Mechanistic insights into enhanced fire and smoke resistance in surface densified wood: a chemical-free thermophysical approach
Bibliographic record
Abstract
Abstract Wood is a sustainable material, but its inherent flammability and smoke emissions limit its practical applications. This study proposes a thermo-physical strategy to enhance fire safety by fabricating surface densified wood (SDW). SDW were fabricated via hydrothermal pretreatment (20–80 °C) followed by thermo-densification, yielding samples with varying compression ratios (10–30 %; e.g., SDW 20 -30 %) and deformation stabilities (e.g., SDW 80 -30 %). Cone calorimetry revealed that the densified-surface-layer effectively suppressed heat and smoke release by promoting early char formation, which acted as a thermal and mass transfer barrier. SDW 20 -30 % showed 20 % and 70 % reductions in total heat release (THR) and total smoke production (TSP) within the first 360 s. Further improvements were achieved with enhanced densified-surface-layer’s stability: compared to SDW 20 -30 %, SDW 80 -30 % exhibited 32 %, 14 %, and 22 % reductions in CO yield, THR, and TSP, respectively, and delayed peak heat release rate by 73 s. Correlation analysis indicated that densified-surface-layer’s deformation stability contributed more significantly to fire hazard mitigation than densification degree. Thermal and chemical analyses confirmed increased crystallinity and compositional evolution in the densified-surface-layer, leading to improved thermal resistance. These findings demonstrate a chemical-free approach to improving wood fire safety and offer insights into the development of safer bio-based materials.
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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".