Laser-induced Breakdown Spectroscopy (LIBS) in the Field: How Rock Moisture Influences Spectral Quality and Plasma Properties
Bibliographic record
Abstract
Before LIBS can be applied to analysis at mine sites under normal weather conditions, a number of practical issues need to be addressed.One of these is the moisture content of rock samples taken directly in the field.To assess the effect of rock moisture content on LIBS measurements, we studied its temporal evolution as the rock dried under ambient laboratory conditions using a series of 1080 laser shots (18 rows × 60 columns) at 2 Hz (8 ns pulse duration, 1064 nm wavelength, with a fluence of ~4 kJ cm -2 ).At maximum moisture, the LIBS spectra are weak, with only a few strong lines emerging from the background noise, while a richer spectrum appears as the rock dries.Color maps of LIBS spectra averaged over wavelength (white light) from the rock surface and net Hα line intensity from the water layer form complex, weakly correlated mosaics whose components depend on local rock properties (e.g., composition, porosity, asperities).However, the time evolution of their average over each of the 18 rows correlates well with that of the rock weight and microwave moisture measurements.Using the Hα line broadening and the ratio between the Mg II 280.27 nm and Mg I 285.21 nm line, the space and time averaged plasma produced in both the dry and wet rock areas is characterized by an electron number density in the range of 10 17 cm -3 and an ionization temperature close to 1 eV.The physical mechanisms involved are discussed.This study highlights the importance of controlling the moisture of the rock at the mining site before starting LIBS measurements, as it has a significant impact on the accuracy of the results obtained.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".