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Record W4389660712 · doi:10.1021/acs.joc.3c01584

Nickel-Catalyzed O-Arylation of Primary or Secondary Aliphatic Alcohols with (Hetero)aryl Chlorides: A Comparison of Ni(I) and Ni(II) Precatalysts

2023· article· en· W4389660712 on OpenAlexafffund
Kathleen M. Morrison, Nicholas J. Roberts, Samantha L. Dudra, Joseph P. Tassone, Michael J. Ferguson, Erin R. Johnson, Mark Stradiotto

Bibliographic record

VenueThe Journal of Organic Chemistry · 2023
Typearticle
Languageen
FieldChemistry
TopicCatalytic C–H Functionalization Methods
Canadian institutionsUniversity of AlbertaDalhousie University
FundersSocial Sciences and Humanities Research Council of CanadaCanadian Institutes of Health ResearchKillam TrustsDalhousie UniversityNatural Sciences and Engineering Research Council of CanadaGovernment of Canada
KeywordsCatalysisCatalytic cycleArylChemistryNickelLimitingLigand (biochemistry)Oxidative additionMedicinal chemistryReductive eliminationPrimary (astronomy)Turnover numberStereochemistryCombinatorial chemistryOrganic chemistryAlkyl

Abstract

fetched live from OpenAlex

A comparative experimental and computational study examining the interplay of the ancillary ligand structure and Ni oxidation state in the Ni-catalyzed C(sp 2 )–O cross-coupling of (hetero)aryl chlorides and primary or secondary aliphatic alcohols is presented, focusing on PAd-DalPhos ( L1 )-, CyPAd-DalPhos ( L2 )-, PAd2-DalPhos ( L3 )-, and DPPF ( L4 )-ligated [( L )NiCl] n ( n = 1 or 2) and ( L )Ni( o -tol)Cl precatalysts. Both L1 and L2 were found to outperform the other ligands examined, with the latter proving to be superior overall. While Ni(II) precatalysts generally outperformed Ni(I) species, in some instances the catalytic abilities of Ni(I) precatalysts were competitive with those of Ni(II). Density-functional theory calculations indicate the favorability of a Ni(0)/Ni(II) catalytic cycle featuring turnover-limiting C–O bond reductive elimination over a Ni(I)/Ni(III) cycle involving turnover-limiting C–Cl oxidative addition.

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 categoriesInsufficient payload (model declined to judge)
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 score0.002
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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)0.0020.000

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 head0.030
GPT teacher head0.289
Teacher spread0.258 · 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.

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

Citations6
Published2023
Admission routes2
Has abstractyes

Explore more

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