Gas evolution in self-extinguishing and insulative nanopolysaccharide-based hybrid foams
Bibliographic record
Abstract
Lightweight, energy-efficient materials in building construction typically include polymeric and composite foams. However, these materials pose significant fire hazards due to their high combustibility and toxic gas emissions, including carbon monoxide and hydrogen cyanide. This study delves into the latter aspects by comparing hybrid systems based on nanofiber-reinforced silica-based Pickering foams with a synthetic reference (polyurethane foams). The extent and dynamics of fire retardancy and toxic gas evolution were assessed, and the results revealed the benefits of combining the thermal insulation of silica with the structural strength of biobased nanofibers, the latter of which included anionic and phosphorylated cellulose as well as chitin nanofibers. We demonstrate that the nanofiber-reinforced silica-based Pickering foams are thermal insulative and provide both fire safety and energy efficiency. The results set the basis for the practical design of hybrid foams to advance environmental sustainability goals by reducing energy consumption in built environments.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".