The Detection Efficiency of the Single Particle Soot Photometer
Bibliographic record
Abstract
A single particle soot photometer (SP2) uses an intense laser to heat individual aerosol particles of refractory black carbon (rBC) to vaporization, causing them to emit detectable amounts of thermal radiation that are used to quantify rBC mass. This approach is well suited for the detection of the majority of rBC mass loading in the ambient atmosphere, which occurs primarily in the accumulation mode (∼ 1–300 fg-rBC/particle). In addition to operator choices about instrument parameters, SP2 detection of rBC number and/or mass can be limited by the physical process inherent in the SP2 detection technique — namely at small rBC mass or low laser intensities, particles fail to heat to vaporization, a requirement for proper detection. In this study, the SP2's ability to correctly detect and count individual flame-generated soot particles was measured at different laser intensities for different rBC particle masses. The flame-generated soot aerosol used for testing was optionally prepared with coatings of organic and non-organic material and/or thermally denuded. These data are used to identify a minimum laser intensity for accurate detection at sea level of total rBC mass in the accumulation mode (300 nW/(220-nm PSL)), a minimum rBC mass (∼ 0.7-fg rBC-mass corresponding to 90 nm volume-equivalent diameter) for near-unity number detection efficiency with a typical operating laser intensity (450 nW/(220-nm PSL)), and a methodology using observed color temperature to recognize laser intensity insufficient for accurate rBC mass detection. Additionally, methods for measurement of laser intensity using either laboratory or ambient aerosol are presented.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".