Water‐Blown Rigid Biofoams from Soy‐Based Biopolyurethane and Microcrystalline Cellulose
Bibliographic record
Abstract
Abstract A novel soy‐based polyurethane biofoam (BioPU) from two polyols (soybean oil‐derived polyols SOPEP and petrochemical polyol Jeffol A‐630 = 1:1 in weight) and poly (diphenylmethane diisocyanate) (pMDI) has been prepared by using a free‐rise method with water as a blowing agent, and microcrystalline cellulose (MCC) as a reinforcement. The photographs of the samples show that the biofoams have similar appearances, and the cell morphology of the resulting biofoams was examined by scanning electron microscope. Density of the composites decreased as a result of increase in MCC content. FTIR study exhibited characteristic peaks for MCC and BioPU. Mechanical properties such as compressive strength, compressive modulus, flexural strength and flexural modulus of the samples were substantially improved with the increase in MCC content. Similarly, improvements in glass transition temperature (Tg) and storage modulus around and after Tg of the neat biofoam were also observed with the composites. Dynamic mechanical analysis results showed an improvement in mechanical properties as well as better thermal stability of the composites over the neat biofoam. Thermogravimetric analysis showed improved thermal stability of the biofoams reinforced with MCC. This research has provided a simple method for preparing the biofoam, while exploring the potential of substituting up to 50 % of the petroleum‐based polyol in biofoam applications.
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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".