Nanocellulose Removes the Need for Chemical Crosslinking in Tannin-Based Rigid Foams and Enhances Their Strength and Fire Retardancy
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Thermal insulation and fire protection are two of the most critical features affecting energy efficiency and safety in built environments. Together with the associated environmental footprint, there is a strong need to consider new insulation materials. Tannin rigid foams have been proposed as viable and sustainable alternatives to expanded polyurethanes, traditionally used in building enveloping. Tannin foams structure result from polymerization with furfuryl alcohol via self-expanding. We further introduce cellulose nanofibrils (CNFs) as a reinforcing agent that eliminates the need for chemical crosslinking during foam formation. CNF forms highly entangled and interconnected nanonetworks, at solid fractions as low as 0.1 wt %, enabling the formation of foams that are ca . 30% stronger and ca . 25% lighter compared to those produced with formaldehyde, currently known as one of the best performers in chemically coupling tannin and furfuryl alcohol. Compared to the those chemically crosslinked, our CNF-reinforced tannin foams display higher thermal degradation temperature (peak shifted upward, by 30–50 °C) and fire resistance (40% decrease in mass loss). Furthermore, we demonstrate partially hydrophobized CNF to tailor the foam microstructure and derived physical–mechanical properties. In sum, green and sustainable foams, stronger, lighter, and more resistant to fire are demonstrated compared to those produced by formaldehyde crosslinking.
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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) | 0.002 | 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".