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Record W2890891280 · doi:10.1177/026248931403300301

A Novel Method to Deliver Natural Fibre for Mechanical Reinforcement of Polyurethane Foam

2014· article· en· W2890891280 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueCellular Polymers · 2014
Typearticle
Languageen
FieldMaterials Science
TopicNatural Fiber Reinforced Composites
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPolyurethaneMaterials scienceComposite materialGlass fiber

Abstract

fetched live from OpenAlex

Introducing glass fibre to polyurethane foams increases the foam stiffness without raising the isocyanate content. This allows glass fibre reinforced polyurethane foams to be used in structural applications. Glass fibre reinforced polyurethane foams can be manufactured on a large scale using a chopper gun spray system. The glass fibre is commonly supplied as a roving (long strands of fibre wound into a spool) and the chopper gun breaks the roving into equal length pieces, which delivers a stream of glass fibre at a constant mass flow rate. Residual natural fibre is cost-effective, abundant and renewable making it an ideal candidate to replace non-renewable glass fibre in reinforcing polyurethane foam. However, residual natural fibre are often supplied as loose tufts and require multiple steps to be made into roving. In this study, a novel concept was developed that can meter natural fibre at a constant mass flow rate. The concept has the potential for developing natural fibre reinforced polyurethane foams on a large scale, with uniform fibre dispersion and high fibre volume fractions. The concept is verified through mathematical simulations and a prototype.

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.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.645
Threshold uncertainty score0.904

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 science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.012
GPT teacher head0.260
Teacher spread0.248 · 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