Thermal stability and performance trends of sustainable lignocellulosic olive / low density polyethylene biocomposites for better environmental green materials
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 it