A laser-ablation dual-comb spectrometer for detection of critical minerals
Bibliographic record
Abstract
Rare-earth elements and other critical minerals are key components to transition economies towards a greener and more sustainable footprint. In all phases of mining operations (exploration, extraction and ore processing), real-time and stand-off sensors with low detection thresholds have the potential to improve the economic and environmental impact of producing critical minerals. Upgrading ore body modelling as well as realizing sorting and processing efficiencies are two use cases. Laser-ablation dual comb spectroscopy is a broadband technique that uses two mode-locked lasers to measure the absorption spectrum of laser-produced plasmas. It requires minimal sample preparation, has millisecond measurement times, and has the resolution of continuous wave laser based spectroscopies. These properties make it an ideal candidate for real-time sensing in mining workflows, yet its potential in mining remains unexplored. This work describes the construction, characterization, and use of a laser-ablation dual comb spectroscopy system optimized for detecting rare-earth elements in ores. The system was built using two commercial mode-locked lasers whose repetition rates were stabilized with respect to one another for measurement repeatability. The systems measurement spectrum is centered around the second harmonic of the two mode-locked lasers, ranging from 518-532 nm, and its spectral resolution ranges from 1-10 pm. Its ability to detect rare earth elements in mineral matrices was tested by measuring a dilution series of pellets made from calibrated ore powders with traces of CeO₂, La₂O₃, and Sm₂O₃. Limits of detection of Ce, La, and Sm were estimated from the measurements to be 50 ± 1 ppm, 65 ± 0.1 ppm, and 63 ± 0.1 ppm, respectively.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".