Enhancing Fire Resistance and Mechanical Properties of Wood Strand Boards by Impregnation with Sodium Bicarbonate and Sodium Borate
Bibliographic record
Abstract
The development of halogen-free flame-retardant formulations for wood-based panels is a promising strategy to improve both fire safety and environmental performance. In this study, oriented strand boards (OSB) were impregnated with aqueous solutions of sodium borate (SBo) and sodium bicarbonate (SBi) to evaluate their combined effects on fire resistance and mechanical properties. Fire performance was assessed using the ASTM D3806 small-scale tunnel test, while mechanical and physical properties were measured according to ASTM D1037. Significant improvements in fire performance were observed: mass loss (ML) during flammability testing decreased by 38% (from 6.9% to 4.3%), flame spread speed (FSS) was reduced by more than 50% (from 6.8 to 3.3 mm/s), and after-flame times (AFT) dropped from 17.2 s to 0 s. Thermogravimetric analysis (TGA) further confirmed enhanced thermal stability, with increased char residue (from 16.9% in untreated boards to 31.5% in treated ones). Mechanical testing revealed a 16% increase in internal bond (IB) strength (from 0.44 to 0.51 MPa), while modulus of rupture (MOR) and modulus of elasticity (MOE) were only slightly affected (decreased by up to 4.2% and 3.6%, respectively). Interestingly, the two additives exerted contrasting effects: SBo reduced strength and bonding performance, whereas SBi improved internal bond strength and dimensional stability. The optimal balance was obtained with treatment P250-50 (250 g SBi and 50 g SBo), which combined enhanced fire resistance with acceptable mechanical integrity. Overall, the results demonstrate that the synergistic use of SBo and SBi offers an effective halogen-free approach to simultaneously enhance the fire resistance and mechanical performance of OSB panels, highlighting its potential for industrial applications.
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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) | 0.001 | 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".