Simultaneous identification of multi-combustion-intermediates of alkanol-air flames by femtosecond filament excitation for combustion sensing
Bibliographic record
Abstract
Laser filamentation produced by the propagation of intense laser pulses in flames is opening up new possibility in application to combustion diagnostics that can provide useful information on understanding combustion processes, enhancing combustion efficiency and reducing pollutant products. Here we present simultaneous identification of multiple combustion intermediates by femtosecond filament excitation for five alkanol-air flames fueled by methanol, ethanol, n-propanol, n-butanol, and n-pentanol. We experimentally demonstrate that the intensities of filament-induced photoemission signals from the combustion intermediates C, C2, CH, CN increase with the increasing number of carbons in the fuel molecules, and the signal ratios between the intermediates (CH/C, CH/C2, CN/C, CH/C2, CN/CH) are different for different alkanol combustion flames. Our observation provides a way for sensing multiple combustion components by femtosecond filament excitation in various combustion conditions that strongly depend on the fuel species.
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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".