Morphology and Raman Spectra of Engine-Emitted Particulates
Bibliographic record
Abstract
The morphology and nanostructure of soot from different engines were studied. The soot samples were collected from a 1.9 L Volkswagen light-duty diesel (LDD) engine for two different fuel types [ultralow sulfur diesel (ULSD) and B20] and six speed/load combinations, as well as from a heavy-duty engine using a pilot-ignited high-pressure direct-injection (HPDI) natural-gas combustion system for three different speed/load combinations. Transmission electron microscopy (TEM) was employed to investigate the soot morphology by using alternative fractal measurement methods. The fractal dimensions (Df ) were computed from the scaling of the projected aggregate dimensions with the number of primary particles (“LW” method) and two-dimensional pair correlation functions. For the soot collected from the LDD, it was found that the fractal dimensions are independent of fuel type, while a higher engine load slightly decreased Df . The soot produced by the HPDI exhibited a similar correlation between Df and engine load. The fractal dimension of the engine-emitted soot was measured in a range of 1.70–1.85 and the fractal prefactor kfLW of 1.08–1.39. Raman spectroscopy was used to characterize the soot nanostructure based on the degree of microstructural disorder. The Raman spectral analysis was done using two-band (“G” at ∼1578 and “D” at ∼1340 cm−1) and five-band (G, D1, D2, D3, and D4 at about 1580, 1350, 1500, 1620, and 1200 cm−1 respectively) combinations. For the soot sampled from the LDD, the results from both methods showed that B20 soot exhibited a greater structural disorder. Likewise, the Raman analysis of the soot from both engines also showed that the increase in engine load condition caused increases in the degree of the structural order of soot. The use of either D/G ratio or D1 width cannot distinguish between the HPDI and the LDD soot. However, on a plot of D/G versus D1, the data fall into distinct clusters. This may indicate the importance of using more than two spectral parameters to characterize the soot samples.
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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".