Speciation of Dissolved Iron(III) and Iron(II) in Water by On-Line Coupling of Flow Injection Separation and Preconcentration with Inductively Coupled Plasma Mass Spectrometry
Bibliographic record
Abstract
A method has been developed for the speciation of trace dissolved Fe(II) and Fe(II) in water by on-line coupling of flow injection separation and preconcentration with inductively coupled plasma mass spectrometry (ICPMS). Selective determination of Fe(III) in the presence of Fe(II) was made possible by on-line formation and sorption of the Fe(III)-pyrrolidinecarbodithioate (PDC) complex in a PTFE knotted reactor over a sample acidity range of 0.07-0.4 mol L(-1) HCl, elution with 1 mol L(-1) HNO3, and detection by ICPMS. Over a sample acidity range of 0.001-0.004 mol L(-1) HCl, the sum of Fe(III) and Fe(II), i.e., Fe(III + II), could be determined without the need for preoxidation of Fe(II) to Fe(III). The concentration of Fe(II) was obtained as the difference between those of Fe(III + II) and Fe(III). With a sample flow rate of 5 mL min(-1) and a 30-s preconcentration time, an enhancement factor of 12, a retention efficiency of 80%, and a detection limit (3s) of 0.08 microg L(-1) were obtained at a sampling frequency of 21 samples h(-1). The relative standard deviation (n = 11) was 2.9% at the 10 microg L(-1) Fe(III) level. Recoveries of spiked Fe(III) and Fe(II) in local tap water, river water, and groundwater samples ranged from 95% to 103%. The concentrations of Fe(III) and Fe(II) in synthetic aqueous mixtures obtained by the proposed method were in good agreement with the spiked values. The result for total iron concentration in the river water reference material SLRS-3 was in good agreement with the certified value. The method was successfully applied to the determination of trace dissolved Fe(III) and Fe(II) in local tap water, river water, and groundwater samples.
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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 | 0.000 | 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 | 0.001 |
| 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".