Sustainable and lightweight biopolyamide hybrid composites for greener auto parts
Bibliographic record
Abstract
Abstract Sustainable bio‐based materials have remarkable environmental and health impacts throughout their life cycles. Over the past few decades, green biocomposites have attained rising attraction in the automotive industry, as they can be customized to meet many of its prime requirements. Polyamides are the most common engineering polymers used in the automotive industry due to their desirable properties. This study focuses on improving thermo‐mechanical properties of wood fibre/carbon fibre biopolyamide hybrid composites for automotive applications. Important material properties such as tensile, flexural, and impact strengths along with density, melt flow index, and heat deflection temperature were studied and correlated with their SEM surface morphologies. The composites were produced by melt‐compounding of the fibres and polymers via extrusion and injection moulding. All hybrid composites exhibited greater thermo‐mechanical properties compared to wood fibre composites. Use of a polymer blend of polyamide and polypropylene matrix in the composites further enhanced performance properties of the composites while reducing the costs. The developed hybrid composites had lower densities compared to the existing materials used in some auto parts. The mechanical properties of polymer blend composites, including tensile, flexural, and impact properties were higher than those of polyamide composites. Image analysis showed efficient fibre‐matrix adhesion with good fibre dispersion in the composites. A significant improvement in heat deflection temperature was observed for the hybrid polymer blend composites. The study indicated that the developed hybrid bio‐based composites are promising candidates with light‐weighting potential for automotive structural applications, where high stiffness and thermal resistance are required.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".