Determination of the optimum coupling agent content for composites based on hemp and high density polyethylene
Bibliographic record
Abstract
It is well known that for polymer composites based on natural fibers, the addition of a coupling agent is necessary to improve fiber dispersion and adhesion with the matrix. Nevertheless, an optimum content must be found which is related to the total surface area created between the fibers and the matrix. But in most cases reported in the literature, a single property (like tensile strength or flexural modulus) is used to determine this optimum value. In this work, high density polyethylene (HDPE) was reinforced with hemp fibers as a typical system. In particular, the addition of a coupling agent based on maleated polyethylene (MAPE) was studied to determine its optimum content. To better detect the specific effect of the selected coupling agent, reinforcement content was limited to 10% wt., while the MAPE content was controlled at different levels (0, 5, 7, 9 and 11% wt. based on total hemp content). Compounding was performed in a twin-screw extruder and the samples were produced by compression molding. From the composites obtained, a complete set of characterization in terms of morphology (SEM), mechanical properties (tension, flexion, and impact), and density (pycnometry) was made. From all the results obtained, it can be shown that 9% wt. MAPE is the optimum content maximizing all the mechanical properties. These results indicate that any physical property can be used to determine the optimum coupling agent content.
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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 | 0.001 |
| 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".