Kinetic Speciation of Co(II), Ni(II), Cu(II), and Zn(II) in Model Solutions and Freshwaters: Lability and the d Electron Configuration
Bibliographic record
Abstract
The kinetic speciation of Co(II), Ni(II), Cu(II), and Zn(II) in model solutions of a well-characterized fulvic acid (Laurentian fulvic acid), freshwater samples from the Rideau River (Ottawa, Ontario), and freshwater samples from the Sudbury (Ontario) area were investigated by the competing ligand exchange method using Chelex 100 as the competing ligand and by inductively coupled plasma-mass spectrometry to measure the dissociation kinetics. The metal species were quantitatively characterized by the rate coefficient for the first-order dissociation of metal complex to free metal ion. This technique can be applied to almost all elements and represents an important advance in our ability to investigate the kinetic availability of metal species in the freshwater environment. The order of the lability of the metal complexes, Co(II) > Ni(II) > Cu(II) < Zn(II), follows the reverse order of the ligand field stabilization energy with the exception of Cu(II); the behavior of Cu(II) is also due to the Jahn-Teller effect, which shortens the equatorial bonds and lengthens the axial bonds of a tetragonally distorted Cu(II)-L6 complex. This study has demonstrated a relationship between the lability of metal-DOM complexes of the 3d transition metals in freshwaters and their d electron configuration. This is the first time that the importance of the d electron configuration on the lability of metal complexes in the freshwater environment has been demonstrated. The slow complexation kinetics of both Ni(II) and Cu(II) suggestthatthe usual equilibrium assumption for freshwaters may be invalid.
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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) | 0.000 | 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) | 0.000 | 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".