Some Mechanical Properties of Coconut Fiber Reinforced Polyethylene Composite to Control Environmental Waste in Ghana
Bibliographic record
Abstract
Polymer products have been applied in all spheres of life and their disposal after use has been a problem. In Ghana, non-biodegradable polymer products in the form of used water-sachet bags is littered everywhere. Coconut husk, which is a natural fiber, is also available as waste. We explore a means of recycling sachet-water bags and coconut husk to yield a useful product. A composite was formed by melting the polyethylene, into which was dispersed coconut fiber, and then allowed to set. Varied masses of fiber were added after which water absorption test, hardness/compressive and flexure tests were conducted on the composite product. The absorption rate of the composite increased with increasing composition of fiber, meaning that the porosity of the material was influenced by the amount of fiber. Increasing the fiber content increased the load needed to compress the sample, indicating an increase in the strength of the composite. The load-bearing capacity increased by 120 % when 450.5 g of fiber was added to the control sample, and further increased to 800 % when the fiber mass was increased to 804.4 g. With an amount of 100 g of fiber added to the polyethylene, the flexure increased by about 5.73 % and by about 31.46 % when 450 g of fiber was added. There was therefore improvement in the mechanical properties of the composite formed, and consequently such waste products can be put to use in applications like the production of ceilings, partition boards, automobile interiors and the likes.
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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".