Fiber-matrix interface improvement via glycidyl methacrylate compatibilization for rotomolded poly(lactic acid)/agave fiber biocomposites
Bibliographic record
Abstract
The growing interest in research and development of eco-friendlier materials makes attractive the use of bio-based and biodegradable polymers such as polylactic acid (PLA). However, the higher cost of PLA compared to conventional polymers limits its applications. Moreover, raw materials for rotational molding must be in a powder form, which further increases their cost. So, the main objective of this study was to use agave fibers to produce lower-cost PLA based rotomolded biocomposites (BC) without compromising its bio-sourced origin and to compare with a standard rotomolding resin: linear medium density polyethylene (LMDPE). To improve the fiber-matrix interface, a chemical surface treatment of the fibers with glycidyl methacrylate grafted polylactic acid (GMA-g-PLA) in solution was evaluated. The results showed that a better biocomposites’ morphology was obtained, especially with the fibers treated twice. The surface treatment was also shown to substantially improve the flexural and tensile properties of treated fiber biocomposites at higher fiber content (25% wt.) compared to those with untreated fiber. The surface treatment also led to a substantial reduction of the biocomposites porosity and water absorption. Overall, the samples were shown to have better mechanical properties than neat LMDPE while being eco-friendlier due to their bio-nature.
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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) | 0.001 | 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".