Banana fiber-LDPE recycled composites for low-cost eco-friendly construction applications
Bibliographic record
Abstract
Currently, McGill is collaborating with University of Western Australia, Moratuwa Univeristy (Colombo, Sri Lanka), and Waste for Life to create affordable eco-friendly building products for Sri Lanka. By using recycled LDPE waste plastics as well as banana fibers made from waste banana trees, composites can be made for basic housing applications. The composite material is to be made using methods easily reproducible in Sri Lanka at a low cost. Tensile properties, flame retardancy and moisture absorption were investigated to create a strong, safe, and durable product. First the plastic matrix (from Sri Lankan and Canadian sources) and banana fiber reinforcement were characterized using differential scanning calorimetry, thermogravimetric analysis, and Fourier-transform infrared spectroscopy. This is to ensure that the processing parameters can be generalized to both countries. Then a manufacturing method using compression molding was developed. Fibers of different length were used to assess their effect on manufacturing and strength. Panels made with 40 wt% of 20 cm long fibers in a random orientation yielded the best results. Then, UL94 fire tests were conducted with ATH mineral filler as a flame retardant. Integrating the flame retardant directly into the composite's layers is the most efficient way of reducing flame spread. Then, different commercially available waterproofing solutions were used to prevent moisture absorption into the composite panels. Sealing the composite with an outer layer of at least 0.3 mm of LDPE was the cheapest and most effective way to prevent water intrusions. Finally, the three aspects of the project (tensile strength, fireproofing, water absorption) were combined in a final product and characterized.
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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) | 0.002 | 0.001 |
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".