Sensitive detection of metals in water using laser-induced breakdown spectroscopy on wood sample substrates
Why this work is in the frame
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Bibliographic record
Abstract
Water contaminated with toxic heavy metals can be a great risk to humans. Laser-induced breakdown spectroscopy (LIBS) is a promising candidate to monitor heavy metals in aqueous solutions on site, but the sensitivity is still a major problem. To perform sensitive analysis of analyte metals in aqueous solutions with LIBS, a thin wood sample substrate was used as a liquid absorber to transform the liquid sample analysis to a solid sample analysis. We focus on investigating the performance of this technique using different laser wavelengths (266, 532, and 1064 nm) with a low pulse energy (<5 mJ) and a different number of shots (from 10 to 1000). We demonstrate that a limit of detection of 30 ppb can be achieved using low energy pulses with a 1000 shot accumulation. This technique provides a potentially simple approach for a portable micro LIBS system to monitor water samples.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it