Inductively Coupled Plasma Mass Spectrometry with On-Line Leaching: A Method To Assess the Mobility and Fractionation of Elements
Bibliographic record
Abstract
A new technique has been developed to assess the mobility and site of specific elements in complex natural materials such as rocks. Concentration profiles during leaching were obtained by pumping reagents (water, 1% HNO3, 10% HNO3, 30% HNO3), either continuously or with flow injection, through a microcolumn of sample while continuously monitoring analyte signals by inductively coupled plasma mass spectrometry (ICPMS). Compared to batch extraction procedures normally used, the approach involves minimal sample preparation and reduced contamination since the leaching is performed in a closed system. Continuous on-line monitoring also allows a greater resolution of the various phases reacting with given reagent. Compared to continuous leaching, flow injection increased the resolution of the various phases using discrete injections of reagents while reducing reagent consumption and minimizing etching of the MS interface. Furthermore, sensitivity was preserved by injecting into air instead of an aqueous carrier. Whether in the continuous or flow injection modes, the proposed approach provides real-time data on what phases are breaking down and what metals are released. It can therefore be used to design effective leaching strategies and to trace isotopic compositions. However, the resulting spectra are complex and the correct determination of some elements requires high-resolution ICPMS.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".