Measurements of total peroxy and alkyl nitrate abundances in laboratory-generated gas samples by thermal dissociation cavity ring-down spectroscopy
Bibliographic record
Abstract
A novel measurement technique, thermal dissociation cavity ring-down spectroscopy (TD-CRDS), for rapid (1 s time resolution) and sensitive (precision approximately 100 parts per trillion by volume (10(-12); pptv)) quantification of total peroxy nitrate (SigmaPN) and total alkyl nitrate (SigmaAN) abundances in laboratory-generated gas mixtures is described. The organic nitrates are dissociated in a heated inlet to produce NO(2), whose concentration is monitored by pulsed-laser CRDS at 532 nm. Mixing ratios are determined by difference relative to a cold inlet reference channel. Conversion of laboratory-generated mixtures of AN in zero air (at an inlet temperature of 450 degrees C) is quantitative over a wide range of mixing ratios (0-100 parts per billion by volume (10(-9), ppbv)), as judged from simultaneous measurements of NO(y) using a commercial NO-O(3) chemiluminescence monitor. Conversion of PN is quantitative up to about 4 ppbv (at an inlet temperature of 250 degrees C); at higher concentrations, the measurements are affected by recombination reactions of the dissociation products. The results imply that TD-CRDS can be used as a generic detector of dilute mixtures of organic nitrates in air at near-ambient concentration levels in laboratory experiments. Potential applications of the TD-CRDS technique in the laboratory are discussed.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".