Detection of Lead in Water Using Laser-Induced Breakdown Spectroscopy and Laser-Induced Fluorescence
Bibliographic record
Abstract
Laser-induced breakdown spectroscopy (LIBS) is a well-known technique for fast, stand-off, and nondestructive analysis of the elemental composition of a sample. We have been investigating micro-LIBS for the past few years and demonstrating its application to microanalysis of surfaces. Recently, we have integrated micro-LIBS with laser-induced fluorescence (LIF), and this combination, laser ablation laser-induced fluorescence (LA-LIF), allows one to achieve much higher sensitivity than traditional LIBS. In this study, we use a 170 microJ laser pulse to ablate a liquid sample in order to measure the lead content. The plasma created was re-excited by a 10 microJ laser pulse tuned to one of the lead resonant lines. Upon optimization, the 3sigma limit of detection was found to be 35 +/- 7 ppb, which is close to the EPA standard for the level of lead allowed in drinking water.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".