Trace Selenium Measurement in Water Using Laser-Induced Fluorescence Assisted by Laser Ablation
Bibliographic record
Abstract
Selenium detection and removal from industrial and mining effluents have gained attention recently due to the negative effects of this trace element on aquatic life. However, the current methods for the detection of selenium in effluents are off-line by nature. In order to fill this gap, we investigated the use of laser ablation-assisted laser-induced fluorescence (LA-LIF) to measure trace amount of selenium in aqueous solutions. LA-LIF measurements are real time, label-free, standoff, and require no consumables as well as no sample preparation. They can provide a field-amenable, versatile tool for the measurement of selenium in the whole water treatment chain. We describe the system utilized, the temporal and fluence optimization studies, and the resulting calibration curve, which is linear over a wide dynamic range from parts-per-billion to tens of parts-per-million concentration levels. We also show that the achieved limit of detection of selenium can reach 32 µg/L using LA-LIF, without any kind of preconcentration or matrix transfer.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.002 | 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".