Thermal Insulation Properties of Milkweed Floss Nonwovens: Influence of Temperature, Relative Humidity, and Fiber Content
Bibliographic record
Abstract
This study investigated the influence of fiber content, temperature, and relative humidity on the thermal insulation properties of nonwoven mats made of seed fibers from Asclepias Syriaca, commonly known as milkweed floss. Nonwoven mats with a 1-inch thickness were produced by uniformly arranging milkweed fibers within a mold. Various quantities of fiber were employed to obtain nonwoven mats with a fiber content ranging from 5 to 35 kg/m3. Thermal conductivity and thermal diffusivity were measured across diverse relative humidity levels and temperatures. Simultaneously, milkweed floss samples were exposed to identical environmental conditions to assess the moisture regain and specific heat capacities of the fiber. The specific heat capacity of milkweed and thermal conductivity of the nonwovens exhibited a linear increase with temperature. The thermal diffusivity and thermal conductivity of the nonwovens decreased with rising fiber content. The thermal insulation properties of the nonwovens remained partially stable below 30% relative humidity but substantially deteriorated at higher levels. The nonwovens exhibited optimal thermal insulation properties at a fiber content between 20 and 25 kg/m3. The results of this study highlighted several technical advantages of employing milkweed floss as a sustainable and lightweight solution for thermal insulation.
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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".