Evaluating Class B Fire Extinguishing Efficacies of Organic Nitro Compounds Based Aerosol Forming Compositions
Bibliographic record
Abstract
Every year, increasing fire accidents worldwide have directed our attention towards an effective fire suppression system. For many decades, Halons have been used as effective fire extinguishing agents. Vienna convention (1985) and Montreal protocol (1987) determined Halons as ozone-depleting agents, and subsequently, their manufacturing is banned by the U.S. Environmental Protection Agency. Thus, against the urgent background of this elimination of Halons, aerosol-based extinguisher resulted as one of the significant Halons substitute technology. The Ozone Depletion Potential and Global Warming Potential values of aerosol extinguishing agents are nearly zero. This has provided thrust for various Universities and research institutions for undertaking numerous projects to develop aerosol-forming pyrotechnic composites for fire fighting applications. In the present work, novel fire extinguishing pyrotechnic compositions were fabricated, in which four different organic nitro compounds, 3,5-dinitrosalicylic acid, 3,5-dinitrobenzoic acid, m-nitrobenzenesulphonate and 3-nitrophthalic acid, were used as the reductant. These nitro compounds undergo rapid redox reaction with strong oxidants to produce aerosol fire extinguishing agents, extinguishing 4.57 KW of small scale n-heptane pool fires in 3 seconds. The fire extinguishing efficacy and thermal characteristics of novel compositions were evaluated against potassium nitrate and phenol formaldehyde resin-based conventional pyrotechnic composition.
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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 | 0.001 | 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".