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Record W2325174723 · doi:10.1021/jp110107p

From Inert to Explosive, The Hydrolytic Reactivity of R−NSO Compounds Understood: A Computational Study

2011· article· en· W2325174723 on OpenAlexaff
Elena V. Ivanova, Heidi M. Muchall

Bibliographic record

VenueThe Journal of Physical Chemistry A · 2011
Typearticle
Languageen
FieldChemistry
TopicChemical Reaction Mechanisms
Canadian institutionsConcordia University
Fundersnot available
KeywordsReactivity (psychology)ChemistryHydrolysisNucleophileNatural bond orbitalComputational chemistryMoleculeResonance (particle physics)ReagentSulfurMedicinal chemistryOrganic chemistryCatalysis

Abstract

fetched live from OpenAlex

We present a computational study on the concerted hydrolysis of several classes of N-sulfinylamines of generic formula R-N═S═O, such as the -amines themselves (R-NSO), -hydrazines (R-NH-NSO), -hydrazides (R-CO-NH-NSO) and -amides (R-CO-NSO), as these species are known to possess a wide range of hydrolytic reactivity. Two possible mechanisms of hydrolysis, with a water dimer across the S═O and N═S bonds, in the gas phase are investigated. The reactivity is discussed with respect to the electronic structures, established with the use of the quantum theory of Atoms in Molecules, Natural Bond Orbital and Natural Resonance Theory approaches. For the inert N-sulfinylhydrazines and the keto tautomers of N-sulfinylhydrazides, extended π-conjugation adds a sulfide-like resonance structure that is responsible for their insensitivity toward moisture. Activation barriers for hydrolysis, where water acts as a nucleophilic reagent, decrease with increasing positive charge on the NSO sulfur atom, a finding that might prove useful as a predictive tool in the determination of the general reactivity of an N-sulfinyl compound by experimentalists.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.014
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.040
GPT teacher head0.268
Teacher spread0.228 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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

Citations11
Published2011
Admission routes1
Has abstractyes

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Same venueThe Journal of Physical Chemistry ASame topicChemical Reaction MechanismsFrench-language works237,207