Comparison of Analytical Methods for the Determination of Uranium in Seawater Using Inductively Coupled Plasma Mass Spectrometry
Bibliographic record
Abstract
Trace element determinations in seawater by inductively coupled plasma mass spectrometry are analytically challenging due to the typically very low concentrations of the trace elements and the potential interference of the salt matrix. In this study, we did a comparison for uranium analysis using inductively coupled plasma mass spectrometry (ICP-MS) of Sequim Bay seawater samples and three seawater certified reference materials (SLEW-3, CASS-5, and NASS-6) using eight different analytical approaches. The methods evaluated include the following: direct analysis, Fe/Pd reductive precipitation, off-line preconcentration using the actinide specific resin, UTEVA, standard addition calibration, on-line automated dilution using an external calibration with and without matrix matching, and on-line automated preconcentration using the seaFast preconcentration resin. The two methods which produced the most accurate results were the method of standard addition calibration and off-line preconcentration using the UTEVA resin, recovering uranium from a Sequim Bay seawater sample at 101 ± 1.2% and 98 ± 2.7%, respectively. The on-line preconcentration method and the automated dilution with matrix-matched calibration method also performed very well. The two least effective methods were the direct analysis and the Fe/Pd reductive precipitation method using sodium borohydride.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 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".