Microstructure and Chemical Composition of Particles from Small-scale Gas Flaring
Bibliographic record
Abstract
Among globally relevant combustion sources, such as diesel emission and biomass burning, gas flaring remains the most uncertain. In this study, small-scale turbulent gas flaring was used to characterize particulate emissions produced under different operating conditions, such as various burner diameters and exit velocities. The composition of the fuel was also varied by modifying the percentage of methane, ethane, propane, butane, N2, and CO2, which are the predominant constituents in the upstream oil and gas industry. A broad suite of physical, chemical, and microscopic techniques was employed for analysis, and scanning electron microscopy showed the generated soot agglomerates to be composed of primary spherules that were 30 ± 10 nm in diameter. Additionally, high-resolution transmission electron microscopy, used to determine the length, tortuosity, and separation of individual graphene fringes on the primary particles, revealed a fullerenic, multiple-nuclei internal structure. Single-particle analysis revealed the dominance of elemental carbon vs. oxidized and metal-contaminated particles, and infrared spectroscopy showed the presence of alkanes and aromatics with oxygenated compounds. Intercomparing the microstructure and the composition, we also concluded that the vast majority of particles are hydrophobic.
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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) | 0.000 | 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 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".