Development of Thermally Insulating Nonwovens from Milkweed Fibers Using an Air-Laid Spike Process
Bibliographic record
Abstract
Milkweed (MW) fiber is a natural fiber that provides tremendous thermal insulation properties due to its lightweight hollow structure. This study aimed to investigate the effect of milkweed fiber as a thermal fiber in nonwovens. Milkweed fibers were blended with a low-melt fiber consisting of a polyethylene terephthalate core, a polyolefin sheath (LM 2.2), and polylactic acid (PLA) fiber. Nonwovens with different fiber contents were manufactured using an air-laid Spike process to determine their effect on thermal and mechanical properties. Then, the nonwovens were compared with Thinsulate® and Primaloft®, two commercially synthetic insulation products. Structural properties, including mass per unit area, thickness, and porosity and thermal properties were studied. Furthermore, compression and short-term compression recovery were also evaluated. The results revealed that milkweed-based nonwovens that contained 50 wt% or 70 wt% of milkweed presented a lower thermal conductivity than synthetic nonwovens. Milkweed nonwovens of the same thickness provided identical thermal resistance as Thinsulate® and Primaloft. Sample 3, composed of 50 wt% MW, 20 wt% LM 2.2, and 30 wt% PLA, demonstrated the same thermal insulation as Thinsulate® with a weight three times lighter. Milkweed nonwovens presented higher moisture regain values than Thinsulate® and Primaloft®, without affecting thermal conductivity.
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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".