Tuning RHO Zeolite Crystallization Time and Precursors for Stable and Improved Methanol Conversion to Dimethyl Ether Compared to Conventional Catalysts
Bibliographic record
Abstract
Energy supply is a significant concern that can be addressed by developing sustainable energy sources. Dimethyl ether (DME) shows remarkable promise in this regard, as it finds applications in diesel fuel engines and LPG systems and serves as an excellent carrier for H 2 . In this study, various RHO samples were synthesized by modifying preparation parameters and evaluated for their performance in this reaction. Through characterization analyses such as XRD, BET, NH 3 -TPD, SEM, EDS, and reactor experiments, it was determined that the optimized sample with the highest crystallization degree (10 days of hydrothermal crystallization) and synthesized with a template:Al 2 O 3 ratio of 0.5:1 possessed an optimum weak/strong acid sites ratio of 1.49 which effectively addressed the limitations of previous catalysts, such as high-temperature requirements and rapid deactivation. The RHO sample achieved the highest conversion rates at temperatures as low as 190 °C, maintaining its efficacy for several days. It was observed that the RHO sample performed superior to common ZSM-5 zeolite for this reaction owing to its higher acidity (2560 μmol g –1 against 1940 μmol g –1 ) and surface area (671 m 2 g –1 vs 230 m 2 g –1 ). These findings highlight the potential of DME as a sustainable energy source and emphasize the importance of optimizing catalyst preparation to improve the DME production efficiency and cost-effectiveness.
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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".