Predicting hygroscopic growth using single particle chemical composition estimates
Bibliographic record
Abstract
Abstract Single particle mass spectral data, collected in Paris, France, have been used to predict hygroscopic growth at the single particle level. The mass fractions of black carbon, organic aerosol, ammonium, nitrate, and sulphate present in each particle were estimated using a combination of single particle mass spectrometer and bulk aerosol chemical composition measurements. The Zdanovskii‐Stokes‐Robinson (ZSR) approach was then applied to predict hygroscopic growth factors based on these mass fraction estimates. Smaller particles with high black carbon mass fractions and low inorganic ion mass fractions exhibited the lowest predicted growth factors, while larger particles with high inorganic ion mass fractions exhibited the highest growth factors. Growth factors were calculated for subsaturated relative humidity (90%) to enable comparison with hygroscopic tandem differential mobility analyzer measurements. Mean predicted and measured hygroscopic growth factors for 110, 165, and 265 nm particles were found to agree within 6%. Single particle‐based ZSR hygroscopicity estimates offer an advantage over bulk aerosol composition‐based hygroscopicity estimates by providing additional chemical mixing state information. External mixing can be determined for particles of a given diameter through examination of the predicted hygroscopic growth factor distributions. Using this approach, 110 nm and 265 nm particles were found to be predominantly internally mixed; however, external mixing of 165 nm particles was observed periodically when thinly coated and thickly coated black carbon particles were simultaneously detected. Single particle‐resolved chemical information will be useful for modeling efforts aimed at constraining cloud condensation nuclei activity and hygroscopic growth.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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".