Collection efficiency of the soot-particle aerosol mass spectrometer (SP-AMS) for internally mixed particulate black carbon
Bibliographic record
Abstract
Abstract. The soot-particle aerosol mass spectrometer (SP-AMS) uses an intra-cavity infrared laser to vaporize refractory black carbon (rBC) containing particles, making the particle beam–laser beam overlap critical in determining the collection efficiency (CE) for rBC and associated non-refractory particulate matter (NR-PM). This work evaluates the ability of the SP-AMS to quantify rBC and NR-PM mass in internally mixed particles with different thicknesses of organic coating. Using apparent relative ionization efficiencies for uncoated and thickly coated rBC particles, we report measurements of SP-AMS sensitivity to NR-PM and rBC, for Regal Black, the recommended particulate calibration material. Beam width probe (BWP) measurements are used to illustrate an increase in sensitivity for highly coated particles due to narrowing of the particle beam, which enhances the CE of the SP-AMS by increasing the laser beam–particle beam overlap. Assuming complete overlap for thick coatings, we estimate CE for bare Regal Black particles of 0.6 ± 0.1, which suggests that previously measured SP-AMS sensitivities to Regal Black were underestimated by up to a factor of 2. The efficacy of the BWP measurements is highlighted by studies at a busy road in downtown Toronto and at a non-roadside location, which show particle beam widths similar to, but greater than that of bare Regal Black and coated Regal Black, respectively. Further BWP measurements at field locations will help to constrain the range of CE for fresh and aged rBC-containing particles. The ability of the SP-AMS to quantitatively assess the composition of internally mixed particles is validated through measurements of laboratory-generated organic coated particles, which demonstrate that the SP-AMS can quantify rBC and NR-PM over a wide range of particle compositions and rBC core sizes.
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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 | 0.001 | 0.002 |
| 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 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".