Hypersonic nozzle for laser-spectroscopy studies at 17 K characterized by resonance-ionization-spectroscopy-based flow mapping
Bibliographic record
Abstract
The in-gas-jet laser spectroscopy method relies on the production of uniform and low-temperature gas jets to fully resolve the atomic hyperfine structure and efficiently determine fundamental nuclear properties of short-lived isotopes from, e.g., the hardly accessible actinide and transactinide elements. In this article we present the studies devoted to designing, producing, and characterizing the flow properties of a convergent-divergent (de Laval) hypersonic nozzle with a superior performance for laser spectroscopy applications. A novel flow mapping technique, based on resonance ionization spectroscopy (RIS), has been employed to characterize the local flow properties of an argon gas jet formed by this nozzle, revealing a 61.5-mm long, highly collimated atomic jet at a uniform low temperature of 16.6(5) K [Mach 8.11(12)] that will enable laser spectroscopy experiments on heavy-exotic nuclei with an unprecedented spectral resolution and a high efficiency. These results have been compared with those obtained by planar laser induced fluorescence spectroscopy (PLIFS) studies and show a good agreement between the two techniques and a significant improvement in efficiency of the RIS mapping method with respect to PLIFS. The data are compared to state-of-the-art fluid-dynamics calculations that were carried out to obtain the nozzle contour and simulate its performance, as well as to explain the observation of a possible onset of argon nucleation.
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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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".