Application of quantum cascade lasers for infrared spectroscopy of jet-cooled molecules and complexes
Bibliographic record
Abstract
The combination of infrared laser spectroscopy with a molecular jet expansion provides a powerful technique to investigate medium sized organic molecules and clusters. The coupling of quantum cascade lasers (QCLs) with two slit jet infrared spectrometers, namely an off-axis cavity enhanced absorption (CEA) spectrometer and a rapid scan spectrometer with an astigmatic multi-pass cell assembly, are described. Two types of QCLs, specifically a continuous wave (cw) liquid nitrogen cooled distributed-feedback QCL at 5.7 μm, and a cw room temperature mode-hop-free external cavity QCL centered at 6.1 μm, were employed as the light sources. A pair of 1 inch highly reflective cavity ring-down mirrors (R = 99.98% at 5.2 μm) separated by 55 cm or a pair of 1.5 inch astigmatic mirrors separated by 20 cm, served as the optical cavities. To automate and to synchronize the timing of the CEA or rapid scan experiments with a pulsed slit jet molecular expansion, two LabVIEW computer programs were developed. For the CEA experiments, one of the cavity mirrors was mounted on a piezoelectric actuator with 1 inch clear aperture to maximize the effective mirror size. The effects of mirror size and laser sweep rate were evaluated. A minimum detection sensitivity of 1.8×10-8 cm-1 was achieved. Jet-cooled molecules were generated using a homemade pulsed slit jet nozzle assembly. A jet-cooled infrared spectrum of methyl lactate was recorded to demonstrate the performance of the CEA spectrometer. Preliminary results obtained with the room temperature QCL coupled to the rapid scan spectrometer are also presented.
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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) | 0.000 | 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 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".