Investigating the use of natural hollow fibers from common milkweed to improve the mechanical and thermal properties of epoxy resin
Bibliographic record
Abstract
Abstract Milkweed floss was used as a reinforcement to develop lightweight epoxy composites with enhanced thermal insulating properties. Seed fibers from Asclepias Syriaca harvested in Quebec were used for this study. Some fibers were treated with acetone to remove their protective hydrophobic coating. Epoxy resin was blended with 15% w/w of native or acetone‐treated milkweed floss to form prepregs. The prepregs were casted into molds, degassed, and compressed to produce the composites. The composites were passed through a post‐curing phase before characterization. The composites and the epoxy resin alone were characterized to determine their density, porosity, thermal conductivity, thermal degradation, mechanical resistance, and thermo‐mechanical behavior. The composites reinforced with native or acetone‐treated milkweed floss were 7.4% and 10.3% lighter than the epoxy resin, respectively. The specific elastic modulus of the resin increased by 9.6% and 20.1% with the addition of native or acetone‐treated fibers, respectively. The thermal conductivity of the epoxi decreased by 7.8% and 15.6% with the use of native or acetone‐treated milkweed floss, respectively. Due to their low weight and reduced thermal conductivity, the milkweed‐reinforced epoxy composites may find applications in the production of structural and insulating elements for vehicles and buildings.
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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".