Green Polyurethanes and Bio-fiber-based Products and Processes
Bibliographic record
Abstract
Green polyurethane (PU), discussed in this chapter, is considered to be ‘green’ because of the foams themselves (rather than the urethane resins) which are widely used in industrial sustainable applications. PU foams can be highly cross-linked and, consequently, blown with chemical blowing agents such as water. Green PU foams are already used in carbon neutral construction as insulation and in automotive seating systems. In view of environmental and carbon emission concerns, there is now greater emphasis on the utilization of vegetable oil or plant oil polyols in PU fabrication, particularly soybean-oil-derived polyols. However, some plant oils, such as epoxidized soybean oil and castor oil, can react through their hydroxyl groups. Due to the limited industrialization of sustainable isocyanates, the basic raw biomaterials for green PU are bio-based polyols and bio-mass such as natural fibers and lignin, which are derived from sustainable sources. Obviously, the introduction of bio-mass into PU manufacturing eventually increases the renewable content of PU products. Green PU foams made from bio-based polyols were initially selected for automotive parts (i.e. seat cushions, headliners, armrests and load floors) because of their low weight, high quality, thermal stability, high R-values and air-sealing properties. In the current market, more petroleum-based PU products have been replaced with new bio-based alternatives without compromising the integrity of the product, while improving bio-degradability. Green PU foam is manufactured in two cell forms which depend on the manufacturing procedures and formulations; the foaming method and formulation determine the foam density and cell quality. Finally, the addition of reactive bio-mass has a significant effect on the foam characteristics as well as on their final performance, with a decrease in product cost.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".