Low temperature reaction kinetics inside an extended Laval nozzle: REMPI characterization and detection by broadband rotational spectroscopy
Bibliographic record
Abstract
Chirped-Pulse Fourier-Transform millimeter wave (CP-FTmmW) spectroscopy is a powerful method that enables detection of quantum state specific reactants and products in mixtures. We have successfully coupled this technique with a pulsed uniform Laval flow system to study photodissociation and reactions at low temperature, which we refer to as CPUF ("Chirped-Pulse/Uniform flow"). Detection by CPUF requires monitoring the free induction decay (FID) of the rotational coherence. However, the high collision frequency in high-density uniform supersonic flows can interfere with the FID and attenuate the signal. One way to overcome this is to sample the flow, but this can cause interference from shocks in the sampling region. This led us to develop an extended Laval nozzle that creates a uniform flow within the nozzle itself, after which the gas undergoes a shock-free secondary expansion to cold, low pressure conditions ideal for CP-FTmmW detection. Impact pressure measurements, commonly used to characterize Laval flows, cannot be used to monitor the flow within the nozzle. Therefore, we implemented a REMPI (resonance-enhanced multiphoton ionization) detection scheme that allows the interrogation of the conditions of the flow directly inside the extended nozzle, confirming the fluid dynamics simulations of the flow environment. We describe the development of the new 20 K extended flow, along with its characterization using REMPI and computational fluid dynamics. Finally, we demonstrate its application to the first low temperature measurement of the reaction kinetics of HCO with O2 and obtain a rate coefficient at 20 K of 6.66 ± 0.47 × 10-11 cm3 molec-1 s-1.
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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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".