Unraveling glyphosate sequestration: The role of natural organic matter fractions in soil-water contamination and retention
Bibliographic record
Abstract
The bioavailability and fate of pesticides in soil are largely influenced by soil's sorption characteristics. Therefore, the adsorption of pesticides, like glyphosate (GBH), onto soil natural organic matter (NOM) was investigated in this study. With the aid of sequential treatment methods of agricultural soil, NOM was modified to yield demineralized matter (DM), nonhydrolyzable carbon (NHC), and black carbon (BC). A comprehensive characterization of NOMs was carried out using BET, ICP-OES, pHpzc, SEM-EDS, XRD, and FTIR, which revealed alterations in the physical and chemical characteristics of NOMs as a result of the extraction and modification procedures. Experimental data demonstrated that the Sips isotherm model provided the best fit for NOM-glyphosate interactions, as indicated by the lowest chi-square values and correlation coefficient. The model suggests a complex interaction between the pesticide and NOMs, driven potentially by π-π interactions, as well as electrostatic interactions between charged NOMs due to their moieties and glyphosate ions in aqueous media. The predicted maximum adsorption capacity improved from 6.8 mg/g (bulk soil) to 8.7 mg/g (BC fraction), with experimental adsorption capacity following the order Bulk < DM < BC < NHC. Sorption was fairly enhanced under acidic conditions and sorption hysteresis was observed. Additionally, the NOM's chemical composition, particularly its percent organic carbon and mineralogy, which influenced the NOM's hydrophobic properties, played a key role in influencing adsorption behavior and potentially irreversible sorption, as reflected in H-indices. This study highlights the impact of different NOM fractions on glyphosate mobility, retention in soil and potential environmental risks.
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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".