Mechanical and morphological properties of cellulose nanocrystal‐polypropylene composites
Bibliographic record
Abstract
In this work, the rheological, mechanical, morphological, and thermal properties of cellulose nanocrystal (CNC)‐polypropylene (PP) composites prepared in the molten state were investigated. All samples contained a maleated PP used as combatibilizer. Degradation of the PP in the presence of CNCs at high processing temperature was shown to have a significant effect on the rheological behavior. For PPs with two different molecular weights and prepared at different temperatures, the tensile modulus of composites containing 2 wt% CNC was improved by about 30% and the tensile strength was increased up to 16%, in comparison with the unfilled matrices. The tensile strain at break of composites decreased by 17% up to 75% with respect to the matrix, depending on the processing conditions and PP used. Preparing the low molecular weight PP composites via twin‐screw extrusion was shown to be more efficient than using an internal batch mixer. The tensile modulus of the PP/CNC composites could be fairly well described by a model proposed by Nielsen based on the Halpin‐Tsai equation. Finally, properties of the PP/CNC composites have been compared to those of a PP reinforced with flax fibers and a PP filled with nanoclay. POLYM. COMPOS., 39:3605–3617, 2018. © 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) | 0.000 | 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".