Solution-state NMR investigation of the sorptive fractionation of dissolved organic matter by alkaline mineral soils
Bibliographic record
Abstract
Environmental context Dissolved organic matter plays a key role in global carbon cycling and environmental contaminant transport. We use one- and two-dimensional solution-state nuclear magnetic resonance spectroscopy to characterise dissolved organic matter before and after binding to alkaline subsoils with low organic carbon content. The results show that the dissolved organic matter is selectively fractionated through preferential binding of specific organic carbon functional groups. Abstract Sorption to clay minerals is a prominent fate of dissolved organic matter (DOM) in terrestrial environments. Previous studies have observed that DOM is selectively fractionated by interactions with both pure clay minerals and acidic mineral soils. However, the specific DOM functional groups that preferentially sorb to mineral surfaces in alkaline soils require further examination because higher basicity could change the nature of these sorptive interactions. Biosolids-derived DOM was characterised using one- and two-dimensional solution-state NMR spectroscopy before and after sorption to three alkaline subsurface mineral soils with varying mineralogy. Carboxylic DOM components sorbed preferentially to all soils, likely due to cation bridging and ligand exchange mechanisms. Aliphatic constituents were selectively retained only by a soil with high clay mineral content, possibly by van der Waals interactions with montmorillonite surfaces. Polar carbohydrate and peptide components of the DOM did not exhibit preferential sorption and may remain mobile in the soil solution and potentially stimulate microbial activity. A relatively low signal from aromatic DOM components prevented a full assessment of their sorption behaviour. The results suggest that DOM is selectively fractionated by similar interactions in both acidic and alkaline soils that may play a key role in the chemical and biochemical processes of subsurface environments.
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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".