Nickel and copper complexation by natural dissolved organic matter – titration of two contrasting lake waters and comparison of measured and modelled free metal ion concentrations
Bibliographic record
Abstract
Environmental context Natural dissolved organic matter strongly influences the biogeochemistry and bioavailability of trace metals in natural waters. Chemical equilibrium models are often used to predict the relative importance of the free metal cation, a recognised indicator of the metal’s bioavailability. Here we show how the nature of the organic matter varies between two lakes, affecting the measured speciation of copper and nickel, a result that challenges existing chemical equilibrium models. Rationale Thermodynamic models such as the Windermere Humic Aqueous Model (WHAM) are often used to estimate the binding of cations by dissolved organic matter (DOM) in natural aquatic systems. Such models require as input data the quantity of DOM but do not consider its quality. Using two well-characterised lakewater samples, we demonstrate, for realistic environmental conditions, that the conditional binding parameters for the complexation of Ni and Cu with natural DOM vary between lakes and we relate these differences to the spectroscopic quality of the DOM. Methodology Waters from two lakes with contrasting types of DOM were titrated with Cu and Ni and the conditional binding parameters were calculated using a two-site ligand model, with associated conditional stability constants implemented in PHREEQC v.3.1.2, and compared between lakes and between metals. The titration curves for each lake were compared to those predicted by WHAM v7.05. Results Binding affinities and capacities of DOM for Cu and Ni were found to differ not only between metals, but also between lakes. Discussion Overall, the titration results suggest that the more aromatic humic-like DOM from allochthonous sources may have a significantly higher complexation affinity for Ni than the more protein-like DOM from autochthonous sources. The differing behaviour of Ni and Cu in the two lakes suggests that they are binding to different types of binding sites within the DOM matrix. More data with various natural DOM samples are needed to capture the diversity of metal–DOM interactions and to improve our ability to predict metal speciation in natural waters.
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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) | 0.000 | 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 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".