Advancing the characterization of organic Cu(II)-binding ligands in Arctic Ocean waters: Integration of IMAC, SPE, HRMS, and fluorescence techniques
Bibliographic record
Abstract
The availability and toxicity of dissolved copper (Cu) in the ocean are largely determined by its complexation with organic ligands, but the structure and origin of these ligands are still largely unknown. In this work, standard and marine Cu(II) complexing ligands were isolated using immobilized metal affinity chromatography (IMAC) and solid phase extraction (SPE), and characterized using high-resolution mass spectrometry (HRMS) and excitation-emission matrix (EEM) fluorescence to obtain information on the size distribution, molecular composition, and fluorescing properties of organic Cu(II)-binding molecules in the Arctic Ocean. We first investigated the retention performance of model ligands and found that N-containing ligands were preferentially isolated on the IMAC-SPE column. Application to Arctic Ocean dissolved organic matter (DOM) showed that the organic ligands accounted for up to 3 % of the DOM, consistent with previous studies. Robust correlations of HMRS peak intensities with EEM signals were used to expand chemical characterization of 511 molecular formulas in the IMAC-SPE and SPE extracts. The pairing based on the molecular classification associated with fluorescence signatures showed that 52 % Spearman correlations were linked to humic-like components. The necessity of multiple molecular annotation methods, like HRMS/fluorescence correlation or van Krevelen class attribution, was demonstrated to yield a better molecular identification. The combination of IMAC, SPE, HRMS, and EEM appears to be a promising approach for the characterization of organic Cu(II)-binding ligands in waters. • Copper organic ligands represented about 2–3 % of DOC in the northern Canada Basin • Only 7–25 % of the molecular peaks were found in both SPE and IMAC-SPE extracts • 61 % of the conjugated double-bond molecules correlated with PARAFAC components in the Cu-binding ligand pool
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".