Mechanical and Thermal Properties of Recycled Fishing Net-Derived Polyamide 6/Switchgrass Fiber Composites for Automotive Applications
Bibliographic record
Abstract
The increasing demand for sustainable materials in automotive applications, coupled with the critical need to address marine plastic pollution, presents an opportunity for innovative material development. This study explores composites made from recycled polyamide 6 (PA6) fishing nets reinforced with switchgrass fibers (0–30 wt%). The composite with 30 wt% switchgrass fibers increased tensile strength by 23% and Young’s modulus by 126% compared to unreinforced recycled PA6, achieving 93% of the tensile strength of commercial automotive-grade neat PA6 and surpassing another grade by 22%. However, higher fiber loading hindered processability, as evidenced by incomplete mold filling and reflected by a decrease in melt flow rate from 19.35 to 8.63 g/10 min. Thermal analysis revealed reduced crystallinity and crystallization temperatures with fiber addition, attributed to restricted polymer chain mobility. While dynamic mechanical analysis demonstrated improved stiffness below the glass transition temperature, scanning electron microscopy indicated optimal fiber-matrix adhesion at up to 20 wt% fiber loading, with aggregation at higher concentrations. These findings establish recycled fishing net-derived PA6/switchgrass fiber composites as a viable alternative to virgin materials in automotive applications, with mechanical properties comparable to commercial grades. Although the composites demonstrate enhanced mechanical strength and modulus, the significant reduction in ductility restricts their use to rigid, semi-structural components where flexibility is not critical. Future research should address processing challenges to enhance fiber dispersion and interfacial adhesion at higher loadings.
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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".