Solid phase extraction and preconcentration of some metal ions using Schiff base immobilised silica gel followed by ICP-OES
Bibliographic record
Abstract
In this study, a simple and efficient solid phase extraction procedure was developed for simultaneous separation and preconcentration of Ba, Cd, Co, Cu, Mn and Ni. The methodology was based on preconcentration of the target analytes on N,N’-bis(4-methoxysalicylidene)-1,3-propanediamine modified silica gel prior to inductively coupled plasma optic emission spectrometry detection. The experimental conditions were as follows: pH of sample 5.00; sample and eluent flow rates 3 mL min−1; sample volume 25 mL; eluent 0.5 mol L−1 HNO3; eluent volume 3.0 mL. Preconcentration factor was achieved as 33.3 for Ba, Co, Mn; 83.3 for Cd, Ni; 166.7 for Cu. Limits of detection were found as 0.33, 0.26, 0.27, 0.36, 0.27 and 0.19 µg L−1 for Ba, Cd, Co, Cu, Mn and Ni, respectively. The relative standard deviations of 2.6–3.8% were obtained via nine parallel analyses. The suggested procedure was successfully validated by the analysis of TMDA-53.3 Lake Ontario water and ERM-CA022a soft drinking water certified reference materials and applied to various natural water samples.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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 teacher head, 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".