Recycled Low Density Polyethylene Reinforced with Deverra tortuosa Vegetable Fibers
Bibliographic record
Abstract
In this work, natural fibers extracted from the medicinal aromatic plant Deverra tortuosa, with different sizes (S1 = 2 mm and S2 = 500 μm), were incorporated into recycled low density polyethylene (rLDPE) to produce sustainable biocomposites. Compounding was performed with different fiber concentrations (0 to 30% wt.) via twin-screw extrusion followed by injection molding. Based on the samples obtained, a comprehensive series of characterization was conducted, encompassing morphological and mechanical (flexural, tensile, hardness, and impact) properties. Additionally, thermal properties were assessed via differential scanning calorimetry (DSC), while Fourier transform infrared spectroscopy (FTIR) was used to elucidate potential chemical interactions and changes with processing. Across the range of conditions investigated, substantial improvements were observed in the rLDPE properties, in particular for the tensile modulus (23% for S1 and 104% for S2), flexural modulus (47% for S1 and 61% for S2), and flexural strength (31% for S1 and 65% for S2). Nevertheless, the tensile strength decreased (15% for S1 and 46% for S2) due to poor fiber–matrix interfacial adhesion. These preliminary results can be used for further development in sustainable packaging materials.
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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".