Mechanical and Insulation Performance of Rigid Polyurethane Foam Reinforced with Lignin-Containing Nanocellulose Fibrils
Bibliographic record
Abstract
Isocyanates are critical components that affect the crosslinking density and structure of polyurethane (PU) foams. However, due to the cost and hazardous nature of the precursor for isocyanate synthesis, there is growing interest in reducing their usage in polyurethane foam production-especially in rigid PU foams (RPUF) where isocyanate is used in excess of the stoichiometric ratio. In this study, lignin-containing nanocellulose fibrils (LCNF) were explored as mechanical reinforcements for RPUF with the goal of maintaining the mechanical performance of the foam while using less isocyanate. Different amounts of LCNF (0-0.2 wt.%) were added to the RPUF made using isocyanate indices of 1.1, 1.05, 1.0, and 0.95. Results showed that LCNF served as a nucleating agent, significantly reducing cell size and thermal conductivity. LCNF addition increased the crosslinking density of RPUF, leading to enhanced compressive properties at an optimal loading of 0.1 wt.% compared to unreinforced foams at the same isocyanate index. Furthermore, at the optimal loading, LCNF-reinforced foams made at lower isocyanate indices showed comparable stiffness and strength to unreinforced foams made at higher isocyanate indices. These results highlight the reinforcing potential of LCNF in rigid polyurethane foams to improve insulation and mechanical performance with lower isocyanate usage.
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 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".