Banana fiber/low-density polyethylene recycled composites for third world eco-friendly construction applications – Waste for life project Sri Lanka
Bibliographic record
Abstract
In the framework of the waste for life Sri Lanka project, low value industrial low-density polyethylene waste plastics and banana fibers made from agricultural waste are upcycled into affordable eco-friendly building products. This creates income for the local communities while mitigating waste disposal and reducing ecological problems. Within this context, a robust manufacturing method using compression molding was developed. Panels with varying fiber content were manufactured using unchopped and chopped fibers. Low-density polyethylene characterization using Fourier transform infrared spectroscopy and differential scanning calorimetry to compare plastic sources was conducted. To reduce cost, no fiber treatments or compatibilizers were used. An estimate of critical fiber length was found to be around 1.45 cm to 2.5 cm. A trend of increasing strength with fiber content (up to 40 wt.%) was achieved by using longer fibers than in the previous research. Handling and dispersion of the fibers were increased by chopping the fibers to 20 cm lengths, which led to an increase in tensile strength due to easier manufacturing. Cross-ply panels made with fibers chopped to a length of 20 cm were found to be strongest peaking at around 40 wt.% with a tensile strength of 32.8 MPa, a fourfold increase compared to a raw low-density polyethylene (0 wt.%) panel.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".