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Record W4308029168 · doi:10.1038/s41598-022-21915-9

Selective conversion of methane to cyclohexane and hydrogen via efficient hydrogen transfer catalyzed by GaN supported platinum clusters

2022· article· en· W4308029168 on OpenAlexafffund
Lida Tan, Hui Su, Jing‐Tan Han, Mingxin Liu, Chao‐Jun Li

Bibliographic record

VenueScientific Reports · 2022
Typearticle
Languageen
FieldChemical Engineering
TopicAmmonia Synthesis and Nitrogen Reduction
Canadian institutionsMcGill UniversityCentre in Green Chemistry and Catalysis
FundersNatural Sciences and Engineering Research Council of CanadaShanghai Jiao Tong University
KeywordsCyclohexaneCatalysisHydrogenMethanePhotochemistryBenzenePlatinumMaterials scienceInorganic chemistryChemistryOrganic chemistry

Abstract

fetched live from OpenAlex

Abstract Non-oxidative liquefaction of methane at room temperature and ambient pressure has long been a scientific “holy grail” of chemical research. Herein, we exploit an unprecedented catalytic transformation of methane exclusively to cyclohexane and hydrogen evolution through effective surface-hydrogen-transfer (SHT) at the heterojunctions boundary consisting of electron-rich platinum cluster (Pt) loaded on methane-activating gallium nitride (GaN) host. The experimental analysis demonstrates that the interface-induced overall reaction starts with methane aromatization to benzene and surface-bound hydrogen initiated by the Ga–N pairs, followed by the hydrogenation of benzene to cyclohexane with surface-bound hydrogen. The in-situ activated hydrogen at electron-rich metal Pt cluster is crucial for the hydrogenation and enables an outstanding selectivity (up to 92%) and productivity (41 μmol g −1 ) towards cyclohexane and hydrogen evolution concurrently at 300 °C, which is well-delivered after 5 recycling runs.

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)0.001
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 score0.003
Threshold uncertainty score0.808

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0000.000
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 head0.006
GPT teacher head0.202
Teacher spread0.196 · 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

Citations11
Published2022
Admission routes2
Has abstractyes

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