Preparation and Characterization of Sustainable Polyurethane Foams from Soybean Oils
Bibliographic record
Abstract
Abstract Polyol derived from soybean oil was made from crude soybean oil by epoxidization and hydroxylation. Soy‐based polyurethane (PU) foams were prepared by the in‐situ reaction of methylene diphenyl diisocyanate (MDI) polyurea prepolymer and soy‐based polyol. A free‐rise method was developed to prepare the sustainable PU foams for use in automotive and bedding cushions. In this study, three petroleum‐based PU foams were compared with two soy‐based PU foams in terms of their foam characterizations and properties. Soy‐based PU foams were made with soy‐based polyols with different hydroxyl values. Soy‐based PU foams had higher Tg (glass transition temperature) and worse cryogenic properties than petroleum‐based PU foams. Bio‐foams had lower thermal degradation temperatures in the urethane degradation due to natural molecular chains with lower thermal stability than petroleum skeletons. However, these foams had good thermal degradation at a high temperature stage because of MDI polyurea prepolymer, which had superior thermal stability than toluene diisocyanate adducts in petroleum‐based PU foams. In addition, soy‐based polyol, with high hydroxyl value, contributed PU foam with superior tensile and higher elongation, but lower compressive strength and modulus. Nonetheless, bio‐foam made with high hydroxyl valued soy‐based polyol had smaller and better distributed cell size than that using low hydroxyl soy‐based polyol. Soy‐based polyol with high hydroxyl value also contributed the bio‐foam with thinner cell walls compared to that with low hydroxyl value, whereas, petroleum‐based PU foams had no variations in cell thickness and cell distributions.
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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) | 0.000 | 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".