Potassium Stable Isotopic Compositions Measured by High-Resolution MC-ICP-MS
Bibliographic record
Abstract
Potassium isotopic (K-41/K-39) compositions are notoriously difficult to measure. TIMS measurements are hindered by variable fractionation patterns throughout individual runs and too few isotopes to apply an internal spike method for instrumental mass fractionation corrections. Internal fractionation corrections via the K-40/K-39 ratio can provide precise values but assume identical K-40/K-39 ratios (e.g. 0.05% (1sigma) in [1]); this is appropriate in some cases (e.g. identifying excess K-41) but not others (e.g., determining mass fractionation effects and metrologically traceable isotopic abundances). SIMS analyses have yielded measurements with 0.25% precisions (1sigma) [2]. ICP-MS analyses are significantly affected by interferences from molecular species such as Ar-38H(+) and Ar-40H(+) and instrument mass bias. Single collector ICP-MS instruments in mode have yielded uncertainties as low as 2% (1sigma, e.g. [3]). Although these precisions may be acceptable for some concentration determinations, they do not resolve isotopic variation in terrestrial materials. Here we present data from a series of measurements made on the Thermo Scientific NEPTUNE Plus multi-collector ICP-MS that demonstrate the ability to make K-41/K-39 ratio measurements with 0.07% precisions (1sigma). These data, collected on NIST K standards, indicate the potential for MC-ICP-MS measurements to look for K isotopic variations at the sub-permil level. The NEPTUNE Plus can sufficiently resolve 39K and 41K from the interfering 38ArH+ and 40ArH+ peaks in wet cold plasma and high-resolution mode. Measurements were made on small but flat, interference-free, plateaus (ca. 50 ppm by mass width for K-41). Although ICP-MS does not yield accurate K-41/K-39 values due to significant instrumental mass fractionation (ca. 6%), this bias can be sufficiently stable over the time required for several measurements so that relative K-41/K-39 values can be precisely determined via sample-standard bracketing. As cold plasma conditions can amplify matrix effects, experiments were conducted to test the matrix tolerance of measurements; the use of clean, matrix-matched samples and standards is critical. Limitations of the cold-plasma high-resolution MC-ICP-MS methodology with respect to matrix tolerance are discussed and compared with the limitations of TIMS methodologies.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".