Modelling on a Biomimetic [Cu−O−Cu]<sup>2+</sup>–mediated Methane–to–Methanol Conversion Unveils the Site for Methane Activation
Bibliographic record
Abstract
Abstract The Cu−O−Cu core exhibits methane‐to‐methanol conversion, mirroring the reactivity of the copper–containing enzyme pMMO. Herein, we computationally examined the reactivity of a biomimetic Cu−O−Cu core towards methane–to–methanol conversion. The oxygen atom of the Cu−O−Cu core abstracts hydrogen present in the C−H bond of methane. The spin density at the bridging oxygen helps to abstract hydrogen from the C−H bond. We modulated the spin density of the bridging oxygen by substituting only a single copper atom of the Cu−O−Cu core by metals (M) such as Fe, Co, and Ag. These substitutions result in bimetallic [Cu−O−M]2+ models. We observed that the energy barriers for the C−H activation step and the subsequent rebound step vary with the metal M. [Cu−O−Ag]2+ exhibits the highest reactivity for M2M conversion, while [Cu−O−Fe]2+ displays the lowest reactivity. To understand the different reactivity of these models towards M2M conversion, we employed distortion‐interaction analysis, orbital analysis, spin density analysis, and quantum theory of atoms in molecules analysis. Orbital analysis reveals that all four adducts follow a hydrogen atom transfer mechanism for C−H activation. Further, spin density analysis reveals that a higher spin density on the bridging oxygen leads to a lower C−H activation barrier.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".