Toward Elimination of Soot Emissions from Jet Fuel Combustion
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Soot from jet fuel combustion in aircraft engines contributes to global warming through the formation of contrail cirrus clouds that make up to 56% of the total radiative forcing from aviation. Here, the elimination of such emissions is explored through N 2 injection (containing 0–25 vol % O 2 ) at the exhaust of enclosed spray combustion of jet fuel that nicely emulates aircraft soot emissions. It is shown that injecting N 2 containing 5 vol % of O 2 enhances the formation of polyaromatic hydrocarbons (PAHs) that adsorb on the surface of soot. This increases soot number density and volume fraction by 25 and 80%, respectively. However, further increasing the O 2 concentration to 20 or 25 vol % enhances oxidation and nearly eliminates soot emissions from jet fuel spray combustion, reducing the soot number density and volume fraction by 87.3 or 95.4 and 98.3 or 99.6%, respectively. So, a judicious injection of air just after the aircraft engine exhaust can drastically reduce soot emissions and halve the radiative forcing due to aviation, as shown by soot mobility, X-ray diffraction, Raman spectroscopy, nitrogen adsorption, microscopy, and thermogravimetric analysis (for the organic to total carbon ratio) measurements.
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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".