Determination of Total Chromium in Seawater by Isotope Dilution Sector Field ICPMS Using GC Sample Introduction
Bibliographic record
Abstract
A method for the accurate determination of total Cr in seawater by isotope dilution (ID) sector field inductively coupled plasma mass spectrometry (SF-ICPMS) using GC as a means of sample introduction is described. Chromium was reduced to Cr(III) by addition of SO(2)-saturated water and derivatized with trifluoroacetylacetonate (TFA) to form volatile Cr(TFA)(3). Derivatized analyte was either extracted into hexane or directly sampled by solid-phase microextraction (SPME) using a poly(dimethylsiloxane)-coated fused-silica fiber for GC/SF-ICPMS analysis. With medium resolution required to efficiently separate argide, argon chloride and oxide interferences, a concentration of 0.154 +/- 0.013 ng mL(-1) (1 SD, n = 4) was obtained for Cr in NRCC seawater CRM CASS-4 using a 1-microL hexane extract, in agreement with the certified value of 0.144 +/- 0.029 ng mL(-1) (95% confidence interval). A detection limit of 20 pg mL(-1) was achieved. Low-resolution GC/SF-ICPMS in combination with solvent-free SPME sampling effectively eliminated spectroscopic interferences, yielding a concentration of 0.132 +/- 0.004 ng mL(-1) (1 SD, n = 4) for Cr in CASS-4 with a method detection limit of 3.9 pg mL(-1). By comparison, SPME sampling with GC/SF-ICPMS in medium-resolution mode provided a concentration of 0.146 +/- 0.013 ng mL(-1) (1 SD, n = 4) and a method detection limit of 9.1 pg mL(-1).
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".