Nuclear Magnetic Resonance Analysis of Changes in Dissolved Organic Matter Composition with Successive Layering on Clay Mineral Surfaces
Bibliographic record
Abstract
Dissolved organic matter (DOM) chemistry and the potential for organic matter (OM) to self-associate with other OM components are important aspects of understanding the mechanisms of DOM sorption to clay surfaces. To investigate this further, we sorbed DOM isolated from peat humic acid onto either kaolinite, montmorillonite and gibbsite via ten sequential batch equilibration sorption experiments. Dissolved organic carbon (DOC) sorption to all minerals increased consistently, suggesting that sorption occurred via mineral-OM interactions at the beginning of the experiment. After six successive DOM loadings, the concentration of DOC sorbed by kaolinite and gibbsite began to plateau, likely due to the saturation of mineral surface sorption sites. Solution-state nuclear magnetic resonance (NMR) analysis of unbound DOM showed that kaolinite and montmorillonite sorbed aliphatic, protein and lignin components initially and primarily aliphatic and aromatic constituents in later sorption experiments, whereas gibbsite sorbed mostly aliphatic compounds during all DOM loadings. Analysis of the organo-clay complexes using 1H high resolution–magic angle spinning (HR-MAS) NMR confirmed the preferential sorption of aromatic and aliphatic components to all three minerals. Overall, these results suggest that OM-OM interactions may be important mechanisms of DOM sorption to clay mineral surfaces.
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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 | 0.001 |
| 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".