Resonant laser-induced breakdown spectroscopy (RLIBS) analysis of traces through selective excitation of aluminum in aluminum alloys
Bibliographic record
Abstract
We investigated laser-induced breakdown spectroscopy for the detection of traces of magnesium and silicon contained in aluminum alloys by using the same 5 ns optical parametric oscillator laser pulse to ablate the sample and excite selectively an atomic transition of vaporized aluminum (Al I 309.27 nm). The excitation energy of aluminum is then transferred to all components of the gas/plasma phase via particle collisions. The optical emission of the trace elements as a function of the laser wavelength exhibits a high peak when the laser is tuned exactly to the aluminum transition. The on-resonance signal-to-noise ratio of magnesium (Mg 285.21 nm) was maximized near the off-resonance threshold fluence for detection of the magnesium line (∼1.78 J cm−2). The detection threshold of the magnesium line decreases below 1.0 J cm−2 when the laser is on resonance for a sample of aluminum alloy containing 150 ppm of magnesium. Under optimal conditions, the limits of detection of magnesium and silicon in aluminum alloy were found to be 0.75 ppm and 80 ppm, respectively, compared to 39 ppm and 5000 ppm, respectively, when the laser was off resonance at the same fluence. The limits of detection obtained by using low fluences and low energy per pulse are similar to those obtained using conventional LIBS but with much higher fluences and higher energy per pulse. The main advantage of this technique is that it allows measuring simultaneously relatively low concentrations of several trace elements while minimizing the damage to the sample.
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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 | 0.001 | 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".