Reactive nitrogen and total organic carbon calibration techniques for the Aerodyne aerosol mass spectrometer
Bibliographic record
Abstract
The Aerodyne aerosol mass spectrometer (AMS) is used extensively to measure the chemical composition of non-refractory submicron aerosol particles for laboratory and atmospheric field studies. Typical AMS calibration methods are mass-based, involving generating pure or mixtures of particles with a known size and number concentration. Here we present a new calibration method using a heated platinum/molybdenum catalyst-based conversion technique that provides an independent measurement of reactive nitrogen (Nr) and total organic carbon (Cy) traceable to gas-phase standards of nitric oxide (NO) and carbon dioxide (CO2), respectively. The calibration method was tested using dried particles composed of pure ammonium nitrate (AN), other ammonium salts, and nitrogen-containing organic species. The nitrate ionization efficiencies determined with the AMS single particle or Nr calibration methods matched within experimental uncertainties (±15%). By measuring the AMS lens transmission efficiency and incorporating light-scattering measurements of the AMS collection efficiency, the catalyst method independently showed that the AMS relative ionization efficiency (RIE) for ammonium was essentially the same among different ammonium containing compounds (±9%), regardless of the corresponding anion and stoichiometry, quantifying support of a major assumption inherent in AMS calibrations. The Nr and Cy measurements were used to calibrate the RIE for 4-nitrocatechol (1.1 ± 0.4), isosorbide mononitrate (1.2 ± 0.1) and triammonium citrate (2.1 ± 0.2), which were within the range of organic RIEs used in AMS quantitation. The combined catalyst system provided more accurate measurements of the elemental carbon-to-nitrogen ratios than the high-resolution AMS elemental analysis using the Improved-Ambient method plus the inorganic nitrogen.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".