Pre-treatment of Flax Fibers for use in Rotationally Molded Biocomposites
Bibliographic record
Abstract
The objective of this study was to determine the effects of pre-treated flax fibers on the performance of the fiber-reinforced composites. Lack of good interfacial adhesion and poor resistance to moisture absorption make the use of natural fiber-reinforced composites less attractive. In order to improve fiber/matrix interfacial properties, fibers were subjected to chemical treatments, namely, mercerization, silane treatment, benzoylation, and peroxide treatment. Selective removal of non-cellulosic compounds constitutes the main objective of the chemical treatments of flax fibers to improve the performance of fiber-reinforced composites. Flax fibers were derived from Saskatchewan-grown flax straws. Composites consisting of high-density polyethylene (HDPE) or linear low-density polyethylene (LLDPE) or HDPE/LLDPE mix, chemically treated fibers and additives were prepared by the extrusion process. The test samples were prepared by rotational molding. The fiber surface morphology and the tensile fracture surfaces of the composites were characterized by scanning electron microscopy (SEM). The effects of the different chemical treatments on the mechanical and the physical properties of natural fiber-reinforced composites were investigated. The differential scanning calorimetry (DSC) was used to measure the melting point of the fiber-reinforced 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".