Comparison of negative-ion proton-transfer with iodide ion chemical ionization mass spectrometry for quantification of isocyanic acid in ambient air
Bibliographic record
Abstract
Isocyanic acid (HNCO) is a trace gas pollutant of potential importance to human health whose measurement has recently become possible through the development of negative-ion proton-transfer chemical ionization mass spectrometry (NI-PT-CIMS) with acetate reagent ion. In this manuscript, an alternative ionization and detection scheme, in which HNCO is quantified by iodide CIMS (iCIMS) as a cluster ion at m/z 170, is described. The sensitivity was inversely proportional to water vapor concentration but could be made independent of humidity changes in the sampled air by humidifying the ion–molecule reaction (IMR) region of the CIMS. The performance of the two ionization schemes was compared and contrasted using ambient air measurements of HNCO mixing ratios in Calgary, AB, Canada, by NI-PT-CIMS with acetate reagent ion from Dec 16 to 20, 2013, and by the same CIMS operated in iCIMS mode from Feb 3 to 7, 2014. The iCIMS exhibited a greater signal-to-noise ratio than the NI-PT-CIMS, not because of its sensitivity, which was lower (∼0.083 normalized counts per second (NCPS) per parts-per-trillion by volume (pptv) compared to ∼9.7 NCPS pptv−1), but because of a much lower and more stable background (3 ± 4 compared to a range of ∼2 × 103 to ∼6 × 103 NCPS). For the Feb 2014 data set, the HNCO mixing ratios in Calgary air ranged from <12 to 94 pptv (median 34 pptv), were marginally higher at night than during day, and correlated with nitrogen oxide (NOx = NO + NO2) mixing ratios and submicron particle volume. The ratios of HNCO to NOx observed are within the range of emission ratios reported for gasoline-powered motor vehicles.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".