Inductively coupled plasma mass spectrometric determination of heavy metals in sea-water samples after pre-treatment with a chelating resin disk by an on-line flow injection method
Bibliographic record
Abstract
A new on-line flow injection (FI) pre-treatment system using a disk-type chelating resin (5 mm diameter, 0.5 mm thickness) was developed for the simultaneous multi-element determination of trace metals in sea-water samples by inductively coupled plasma mass spectrometry (ICP-MS). A chelating resin possessing an iminodiacetate (IDA) functional group was used for the collection of trace elements and the elimination of alkali and alkaline earth metals in highly concentrated salt solution. A 1 ml volume of a sea-water sample (pH 5.5) was applied to the chelating resin disk. Considering the removal efficiency for Ca, 50 mM ammonium acetate buffer solution (pH 5.5) was chosen as a sample carrier. The enriched trace metals were eluted with 0.1 M nitric acid and the eluate flowed into the ICP-MS system. The processing time for one sample was < 6 min (350 s). One of the important observations is the possibility of working with a low recovery, even lower than 50%. For example, several elements such as Mn, Cr, As, Mo, Ba and U, the recovery of which was < 50% in a batch-wise method, showed good linearity and reproducibility. The proposed method was evaluated by analyzing two kinds of sea-water certified reference materials, CASS-4 and NASS-5. Analytical data for eight heavy metals, V, Mn, Co, Ni, Cu, Mo, Cd and U, obtained from the present study agreed well with the certified values.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".