Physical, Thermal and Mechanical Characterization of a New Material Composite Based on Fibrous Wood Particles of Date Palm Tree
Bibliographic record
Abstract
The objective of this work is to valorize the waste from date palms tree which is often abandoned in the palm groves The aim is to produce a new composite based on this waste that can be used as an alternative material to the conventional insulators. The approach consists in making a composite material from petiole wood (WPC) in different particle sizes (0 to 1, 1 to 3 and 3 to 5) mm. We then characterized the physical, thermal and mechanical properties of this new material (WPC). The results obtained proved the relative anisotropy of the material and the effect of the particle size distribution on these properties. The composites (WPC) had low density in the range (0.16-0.56) g/cm3 and also exhibited low thermal conductivity, in the range (0.109-0.122)W/mK°. These weak properties make it possible to use (WPC) as an effective insulator. These characteristics were quite acceptable in comparison with other thermal insulation materials such as cork agglomerate and traditional wood. The interesting mechanical properties of the new composite (WPC) have been shown by the tensile tests and the three-point flexural tests. These results make it possible to valorize these materials (WPC) for possible industrial applications.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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".