Exploring the affinity and selectivity of sedimentary mackinawite (FeS) towards natural organic matter
Bibliographic record
Abstract
While iron oxides have been thoroughly explored in terms of their ability to sorb and sequester organic carbon (OC) in sediments, the role of iron sulfide (Fe-S) minerals in the long-term sequestration of OC remains poorly defined. In this study, we assessed the affinity of different types of natural organic matter (NOM) towards synthetic Fe-S minerals using sorption isotherms. We found affinities and sorption capacities varying in the following order: plankton NOM > corn leaves NOM > aged terrestrial NOM. Scanning electron microscopy showed that NOM increases the size and surface area of Fe-S aggregates, likely also influencing their surface reactivity. High NOM contents in Fe-S minerals protected Fe(II) from oxidation after exposure to atmospheric oxygen. Analysis of the synthetically prepared Fe-S-NOM complexes by synchrotron scanning transmission X-ray microscopy (STXM) coupled to near-edge X-ray absorption fine-edge structure (NEXAFS) spectroscopy revealed strong interactions between Fe-S minerals and NOM extracted from plankton, specifically with amide and carboxylic functional groups. We also attempted to identify and characterize interactions between OC and Fe-S minerals in natural sulfidic sediments from the St. Lawrence Estuary and the Saguenay Fjord, hence linking our work on synthetic iron sulfides to what is occurring in natural environments, although this effort proved more challenging owing to the presence of Fe(III) minerals even at depth and the difficulty in distinguishing FeS from other Fe(II) minerals. We present depth concentration profiles of dissolved OC, iron, and sulfur in the liquid-phase (pore water) along with speciation data from sequential extractions of sulfur in the solid-phase collected from sediment cores. We found a clear association between mixed Fe(II)/Fe(III) minerals and OC in sediments, which, combined with the results of the synthetic FeS experiment, suggests that Fe-S minerals can promote OC sequestration in sediments.
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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".