Oxide Formation and Instrumental Mass Bias in MC‐ICP‐MS: An Isotopic Case Study of Neodymium
Bibliographic record
Abstract
High rates of oxide formation affect the magnitude and behaviour of instrumental mass bias for Nd isotopic measurements performed with multi‐collector inductively coupled plasma‐mass spectrometry (MC‐ICP‐MS) instruments, causing the traditional correction methods (e.g., internal and external normalisation) to fail. Here, we investigate the instrumental conditions that govern oxide formation and provide an extensive data set describing how different oxide formation rates affect the measurement error of Nd isotopic ratios. Results are reported for several instrumental set‐ups including wet and dry plasma, different introduction methods, the addition of N 2 , and various sampler and skimmer cone geometries. The differences in the behaviour of Nd isotopic ratios observed for dry and wet plasma require several reaction mechanisms to explain why oxide formation is associated with a non‐linear mass bias for some rare earth elements. We developed a simple mathematical model to describe the behaviour of Nd isotopic ratios for a range of oxide formation rates and different instrumental settings and present a qualitative model that predicts the isotopic offsets of Nd ratios based on the cumulative contributions of the major sources of mass bias. A series of analytical recommendations for the determination of accurate and precise Nd ratios by MC‐ICP‐MS is presented.
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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 | 0.003 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".