A computational study of methanol-to-hydrocarbon conversion — Towards the design of a low-barrier process
Bibliographic record
Abstract
Computational quantum chemistry has been employed to examine the production of ethylene with methanol-to-hydrocarbon (MTH) processes via a carbon pool mechanism. We find that the M05-2X functional performs well for the types of reactions that are involved. The methylation reactions of the aromatic cocatalyst are the most energy-demanding steps in the process. For the subsequent production of C 2 H 4 , we have identified a low-energy pathway that involves multiple methyl shifts, followed by concerted deprotonation and C 2 H 4 elimination. The substitutions of the Al and Si atoms in the participating Si–OH–Al moiety of zeolite catalysts with Ga and Ge do not lead to lower barriers for the methylation reactions, nor does the use of a more electron-rich aromatic cocatalyst. However, we find that the use of two cocatalysts, a nucleophile and an aromatic carbon pool, can provide an overall low-energy pathway for the MTH process.
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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 | 0.001 | 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 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".