A Comparative Analysis of Tannin and Commercial Fire Retardants in Wood Fire Protection
Bibliographic record
Abstract
In civil construction, one of the primary challenges associated with wood application is its high flammability and low durability during fires. Although chemical treatment with fire-retardant properties exists, they are expensive and of non-renewable origin. Tannin, a wood extractive, being a phenolic compound, holds promise for enhancing the thermal properties of wood. This study aimed to assess the efficacy of tannin as a fire retardant and compare it with a commercial product, as well as comparing different application techniques for these products. Wood samples from the Simarouba amara Aubl. species were utilized. Tannin and a commercial flame retardant were applied via immersion, vacuum impregnation, brushing, and a treatment of tannin incorporated into water-based wood varnish. Alongside the burning test performance, assessments of the wood’s physical properties, such as colorimetry, thermal stability, and mass retention, were conducted. The application of tannin altered the wood’s color and reduced the spread of fire; its presence significantly reduced the flame duration and maintained the wood’s structural integrity. However, tannin retention was lower compared to commercial flame retardant, leading to reduced fire retardancy. Among the methods tested, immersion proved to be the most effective in enhancing the wood’s resistance to flame contact.
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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 | 0.001 |
| 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".