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Record W4416672039 · doi:10.26434/chemrxiv-2025-5l3cz

A Combinatorial Ab Initio Study of Methane Dehydroaromatization Pathways on Wurtzite Gallium Nitride

2025· article· W4416672039 on OpenAlexaff
Sylvester Zhang, Peter H. McBreen, Chao‐Jun Li, Rustam Z. Khaliullin

Bibliographic record

VenueChemRxiv · 2025
Typearticle
Language
FieldChemical Engineering
TopicCatalysis and Oxidation Reactions
Canadian institutionsUniversité LavalMcGill University
Fundersnot available
KeywordsWurtzite crystal structureAb initioAcetyleneDehydrogenationCatalysisMethaneEthyleneGallium nitride

Abstract

fetched live from OpenAlex

Methane dehydroaromatization (MDA) offers a direct route for the non-oxidative valorization of methane into aromatic hydrocarbons. Wurtzite gallium nitride has been reported experimentally to catalyze MDA near 450°C, yet the later stages of the reaction remain poorly understood. Here, we developed and applied a high-throughput combinatorial ab-initio framework to propose two thermally accessible MDA pathways on GaN: the C₃+C₃ and C₄+C₂ mechanisms. Methylene and ethylene, which form readily with barriers of 1.79eV and 1.44eV, respectively, emerge as key C₁ and C₂ intermediates. The rate-determining step in the C₃+C₃ pathway is ethyl formation (3.41eV), whereas the C₄ + C₂ route proceeds through a lower-barrier vinyl dehydrogenation (2.89eV) involving strongly bound acetylene intermediates. The unique geometry of the GaN surface, characterized by the spacing and orientation of surface Ga-N bonds, stabilizes both C₃ and C₄ intermediates and promotes the coupling steps that yield benzene. Several thermodynamically stable intermediates may act as kinetic traps, rationalizing observed by-products such as ethylene and cyclohexane. These findings provide a comprehensive mechanistic framework for MDA on GaN and underscore the catalytic potential of non-oxide nitrides for hydrocarbon activation and coupling.

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)0.001
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 score0.027
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
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 head0.017
GPT teacher head0.254
Teacher spread0.237 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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