High-throughput generation of aircraft-like soot
Bibliographic record
Abstract
High-throughput, laboratory units for generation of aircraft-like soot are needed to quantify and understand the impact of such emissions on public health and climate change due to the high costs and limited access to aircraft engines. Enclosed spray combustion of jet fuel is used here to generate high soot concentrations, up to 255 mg/m3, three orders of magnitude higher than those typically obtained by widely used soot generators that use vapor-fed flames. This is attributed mostly to the use of real jet fuels. The large mass concentrations enable routine characterization of the soot specific surface area (SSA) and pore size distribution (PSD) by N2 adsorption. The geometric mean mobility diameter, d¯m, of soot agglomerates was systematically varied from 15 to 180 nm by varying the equivalence ratio (EQR) at constant fuel feed rates, while the organic to total carbon (OC/TC) ratio is low (<20%) at all conditions. The geometric mean primary particle (PP) diameter, d¯p, standard deviation, σg,p, and mass-mobility exponent, Dfm, were hardly altered in that EQR range. These measured Dfm and σg,p indicate that soot PPs were sinter-bonded by surface growth, in agreement with aircraft emissions literature. Most importantly, soot made at EQR ≤ 1.34 has mainly small pores (<2 nm) and similar morphology (Dfm = 2.52 ± 0.17), SSA (160 − 239 m2/g), OC/TC (< 20%), d¯m (15 − 61 nm) and d¯p (14 nm) with those from high-thrust aircraft emissions.
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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".