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Record W2046146995 · doi:10.1139/v08-178

<sup>31</sup> P NMR and ESI-MS studies of metal ion-phosphorus pesticide residue complexes

2009· article· en· W2046146995 on OpenAlexaffvenue
In Sun Koo, Dildar Ali, Kiyull Yang, Yong Il Park, Abdelhamid A. Esbata, Gary W. vanLoon, Erwin Buncel

Bibliographic record

VenueCanadian Journal of Chemistry · 2009
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPesticide Residue Analysis and Safety
Canadian institutionsQueen's University
Fundersnot available
KeywordsChemistryChemical shiftElectrospray ionizationMetalStoichiometryIonic bondingMass spectrometryAnalytical Chemistry (journal)IonNuclear magnetic resonance spectroscopyCrystallographyNuclear chemistryStereochemistryPhysical chemistryOrganic chemistryChromatography

Abstract

fetched live from OpenAlex

31 P NMR and ESI-MS structural studies have been carried out for O,O-dimethylphosphorothioate anion (PA – ) interacting with Ag + , Hg 2+ , and Na + cations. Evidence is presented for the formation of PA – ···Ag + and PA – ···Hg 2+ metal complexes and for ionic PA – Na + , with 31 P chemical shift values of 43, 40, and 65 ppm, respectively. The 31 P chemical shifts for PA – ···Ag + and PA – ···Hg 2+ complexes exhibit an initial sharp rise as the [Ag + ]/[PA] and [Hg 2+ ]/[PA] ratio increases from 0 to 1.0 and 0 to 0.5, respectively, and remain almost constant beyond these ratios. These results indicate the formation of 1:1 PA – ···Ag + and 2:1 (PA – ) 2 ···Hg 2+ complexes. Electrospray ionization mass spectroscopy (ESI-MS) revealed peaks corresponding to [O-P(=S)(OCH 3 ) 2 + Ag + H] + and {2[O-P(=S)(OCH 3 ) 2 ] + Hg + H} + for PAH interacting with Ag + and Hg 2+ , supporting the 1:1 PA – ···Ag + and 2:1 (PA – ) 2 ···Hg 2+ stoichiometry. The experimental results support the predominance of species with greater P=O double bond and P–S single bond character, relative to P-O/P=S. Our results on 31 P NMR chemical shifts have a bearing on the use of 31 P chemical shifts as a tool for organophosphorus pesticide identification.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.656
Threshold uncertainty score0.258

Codex and Gemma teacher scores by category

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

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 head0.021
GPT teacher head0.236
Teacher spread0.215 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations30
Published2009
Admission routes2
Has abstractyes

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