Impact of surface delignification on fire retardancy of wood treated with polyelectrolyte complexes
Bibliographic record
Abstract
Abstract Wood is a natural composite widely employed as a residential building interior finishing. Although wood is readily available and offers benefits to the occupants, such as enhanced well-being, it is rarely employed in commercial construction due, amongst others, to the potential hazard of fire propagation. The application of flame retardant (FR) treatments leads to a reduction of wood flammability and supports wood as interior finishing. Polyelectrolyte complexes (PECs) deposition is an innovative surface treatment that has already proven its efficiency for fabrics. For wood, recent studies have highlighted that the weight gain impacted the fire-retardancy, and a minimum of 2 wt.-% was set to obtain fire protection. This study explored the potential of surface delignification to activate the wood surface and facilitate the PEC impregnation. Yellow birch ( Betula alleghaniensis , Britt.) was surface delignified (0.3 mm) using sodium chlorite. The treatment impact on wood was evaluated by spectroscopy analysis (FTIR, Raman), and the increase in wood wettability was demonstrated (contact angle decreases from 50° to 35° after the surface delignification). Then, PECs consisting of polyethyleneimine and sodium phytate were surface impregnated in wood and delignified wood. The flame retardancy was evaluated using a cone calorimeter. Despite the increase in weight gain (1.5 wt.-% ± 0.3 wt.-% to 4.3 wt.-% ± 2.5 wt.-%), fire performance was not improved. This study demonstrates that lignin strongly affects char formation, even in the presence of PECs.
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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".