate dispersion of filler particles after the addition of MCC to the CA matrix. The addition of 4% MA- g -CA to 15 wt % MCC green composites exhibited an improved fiber-matrix adhesion. This gave comparable tensile strength and enhanced tensile modulus compared to the neat matrix. Also, the 7% MA- g -CA-added samples showed the highest extensional viscosity of the resulting green composites.
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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) | 0.001 | 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".