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Record W2007211476 · doi:10.1021/es7024356

Application of Saturation Transfer Double Difference NMR to Elucidate the Mechanistic Interactions of Pesticides with Humic Acid

2008· article· en· W2007211476 on OpenAlexafffund
Azadeh Shirzadi, Myrna J. Simpson, Yunping Xu, André J. Simpson

Bibliographic record

VenueEnvironmental Science & Technology · 2008
Typearticle
Languageen
FieldEnvironmental Science
TopicPesticide and Herbicide Environmental Studies
Canadian institutionsThe Scarborough HospitalUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of CanadaGovernment of Ontario
KeywordsChemistryElectronegativityHumic acidHalogenBinding energyNuclear magnetic resonance spectroscopyHydrogen bondPesticideMoleculeComputational chemistryStereochemistryOrganic chemistryAlkyl

Abstract

fetched live from OpenAlex

Elucidation of mechanistic interactions of anthropogenic chemicals is critical to understanding and eventually predicting their behavior in the environment Here, a recently developed technique, saturation transfer double difference (STDD) NMR spectroscopy is employed to determine the interactions of pesticides with humic acid (HA) at the molecular level. The degree of interaction at each NMR observable nucleus in the pesticide can be quantified in the form of an epitope map, which depicts the mechanism of the pesticide-HA interaction. Our results indicate that, at pH 7, halogen atoms (F and Cl) in water-soluble pesticides (diflufenzopyr, acifluorfen, and chlorsulfuron) play a dominant role in influencing binding to HA, whereas carboxyl groups likely play a secondary role when halogen atoms are also present in the molecule, as observed with diflufenzopyr and acifluorfen. However, when present on its own, the carboxyl group dominates in binding affinityto HA (e.g., imazapyr). Electronegativity and electron density appear to play a key role in the mechanism of binding and results suggest that polar bonds are the primary points of HA contact in the water soluble pesticides investigated. Likely interactions may include hydrogen bonding and dipole-dipole interactions.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.300
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.004
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.009
GPT teacher head0.219
Teacher spread0.209 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations66
Published2008
Admission routes2
Has abstractyes

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