A Comprehensive Study of the Cobalt(II) Chelation Mechanism by an Iminodiacetate-Decorated Disaccharide Ligand
Bibliographic record
Abstract
We report an investigation on the cobalt(II) chelation mechanism by a modified α-maltoside ligand 9 decorated with two iminodiacetate (IDA) residues on C6,C6′ positions. Herein we uncovered the capacity of this biodegradable ligand to chelate cobalt(II), an ionic metal contaminant in the environment that is used, in particular, in lithium-ion batteries. The interactions between cobalt(II) and synthesized ligand 9 were systematically studied using different analytical methods such as 1H and 13C NMR, potentiometry, spectrophotometry, ITC, and ICP-AES. We observed a high affinity for the 1:1 complex, one cobalt(II) associated with two iminodiacetate groups, which is 10-fold higher than the 2:1 complex, where each of the two IDA groups interacts alone with a cobalt(II). Taking into account the log βCoL value obtained (≈12.3) with the stoichiometry 1:1, the strength of this complexation with cobalt(II) can be ranked as follows for the most common ligands: IDA < MIDA < NTA < 9 < EDTA < TTHA < DTPA. We further completed a preliminary remediation test with water contaminated with cobalt(II) and recovered cobalt(II) metal using Chelex® resin, which allowed a recycling of the synthetic ligand for future recovering experiments. The results shed light on the great potential of using this synthetic ligand as an effective and green remediation tool.
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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) | 0.001 | 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".