Non‐oxidative Methane Activation over Molybdenum and Tungsten Nitride Catalysts
Bibliographic record
Abstract
Abstract In this study, molybdenum (Mo), tungsten (W), and mixed metal oxide and ‐nitride catalysts are synthesized via incipient wetness impregnation on silica support (SBA‐15) with a total target metal loading of 4 wt %. The effect of the Mo : W ratio on the non‐oxidative methane conversion, product selectivity, and coke formation is evaluated. The reduced Mo/SBA‐15 and MoW/SBA‐15 catalysts show higher CH4 conversion activity to benzene (selectivity of ~75.5 %) than for ethylene (selectivity of ~9.5 %) with a rather fast coke formation rate of 19–24 mgCoke gCat−1 h−1 without a clear influence of the Mo : W ratio. In contrast, the metal nitrides have a much smaller coke formation rate. The smallest rate of 0.45 mgCoke gCat−1 h−1 is achieved for the WN/SBA‐15 sample, which increases with the molybdenum content to 4.7 and 8.6 mgCoke gCat−1 h−1 for the MoWN(5 : 1) and MoN/SBA‐15 catalysts, respectively. There seems to be a yet unknown but negative correlation between the coke formation rate and the number of silanamine functional groups formed during the catalyst nitridation (Si−OH→≡Si−NH2→≡Si−NH−Si≡). The C2H4 selectivity is significantly increased with the relative amount of tungsten, with the highest C2H4 selectivity of 63 % for mixed‐metal nitride catalyst containing Mo : W ratio of 1 : 5. While the Mo/SBA‐15 the MoN/SBA‐15 achieve a C2H4 selectivity of less than 10 %.
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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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".