Thermal stability and performance trends of sustainable lignocellulosic olive / low density polyethylene biocomposites for better environmental green materials
Bibliographic record
Abstract
The current trend in deteriorating mechanical performance of green polymeric-based materials has made it essential for designers to establish more reliable and sustainable bio-products. Here, the mechanical performance of Jordanian lignocellulosic olive fibers in polymeric-based composites has been methodically investigated. The outcomes of different reinforcement conditions on the desired mechanical performance of the olive leaf’s lignocellulosic fibers with low-density polyethylene (LDPE) composites have been examined, including the properties of tensile strength, tensile modulus, mechanical strain, impact strength, and the intensity per composite volume. This has been accomplished to determine the optimum reinforcement condition for the desired mechanical behavior as well as to establish the performance deterioration and enhancement trends of such bio-materials in a more consistent manner. The results signify that lignocellulosic olive fibers have exhibited various enhancements in terms of mechanical performance. Both the tensile strength and modulus of elasticity have been dramatically improved at 20 wt.% fiber content. This was the most desired reinforcement condition among all considered cases. The olive fibers also possess the capability of maintaining relatively high ductility and impact strength properties, making them suitable for various industrial applications where high ductility is necessary. Thermal stability analysis using TGA and DTG has been employed to obtain accurate results.
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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".