Scaling to millijoule energies for laser-induced breakdown spectroscopy of water samples
Bibliographic record
Abstract
The capabilities of laser-induced breakdown spectroscopy (LIBS) for analysis of water samples with low energy laser pulses was investigated using 355 nm, 10 ns pulses with energies from 3.5 to 100 mJ. In order to optimize the detection limit, the spatial and temporal dependence of the line emission from a sodium solution water jet target in air has been measured, allowing the identification of optimum gating time and observation position for sodium. Careful characterization of the background noise sources in the LIBS detection system has been undertaken, including the continuum emission from the plasma, dark current in the detector array and electron emission noise in the image intensifier. The energy dependence of the limit of detection for sodium in water has been investigated. Single shot detection limits for sodium have been measured ranging from 2 ppm to 200 ppm for laser pulse energies of 100 mJ to 3.5 mJ respectively. For aluminium, the detection limits are over an order of magnitude poorer than for sodium.
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 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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".