Absolute Measurements of Total Peroxy Nitrate Mixing Ratios by Thermal Dissociation Blue Diode Laser Cavity Ring-Down Spectroscopy
Bibliographic record
Abstract
Peroxycarboxylic nitric anhydrides (PANs) have long been recognized as important trace gas constituents of the troposphere. Here, we describe a blue diode laser thermal dissociation cavity ring-down spectrometer for rapid and absolute measurements of total peroxyacyl nitrate (SigmaPAN) abundances at ambient concentration levels. The PANs are thermally dissociated and detected as NO2, whose mixing ratios are quantified by optical absorption at 405 nm relative to a reference channel kept at ambient temperature. The effective NO2 absorption cross-section at the diode laser emission wavelength was measured to be 6.1 x 10(-19) cm2 molecule(-1), in excellent agreement with a prediction based on a projection of a high-resolution literature absorption spectrum onto the laser line width. The performance, i.e., accuracy and precision of measurement and matrix effects, of the new 405 nm thermal dissociation cavity ring-down spectrometer was evaluated and compared to that of a 532 nm thermal dissociation cavity ring-down spectrometer using laboratory-generated air samples. The new 405 nm spectrometer was considerably more sensitive and compact than the previously constructed version. The key advantage of laser thermal dissociation cavity ring-down spectroscopy is that the measurement can be considered absolute and does not need to rely on external calibration.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".