Fire‐Retardant and Thermal‐Insulating Cellulose Nanofibril Aerogel Modified by In Situ Supramolecular Assembly of Melamine and Phytic Acid
Bibliographic record
Abstract
The widespread utilization of cellulose nanofibril (CNF) has been significantly hindered by its inherent flammability. To explore the potential of using CNF aerogel as sustainable material with good fire‐retardant and thermal‐insulating properties, CNF aerogel is modified by in situ supramolecular assembly of melamine (MEL) and phytic acid (PA). This strategy addresses CNF's flammability and avoids the environment issues associated with the incorporation of traditional fire‐retardant. The modified aerogel exhibits highly porous honeycomb structure with low density and good mechanical properties. After modification with MEL–PA, the aerogel exhibits highly improved shape integrity during burning, higher thermal stability, and favorable combustion behavior for fire retardancy. The heat transfer of the modified aerogel is well hindered, which demonstrated effective thermal insulation performance. In view of the excellent thermal and fire‐retardant properties, the MEL–PA/CNF composite aerogel can be a potential fire‐retardant and thermal‐insulating material for applications such as clothing, building, and electronic devices.
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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) | 0.000 | 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".