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Record W4401178530 · doi:10.1038/s41467-024-50801-3

Photosynthesis of CH3OH via oxygen-atom-grafting from CO2 to CH4 enabled by AuPd/GaN

2024· article· en· W4401178530 on OpenAlexafffund
Hui Su, Jing‐Tan Han, Botong Miao, Mahdi Salehi, Chao‐Jun Li

Bibliographic record

VenueNature Communications · 2024
Typearticle
Languageen
FieldEnergy
TopicAdvanced Photocatalysis Techniques
Canadian institutionsMcGill UniversityCentre in Green Chemistry and Catalysis
FundersShanghai Jiao Tong UniversityCanada Foundation for InnovationNatural Sciences and Engineering Research Council of CanadaCanada Research ChairsMcGill University
KeywordsMethaneOxygenYield (engineering)CatalysisCarbon fibersArtificial photosynthesisCarbon dioxideNanotechnologyMaterials scienceGreenhouse gasMethanolAtom (system on chip)PhotosynthesisGraftingChemical engineeringChemistryComputer scienceOrganic chemistryPhotocatalysis

Abstract

fetched live from OpenAlex

The direct co-conversion of methane and carbon dioxide into valuable chemicals has been a longstanding scientific pursuit for carbon neutrality and combating climate change. Herein, we present a photo-driven chemical process that reforms these two major greenhouse gases together to generate green methanol and CO, two high-valued industrial chemicals. Isotopic labeling and control experiments indicate an oxygen-atom-graft occurs, wherein CO2 transfers one O into the C–H bond of CH4 via photo-activated interfacial catalysis with AuPd nanoparticles supported on GaN. The photoexcited AuPd/GaN interface effectively orchestrates the CH4 oxidation and the CO2 reduction producing 13.66 mmol g−1 of CH3OH yield over 10 h. This design provides a solid scientific basis for the photo-driven oxygen-atom-grafting process to be further extended to visible light region. The direct co-conversion of CH4 and CO2 into valuable chemicals has been a longstanding scientific pursuit. Here, the authors present a photo-driven process that reforms these greenhouse gases together to generate CH3OH and CO over AuPd/GaN.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.503
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 science0.0020.000
Research integrity0.0000.001
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 head0.012
GPT teacher head0.298
Teacher spread0.286 · 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

Citations16
Published2024
Admission routes2
Has abstractyes

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