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Record W2566916576 · doi:10.1021/acs.organomet.6b00823

Cobalt- and Iron-Catalyzed Isomerization–Hydroboration of Branched Alkenes: Terminal Hydroboration with Pinacolborane and 1,3,2-Diazaborolanes

2016· article· en· W2566916576 on OpenAlexafffund
Takahiko Ogawa, Adam J. Ruddy, Orson L. Sydora, Mark Stradiotto, Laura Turculet

Bibliographic record

VenueOrganometallics · 2016
Typearticle
Languageen
FieldChemistry
TopicOrganoboron and organosilicon chemistry
Canadian institutionsDalhousie University
FundersNatural Sciences and Engineering Research Council of CanadaDalhousie University
KeywordsHydroborationChemistryIsomerizationAlkeneSelectivityCobaltCatalysisOrganic chemistryCombinatorial chemistryMedicinal chemistry

Abstract

fetched live from OpenAlex

The synthesis and characterization of a series of structurally varied N -phosphinoamidinate-ligated cobalt complexes is described, along with the successful application of these and a related iron complex as precatalysts in the isomerization–hydroboration of terminal, geminal, and internal alkenes. These reactions proceed under mild conditions (23–65 °C), at relatively low base-metal loadings (1–5 mol %), typically without cosolvent, and with high terminal hydroboration selectivity across a broad spectrum of branched alkenes. With some of the alkene substrates examined, the N -phosphinoamidinate-ligated precatalysts employed herein are shown to provide alternative terminal selectivity versus other previously reported precatalyst classes for such transformations. Reports of terminal-selective metal-catalyzed alkene isomerization–hydroboration disclosed thus far in the literature employ pinacolborane (HBPin); while effective in the system herein, we also report the first examples of such transformations employing either 1,3-dimethyl-1,3-diaza-2-boracyclopentane or benzo-1,3,2-diazaborolane. The application of these 1,3,2-diazaborolanes in place of HBPin in some instances enables novel terminal selectivity in the isomerization–hydroboration of branched alkenes.

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 score0.013
Threshold uncertainty score0.967

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.005
GPT teacher head0.197
Teacher spread0.192 · 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

Citations74
Published2016
Admission routes2
Has abstractyes

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