Gas phase chemical analysis using long-wavelength vertical-cavity surface-emitting lasers
Bibliographic record
Abstract
A method of gas phase chemical analysis using direct absorption spectroscopy is implemented with long-wavelength vertical-cavity surface-emitting lasers (VCSELs) operating near 1577 nm. The method is based on a linear dependence of widths of collisionally broadened absorption lines on gas pressure. It is shown that the absolute gas concentrations in multicomponent gas mixtures can be extracted from the line widths of all compounds measured simultaneously. The concentrations of both absorbing and nonabsorbing compounds are extracted from the results of simultaneous measurements of peak absorption and line width of the absorbing compound. Long-wavelength VCSELs with a buried tunnel junction (Vertilas, Germany) are used, for the first time, for multispecies and trace gas detection. Continuous single-mode tuning of the VCSELs up to 30 cm–1 is achieved with temperature and injection current varied in the range 0 to 50 °C and 1.3 to 6.5 mA, respectively. A fractional absorption of ~10–4 (600 ppm of CO2 in air) is measured with a single-beam spectroscope. The method described can be used for laser chemical analysis of gas mixtures with relatively high concentrations of target compounds and for open-path trace gas sensing. Compact sensors based on long-wavelength VCSELs can be developed for environmental and industrial gas monitoring.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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 | 0.000 |
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".