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Record W3045978561 · doi:10.1016/j.isci.2020.101419

Light-Driven Metal-Free Direct Deoxygenation of Alcohols under Mild Conditions

2020· article· en· W3045978561 on OpenAlexafffund
Dawei Cao, Zhangpei Chen, Leiyang Lv, Huiying Zeng, Yong Peng, Chao‐Jun Li

Bibliographic record

VenueiScience · 2020
Typearticle
Languageen
FieldChemistry
TopicChemical Synthesis and Reactions
Canadian institutionsMcGill UniversityCentre in Green Chemistry and Catalysis
FundersFonds Québécois de la Recherche sur la Nature et les TechnologiesNatural Sciences and Engineering Research Council of CanadaCanada Research ChairsCanada Foundation for InnovationLanzhou University
KeywordsDeoxygenationChemistryCatalysisFunctional groupBond cleavageDissociation (chemistry)Combinatorial chemistryMoleculeOrganic chemistryMetalPolymer

Abstract

fetched live from OpenAlex

Hydroxyl is widely found in organic molecules as functional group and its deprivation plays an inevitable role in organic synthesis. However, the direct cleavage of Csp 3 -O bond in alcohols with high selectivity and efficiency, especially without the assistance of metal catalyst, has been a formidable challenge because of its strong bond dissociation energy and unfavorable thermodynamics. Herein, an efficient metal-free strategy that enables direct deoxygenation of alcohols has been developed for the first time, with hydrazine as the reductant induced by light. This protocol features mild reaction conditions, excellent functional group tolerance, and abundant and easily available starting materials, rendering selective deoxygenation of a variety of 1° and 2° alcohols, vicinal diols, and β -1 and even β -O-4 models of natural wood lignin. This strategy is also highlighted by its "traceless" and non-toxic by-products N 2 and H 2 , as readily escapable gases. Mechanistic studies demonstrated dimethyl sulfide being a key intermediate in this transformation.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

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)0.0020.001

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 head0.038
GPT teacher head0.272
Teacher spread0.234 · 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 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

Citations33
Published2020
Admission routes2
Has abstractyes

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