Novel approach for silane treatment of flax fiber to improve the interfacial adhesion in flax/bio epoxy composites
Bibliographic record
Abstract
Natural fibers have been approved as an excellent alternative for traditional reinforcements in polymer composites. However, the main disadvantage of natural fibers as reinforcements is their poor interaction with polymeric matrices due to their hydrophilic nature. In order to improve their compatibility as composite reinforcement, surface treatments are required. Silane treatment has been widely used to modify hygroscopic properties of natural fibers. In this study, a new method of silane treatment has been developed without the prehydrolysis and curing/dehydration of silanol groups. To do so, flax fibers were modified through 2,2,6,6-tetramethylpiperidine-1-oxy radical (TEMPO)-mediated oxidation followed by direct application of the amino-silane. The fiber/matrix interfacial adhesion was examined by measuring interlaminar shear strength. Thermogravimetric analysis and dynamic mechanical analysis were performed to study the thermal resistance and thermomechanical properties of the composites. The results revealed that fiber/matrix adhesion was improved in the oxidized silanized fiber composite. In addition, water absorption was significantly reduced (20%) in the oxidized silanized fiber reinforced composite compared with the as-received one.
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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".