Development of Bio-Composites from Milkweed Fibers Using Air-Laid Spike Process for Automobile Dashboard Applications
Bibliographic record
Abstract
This study focused on examining the reinforcement of milkweed fibers in polylactic acid (PLA) bio-composites used for dashboards in car interiors. Milkweed fiber is a natural fiber with a hollow structure that provides tremendous thermal insulation and noise resistance properties. Firstly, the milkweed fibers were blended with PLA fibers in a weight ratio of 75:25 using an air-laying process. Then, several layers of nonwoven material were compressed in a hydraulic press to obtain bio-composites. Finally, three bio-composites were obtained with different numbers of layers. The density, microstructure, thermal conductivity, sound transmission loss (STL), mechanical properties, dynamic mechanical analysis (DMA), and contact angles of the bio-composites were evaluated. The microstructure analysis revealed that some milkweed fibers collapsed due to the high-pressure molding process, which does not affect the bio-composite properties. The bio-composite with a higher number of nonwoven layers presented a poor interface between PLA and milkweed fibers, thus making it less homogeneous. This bio-composite showed a decrease of 5% in thermal conductivity values and a 19% increase in STL values. In addition, it exhibited a 160% increase in specific flexural strength and a 335% increase in specific flexural modulus compared to samples with a lower number of nonwoven layers. Therefore, it offers the best mechanical-property-to-density ratio, with values that conform to the specifications required for automotive dashboards.
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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) | 0.000 | 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 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".