Influence of surrounding gas, composition and pressure on plasma plume dynamics of nanosecond pulsed laser-induced aluminum plasmas
Bibliographic record
Abstract
In this article, we present a comprehensive study of the plume dynamics of plasmas generated by laser ablation of an aluminum target. The effect of both ambient gas composition (helium, nitrogen or argon) and pressure (from ∼5 × 10−7 Torr up to atmosphere) is studied. The time- and space- resolved observation of the plasma plume are performed from spectrally integrated images using an intensified Charge Coupled Device (iCCD) camera. The iCCD images show that the ambient gas does not significantly influence the plume as long as the gas pressure is lower than 20 Torr and the time delay below 300 ns. However, for pressures higher than 20 Torr, the effect of the ambient gas becomes important, the shortest plasma plume length being observed when the gas mass species is highest. On the other hand, space- and time- resolved emission spectroscopy of aluminum ions at λ = 281.6 nm are used to determine the Time-Of-Flight (TOF) profiles. The effect of the ambient gas on the TOF profiles and therefore on the propagation velocity of Al ions is discussed. A correlation between the plasma plume expansion velocity deduced from the iCCD images and that estimated from the TOF profiles is presented. The observed differences are attributed mainly to the different physical mechanisms governing the two diagnostic techniques.
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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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".