Development of cellulose nanocrystal‐reinforced polylactide: A comparative study on different preparation methods
Bibliographic record
Abstract
A masterbatch of polylactide (PLA) containing cellulose nanocrystals (CNCs) prepared via a solution‐cast method was further diluted with neat PLA in the melt state using either a twin‐screw extruder or an internal batch mixer to reach a final CNC content of 4 wt%. For the sake of comparison, a direct melt mixing method was employed to prepare PLA–CNC composites. Then, the efficiency of the preparation methods was assessed by comparing the morphological, rheological and thermomechanical properties of the compounded samples. Scanning electron microscopy showed the disappearance of large agglomerates when using the PLA–CNC masterbatch. Transmission electron microscopy revealed the existence of well‐dispersed CNCs within the PLA matrix at a nanoscale for masterbatch‐based nanocomposites. The rheological properties of the nanocomposites containing the PLA–CNC masterbatch were significantly increased for both steady and small‐amplitude oscillatory shear flow fields, compared to the composites prepared via direct melt mixing. In addition, the masterbatch‐based nanocomposites exhibited pronounced overshoots in the transient start‐up viscosity. The crystalline content of the PLA in the nanocomposites and the crystallization temperature increased when the CNCs were well dispersed, which showed the nucleating effect of the CNCs. In dynamic mechanical thermal analysis, the storage modulus of the nanocomposites increased up to 41 and 128% in the glassy and rubbery regions, respectively. These results show that hydrophilic CNCs can be well dispersed and reinforce PLA using efficient preparation methods. POLYM. COMPOS., 40:E342–E349, 2019. © 2017 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) | 0.001 | 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".