Non-oxidative methane activation over molybdenum and tungsten nitrides
Bibliographic record
Abstract
The abundance of natural gas (methane) has gained enormous attention towards its use as a feedstock replacement in petrochemical industries. Canada is one world’s top chemical and plastic manufacturing industries. Ethylene and aromatics are used largely (52% and 30% respectively) as feedstocks in petrochemical industries. Due to volatile oil price and its depletion, it makes it essential for refiners to look for a reliable feed replacement for optimum revenue, making methane gas a good candidate.Direct non-oxidative conversion of methane to olefins and aromatics can be a potential alternative to the conventional intermediate synthesis-gas production route. This process does not produce greenhouse gas and could also be used for clean hydrogen gas production. However, activation of the C–H bonds in CH4 is extremely challenging and thermodynamically limited, requiring high temperatures (700–1000°C) and an efficient catalyst. Also, selectivity and coking of the catalyst are considered a major challenge. In this master’s thesis, the metal catalysts chosen are Ga, Mo, and W. Each of these metals has shown to be active towards methane activation. Metal nitrides are an alternative to noble-metal catalysts; our group has recently reported catalytic activity of gallium nitride towards methane activation. In this study, the catalytic performance of Mo, W, and mixed-metal Mo/W nitrides was investigated. The effect of atomic ratios of Mo and W on the methane conversion, product selectivity, and coke formation was evaluated. The study on gallium nitride catalyst and its regeneration capability was studied as collaborative work.The metal and mixed-metal nitride catalysts were synthesized via impregnation of the corresponding precursors onto SBA-15 with a total target metal loading of 4 wt% and varying atomic ratios between Mo and W. Impregnated catalysts were dried and subsequently calcined at 750°C to form metal oxides. Prior to the methane activation experiments, the metal oxides were either reduced in H2 at the 700°C for 1 h or nitridated under NH3 gas at 700°C for 3 h to form the corresponding metal nitrides. Reduction, nitridation, and activity experiments were carried out in the same fixed bed reactor setup at the same temperature (700°C) and GHSV of 1160 h−1 at 1 barabs. The product gas mixture was analyzed via a calibrated mass spectrometer, while the amount of coke was determined via temperature-programmed oxidation in a thermogravimetric analyzer (TGA). The catalysts were characterized using N2 adsorption/desorption, chemisorption, DRIFTS, XRD, SEM-EDS, and XPS to understand the structure-activity relationship.The methane conversion to hydrocarbons along with the coke formation and product selectivity of the catalysts were evaluated. Mo, W, and MoW catalysts have higher activity than the corresponding nitrides with Mo and MoN achieving a CH4 conversion to hydrocarbon values of 0.85 % and 0.55%, respectively. W and WN catalysts are slightly less active with CH4 conversions of 0.55% and 0.14%, respectively. Despite the lower CH4 conversion, the metal nitrides have a much lower coke formation and a much higher selectivity towards ethylene as aromatic species are more prone for coke formation. The lowest coke deposition was 8 mgCoke gCat−1 on WN/SBA-15 for the nitride compared to 110 mgCoke gCat−1 for the metal catalysts. The C2H4 selectivity is greatly increased with the relative amount of W. Mo and MoN achieve C2H4 selectivity of 10% and almost zero, respectively, while W and WN achieve 25% and 42% C2H4 selectivity. The highest C2H4 selectivity of 65% was reached with the mixed-metal nitride catalyst containing Mo:W ratio of 5:1. Using TGA, it was shown that the gallium nitride (GaN) catalyst could be successfully regenerated with air while maintaining a stable ethylene yield. The DRIFTS experiments confirmed the DFT work that ethylene is formed first followed by benzene formation over gallium in non-oxidative CH4 conversion
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How this classification was reachedexpand
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 | 0.000 | 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) | 0.005 | 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 teacher head, 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".