Application of laser induced breakdown spectroscopy (LIBS) for detection of lead contaminants in water using wood sample substrates
Bibliographic record
Abstract
Toxic heavy metals can be a significant risk to human health. Lead poisoning has been a recognized health hazard for more than 2000 years. Exposure can occur through drinking water, food, air, soil, and dust from old paint containing lead. Laser-induced breakdown spectroscopy (LIBS) is a powerful analytical technique to monitor heavy metals in all forms including aqueous solutions. Our goal is to develop a LIBS sampling technique for the measurement of heavy metal contaminants in water that has sufficient sensitivity to measure contamination at the allowable limit for drinking water (e.g. 15 ppb for lead). To perform fast and sensitive trace metal detection in aqueous solution with LIBS, a thin wood sample has been used as a liquid absorber to transform liquid sample analysis to solid sample analysis [1]. Initial studies have been carried out for lead in water. The plasma was generated by focusing Nd: YAG laser pulses with ~7 mJ energy at 1064nm on test samples in air. We also tried to further improve the sensitivity by carrying out experiments in argon gas in a sample chamber. The dependence of the elemental spectra on time delay and laser beam energy was also investigated. Initial results for lead in water indicate that a 3-sigma limit of detection (LOD) of the order of 10 ppb is achievable with a 1000 lasers shots, which is readily achievable with moderate repetition rate laser systems today. The experimental results will be presented and discussed.
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How this classification was reachedexpand
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 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".