Polymerization compounding of hemp fibers to improve the mechanical properties of linear medium density polyethylene composites
Bibliographic record
Abstract
Improved mechanical properties play an important role allowing the expansion of natural fiber composites in automotive interior parts and construction industries. Unfortunately, their hydrophilic nature leads to incompatibility with hydrophobic matrices limiting their full potential. But this can be overcome by fiber surface modification. So the objective of this work was to modify hemp fibers by an advanced process called surface‐initiated catalytic polymerization and to evaluate its effect in terms of morphological, rheological, and mechanical properties for linear medium density polyethylene (LMDPE) composites. The results showed that catalytic polymerization of hemp fibers was successful as the fibers were coated with grafted polymer chains (polyethylene). This was confirmed by an increase of the C‐H and C‐C groups by FTIR on the fibers surface, as well as the presence of an extra peak in DTG curves between 400 and 500°C. This was also confirmed by SEM pictures and density change owing to the presence of these grafted PE molecules. The composites morphology (SEM) and mechanical properties in the solid ( E ' and tan δ ) and melt (van Gurp‐Palmen plot, complex and transient viscosity, and activation energy) state showed that, the presence of PE molecules on the surface of treated hemp fibers led to a significant improvement of the fiber‐matrix interfacial quality producing significant increases of the composite's (30% wt.) Young's modulus (8%) and tensile strength (43%) with respect to the composite based on untreated fibers, which represents a 125% and 58% increases with respect to the neat polymer. POLYM. COMPOS., 39:2860–2870, 2018. © 2017 Society of Plastics Engineers
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".