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Record W4402425139 · doi:10.1002/ange.202410555

Visible Light‐Switchable Lattice Oxygen Sites for Selective C−H and C(O)−C Bond Electrooxidation

2024· article· en· W4402425139 on OpenAlexafffund
Keping Wang, Jinshu Huang, Jinguang Hu, Mei Wu, Yuhe Liao, Song Yang, Hu Li

Bibliographic record

VenueAngewandte Chemie · 2024
Typearticle
Languageen
FieldChemistry
TopicOxidative Organic Chemistry Reactions
Canadian institutionsUniversity of Calgary
FundersCanada First Research Excellence FundNational Natural Science Foundation of China
KeywordsChemistryVisible spectrumOxygenPhotochemistryCrystallographyMaterials scienceOptoelectronicsOrganic chemistry

Abstract

fetched live from OpenAlex

Abstract Lattice‐oxygen is highly oxidizable, ideal for electrocatalytic C−H oxidation but insufficient alone for C(O)−C bond cleavage due to the non‐removable nature of lattice sites. Here, we present a visible light‐assisted electrochemical method of in situ formulating removable lattice‐oxygen sites in a nickel‐oxyhydroxide (ESE‐NiOOH) electrocatalyst. This catalyst efficiently converts aromatic alcohols and carbonyls with C(O)−C fragments from lignin and plastics into benzoic acids (BAs) with high yields (83–99 %). Without light irradiation, ESE‐NiOOH's intrinsic lattice‐oxygen is non‐removable and inert for C(O)−C bond cleavage. In situ characterizations show light‐induced lattice‐oxygen removal and regeneration via OH − refilling. Theoretical calculations identify the nucleophilic oxygen attack on ketone‐derived carbanion as a rate‐determining step, which can be remarkably facilitated by removable lattice‐oxygen to activate α ‐C−H bonds. As a proof‐of‐concept, an “electrochemical funnel” strategy is developed for high‐efficiency upgrading aromatic mixtures with C(O)−C moieties into BA with up to 94 % yield. This in situ removal‐regeneration approach for lattice sites opens an avenue for the tailored design of interfacial electrocatalysts to selectively upcycle waste carbon sources into valuable products.

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 categoriesMeta-epidemiology (narrow)
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 score0.115
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
Bibliometrics0.0000.000
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 head0.015
GPT teacher head0.259
Teacher spread0.245 · 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

Citations1
Published2024
Admission routes2
Has abstractyes

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