Preconcentration of Cu(II), Co(II), and Ni(II) using an Optimized Enrichment Procedure: Useful and Alternative Methodology for Flame Atomic Absorption Spectrometry
Bibliographic record
Abstract
In this paper, a new solid phase extraction procedure is described for Cu(II), Co(II), and Ni(II). Silica gel which was coated with N,N'-bis(4-methoxysalicylidene) ethylenediamine (MSE) is used as a sorbent. Three independent variables were optimized using central composite design (CCD) for sorption and elution of metal ions. The optimum values of sorption and elution variables allowed simultaneous preconcentration of the ions in same conditions as follows, for sorption, pH 6.9, flow rate 5.4 mL min(-1), sample volume 50.0 mL, and for elution, flow rate 2.6 mL min(-1), eluent concentration 1.0 mol L(-1), eluent volume 5.0 mL. The detection limits (LOD) were found to be 1.1 µg L(-1) for Cu(II), 7.4 µg L(-1) for Co(II), and 7.5 µg L(-1) for Ni(II) and preconcentration factor was 200 for each of the ions. The accuracy of the method was tested with Lake Ontario water and multi-element standard solution. The proposed method was also applied to various water samples. The proposed method can be alternatively suggested as accurate, precise, easy, and a cheap method for Cu(II), Co(II), and Ni(II) determination.
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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 | 0.000 |
| 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) | 0.000 | 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".