Comparison of radium‐226 separation methods based on chromatographic and extraction resins for its determination by ICP‐MS in drinking waters
Bibliographic record
Abstract
Abstract Over the past century, human activities have contributed to the release of 226 Ra (t ½ = 1,600 y) in the environment, increasing the potential risks for human exposure and thus prompting scientists to monitor it. Inductively coupled plasma mass spectrometry (ICP‐MS) is an alternative to alpha‐spectrometry for the quantification of 226 Ra. However, the performances of radioanalytical procedures are rarely compared in a rigorous framework, which means that researchers may choose one on subjective factors or guesses. This article compares five published methods for the separation and preconcentration of 226 Ra in drinking waters based on chromatographic and extraction resins prior to its analysis by ICP‐MS. We evaluated the turnaround time, generated wastes, and final cost of each protocol as the economic aspect can be an important criterion when selecting a method, particularly for sustainable environmental monitoring. Our results showed that 226 Ra was successfully separated and preconcentrated, yielding recoveries ranging between 84% and 105%. Method detection and quantification limits of respectively 2–7 fg L −1 (0.1–0.3 mBq L −1 ) and 6–24 fg L −1 (0.2–0.9 mBq L −1 ) were achieved when the separation method was coupled with ICP‐MS. The turnaround times ranged between 6 and 21 hours, whereas the cost of the methods varied between 40 and 132 USD. This study highlights for the first time that methodologies recently published on the evaluation of 226 Ra levels in drinking water by ICP‐MS have comparable figures of merit. Our results offer essential insights into the selection of the most suitable separation method.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".