Evaluating the Properties of Native and Modified Milkweed Floss for Applications as a Reinforcing Fiber
Bibliographic record
Abstract
The use of natural fibers is a sustainable alternative for developing reinforced-polymer composites. It is believed that the seed flosses of common milkweed, Asclepias Syriraca, may be a promising reinforcing fiber given its uncommon hollow microstructure that is associated with both high specific properties and outstanding insulating capacities. This study presents an overview of the properties of milkweed floss and its potential use in reinforced-polymer composites. Milkweed flosses from Quebec were analyzed to determine their overall dimensions, density, porosity, coefficient of acoustic absorption, thermal conductivity, thermal resistance, and elastic modulus. In parallel, a portion of milkweed fibers was treated with acetone to modify their surface, and the properties of the treated fibers were measured and compared against the characteristics of the original fibers. Infrared spectroscopy was employed to assess differences between the chemical groups on the surface of treated and native fibers. The treatment with acetone removed fatty acids, waxes, and free sugars from the fibers’ surface. The acetone treatment did not affect the fibers’ microstructure nor their acoustic absorption capacity. The acetone-treated fibers showed greater thermal resistance and a higher thermal conductivity than native milkweed floss. The elastic modulus of milkweed decreased by nearly 49% after the acetone treatment.
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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".