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Record W4413377950 · doi:10.3390/ma18163821

Milkweed Fiber Nonwovens for Sustainable Thermal and Acoustic Building Insulation

2025· article· en· W4413377950 on OpenAlexafffundabout
Deborah Lupescu, Mathieu Robert, Saïd Elkoun

Bibliographic record

VenueMaterials · 2025
Typearticle
Languageen
FieldMaterials Science
TopicTextile materials and evaluations
Canadian institutionsUniversité de Sherbrooke
FundersMitacs
KeywordsMaterials scienceComposite materialFiberThermal conductivityPolyethylene terephthalatePolyolefinThermal insulationGlass woolPolylactic acidPolyethyleneThermal resistanceThermalSoundproofingPolymer

Abstract

fetched live from OpenAlex

This study investigates the use of a local fiber, specifically milkweed that grows in Quebec, Canada, for nonwoven building applications. Milkweed is a natural fiber with an ultra-lightweight hollow structure that provides excellent acoustic and thermal insulation properties. To provide three-dimensional stability to nonwovens, milkweed fibers were blended with a low-melt fiber composed of a polyethylene terephthalate core and a polyolefin sheath (LM 2.2), and polylactic acid (PLA) fibers. Several nonwovens with different fiber contents were manufactured using an air-laid Spike process. The nonwovens were compared with a commercially available thermal insulation material made of 100% hemp. The thermal conductivity and thermal resistance were measured at different temperatures. The sound absorption coefficient of the nonwovens was determined both using an impedance tube and the Johnson-Champoux-Allard (JCA) acoustic model. The results showed that all nonwovens exhibit thermal conductivity values below 70 mW/m·K at temperatures ranging from -4 °C to 24 °C, which are lower than many materials commonly used in building applications. A sample presented a thermal resistance that is 8%, 10%, and 45% higher than those of rock wool, polyisocyanurate (PIR), and fiberglass, respectively.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.015
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.014
GPT teacher head0.289
Teacher spread0.274 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2025
Admission routes3
Has abstractyes

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