Morphology and Microstructure of Engine-Emitted Particulates
Bibliographic record
Abstract
The scattering properties (influenced by morphology) and refractive index (dependent on microstructure) of engine-emitted soot influences its effect on climate, as well as how we interpret optical measurements of aerosols. The morphology and microstructure of soot from two different engines were studied. The soot samples were collected from a 1.9L Volkswagen TDI engine for two different fuel types (ULSD and B20) and six speed/load combinations., as well as from a Cummins ISX heavy-duty engine using the Westport pilot-ignited high-pressure direct-injection (HPDI) natural-gas fuelling system for three different speed/load combinations. The transmission electron microscopy (TEM) was employed to investigate the soot morphology, emphasizing the fractal properties. Image processing was used to extract the geometrical properties of the thirty-five randomly chosen aggregates from each sample. The morphologies obtained from the TEM analysis were compared to the equivalent size measurements from differential mobility analyzers. The fractal dimensions (Df) were computed using the aggregate dimension and pair correlation methods. For the soot collected from VW engine, it was found that the fractal dimensions are independent of fuel type, while engine load conditions had slight influence on Df. For the soot produced by the HDPI gas engine, similar relations between fractal dimension and engine load were also observed. Raman spectroscopy was used to discriminate the soot microstructure based on the degree of microstructural disorder. The Raman spectral analysis was done using two-band (“G” at ~1578 and “D” ~1340 cm−1) and five-band (G, D1, D2, D3, D4 at about 1580,1350, 1500, 1620 and 1200 cm−1 respectively) combinations. For the soot sampled from VW engine, the results from both methods showed that B20 soot exhibited much greater structural disorder and. Likewise, the Raman analysis of the soot from both engines also showed that the increased in engine load condition caused increases in the degree of the structural order of soot.
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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 | 0.001 | 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".