Assessing the Suitability of Agro-Waste from Oil Palm Empty Fruit Bunches as Quality Eco-Composite Boards
Bibliographic record
Abstract
The properties of eco-composite boards from agro-waste of oil palm empty fruit bunches were studied. The oil palm empty fruit bunches (EFB) obtained from a private oil palm plantation. By using fibre cutter and particle crusher, these EFB refined. Hardeners and wax added at 1% and 3% during the mixing process. Boards with densities of 500, 600 and 700 kg/m3 produced using resin urea formaldehyde as the bonding agent at 10, 12 and 14%. The boards conditioned in a conditioning chamber set at 20±2 °C and 65% relative humidity before undergoing subsequent testing. The EN Standards specifications applied in the preparation of test samples and testing. Results showed the highest modulus of rupture (MOR) and modulus of elasticity (MOE) achieved in this study were 22.91 N/mm2 and 2059.56 N/mm2. The internal bonding was found to be at 0.98 N/mm2, and 467.47 N/mm2 and 512.37 N/mm2 respectively for the edge and face screw withdrawal. Boards with 700 kg/m3 density and 14% resin content met the requirement of standard specifications. Scanning electron microscopy machine used to study the resin-fibre bonding property. Resin and fibre in the board inspected carefully, and voids appeared at the cross-section of the board with density 500 kg/m3 at 10% resin suggesting moisture penetrated into the board via the open spaces and weakened the linkages existed, thus cause the board to have low properties. The thermogravimetric analysis indicates maximum rate of decomposition for the EFB boards occurred at 380.83 °C. This study shows that the board’s density and resin content applied influence on the board’s overall properties with boards produced at 700 kg/m3 density with 14% resin content showed excellent overall properties with good dimensional stability.
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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 | 0.000 | 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".