Mechanical properties of biodegradable composites from poly lactic acid (PLA) and microcrystalline cellulose (MCC)
Bibliographic record
Abstract
Abstract Biodegradable composites were prepared using microcrystalline cellulose (MCC) as the reinforcement and polylactic acid (PLA) as a matrix. PLA is polyester of lactic acid and MCC is cellulose derived from high quality wood pulp by acid hydrolysis to remove the amorphous regions. The composites were prepared with different MCC contents, up to 25 wt %, and wood flour (WF) and wood pulp (WP) were used as reference materials. Generally, the MCC/PLA composites showed lower mechanical properties compared to the reference materials. The dynamic mechanical thermal analysis (DMTA) showed that the storage modulus was increased with the addition of MCC. The X‐ray diffraction (XRD) studies on the materials showed that the composites were less crystalline than the pure components. However, the scanning electron microscopy (SEM) study of materials showed that the MCC was remaining as aggregates of crystalline cellulose fibrils, which explains the poor mechanical properties. Furthermore, the fracture surfaces of MCC composites were indicative of poor adhesion between MCC and the PLA matrix. Biodegradation studies in compost soil at 58°C showed that WF composites have better biodegradability compared to WP and MCC composites. The composite performances are expected to improve by separation of the cellulose aggregates to microfibrils and with improved adhesion. © 2005 Wiley Periodicals, Inc. J Appl Polym Sci 97: 2014–2025, 2005
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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".