Synthesis of polyurethanes from solvolysis lignin using a polymerization catalyst: Mechanical and thermal properties
Bibliographic record
Abstract
Two methods of synthesis, namely, using a polymerization catalyst versus a non-catalytic route, were investigated to produce lignin-based polyurethanes. The films were characterized with respect to crosslink density, ultimate tensile behavior and glass transition temperature. The results indicated that use of the catalyst for polymerization is an effective way for producing films with consistent properties, even at lignin contents as high as 45 to 50 wt%. To illustrate the catalyst effectiveness, crosslink densities of catalyzed films with 20 wt% of lignin content increased drastically from 0.2-0.3 to 1.7-2.7 mmol/cm 3 when the NCO/OH molar ratio increased from about 1.3 to 3.0, without much increase in the corresponding crosslink densities of the non-catalyzed films. Also, when the NCO/OH molar ratio increased from 1.2 to 3.2, the tensile strength increased from 1.9 MPa to a maximum of 55 MPa (NCO/OH=2.6) before decreasing. Also, for same NCO/OH ratios, ultimate strain decreased drastically from 174.4% to 4.3%, with a corresponding increase in Young's Modulus from 0.03 GPa to 2.8 GPa. The glass transition temperatures of the catalyzed films also increased from 35°C to 89°C. Without the catalyst, only polyurethanes with low NCO/OH ratios, low lignin contents, and inferior mechanical properties, could be synthesized.
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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) | 0.001 | 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".