New insights on CO and CO2 hydrogenation for methanol synthesis: The key role of adsorbate-adsorbate interactions on Cu and the highly active MgO-Cu interface
Bibliographic record
Abstract
By coupling density functional theory calculations (DFT) with microkinetic modeling, we address two controversial problems pertaining to methanol synthesis: i) Which of the CO or CO2 hydrogenation routes dominates the synthesis rate, and ii) what makes irreducible, inert oxides like MgO an efficient promoter for the CO hydrogenation process? We determine that the inconsistency between the experimental activity trend and the previous theoretical results in the literature is attributed to the absence of interactions between adsorbed formate and intermediates, which would underestimate the rate of CO2 route by having a too high formate coverage. We show that when adsorbate-adsorbate interactions, especially the derived H bond, are included, the CO2 hydrogenation dominates for pure Cu catalysts, which is consistent with experiments. In addition, a new transition state for hydrogenation of adsorbed HCOOH* is discovered, which is further stabilized by hydrogen bonding. We also identify the MgO/Cu interface as a highly active site and propose a novel lattice-oxygen involved reaction mechanism at the interface for methanol formation. The CO hydrogenation reactivity can then be enhanced by stabilizing HCO* in the formate-type species, while the CO2 hydrogenation is inhibited by the poisoning of CO2* and HCOO*.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".