Effect of a Novel Coupling Agent, Polybutadiene Isocyanate, on Mechanical Properties of Wood-Fiber Polypropylene Composites
Bibliographic record
Abstract
The scope of the present paper is to study the effect of adding a novel coupling agent, polybutadiene isocyanate (PBNCO), on the mechanical properties of hardwood aspen fiber/polypropylene (PP) composites. The resulting properties were compared to those obtained with the most commonly used coupling agent, maleic anhydride modified polypropylene (MAPP). In this study, we determined a value of 24.75 MPa for the tensile strength of pure PP and 22 MPa for the composite containing 30 or 40wt% unmodified fibers. These results indicate that wood fiber behaves merely as filler when incorporated into PP and no reinforcing effect was observed in this case. This occurs because of the chemical incompatibility between the thermoplastic PP and the polar fiber, resulting in low interfacial adhesion. However, it was verified that addition of 3% MAPP and 5% PBNCO to this formulation produced composites with better performance, since the tensile and impact properties were increased up to 30 MPa and 22 J/m 2 , respectively. This behavior can be attributed to the enhanced interfacial bond between reinforcing fibers and polymer matrix modified MAPP and PBNCO treatments, play a significant role in improving the mechanical properties of the composites. The increase in mechanical properties demonstrated that PBNCO is an effective coupling agent for wood fiber/PP composites.
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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".