Influence of fibre extraction method, alkali and silane treatment on the interface of Agave americana waste HDPE composites as possible roof ceilings in Lesotho
Bibliographic record
Abstract
Surface treatment is often necessary for strong composites. But the challenge for developing countries is to find chemicals and treatment procedures that are cheap and simple but maintain good composite properties. Mercerization followed by silane treatment of natural fibres is among the simplest and cheapest methods used to improve composite interfaces. This study investigates the effectiveness of this method to improve the bond between Agave americana fibres and post consumer HDPE. The influence of fibre extraction method, mercerization and mercerization followed by silane treatment on interfacial shear strength (ISS) and fibre properties is determined. The results indicate that ISS values are generally low but mercerization doubles the ISS values between Agave americana fibres extracted by traditional boiling of leaves and post consumer HDPE. Mercerization also improves fibre tensile and thermal properties. While triethoxyvinylsilane treatment of fibres after mercerization does not improve the ISS, it does not reduce it either, nor does it reduce tensile and thermal strengths of mercerized fibres. Fibres from non-boiled leaves resulted in poor fibre tensile strengths but improved ISS. There is a potential to use mercerization as cheap, simple technique to make Agave americana HDPE composites to provide cheap roof ceilings in Lesotho.
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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 | 0.001 | 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".