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Record W2808446465 · doi:10.1021/acscatal.8b01153

Revised Mechanisms for Aldehyde Disproportionation and the Related Reactions of the Shvo Catalyst

2018· article· en· W2808446465 on OpenAlexafffund
Dmitry G. Gusev, Denis M. Spasyuk

Bibliographic record

VenueACS Catalysis · 2018
Typearticle
Languageen
FieldChemistry
TopicAsymmetric Hydrogenation and Catalysis
Canadian institutionsCanadian Light Source (Canada)Wilfrid Laurier University
FundersNatural Sciences and Engineering Research Council of CanadaCanada Foundation for Innovation
KeywordsDisproportionationCatalysisChemistryHydrideAlkoxideAldehydeDimerRutheniumPhotochemistryMetalOrganic chemistry

Abstract

fetched live from OpenAlex

It is widely believed that the Shvo catalyst (1) dissociates to form two active species in solution: the 18-electron hydride RuH(CO)2[η5-C5(OH)Ph4] (2) and the naked 16-electron complex Ru(CO)2[η4-C5(═O)Ph4] (3). This combined experimental/computational study demonstrates that a sustained presence of 3 is not viable in the reactions of alcohols and organic carbonyls; thus, 3 is better treated as nonexistent under the typical catalytic conditions. We propose a modified view where the key catalytic species are the hydride 2 and the 18-electron metal alkoxide intermediate Ru(OR)(CO)2[η5-C5(OH)Ph4] existing in equilibrium with the corresponding alcohol complex. An X-ray crystallographic study of 2 revealed an interesting dihydrogen-bonded dimer structure in the solid state. The mechanistic ideas of this paper explain the highly efficient Tishchenko-like aldehyde disproportionation reaction with the Shvo catalyst. Additionally, our observations explain why 1 is inefficient for hydrogenation of ethyl acetate and for the acceptorless dehydrogenative coupling of ethanol. Our findings provide practical guidance for future catalyst design on the basis of the Shvo ruthenium dimer prototype.

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)0.001
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.280
Threshold uncertainty score0.322

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.007
GPT teacher head0.228
Teacher spread0.221 · 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

Citations31
Published2018
Admission routes2
Has abstractyes

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