Development of laser-induced breakdown spectroscopy for microanalysis applications
Bibliographic record
Abstract
Abstract Laser induced breakdown spectroscopy is a fast non-contact technique for the analysis of the elemental composition of any sample. Our focus is to advance this technique into a regime where we use pulse energies below 100 µJ. This regime is referred to as micro-laser-induced breakdown spectroscopy or µLIBS. At present we have concentrated on two application areas : (1) The imaging of latent fingerprints and (2) the extension to laser ablation followed by laser-induced fluorescence (LA-LIF) for very high sensitivity analysis of contaminants in water. Preliminary pulse emission scaling of Na in latent fingerprints has been investigated for ~130 fs, 266 nm pulses with energies below 15 µJ. The lowest energy for reliable single shot detection of Na is approximately 3.5 µJ. A 2D map of a fingerprint on a Si wafer has been successfully demonstrated using 5 µJ pulses. In LA-LIF the detection sensitivity of micro-laser-induced breakdown spectroscopy (µLIBS) is improved by coupling it with a second resonant probe pulse. This technique was investigated for the detection of Pb at low concentrations when ablated by 266 nm, 170 µJ pulses. After a short delay the resulting plume was re-excited with a nanosecond laser pulse tuned to a specific transition of Pb. In the case of the resonant dual-pulse LIBS the limit of detection was found to be approximately 60 ppb for Pb in water for 1000 shots. It is expected that this result could be implemented with fiber or microchip lasers with multi-kHz repetition rates and fiber Bragg grating tuning elements. The results are promising for the development of portable µLIBS water monitoring systems and portable fingerprint scanners.
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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 | 0.001 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".