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Record W2565811307 · doi:10.1021/jacs.6b09705

Electrocatalytic Alcohol Oxidation with Ruthenium Transfer Hydrogenation Catalysts

2016· article· en· W2565811307 on OpenAlexfundno aff
Kate M. Waldie, Kristen R. Flajslik, Elizabeth A. McLoughlin, Christopher E. D. Chidsey, Robert M. Waymouth

Bibliographic record

VenueJournal of the American Chemical Society · 2016
Typearticle
Languageen
FieldChemistry
TopicAsymmetric Hydrogenation and Catalysis
Canadian institutionsnot available
FundersDivision of ChemistryNatural Sciences and Engineering Research Council of CanadaStanford University
KeywordsChemistryRutheniumCyclic voltammetryCatalysisButaneBulk electrolysisTransfer hydrogenationInorganic chemistryElectrolysisElectrochemistryHydrideAcetonePotassiumAlcohol oxidationFaraday efficiencyAlcoholElectrolyteHydrogenOrganic chemistryElectrode

Abstract

fetched live from OpenAlex

Octahedral ruthenium complexes [RuX(CNN)(dppb)] ( 1, X = Cl; 2, X = H; CNN = 2-aminomethyl-6-tolylpyridine, dppb = 1,4-bis(diphenylphosphino)butane) are highly active for the transfer hydrogenation of ketones with isopropanol under ambient conditions. Turnover frequencies of 0.88 and 0.89 s –1 are achieved at 25 °C using 0.1 mol % of 1 or 2, respectively, in the presence of 20 equiv of potassium t -butoxide relative to catalyst. Electrochemical studies reveal that the Ru–hydride 2 is oxidized at low potential (−0.80 V versus ferrocene/ferrocenium, Fc 0/+ ) via a chemically irreversible process with concomitant formation of dihydrogen. Complexes 1 and 2 are active for the electrooxidation of isopropanol in the presence of strong base (potassium t -butoxide) with an onset potential near −1 V versus Fc 0/+ . By cyclic voltammetry, fast turnover frequencies of 3.2 and 4.8 s –1 for isopropanol oxidation are achieved with 1 and 2, respectively. Controlled potential electrolysis studies confirm that the product of isopropanol electrooxidation is acetone, generated with a Faradaic efficiency of 94 ± 5%.

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 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 score0.008
Threshold uncertainty score0.320

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
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 head0.224
Teacher spread0.217 · 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

Citations61
Published2016
Admission routes1
Has abstractyes

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Same venueJournal of the American Chemical SocietySame topicAsymmetric Hydrogenation and CatalysisFrench-language works237,207