Assessment of labilities of metal complexes with the dynamic ion exchange technique
Bibliographic record
Abstract
Environmental context In natural waters, the impact of metals on biota is modulated by their binding with ligands. Ion-exchange techniques can provide information about metal-ligand complexes in solution, which can be linked to metal bioavailability in natural waters. We investigate modelling approaches to interpreting data from ion-exchange experiments to help elucidate the contribution of a particular complex to the overall metal uptake. Abstract The dynamic ion exchange technique (DIET) is proposed to provide speciation information, which can be used to establish links with metal bioavailability in natural waters. The experimental setup consists of a few milligrams of a sulfonic acid type ion exchange resin packed in a plastic microcolumn that is coupled to a peristaltic pump for a sample to interact with the resin which is subsequently eluted. The evolution of both the accumulated number of moles in the resin and the concentration of the effluent can provide information on the dissociation of different metal-ligand complexes when compared with the transport properties. This information can be converted into the lability degree of a given complex or the DIET concentration cDIET, which accounts for the labile fraction contributing to the metal accumulation by the resin column at the operation conditions. cDIET can be extended to columns containing chelating resins (such as those with Chelex) or to chromatography. A comprehensive modelling of the involved phenomena (such as diffusion, advection, reaction kinetics and electrostatic partitioning) leads to the quantitative interpretation of the accumulation time series (accumulation curves) or effluent evolution (breakthrough curves). Particularly simple analytical expressions can be used for short exposure times, when a (quasi) steady-state is attained. These models have been checked against the results from complexes of Cu and Ni with ligands, such as ethylenediamine, and ethylenediaminetetraacetic, iminodiacetic, glutamic, salicylic, malonic and malic acids, which yield complexes with contrasting charges. Caution is advised when estimating the free metal fraction from DIET measurements, as cDIET and the free metal concentration can be considered to be equal only in the case of extremely inert complexes.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 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".