Polyurethane Foams Reinforced with Biobased Materials: Properties and Applications
Bibliographic record
Abstract
Background: Today, polyurethane foams can be found in various commercial products such as bedding, home furniture, automotive interiors and even construction materials. From a chemical point of view, polyurethane foams are made from a chemical reaction between a polyol (molecules with more than one hydroxyl group) and a diisocyanate in the presence of a blowing agent. Objective: Because of their highly stable bonds, polyurethane foams are considered as nondegradable leading to some environmental impact. To address this concern different bio-based fillers have been used to create "greener" polyurethane materials. This review presents an overview of different bio-based fillers and containing natural polyols for polyurethane foams formulation with respect to their natural properties, sizes, geometries and contents. Method: A wide range of bio-based fillers derived from wood and non-wood sources are summarized based on their physico-mechanical properties. Then, possible applications are presented and future trends are discussed for the research and development of these complex (multiphase systems) materials (polymer composite foams). Conclusion: Beside traditional polyurethane foams applications including automotive, building, home furniture and package, bio-based filler addition could bring new feature and widen their applications such as shape memory and medication, as well as oil absorbent.
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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 | 0.001 | 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".