Rheological, Mechanical, and thermal properties of polylactide/cellulose nanofiber biocomposites
Bibliographic record
Abstract
Biocomposites based on polylactide (PLA) and cellulose nanofibers (CNFs) were prepared via a solution method. The effects of CNFs on rheological, mechanical, thermal, and optical properties of PLA were investigated. Scanning electron microscopy showed that the CNFs were fairly dispersed/distributed in the PLA. Significant increases in the rheological properties of PLA/CNF composites and a remarkable shear‐thinning behavior were observed. Also, apparent yield stress and a transition from liquid‐ to solid‐like behavior indicated a strong 3D network of CNFs. At room temperature, the storage and Young moduli were increased by 50% for the composite containing 5 wt% CNFs as compared to the neat PLA, whereas the tensile strength was increased up to 31%. The Krenchel model was shown to predict well the Young modulus for lower concentrations of CNFs. Moreover, relative to the neat PLA the storage modulus in flexion at 70°C increased by 264% for PLA containing 5 wt% CNFs. Increased crystalline content and a positive shift of the crystallization temperature by incorporating the CNFs in PLA were observed. Also, good light transparency was retained for these PLA/CNF biocomposites. These results show that the preparation method employed in this work leads to PLA/CNF composites with considerably enhanced properties. POLYM. COMPOS., 39:1752–1762, 2018. © 2016 Society of Plastics Engineers
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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".