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Record W4400949665 · doi:10.1021/acs.orglett.4c02207

PPh<sub>3</sub>-Promoted Direct Deoxygenation of Epoxides to Alkenes

2024· article· en· W4400949665 on OpenAlexafffund
Dawei Cao, Shumei Xia, Lijuan Li, Huiying Zeng, Chao‐Jun Li

Bibliographic record

VenueOrganic Letters · 2024
Typearticle
Languageen
FieldChemistry
TopicChemical Synthesis and Reactions
Canadian institutionsMcGill UniversityCentre in Green Chemistry and Catalysis
FundersFundamental Research Funds for the Central UniversitiesNatural Sciences and Engineering Research Council of CanadaQinghai Provincial Department of Science and TechnologyFonds Québécois de la Recherche sur la Nature et les TechnologiesCanada Foundation for InnovationNational Natural Science Foundation of ChinaCanada Research Chairs
KeywordsChemistryDeoxygenationYield (engineering)Combinatorial chemistryOrganic chemistryMedicinal chemistryCatalysis

Abstract

fetched live from OpenAlex

Deoxygenation of epoxides into alkenes is one of the most important strategies in organic synthesis, biomass conversions, and medicinal chemistry. Although metal-catalyzed direct deoxygenation provides one of the most commonly encountered protocols for the conversion of epoxides to alkenes, the requirement of expensive catalysts and extra reductants has largely limited their universal applicability. Herein, we report an efficient PPh 3 -promoted metal-free strategy for deoxygenation of epoxides to generate alkene derivatives. The success of deoxyalkenylation of epoxides bearing a wide range of functional groups to give terminal, 1,1-disubstituted, and 1,2-disubstituted alkenes manifests the powerfulness and versatility of this strategy. Moreover, gram-scale synthesis with excellent yield and modification of biologically active molecules exemplifies its generality and practicability.

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 score0.009
Threshold uncertainty score0.445

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)0.0000.000

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 head0.205
Teacher spread0.198 · 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

Citations6
Published2024
Admission routes2
Has abstractyes

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