Establishing the thermal changes in the foam layer of a biocomposite coating upon the addition of potassium nitrate
Bibliographic record
Abstract
The object of research is the fire-retardant properties of biocomposite coating for wood when potassium nitrate is added during thermal action. The problem of fire resistance of biocomposites, which is aimed at increasing the efficiency of fire protection of wooden structures, consists in the formation of fire-resistant coatings by adding inorganic compounds. Such compounds are characterized by thermal stability, reduced smoke-forming ability and toxicity of combustion products and are mostly used as synergistic additives. Therefore, the work proves that during thermal action on wood treated with biocomposite coating, intensive formation of a foam coke layer began after 62 s to a height of 16 mm, which thermally insulated the wood. Instead, after the radiation panel was exposed to a sample of wood treated with a biocomposite coating with the addition of potassium nitrate, the formation of a foam coke layer began after 48 s of thermal exposure, the height of which was 15 mm, and the temperature of the flue gases did not exceed 100°C. At the same time, chemical reactions of potassium nitrate begin in the coating with the release of carbon monoxide, with the subsequent destruction of ammonium polyphosphate, which interacts with the biopolymer. The latter, in turn, affects the dehydration of pentaerythritol with the formation of coke, and the simultaneous decomposition of melamine causes the coke to foam. The composition of the gaseous products of thermal destruction of the biocomposite coating showed that when potassium nitrate was added, the amount of combustible gases decreased by more than 19%, and the amount of nitrogen increased by more than 10%. The practical significance is that the results obtained were taken into account when developing a fire-retardant coating for construction. Thus, there are grounds to argue about the possibility of effective protection of wood with a coating containing biopolymers
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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) | 0.000 | 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) | 0.001 | 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".