Cavity-enhanced dual-comb spectroscopy in the molecular fingerprint region using free-running quantum cascade lasers
Bibliographic record
Abstract
Cavity-enhanced dual-comb spectroscopy promises broadband, high-resolution, and highly sensitive spectroscopic measurements on sub-millisecond time scales, making it highly attractive for trace gas monitoring. In this work, we demonstrate cavity-enhanced dual-comb spectroscopy in the molecular fingerprint region using two quantum cascade lasers (QCLs) operating as optical frequency combs centered at 1063cm −1 spanning 56cm −1 . The high-finesse bow-tie cavity provided a 285 m effective path length, and the high power-per-mode of the QCL combs granted a strong multi-heterodyne signal of the swept-cavity transmission. This ultimately resulted in a noise equivalent absorption per spectral element of 1.8×10 −9 cm −1 Hz −1/2 , when considering the active measurement time. Measurements of the ν 8 fundamental band of methanol determined concentrations as low as 1.3 ppm in a single shot, which were captured in a 15 ms sweep of the cavity PZT. The detection limit after averaging 30 measurements was 20 ppb, which took 450 ms in measurement time and 70 s in wall time. This demonstrates the potential of cavity-enhanced dual-comb spectroscopy in challenging applications such as free radical kinetics and environmental 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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".