Online derivatization for hourly measurements of gas- and particle-phase semi-volatile oxygenated organic compounds by thermal desorption aerosol gas chromatography (SV-TAG)
Bibliographic record
Abstract
Abstract. Laboratory oxidation studies have identified a large number of oxygenated organic compounds that can be used as tracers to understand sources and oxidation chemistry of atmospheric particulate matter. Quantification of these compounds in ambient environments has traditionally relied on low-time-resolution collection of filter samples followed by offline sample treatment with a derivatizing agent to allow analysis by gas chromatography of otherwise non-elutable organic chemicals with hydroxyl groups. We present here an automated in situ instrument for the measurement of highly polar organic semi-volatile and low-volatility compounds in both the gas- and particle-phase with hourly quantification of mass concentrations and gas–particle partitioning. The dual-cell semi-volatile thermal desorption aerosol gas chromatograph (SV-TAG) with derivatization collects particle-only and combined particle-plus-vapor samples on two parallel sampling cells that are analyzed in series by thermal desorption into helium saturated with derivatizing agent. Introduction of MSTFA (N-methyl-N-(trimethylsilyl)trifluoroacetamide), a silylating agent, yields complete derivatization of all tested compounds, including alkanoic acids, polyols, diacids, sugars, and multifunctional compounds. In laboratory tests, derivatization is found to be highly reproducible (< 3% variability). During field deployment, a regularly injected internal standard is used to correct for variability in detector response, consumption of the derivatization agent, desorption efficiency, and transfer losses. Error in quantification from instrument fluctuations is found to be less than 10% for hydrocarbons and less than 15% for all oxygenates for which a functionally similar internal standard is available, with an uncertainty of 20–25% in measurements of particle fraction. After internal standard corrections, calibration curves are found to be linear for all compounds over the span of 1 month, with comparable response on both of the parallel sampling cells.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".