A novel inversion method to determine the mass distribution of non-refractory coatings on refractory black carbon using a centrifugal particle mass analyzer and single particle soot photometer
Bibliographic record
Abstract
A novel inversion method is presented, which derives the two-variable number distribution for black carbon aerosol, using a coupled centrifugal particle mass analyzer (CPMA) and single particle soot photometer (SP2). The CPMA classifies all particles by their mass-to-charge ratio, and the SP2 detects the mass of refractive black carbon (rBC) in each individual particle. The results of the inversion are the simultaneous number distributions of both rBC mass and total particle mass. Using the distribution, the coating distribution on a population of rBC particles can be identified visually. Furthermore, the distribution can be integrated to find one-variable mass and number concentration distributions as a function of total or rBC particle mass. These capabilities were demonstrated via smog chamber experiments, where an organic (non-rBC) coating was grown onto uncoated rBC aerosol over several hours via photo-oxidation of p-xylene. The particle distributions were constructed using the inversion over a range of 1–60 fg of total particle mass. As the non-rBC coating thickness increased over time, a shift in the number distribution toward higher total mass was observed. At the end of the experiment, uncoated rBC was injected into the chamber, and the distribution was clearly resolved using the inversion. The CPMA-SP2 method offers several advantages over “SP-2 only” methods, namely, (i) coating mass information can be obtained over a wider range of total particle mass, (ii) total particle mass is measured directly, and (iii) it does not make core–shell morphology assumptions.Copyright © 2018 American Association for Aerosol Research
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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 | 0.001 |
| 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 | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".