The Effect of Fiber Surface Treatment on the Performance of Hemp Fiber/Acrylic Composites for Automotive Structural Parts
Bibliographic record
Abstract
The use of natural fibers for polymer composite materials has increased tremendously in the last few years. This type of reinforcements offers many advantages such as low density, low cost, high specific strength and low environmental impacts. The performance of the natural fiber composites are affected by the fiber loading, the individual mechanical properties of each component (fiber and matrix), and the fiber and matrix adhesion. Concerning the interfacial interaction, natural fibers present a major drawback because of poor compatibility of fibers with most hydrophobic thermoplastic and thermoset matrix. Hemp fiber/acrylic composites were manufactured with sheet molding technique recently. Although mechanical tests give promising results, they exhibit low tensile strength resulting from a poor fiber/matrix adhesion. The moisture resistance property of the sheet molded composites also needs further improvement. In the present work, we propose surface modification of the hemp fibers to improve the surface adhesion and moisture absorption properties of these structural composites. The influence of various chemical treatment such as alkali, silane, SMA Imide resin (copolymers of styrene and dimethylamionopropylamine (DMAPA) maleimide), and fungal treatment were investigated. All type of treatments improved substantially the hydrophobic behavior of the fibers. The SMA treated samples show maximum improvement in their tensile strength and modulus.
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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".