Observation of Mass-Independent Fractionation in MC-ICPMS and its Implication for Accurate Isotope Ratio Measurements
Bibliographic record
Abstract
Today, MC-ICPMS has become a powerful research tool for the high precision isotope ratio measurements with over five-hundred papers published annually in the past several years1-2. However, MC-ICPMS suffers approximately tenfold larger bias (up to 25 % for lithium3) in isotope amount ratio measurements as compared to thermal ionization mass spectrometry (TIMS). This bias needs to be properly corrected in order to obtain accurate isotope amount ratio measurements. The choice of the fractionation law to calibrate (correct) measured isotope ratios is crucial in isotope science. Over the last decades, the Russell law mass bias correction model (Eq. 1)4 which is applicable only for mass-dependent fractionation and assumes identical mass bias for both the calibrator and measurand elements, has become a standard curriculum in isotope ratio measurements. However, it has been reported that not only mass bias is different for different elements but also that the mass bias is different for different isotope pairs of a same element5-6. Mass-independent fractionation in MC-ICPMS has been observed for elements such as Nd, W and Cd5-7. Ri,j = ri,j(mi/mj)-f (1) Here Ri,j = n(iE)/n(jE), ri,j is the measured (uncorrected) isotope ratio and E is the element of interest, f is the fractionation function and mi,mj are the nuclide masses. In this talk, recent research results on MIF observed for Ge, Hg and Pb in MC-MCP in our group8 will be presented and its implication for Russell law mass bias correction for isotope amount ratio measurements will be presented and discussed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 teacher head, 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".