Soot Formation in Co- and Counter-flow Laminar Diffusion Flames of Binary Mixtures of Ethylene and Butane Isomers and Synergistic Effects
Bibliographic record
Abstract
Soot volume fractions of binary mixtures of butane isomers, ethylene−butane isomers, and propane−butane isomers were evaluated experimentally in diffusion flames on both co- and counter-flow burners. Soot volume fractions were measured by two-dimensional line of sight attenuation of a broadband arc lamp generated light in co-flow flames, whereas in counter-flow flames, attenuation of a small radius laser beam was used. Binary mixtures of iso-butane and n -butane did not show any synergistic effects on soot formation. On the other hand, either n -butane or iso-butane addition to ethylene caused a strong synergistic effect in both types of flames that the soot volume fractions were higher than those of the individual mixture components under the same flame conditions. Binary mixtures of propane and butane isomers, however, did not display any measurable synergistic effect on soot formation. These observations were discussed in the light of mechanisms proposed by previous investigators to explain the synergistic effects detected in the flames of binary mixtures. Current results cast doubt on the universality of the dominance of any of the mechanisms previously proposed to explain the synergistic effects observed with some binary hydrocarbon mixtures.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| 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".