Interaction between metal(loid)s and soil mineral-organic matter associations
Bibliographic record
Abstract
Mineral-organic matter (OM) associations sequester metal(loid)s in the environment, controlling their mobility and bioavailability in soils. This review describes processes and mechanisms operating at the interfaces between OM and mineral phases in mineral-metal(loid)-OM and metal(loid)-OM-mineral associations. It will demonstrate the assumption that metal(loid)s interact with “pure” mineral and OM matrixes in soils is overly simplistic as numerous interactions such as adsorption, precipitation and incorporation of metal(loid)s on the surfaces, interfaces or within minerals and OM can occur. For example, multi-layer organic compounds can control the incorporation of minerals and regulate the carbon and metal(loid)s mobility and sequestration. To simplify complex metal(loid)-mineral-OM interactions, we propose five nanometric mechanisms for the sequestration of metal(loid)s by mineral-OM associations: I. heterogeneous nucleation of metal(loid)s-bearing nanomaterials (NMs) within the organic matrix, II. nucleation of NMs via reduction of metal(loid) species on OM/mineral surfaces, III. nucleation of NMs via transformation of preexisting NMs, IV. heterogeneous nucleation of NMs on mineral inclusions and V. diffusion and homogeneous distribution of metals in mineral inclusions and organic matrix. This review helps to develop potential materials for (a) remediation techniques that allow the effective removal of metal pollutants from water and soil and (b) biomass carbon removal and storage techniques that can sequestrate and stabilize carbon in terrestrial systems and thus help fight the current climate change. Future studies need to identify the spatial and functional complexity and the heterogeneous structure and composition of metal(loid)-mineral-OM associations, in which matrixes and adsorbed or incorporated metal(loid)s can transform during multi-dynamic processes.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".