Raman spectroscopy and TEM characterization of solid particulate matter emitted from soot generators and aircraft turbine engines
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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 it