Multiscale Design for Robust, Thermal Insulating, and Flame Self‐Extinguishing Cellulose Foam
Bibliographic record
Abstract
Abstract Cellulose foams are in high demand in an era of prioritizing environmental consciousness. Yet, transferring the exceptional mechanical properties of cellulose fibers into a cellulose network remains a significant challenge. To address this challenge, an innovative multiscale design is developed for producing cellulose foam with exceptional network integrity. Specifically, this design relies on a combination of physical cross–linking of the microfibrillated cellulose (MFC) networks by cellulose nanofibril (CNF) and aluminum ion (Al 3+ ), as well as self‐densification of the cellulose induced by ice‐crystal templating, physical cross–linking, solvent exchange, and evaporation. The resultant cellulose foam demonstrates a low density of 40.7 mg cm –3 , a high porosity of 97.3%, and a robust network with high compressive modulus of 1211.5 ± 60.6 kPa and energy absorption of 77.8 ± 1.9 kJ m −3 . The introduction of CNF network and Al 3+ cross–linking into foam also confers excellent wet stability and flame self‐extinguish ability. Furthermore, the foam can be easily biodegraded in natural environments , re‐entering the ecosystem's carbon cycle. This strategy yields a cellulose foam with a robust network and outstanding environmental durability, opening new possibilities for the advancement of high‐performance foam materials.
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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".