Spatial Associations and Chemical Composition of Organic Carbon Sequestered in Fe, Ca, and Organic Carbon Ternary Systems
Bibliographic record
Abstract
Organo-mineral associations of organic carbon (OC) with iron (Fe) oxides play a major role in environmental OC sequestration, a process crucial to mitigating climate change. Calcium has been found to have high coassociation with OC in soils containing high Fe content, increase OC sorption extent to poorly crystalline Fe oxides, and has long been suspected to form bridging complexes with Fe and OC. Due to the growing realization that Ca may be an important component of C cycling, we launched a scanning transmission X-ray microscopy (STXM) investigation, paired with near-edge X-ray absorption fine structure (NEXAFS) spectroscopy, in order to spatially resolve Fe, Ca, and OC relationships and probe the effect of Ca on sorbed OC speciation. We performed STXM-NEXAFS analysis on 2-line ferrihydrite reacted with leaf litter-extractable dissolved OC and citric acid in the absence and presence of Ca. Organic carbon was found to highly associate with Ca ( R 2 = 0.91). Carboxylic acid moieties were dominantly sequestered; however, Ca facilitated the additional sequestration of aromatic and phenolic moieties. Also, C NEXAFS revealed polyvalent metal ion complexation. Our results provide evidence for the presence of Fe–Ca–OC ternary complexation, which has the potential to significantly impact how organo-mineral associations are modeled.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".