In Situ Imaging Studies of Combustor Pressure Effects on Soot Oxidation
Bibliographic record
Abstract
Airborne soot is a product of incomplete combustion from engines and industrial processes. Unburnt soot is carcinogenic, a major contributor to climate change, and detrimental to combustor lifespan and efficiency. An understanding of how high-pressure combustion affects the oxidation properties of soot is crucial for the design of clean-burning, high-pressure engines and downstream soot filtration technologies. This paper presents the first real-time look at the oxidation of soot particles formed at high pressure and demonstrates that the oxidation pathway changes as combustor pressures increase. Soot particles were formed in an ethylene-fueled diffusion flame, with pressures ranging from 1 to 25 bar, and were subsequently sampled and oxidized inside an ETEM allowing for the nanoscale, real-time observation of oxidation pathways. The high-pressure generated soot grew larger in diameter, formed larger aggregates, and developed graphitic outer shells, protecting the reactive amorphous carbon core. The graphitic shell structure fundamentally changed the oxidation pathway of soot from diffusion driven internal oxidation at lower pressures to highly nonreactive surface oxidation reactions at high pressures. This work demonstrates that as combustor pressures increase to achieve higher thermodynamic efficiencies, highly resilient oxidation-resistant soot is produced.
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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".