Effects of an Iron Pentacarbonyl Additive on Counterflow Natural Gas and Ethanol Flames
Bibliographic record
Abstract
The addition of metallic precursors to flames evinces interest because of their potential ability to catalyze methane and ethanol combustion by means of supplemental gas-phase and surface reactions. A counterflow flame burner is used to spatially characterize and analyze the emissions from iron-pentacarbonyl-borne ethanol and methane combustion. Samples of the flue gases are obtained from these laminar and planar flames and are quantified using gas chromatography (GC) and Fourier transform infrared (FTIR) spectroscopy, while solid particles are examined through X-ray diffraction (XRD). Measurements from ethanol and methane flames are compared and analyzed, to investigate the role of metal particles derived from iron pentacarbonyl. Experimental data demonstrate, in both flames, a significant influence of the additive on combustion emissions, such as NO and soot precursors. The addition of iron pentacarbonyl is found to be more effective in restricting soot precursors in methane flames compared to ethanol flames. An enhanced production of acetaldehyde in the ethanol flame is observed under catalytic conditions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".