Development of Flax Fibre Reinforced Biocomposites for Potential Application for Automotive Industries
Bibliographic record
Abstract
{ Natural fibre-reinforced composite has the potential to replace current materials used for automotive industrial applications. Oilseed flax fibre could be used as reinforcement for composites because it is readily available, environmentally friendly and possesses good mechanical properties. In this research, oilseed flax fibre reinforced-LLDPE and -HDPE biocomposites were developed through extrusion and injection molding. The flax fibre was chemically treated to improve the bond between the fibre and polymer. Flax fibre was mixed with low linear density polyethylene (LLDPE) and high density polyethylene (HDPE) with fibre content varying from 10 to 30% by mass and processed by extrusion and injection molding to biocomposites. The mechanical properties, surface properties, and thermal properties of biocomposites were measured to analyze the treatment and processing effect and to compare the effect of different flax fibre concentrations on the biocomposites. Results showed that developed biocomposites had good properties compared to with pure plastic. Increasing fibre content increased tensile strengths and water absorption; it also decreased molding shrinkage and melting point of biocomposites. The factors in the injection process were also studied to achieve better biocomposite properties.
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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".