Morphology and Raman Spectra of Engine-Emitted Particulates
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| 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)
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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 it