Investigation of Treated Cellulose Filaments as Flame Retardants in Rigid Insulating Polyurethane Foam
Bibliographic record
Abstract
The flame retardant commonly used in spray polyurethane foam is tris(1-chloro-2-propyl)phosphate (TCPP), a petroleum-derived chlorinated compound that emits toxic fumes during fires. The objective of this study was to explore the viability of treating cellulose filaments as a sustainable flame-retardant solution to enhance the environmental performance of polyurethane foam and reduce the smoke emission rate in the event of a fire. Cellulose filaments (CFs) were treated using nitrogen- and phosphorus-based compounds, yielding polyelectrolyte complexes (PEC) and layer-by-layer (LbL) products. The morphology, thermogravimetric analysis, fire behavior, and water vapor sorption of the resulting polyurethane foams and treated cellulose were studied. Despite low levels of phosphorus and nitrogen treatment (0.75% and 1.4% phosphorus in PEC, and 7.47% and 4.5% in LbL), the treated CFs showed promising properties. At equivalent phosphorus levels, treated CFs exhibited residue levels comparable to the commercial flame retardant TCPP (28% residue under inert atmosphere in TGA and 44% in cone calorimeter analysis) and produced lower total smoke emissions (TSR of 400 m2/m2 for PEC, 383 m2/m2 for LbL, compared to 432 m2/m2 in TCPP foams and 531 m2/m2 in commercial foams). Further impregnation may be necessary to improve flame-retardant properties.
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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".