MétaCan
Menu
Back to cohort
Record W4313680712 · doi:10.1016/j.isci.2023.105942

In aqua dual selective photocatalytic conversion of methane to formic acid and methanol with oxygen and water as oxidants without overoxidation

2023· article· en· W4313680712 on OpenAlexafffund
Jing‐Tan Han, Hui Su, Lida Tan, Chao‐Jun Li

Bibliographic record

VenueiScience · 2023
Typearticle
Languageen
FieldEnergy
TopicAdvanced Photocatalysis Techniques
Canadian institutionsMcGill UniversityCentre in Green Chemistry and Catalysis
FundersFonds de recherche du Québec – Nature et technologiesNatural Sciences and Engineering Research Council of CanadaShanghai Jiao Tong UniversityMcGill UniversityFonds Québécois de la Recherche sur la Nature et les TechnologiesCanada Foundation for InnovationCanada Research Chairs
KeywordsFormic acidChemistryMethanolOxygenateMethanePhotocatalysisRadicalPhotochemistryOxygenOrganic chemistryCatalysis

Abstract

fetched live from OpenAlex

The direct and selective transformation of naturally abundant methane (CH 4 ) into high-value-added oxygenates, e.g., methanol, ethanol, and formic acid, is one of the "Holy Grails" in chemistry and chemical productions. However, complex mixtures of products, often due to over-oxidations, make such transformations highly challenging. Herein, gallium nitride (GaN), a methane-active semiconductor, catalyzes the photooxidation of methane and empowers the fine-controlling of chemoselectivity toward methanol and formic acids, simply by regulating the O 2 content in water. In contrast to previous methods, no overoxidation products (CO 2 and CO) were observed in this process. Mechanistic investigations and the corresponding quantitative experiments indicated that the controllable generation of moderately reactive oxygen radicals (•OOH and •OH) in combination with the direct methane activation triggered by GaN is responsible for the highly selective reactivity and tunability through a photo-generated radical process.

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 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 score0.399

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 communication0.0000.001
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 head0.013
GPT teacher head0.284
Teacher spread0.272 · 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

Citations19
Published2023
Admission routes2
Has abstractyes

Explore more

Same venueiScienceSame topicAdvanced Photocatalysis TechniquesFrench-language works237,207