High precision isotope ratio analysis for Cosmochemistry applications using the Thermo Scientific Neoma MC-ICP-MS.
Bibliographic record
Abstract
Within the first five years of the initial introduction of multicollector inductively coupled plasma mass spectrometry (MC-ICP-MS) with the VG Elemental Plasma 54 the potential for the technique to be applied to Isotope Cosmochemistry applications had already been recognized1. Early work focused on the 182Hf-182W chronometer, which had up until the introduction of MC-ICP-MS, been extremely difficult to measure with existing thermal ionization mass spectrometry (TIMS) techniques due to the extremely high first ionization potential of W.In the following 25 years the use of MC-ICP-MS for Isotope Cosmochemistry applications has expanded to numerous other isotopic systems, including, but not limited to 26Al-26Mg, 146Sm-142Nd and 60Fe-60Ni, not just 182Hf-182W. A general feature of these measurements is typically a reliance on excellent precision, on the order of a few ppm, 2RSD [2]. The high count rates required to achieve such excellent precision take time in order to achieve. Excellent precision over such time scales require a high performance mass spectrometer, capable of high sensitivity, stable throughout the course of the measurement and equipped with a low-noise detection array.For over 20 years Thermo Fisher Scientific has pioneered developments in multicollector inductively coupled plasma mass spectrometry (MC-ICP-MS). The latest generation Thermo Scientific™ Neoma™ MC-ICP-MS, blends cutting-edge and field proven technology from the Ultra™ HR-IRMS and iCAP Qnova™ Series ICP-MS [3] and has the capability to complement and enhance Isotope Cosmochemistry applications [4]. Here we report our investigations into using the Neoma MC-ICP-MS and Neoma MS/MS MC-ICP-MS for a selection of Isotope Cosmochemistry application, including high precision Mg, Fe, Cu, Zn and W measurements.[1] A. Halliday, D. Lee, J. Christensen, M. Rehkämper, W. Yi, X. Luo, C. Hall, C. Ballentine, T. Pettke, C. Stirling, Geochimica et Cosmochimica Acta, 1998, 62, 919-940.[2] S. Goderis, R. Chakrabarti, V. Debaille, J. Kodolányi, 2016, J. Anal. At. Spectrom., 2016, 31, 841-862.[3] Thermo Fisher Scientific, 2020, BR30600-EN 0520C: Neoma Multicollector ICP-MS [pdf], Thermo Fisher Scientific.[4] Z. Deng, M. Schiller, M. G. Jackson, M-A. Millet, L. Pan, K. Nikoljsen, N. S. Saji, D. Huang, M. Bizzarro, Nature, 2023, 621, 100-104
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 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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.018 | 0.017 |
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".