Raman spectroscopy and TEM characterization of solid particulate matter emitted from soot generators and aircraft turbine engines
Bibliographic record
Abstract
To obtain reliable mass concentrations of solid particulate matter (PM) in the exhaust emissions from engines using optical instruments, it is essential that the solid PM used for instrument calibration has similar optical properties to the solid PM emitted from the engines being tested. The solid PM emitted from combustion engines is predominantly soot. The optical properties of soot are dictated by its chemical structure, size, and morphology. In this work, the chemical bond structure, primary-particle diameters, aggregate sizes, and morphological parameters of the soot emitted from two laboratory soot generators, widely used for calibrating instruments, are compared to those of soot emitted from three aircraft turbine engines using Raman spectroscopy and transmission electron microscopy. The Raman spectral properties, size, and morphology of soot emitted from aircraft engines are distinctly different from the properties of soot emitted from the soot generators operating under globally near-stoichiometric and fuel-rich conditions. These differences can be attributed to the variations in the size and orientation of the graphitic crystallites, amorphous-carbon content, amount of polyacetylene compounds, deposition of organic material, and extent of oxidation. Conversely, general agreement is observed between the chemical structure, size, and morphology of soot emitted from aircraft engines and the soot emitted from the soot generators operating at globally fuel-lean conditions. The findings of this investigation can be useful for identifying suitable soot particles for the calibration of instruments to measure the mass concentration of solid PM emissions from engines, and for other types of soot.Copyright © 2017 Crown Copyright
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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) | 0.000 | 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".