A Novel Method to Deliver Natural Fibre for Mechanical Reinforcement of Polyurethane Foam
Bibliographic record
Abstract
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.
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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 | 0.001 |
| 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".