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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".