Foam stability and thermo-mechanical properties of micro/nano filler loaded castor oil based flexible polyurethane foam
Bibliographic record
Abstract
Abstract Use of fillers in polymers is to improve thermo-mechanical properties of the resulting material. Fillers are also used in polymeric foam as cell openers. Flexible polyurethane (PU) foams undergo major loss in structural stability when synthetic polyol is replaced with castor oil in the formulation as an alternate polyol. This study probes the effect of various micro and nano-fillers on PU foams prepared using blend polyol containing castor oil and synthetic polyol at a ratio of 1:1. Physical and cellular properties such as foam height, cell diameter, strut thickness and cell number density were evaluated to probe the structural stability of the foam. All foams prepared were found stable while it was found that the densities of the PU foams synthesized were greater than that of the conventional PU foams. Addition of fillers found to enhance thermal and mechanical properties of the foam. Moreover, all foam samples were found to observe thermal stability over and above 258 °C. Minimum glass transition temperature was recorded for 15% HG samples (i.e., −35.5 °C). Highest tensile strength was observed for 15% Si samples whereas, highest elongation was observed for 10% NC.
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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".