Microplasmas produced with sub-millijoule laser pulses for laser induced breakdown spectroscopy
Bibliographic record
Abstract
Summary form only given, as follows. Laser induced breakdown spectroscopy (LIBS) is a powerful technique for fast determination of the chemical composition of materials based on measuring line emission from ions and neutral atoms in a transient laser produced plasma. The technique is considered almost non-destructive since only a small amount of material is ablated. In the current study we are undertaking to optimize the plasma conditions of a sub-millijoule laser produced plasma to optimize the emission of radiation for material identification and to achieve high spatial resolution on the 10 micron scale. Scaling studies have been carried out of the emission levels from metallic target materials as a function of laser pulse energy in the range of 10 to 500 microjoules and as a function of observation delay time for ultraviolet (248 nm) KrF laser pulses of 10 ns duration. Our data show that as a consequence of the smaller amount of the heated mass, time constants of the plasma emission are much shorter than the ones typically encountered in LIBS using much higher laser energies. We are currently conducting LIBS experiments with 50 ps pulses, at 248 nm in the same energy range. Few studies have looked at the scaling of plasma emission with pulse length in the picosecond regime for UV pulses. We present initial experimental results and compare these to expectations based on the scaling of the laser plasma parameters.
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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".