Multi-species gas detection with long-wavelength VCSEL
Bibliographic record
Abstract
A long-wavelength VCSEL has been used for the first time for multi-species gas detection and for trace gas sensing. The VCSEL with a buried tunnel junction (VERTILAS, Germany) was capable of covering a spectral range of 7 nm or 28 cm-1 (central wavelength at 1576.3 nm) with the laser temperature and injection current varied between 0-50 °C and 0.5-5 mA respectively. The pressure of CO:CO2=3:2 gas mixtures buffered with N2 (N2 content 0 - 90 %) was varied from 1 mBar up to 1 Bar. To avoid a non-linear dynamic tuning, the combination of a direct injection current with a saw-tooth waveform was used to sweep the laser frequency across absorption lines. A LabVIEW-based computer code was developed for multi-species gas analysis in time domain. Absorption spectra were averaged over 102-103 laser scans. It has been shown that a cross interference from all collisional partners should be taken into account for accurate multi-component gas detection. A concentration of 600 ppm of CO2 in atmospheric air (fractional absorption ~ 10-4) was detected with laser output power of 120 uW. Long-wavelength VCSELs can be used both for multi-species gas detection in a wide range of pressures and for trace 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 | 0.000 |
| 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 | 0.000 | 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".