Lignin‐Based Polyurethane: Recent Advances and Future Perspectives
Bibliographic record
Abstract
Polyurethane (PU), as a polymer material with versatile product forms and excellent performance, is used in coatings, elastomers, adhesives, and foams widely. However, the raw materials (polyols and isocyanates) of PU are usually made using petroleum-derived chemicals. With the concern for depletion of petroleum resources and the associated negative impact on the environment, developing technologies that can use renewable raw materials as feedstock has become a research hotspot. Lignin, as an abundant, natural, and renewable organic carbon resource, has been explored as raw material for making polyurethanes because it possesses rich hydroxyl groups on its surface. Meanwhile, compared to vegetable oils, lignin does not compete with food supply and performance of the resulting products is superior. Lignin or modified lignin has been shown to impart the polyurethane material with additional functionalities, such as UV-blocking ability, hydrophobicity, and flame retardancy. However, the utilization of lignin has encountered some challenges, such as product isolation, heterogeneity, aggregation, steric hindrance, and low activity. This paper summarizes recent research progress on utilizing lignin and modified lignin for bio-based polyurethane synthesis with a focus on elastomers and foams. Opportunities and challenges for application of the lignin-based polyurethanes in various fields are also discussed.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".