Size-resolved measurements of the light absorption properties of soot aerosols from a gas turbine engine
Bibliographic record
Abstract
Abstract The optical properties of soot are crucial in estimating its climate impact through direct radiative forcing. Soot light absorption is typically quantified by the mass absorption cross-section (MAC λ ) or the absorption function E( m λ ), which are wavelength dependent. Light absorbed by soot can be predicted from its MAC λ using mass-concentration measurements, or from its E( m λ ) using material density and an optical model accounting for soot-aggregate morphology. Recent work has shown that the soot MAC λ shows a size dependency, due to a size-dependent degree of graphitization. We therefore hypothesized here that a similar size dependency may be observed for E( m λ ), which we quantify here. To test this hypothesis, we present a novel approach to obtain size-resolved MAC λ and E( m λ ) of soot from a gas turbine engine by combining pulsed laser-induced incandescence signals with total mass-concentration measurements. E( m λ ) was found to vary with soot-particle size, with values ranging between 0.23 to 0.31 for the smallest (≈ 0.13 fg) and largest (≈ 3 fg) particles measured. To our knowledge, these measurements are the first to demonstrate that E( m λ ) not only varies between soot samples, but also within a population of soot particles, which impacts the interpretation of optical diagnostics and prediction of the radiative properties 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".