Competition of Ca(II) and Mg(II) with Ni(II) for Binding by a Well-Characterized Fulvic Acid in Model Solutions
Bibliographic record
Abstract
Competition of Ca(II) and Mg(II) with Ni(II) ions for binding sites of a well-characterized fulvic acid (FA) in model solutions at constant pH and ionic strength was investigated. The Competing Ligand Exchange Method with Chelex-100 and dimethyl glyoxime as the competing ligands was employed to measure the rate of free Ni 2+ ion release using graphite furnace atomic absorption spectrometry and adsorptive cathodic stripping voltammetry, respectively. The Windermere Humic Aqueous Model was used to predict the effect of competition of Ca(II) and Mg(II) on the binding of Ni(II) by the FA. The results show that the presence of high concentrations of Ca(II) and Mg(II) in model solutions has considerable effects on the binding of Ni(II) by the FA. Since the concentrations of Ca(II) and Mg(II) used are 4 orders of magnitude higher than those of Ni(II), Ca(II) and Mg(II) can outcompete Ni(II) for sites where electrostatic interactions dominate, resulting in Ni(II) forming weak Ni(II)−FA complexes that are labile. The significance is that in freshwaters containing humic substances and trace quantities of nickel and major cations, Ca 2+ and Mg 2+, the competition of Ca 2+ and Mg 2+ with Ni 2+ for binding sites of humic substances produces weak Ni(II)−humate complexes that are labile, releasing free Ni 2+ ions.
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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 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".