Molecular Interactions of Pesticides at the Soil−Water Interface
Bibliographic record
Abstract
High-resolution magic angle spinning (HR-MAS) NMR spectroscopy combined with saturation-transfer double difference (STDD) NMR can be used to analyze the molecular-level interactions of pesticides and whole soils occurring at the soil-water interface. Here 1H HR-MAS STDD NMR has been applied to some common pesticides (trifluralin, acifluorfen, and (4-nitro-3-(trifluoromethyl) phenol) and a pesticide degradation product (1-naphthol). Results indicate that dipolar interactions, H-bonding, hydrophobic associations, and potentially pi-pi interactions are the predominant sorption mechanisms for these molecules at the soil-aqueous interface. It is evident that the physical and chemical characteristics of soil are highly influential in determining the mechanisms of pesticide sorption, as they significantly affect soil conformation. In particular, different binding mechanisms were observed for 1-naphthol in soil swollen using a buffer versus D2O, indicating that the K(oc) alone may not be enough to accurately predict the behavior of a molecule in a real soil environment. Preliminary kinetic-based studies suggest that both the swelling solvent and soil moisture content significantly influence the sequestration of trifluralin. These studies demonstrate that HR-MAS and STDD NMR are powerful and versatile tools which can be applied to expand our knowledge of the mechanistic interactions of agrochemicals at the molecular level.
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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 | 0.001 |
| 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".