Olive Cellulosic Fibre Based Epoxy Composites: Thermal and Dynamic Mechanical Properties
Bibliographic record
Abstract
This study deals with the evaluation of the impact of three different olive tree residues: olive tree small branch (OTS), olive tree big brunch (OTB) and olive tree leaves (OTL) as a filler on thermal properties of olive/epoxy biocomposites. Olive residue-based epoxy composites were processed at 40% filler loading to fabricate biocomposites by hand lay-up techniques. The thermal stability was investigated by thermal gravimetric analysis (TGA) while dynamic mechanical properties and thermal expansion of fiber composites were analyzed by the dynamic mechanical analyzer (DMA) and thermomechanical analyzer (TMA). The OTL/epoxy composite showed improvement in thermal and DMA (storage modulus, loss modulus, and damping factor) as compared to OTB and OTS/epoxy composites. On the other hand, OTS filled epoxy matrix exhibited the greatest thermal degradation temperature while CTE was the lowest and greatest dynamic mechanical properties over all composites. DMA results revealed that the OTS/epoxy composite possesses the highest storage modulus in view of the strong fiber/matrix interfacial. It is evident from obtained results that the incorporation of olive biomass enhanced thermal, dimensional, and dynamic mechanical characterizations of epoxy composites and appropriate use for automotive or materials applications of building that mandate high-dimensional stability and dynamic mechanical characterizations.
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) | 0.001 | 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".