Mechanical, Morphological, Thermal and Dynamic Study of Composites of Unsaturated Polyesters-Date Palm Leaf Fiber DPLF
Bibliographic record
Abstract
In order to improve the properties of unsaturated polyesters, this study discusses the possibilities of developing a natural waste, date palm leaf fiber DPLF, produced in the northern Algerian Sahara, associated with the polymer matrix of a thermosetting polyester resin UP. For this purpose, composite plates containing virgin fiber at rates of 6 and 10% were treated with an alkaline solution of 6% NaOH on the one hand, and a silane compound on the other. In this research, a mechanical study of strength and elongation at break was carried out. In addition, morphological behavior was followed by SEM scanning electron microscopy. ATG thermogravimetric analysis and energy flow were monitored by DSC differential scanning calorimetry. Also, a study of the water absorption capacity has been conducted. In addition, a dynamic mechanical analysis DMA was carried out. The findings of this study show that there is a favorable mechanical behavior for the composites containing the 6% and 10% DPLF fiber, with alkaline NaOH and Silane treatment. They also show that the chemical treatment with alkaline solution and silane gives composites certain thermal stability compared to those with untreated fiber. Findings also explore that the absorption of water by the various composites shows that the chemical treatment promotes some intermolecular associations with water. Findings also show that the storage modulus (E') increases when the composite contains 10% DPLF, treated and untreated, and the maximum value of the tangent moves towards the high temperature for the treated and untreated fiber composite.
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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".