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Record W2958583734 · doi:10.1021/acscatal.9b02483

Nickel-Catalyzed Regioselective Hydrobenzylation of 1,3-Dienes with Hydrazones

2019· article· en· W2958583734 on OpenAlex
Leiyang Lv, Dianhu Zhu, Zihang Qiu, Jianbin Li, Chao‐Jun Li

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueACS Catalysis · 2019
Typearticle
Languageen
FieldChemistry
TopicCatalytic C–H Functionalization Methods
Canadian institutionsMcGill UniversityCentre in Green Chemistry and Catalysis
FundersFonds de recherche du Québec – Nature et technologiesNatural Sciences and Engineering Research Council of CanadaCanada Research ChairsCentre in Green Chemistry and Catalysis
KeywordsRegioselectivityNucleophileChemistryCarbanionCatalysisSteric effectsArylHeteroatomCombinatorial chemistryNickelHydrideCatalytic cycleOrganic chemistryRing (chemistry)

Abstract

fetched live from OpenAlex

Hydroalkylation of unsaturated hydrocarbons with unstabilized carbon nucleophiles is difficult and remains a major challenge. The disclosed examples so far have mainly focused on the involvement of heteroatom and/or stabilized carbon nucleophiles as efficient reaction partners. Reported here is an unprecedented regioselective nickel-catalyzed hydrobenzylation of 1,3-dienes with hydrazones, generated in situ from abundant aryl aldehydes and ketones and acting as both the sources of unstabilized carbanion equivalent and hydride. With this strategy, both terminal and sterically hindered internal dienes are hydroalkylated efficiently in a highly selective manner, thus providing a reliable catalytic method to construct challenging C(sp3)–C(sp3) bonds.

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.

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 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 score0.070
Threshold uncertainty score1.000

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 head0.011
GPT teacher head0.244
Teacher spread0.233 · 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