Laser-induced emission spectra of stainless steels and aluminum irradiated with nanopulse lasers without setting delay: potential applications to remote sensing and laser micromachining
Bibliographic record
Abstract
Spectral lines from the impurities held in stainless steel and in aluminum can be clearly identified in the UV and the visible spectra when emission is laser induced. These spectroscopic lines can be initiated in metal irradiated at moderate laser optical densities of about 2.5 × 10 8 W / c m 2 . In addition to the lines arising from impurities found in some metals, it was found that some spectroscopic lines from iron oxide formed during irradiation were also detected at the above-mentioned power density. It was found that lines observed from iron oxide are consistent with what is reported in the literature. The investigations reported were produced on samples at optical densities that are sufficient to create an electric field that is about 10 times the air electrical breakdown near the focal point. The results reported were obtained without setting any delay between the laser Q -switch and the data acquisition. The spectroscopic data are comparable to those shown in the literature by laser-induced breakdown spectroscopy in term of signal-to-noise ratio and are promising in detecting impurities such as heavy metals in remote sensing applications, where pulse delay is not always practical due to atmospheric conditions and power requirements. As a marking procedure is used during the investigations, the method demonstrates how spectroscopic monitoring in real time can be applied during a procedure in laser micromachining applications.
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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) | 0.004 | 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".