Ab Initio Insights into the Early Stages of Methane Dehydroaromatization on Gallium Nitride
Bibliographic record
Abstract
Methane dehydroaromatization (MDA) offers a promising non-oxidative route for converting methane into value-added aromatic hydrocarbons. Wurtzite gallium nitride (GaN) has demonstrated catalytic activity for this transformation at temperatures as low as 450°C, although the underlying mechanism remains incompletely understood. In this study, the initial steps of MDA on the GaN m-plane surface were investigated using density functional theory. A low-barrier pathway for methylene (CH₂) formation was identified, enabled by the migration of surface methyl and hydrogen species with similarly low barriers. This mechanism significantly lowers the activation barrier relative to previous estimates. Beyond dehydrogenation and migration, we examined the coupling of C₁ intermediates to form C₂ species, including ethyl, ethane, and ethylene. C-C bond-formation steps leading to ethane and ethylene were found to proceed with barriers comparable to that of CH₂ formation. These results indicate that methane activation on GaN is not governed by a single high-barrier step; instead, dehydrogenation, migration, and coupling each contribute comparable kinetic bottlenecks. Overall, the findings revise the mechanistic understanding of methane activation on GaN and underscore the importance of site availability and adsorbate mobility in enabling selective low-temperature reactivity.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".