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Record W2900708124 · doi:10.1021/acs.organomet.8b00674

Ta-Catalyzed Hydroaminoalkylation of Alkenes: Insights into Ligand-Modified Reactivity Using DFT

2018· article· en· W2900708124 on OpenAlexafffund
Damon J. Gilmour, Jean Michel P. Lauzon, Eric Clot, Laurel L. Schafer

Bibliographic record

VenueOrganometallics · 2018
Typearticle
Languageen
FieldChemistry
TopicAsymmetric Hydrogenation and Catalysis
Canadian institutionsUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of CanadaAmerican Chemical Society Petroleum Research Fund
KeywordsChemistrySteric effectsReactivity (psychology)ProtonolysisLigand (biochemistry)ElectrophileAlkeneCatalytic cycleCatalysisElectronic effectOxidative additionDensity functional theoryComputational chemistryCombinatorial chemistryPhotochemistryStereochemistryOrganic chemistry

Abstract

fetched live from OpenAlex

Density functional theory (DFT) calculations were performed to probe the mechanism of tantalum-catalyzed hydroaminoalkylation, a reaction that affords Csp3–Csp3 bond formation via α-alkylation of a secondary amine with an alkene. Electronic effects in catalyst design were probed to reveal key features in the energy profile of the proposed mechanism, corroborating experimental trends in which electrophilic metal centers demonstrate enhanced reactivity. Modeling of the energy profile with an N,O-chelating amidate Ta catalyst (I) revealed profound differences in relative energetics, rationalizing improvements that have been observed using sterically and electronically varied ligands. N,O-Chelating ligands electronically promote preferential reactivity in the equatorial plane. The turnover-limiting step can be completely changed depending upon the ligand; for sterically bulky monoamidate complexes, the protonolysis of an intermediate metallacycle is the turnover-limiting step rather than C–H activation, as has been found for systems lacking steric bulk. Unproductive off-cycle pathways were also modeled to compare with experimental studies. These insights contribute to a theoretical understanding of key features in ligand design for developing improved catalysts for hydroaminoalkylation.

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 score0.018
Threshold uncertainty score0.912

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0010.000

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 head0.022
GPT teacher head0.262
Teacher spread0.240 · 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

Citations32
Published2018
Admission routes2
Has abstractyes

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