Methane Nonoxidative Aromatization over Ru−Mo/HZSM-5 in a Membrane Catalytic Reactor
Bibliographic record
Abstract
Low-temperature oxygen-free methane aromatization was carried out over Ru−Mo/HZSM-5 in a catalytic membrane reactor. The 0.5% Ru−3% Mo/HZSM-5 catalyst, prepared by incipient wetness coimpregnation, was highly selective toward benzene production. Methane aromatization was evaluated under two sets of conditions: (i) without hydrogen permeation in a fixed-bed conventional catalytic reactor (CR) and (ii) with hydrogen permeation in a catalytic membrane reactor (CMR). In CR mode, the catalyst exhibited remarkable stability with no significant deactivation for 24 h on stream. Switching to CMR mode gave rise to a significant increase in conversion, which reached levels well beyond the thermodynamic conversion. The continuous withdrawal of coproduced H 2 promoted the formation of carbonaceous species with a low H/C ratio and led to a decrease in benzene production. At a methane space velocity of 270 mL (STP)·h -1 ·g -1 and a temperature of 873 K, the CR mode yielded a maximum conversion of methane to benzene equal to 3.8%, i.e., 73% of the thermodynamic equilibrium conversion (5.2%). Under similar conditions, the maximum conversion to benzene attained in CMR mode was 9%. Alternating CR and CMR sequences under moderate feed flow rates proved to be a viable strategy for maintaining high catalyst activity toward benzene production for more than 100 h on stream. The CR step, through an increased hydrogen concentration, helped regenerate the active sites by hydrogenating the carbonaceous species, while the CMR step contributed in the overshoot of benzene conversion due to the equilibrium shift brought about by hydrogen withdrawal. A two-active-site model was proposed to rationalize the experimental observations. Further tests dealing with hydrogen addition to the methane feed flow clearly demonstrated the beneficial influence of hydrogen on the catalyst activity.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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".