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Record W1980349434 · doi:10.1139/v07-099

Methanolysis of organophosphorus esters promoted by an M<sup>2+</sup> catalyst supported on polystyrene-based copolymers

2008· article· en· W1980349434 on OpenAlexfundvenueno aff
Benoît Didier, Mark F. Mohamed, Elizabeth Csaszar, Kate G Colizza, Alexei A. Neverov, R. S. Brown

Bibliographic record

VenueCanadian Journal of Chemistry · 2008
Typearticle
Languageen
FieldChemistry
TopicRadioactive element chemistry and processing
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsChemistryCatalysisPolystyreneMethanolPolymerPhosphonateCopolymerPorosityMetalPolymer chemistryReactivity (psychology)Organic chemistry

Abstract

fetched live from OpenAlex

The methanolysis of three neutral organophosphorus esters (a phosphonate, a phosphonothioate, and a phosphorothionate) promoted by several polymer-supported Zn(II) or Cu(II) containing catalysts was studied. The catalysts consist of a Zn(II) or Cu(II) complex with 1,5,9-triazacyclododecane or phenanthroline attached to a porous polystyrene resin. In each case, the polymer supported catalyst showed activity at near neutral s s pH in methanol (8.38) and ambient temperature and provided accelerations of up to a factor of 2.9 × 10 6 relative to the background reaction at s s pH 9.05. The solid materials could be reused several times and could be reactivated when the activity diminished. Various polymers of different porosity and extent of cross-linking were studied, with the net result being that larger porosities offer the best reactivity for catalyzed methanolysis of these OP species in methanol. This is explained by different parameters including the accessibility to reactive sites, the increase of concentration of catalytic sites on the surface of the polymer, and some cooperative effects between neighboring catalytic groups.Key words: functionalized polymer, metal containing, methanolysis, organophosphorus pesticides and CW agents, catalyst.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
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 score0.005
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.011
GPT teacher head0.217
Teacher spread0.206 · 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

Citations4
Published2008
Admission routes2
Has abstractyes

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