Evaluation of Immobilized Metal-Ion Affinity Chromatography and Electrospray Ionization Tandem Mass Spectrometry for Recovery and Identification of Copper(II)-Binding Ligands in Seawater Using the Model Ligand 8-Hydroxyquinoline
Bibliographic record
Abstract
Complexation by organic ligands dominates the speciation of iron (Fe), copper (Cu), and other bioactive trace metals in seawater, controlling their bioavailability and distribution in the marine environment. Several classes of high-affinity Fe-binding ligands (siderophores) have been identified in seawater but the chemical structures of marine Cu-complexing ligands remain unknown. Immobilized metal-ion affinity chromatography (IMAC) allows Cu ligands to be isolated from bulk dissolved organic matter (DOM) in seawater and separated into fractions which can be characterized independently using electrochemical and spectroscopic techniques. Attempts have been made to combine IMAC with electrospray ionization mass spectrometry (ESI-MS) to characterize marine Cu ligands, but results have proven inconclusive due to the lack of tandem mass spectrometry (MS/MS) data to confirm ligand recovery. We used 8-hydroxyquinoline (8-HQ), a well-characterized model ligand that forms strong 1:2 metal:ligand complexes with Cu2+ at pH 8 (log β2 = 18.3), to evaluate Cu(II)-IMAC and ESI-MS/MS for recovery and identification of copper(II)-complexing ligands in seawater. One-litre samples of 0.45µm-filtered surface seawater were spiked with 8-HQ at low concentrations (up to 100 nM) and fractionated by IMAC. Fractions eluted with acidified artificial seawater were desalted and re-suspended in methanol via solid-phase extraction (SPE) to obtain extracts suitable for ESI-MS analysis. Recovery of 8-HQ by Cu(II)-IMAC was confirmed unambiguously by MS/MS and found to average 81% based upon accurate quantitation via multiple reaction monitoring (MRM). Cu(II)-IMAC fractionation of unspiked seawater using multiple UV detection wavelengths suggests an optimal fraction size of 2 mL for isolating and analyzing Cu ligands with similar properties.
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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 | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".