Effect of mixing conditions on the morphology and performance of fiber-reinforced polyurethane foam
Bibliographic record
Abstract
Polyol derived from soybean oil was used as a natural source preparation to create an environmentally friendly polyurethane foam. In order to stiffen these foams, wood fibers were added to provide reinforcement at low cost, while preserving the environmental friendly nature of the material. Mixing is a crucial step in the manufacture of reinforced foams and determines the fiber distribution and cell structure and hence the performance of the material. In this study, the effect of stirring variables on the mechanical properties of polyurethane foams was investigated and foams made using hand mixing were compared to foams made with a mechanical stirrer. Cell morphologies of the reinforced foams were characterized using scanning electron microscopy, X-ray computed tomography, and 3D stereo microscopy. The mechanical performance of the reinforced foam was essentially independent of stirring time and mainly depended on the variation in stirring rate. For foams made with high fiber fractions using a mechanical stirrer, the fibers were not as effective in increasing the compressive strength and modulus as they were in foams prepared by hand stirring. This suggests that mechanical stirring causes damage to the fibers, particularly at high fiber content.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 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".