Advancing Ca isotopic analysis: Direct measurement of 44Ca/40Ca isotopic composition by MC-MICAP-MS with nitrogen plasma
Bibliographic record
Abstract
Background: In conventional multi-collector inductively coupled plasma mass spectrometry (MC-ICP-MS), major isobaric interference from 40Ar+ ions generated in the plasma prevents the direct measurement of 40Ca+ ions. To address this limitation, we investigated the performance of a microwave inductively coupled atmospheric-pressure plasma (MICAP) ion source, operating with N2 as plasma gas, recently integrated with multi-collector mass spectrometry (MC-MICAP-MS), for the measurement of 44Ca/40Ca and 44Ca/42Ca isotope abundance ratios. Results: The use of a N2 plasma effectively eliminates Ar-related interferences, enabling direct measurement of 44Ca/40Ca and 44Ca/42Ca isotopic composition under low-resolution conditions with an intermediate precision of 0.10 ‰ (2 SD). The method exhibits high tolerance to K+ interference (Ca/K ratio 30), MgO+ interferences (Ca/Mg ratio 20), and Sr++ interferences (Ca/Sr ratio 350), which makes MC-MICAP-MS a more robust approach for the direct measurement of 44Ca/40Ca isotopic composition than other plasma-based methods such as cold plasma MC-ICP-MS and CRC-MC-ICP-MS. The accuracy was validated by comparison with data from established techniques obtained for four biological reference materials (bone, hair, and liver), showing consistent results. For the bovine liver material NIST SRM 1577c, we propose a δ44Ca/40CaSRM915a value of 0.50 ‰ 0.09 ‰ (U, k = 2) and a δ44Ca/42CaSRM915a value of 0.21 ‰ 0.07 ‰ (U, k = 2). Significance: The MC-MICAP-MS approach offers a simple, robust, and reliable alternative for high-precision Ca isotope abundance measurements that has the potential to significantly advance the applications of stable Ca isotope research in various fields.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| 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".