Chemical and microphysical characterization of ambient aerosols with the aerodyne aerosol mass spectrometer
Bibliographic record
Abstract
The application of mass spectrometric techniques to the real-time measurement and characterization of aerosols represents a significant advance in the field of atmospheric science. This review focuses on the aerosol mass spectrometer (AMS), an instrument designed and developed at Aerodyne Research, Inc. (ARI) that is the most widely used thermal vaporization AMS. The AMS uses aerodynamic lens inlet technology together with thermal vaporization and electron-impact mass spectrometry to measure the real-time non-refractory (NR) chemical speciation and mass loading as a function of particle size of fine aerosol particles with aerodynamic diameters between approximately 50 and 1,000 nm. The original AMS utilizes a quadrupole mass spectrometer (Q) with electron impact (EI) ionization and produces ensemble average data of particle properties. Later versions employ time-of-flight (ToF) mass spectrometers and can produce full mass spectral data for single particles. This manuscript presents a detailed discussion of the strengths and limitations of the AMS measurement approach and reviews how the measurements are used to characterize particle properties. Results from selected laboratory experiments and field measurement campaigns are also presented to highlight the different applications of this instrument. Recent instrumental developments, such as the incorporation of softer ionization techniques (vacuum ultraviolet (VUV) photo-ionization, Li+ ion, and electron attachment) and high-resolution ToF mass spectrometers, that yield more detailed information about the organic aerosol component are also described.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".