Complexation of diazinon, an organophosphorus pesticide, with α-, β-, and γ-cyclodextrin NMR and computational studies
Bibliographic record
Abstract
Complexation of the organophosphorus pesticide, diazinon, with α-, β- and γ- cyclodextrin has been investigated through NMR and computational methodologies. Binding constants (Kb) determined by 1H and 31P NMR follow the order γ-CD > α-CD = β-CD, in contrast with reported Kb data for other pesticides and thus indicative of steric encumbrance by the isopropyl group in diazinon being an important factor influencing binding constants. The interaction of diazinon with the CDs has also been investigated through computational studies via molecular dynamics molecular mechanics (MDMM2) and density functional theory (DFT), B3LYP/6-31G*. It is shown that the most favorable orientation in binding corresponds to the hydrophobic heterocyclic residue of diazinon being pulled deepest into the CD cavity, in agreement with the experimentally determined order of binding constants. Moreover, the computations show that it is only with γ-CD that the heterocyclic residue of diazinon and the phosphoryl residue are both largely encrypted in the CD cavity, marking a clear differentiation with α-CD and β-CD where the phosphoryl residue is located largely outside the cavity. Thus, the computational results are in essential agreement with the experimental binding constants where γ-CD stands out with the highest Kb value. Our work could point to the potential usefulness of computational studies to be undertaken in tandem with experimental work in environmental situations such as soil remediation.Key words: organophosphorus pesticides, diazinon complexation, cyclodextrins, computational studies, molecular mechanics.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".