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Record W3211886061 · doi:10.1002/anie.202112770

Deoxygenative Functionalizations of Aldehydes, Ketones and Carboxylic Acids

2021· review· en· W3211886061 on OpenAlexafffund
Jianbin Li, Chia‐Yu Huang, Chao‐Jun Li

Bibliographic record

VenueAngewandte Chemie International Edition · 2021
Typereview
Languageen
FieldChemistry
TopicCatalytic C–H Functionalization Methods
Canadian institutionsMcGill UniversityCentre in Green Chemistry and Catalysis
FundersNatural Sciences and Engineering Research Council of CanadaFonds Québécois de la Recherche sur la Nature et les TechnologiesCanada Excellence Research Chairs, Government of CanadaCanada Foundation for Innovation
KeywordsDeoxygenationElectrophileChemistryOrganic chemistryNucleophileCarboxylic acidReactivity (psychology)AlkylCombinatorial chemistryCatalysis

Abstract

fetched live from OpenAlex

The simple and efficient conversion of carbonyl compounds into functionalized alkanes via deoxygenation is highly enabling in chemical synthesis. This Review covers the recent methodology development in carbonyl and carboxyl deoxygenative functionalizations, highlighting some representative and significant contributions in this field. These advances are categorized based on the reactivity patterns of some oxygenated feedstock compounds, including aldehydes, ketones and carboxylic acids. Four types of reactive intermediates arising from aldehydes and ketones during the deoxygenation, namely, bis-electrophiles, carbenoids, bis-nucleophiles and alkyl radical equivalents, are presented, while the carboxylic acids mainly behave as tris-electrophiles when deoxygenated. In each subcategory, selected examples are organized according to the type of bond formation and discussed from a generalized mechanistic perspective.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.051
GPT teacher head0.344
Teacher spread0.293 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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

Citations97
Published2021
Admission routes2
Has abstractyes

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