Hyphenation of flow injection on-line preconcentration and ICP-MS for the rapid determination of 226Ra in natural waters
Bibliographic record
Abstract
A novel, sensitive, and rapid method is developed for the preconcentration/separation of 226Ra in natural waters using a flow injection (FI) design based on a sequential on-line ion exchange preconcentration coupled with ICP-MS. 226Ra is extracted using an Ln resin (HDEHP) from the sample at pH 10.6 in the presence of ethylenediamine tetraacetic acid (EDTA) diammonium salt, whereas Ca and Mg, which form stable complexes with EDTA, are not retained on the column. Quantitative elution of 226Ra from the Ln resin is achieved using 5 M HNO3. The separation of Ra from Ba and Sr is subsequently performed by the selective retention of Sr and Ba on an Sr*Spec resin, placed between the Ln resin and the Apex-Q system. In addition to being accurate and rapid for 226Ra determination, this method is effective in eliminating spectral (88Sr138Ba) and non-spectral (Ca, Mg) interferences. Using a 20 mL sample, a detection limit (3σ) of 457 fg L−1 (16.92 mBq L−1) was obtained with a sample throughput of 3.5 samples h−1. The precision for 6 replicate measurements of 5 pg L−1 (185.18 mBq L−1) 226Ra was better than 4%. The method was applied to the analysis of natural waters, where the concentration of 226Ra was found to be in the range 1.2 to 2.4 pg L−1 (44.4 to 88.8 mBq L−1), lower than the maximum acceptable concentration (MAC) for 226Ra of 16.2 pg L−1 (600 mBq L−1), recommended in the Guideline for Canadian Drinking Water Quality.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".