CO<sub>2</sub> Hydrogenation to CH<sub>3</sub>OH on Metal‐Doped TiO<sub>2</sub>(110): Mechanisms, Strain Effect and a New Thermodynamic‐Kinetic Relation
Bibliographic record
Abstract
Abstract Surface strain and linear thermodynamic‐kinetic relation are interesting topics in catalysis. Development of low temperature methanol catalysts of high activity and selectivity is of particularly importance for conversion of CO2 to methanol. In the present paper CO2 hydrogenation to methanol on Znx@TiO2(110) (x=0–2) was explored using density functional calculations and microkinetic simulations. The reaction mechanisms on the three model systems were determined and it is shown that Zn2@TiO2(110) is the most active. The most favorable pathway on Zn2@TiO2(110) is identified and CO2+H to HCOO is found to be the rate‐controlling step. It is demonstrated that there is a linear relation (named AEB relation) between the adsorption energies of the initial states and the barriers for the controlling step on the 18 systems studied. Calculations on strained surfaces show that the AEB relation exists within ±1 % strain. Sr2@TiO2(110) and −1 % strained CaZn and ZnCu doped TiO2(110) are potential good low temperature catalysts and deserve experimental testing.
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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".