Mechanical recycling of biobased polyethylene-agave fiber composites
Bibliographic record
Abstract
Biobased polymers have emerged as a promising alternative to petroleum-based polymers in terms of lower environmental impact. However, to improve their carbon footprint, it is important to study strategies, such as recycling, extending the useful life of these biopolymers, and mitigate their higher costs compared to petroleum-based polymers. Adding agro-industrial wastes as fillers or reinforcements is another option to reduce the cost and increase the biobased content to produce composites. This study aimed to evaluate the addition of agave fibers to biobased linear low-density polyethylene (bio-LLDPE) and their effect on its reprocessing by extrusion, i.e., close-loop mechanical recycling. The results revealed that it was possible to reprocess the bio-LLDPE alone as limited changes in their physical properties were observed up to 34 cycles. However, for the composites, the viscosity changed in the first eight cycles mainly due to fiber break-up (lower aspect ratio). The dimensions of the agave fibers are modified by reprocessing. In the initial 8 cycles, there is a notable decrease in fiber dimensions, affecting the tensile, flexural, and impact properties of the composites. The water uptake was found to decrease with each cycle due to better fiber dispersion and the reduction of interfacial voids/defects. Nevertheless, the color of the bio-LLDPE and its composites showed significant changes by reprocessing, which is associated with thermal and oxidation degradation. Despite minor property losses, the study reveals that bio-LLDPE/agave fiber composites exhibit a commendable level of sustainability. This characteristic enables their extended reuse and reprocessing over a prolonged duration.
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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".