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Record W2513142422 · doi:10.1039/c6sc03045a

Tailored oxido-vanadium(V) cage complexes for selective sulfoxidation in confined spaces

2016· article· en· W2513142422 on OpenAlexfundno aff
Dawei Zhang, Kelsey Jamieson, Laure Guy, Guohua Gao, Jean‐Pierre Dutasta, Alexandre Martinez

Bibliographic record

VenueChemical Science · 2016
Typearticle
Languageen
FieldMaterials Science
TopicMagnetism in coordination complexes
Canadian institutionsnot available
FundersAgence Nationale de la RechercheOttawa Hospital Research Institute
KeywordsCageVanadiumChemistryCrystallographyStereochemistryInorganic chemistryCombinatorial chemistryCombinatoricsMathematics

Abstract

fetched live from OpenAlex

Five sets of oxido-vanadium(V) complexes, which include both cages and open structures, were prepared and tested in the catalytic oxidation of sulfides. It was found that the hemicryptophane complexes, which are simultaneously comprised of cyclotriveratrylene (CTV), binaphthol and oxido-vanadium(V) moieties, are the most efficient supramolecular catalysts. The specific shape of the confined hydrophobic space above the metal center leads to a strong improvement in the yield, selectivity and rate of the reaction, compared to the other catalysts investigated herein. A remarkable turnover number (TON) of 10 000 was obtained, which can be attributed to both the high reactivity and stability of the catalyst. Similarly to enzymes, the kinetic analysis shows that the mechanism of oxidation with the supramolecular catalysts obeys the Michaelis-Menten model, in which initial rate saturation occurs upon an increase in substrate concentration. This enzyme-like behavior is also supported by the competitive inhibition and substrate size-selectivity observed, which underline the crucial role played by the cavity.

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)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
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 score0.000
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.019
GPT teacher head0.274
Teacher spread0.254 · 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 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

Citations42
Published2016
Admission routes1
Has abstractyes

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