Precise isotope ratio determination of common Pb using quadrupole LA-ICP-MS with optimized laser sampling conditions and a robust mixed-gas plasma
Bibliographic record
Abstract
This work presents a method for the precise (0.2% RSE) determination of common (i.e. non-radiogenic) Pb isotope ratios using quadrupole Nd:YAG (266 nm) LA-ICP-MS at low (∼2 ppm) Pb concentrations. Laser sampling conditions significantly influence the precision of Pb isotope ratio measurements. This paper presents a set of optimum sampling conditions for the described system. Setting the laser focus above the sample surface significantly improves the precision of ratio measurements due to increased count rates and a reduction in the heterogeneity of particulate matter produced by fracturing at the site of ablation. In addition, using a more robust, mixed-gas (Ar–N2) plasma significantly increases sensitivity and reduces mass bias. With a mixed Ar–N2 plasma and optimized laser sampling conditions, single collector quadrupole LA-ICP-MS can be superior, for some applications (e.g., where micrometre-scale spatial resolution is important), to TIMS, and at low Pb concentrations is a cost-effective alternative to LA-MC-ICP-MS. The application of single detector, quadrupole LA-ICP-MS to the precise determination of common Pb isotope ratios in minerals has not been previously documented. This method is a powerful tool for use in isotope tracer studies of ore deposits and has potential applications to a range of environmental problems.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".