Improved analysis of micro‐ and nanomole‐scale sulfur multi‐isotope compositions by gas source isotope ratio mass spectrometry
Bibliographic record
Abstract
RATIONALE: Multiple sulfur isotope compositions are usually measured on relatively large samples (in the range of micromoles); however, sometimes only small amounts are available and thus it is necessary to analyze small (sub-micromole) samples. We report an improved method to measure multiple sulfur isotope compositions: δ(33) S, δ(34) S and δ(36) S values on the SF6 molecule (m/z 127, 128, 129, 131) for quantities down to 0.1 micromole, and δ(33) S and δ(34) S values for quantities down to 20 nanomoles. METHODS: Multiple sulfur isotope analyses including fluorination and purification of two international Ag2 S standards, IAEA-S1 and IAEA-S3, were carried out at various low concentrations on a dual-inlet isotope ratio mass spectrometer using a microvolume and modified resistor capacities. RESULTS: The analyses yielded a narrow range of δ(34) S values vs CDT (the international standard), with an overall standard deviation of ±0.2 ‰, which was within the range of certified values. This demonstrates the feasibility of determining both Δ(33) S and Δ(36) S values on the sub-micromole scale, and Δ(33) S values on the nanomole scale with similar accuracy to conventional dual-inlet analyses. CONCLUSIONS: The analysis of the three S-isotope ratios on the SF6 molecule using the so-called conventional fluorination method and dual-inlet ion ratio mass spectrometry is reliable for sample sizes down to ~20 nanomoles. Despite being close to the theoretical limits for maintaining the viscous flow regime of gas in the capillary, errors were not limited by counting statistics, but probably relate to sample gas purification. Copyright © 2016 John Wiley & Sons, Ltd.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 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".