Single Atom Catalysts for Selective Methane Oxidation to Oxygenates
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Direct conversion of methane (CH4) to C1–2 liquid oxygenates is a captivating approach to lock carbons in transportable value-added chemicals, while reducing global warming. Existing approaches utilizing the transformation of CH4 to liquid fuel via tandemized steam methane reforming and the Fischer–Tropsch synthesis are energy and capital intensive. Chemocatalytic partial oxidation of methane remains challenging due to the negligible electron affinity, poor C–H bond polarizability, and high activation energy barrier. Transition-metal and stoichiometric catalysts utilizing harsh oxidants and reaction conditions perform poorly with randomized product distribution. Paradoxically, the catalysts which are active enough to break C–H also promote overoxidation, resulting in CO2 generation and reduced carbon balance. Developing catalysts which can break C–H bonds of methane to selectively make useful chemicals at mild conditions is vital to commercialization. Single atom catalysts (SACs) with specifically coordinated metal centers on active support have displayed intrigued reactivity and selectivity for methane oxidation. SACs can significantly reduce the activation energy due to induced electrostatic polarization of the C–H bond to facilitate the accelerated reaction rate at the low reaction temperature. The distinct metal–support interaction can stabilize the intermediate and prevent the overoxidation of the reaction products. The present review accounts for recent progress in the field of SACs for the selective oxidation of CH4 to C1–2 oxygenates. The chemical nature of catalytic sites, effects of metal–support interaction, and stabilization of intermediate species on catalysts to minimize overoxidation are thoroughly discussed with a forward-looking perspective to improve the catalytic performance.
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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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 it