Ni on the CeO<sub>2</sub>(110) and (100) surfaces: adsorption vs. substitution effects on the electronic and geometric structures and oxygen vacancies
Bibliographic record
Abstract
We report density functional theory (DFT) calculations of the interactions of both Ni adsorbate and substitutional dopant with the ceria (110) and (100) surfaces to explain the origin of the activity of Ni/ceria catalysts. Our results indicate that the Ni adatom on the (110) surface prefers to adsorb on a two-fold bridge site over a hollow site up to 0.25 ML coverage, and the most stable position of a Ni adsorbate on the (100) surface was found to be the bridge site where the Ni atom is coordinated to two surface O atoms. The Ni(+) oxidation state for the Ni adatom on the (110) surface was found to be more favorable than the Ni(2+) state on the two-fold bridge site while on the (100) surface, a Ni adatom prefers its Ni(2+) oxidation state over the Ni(+) oxidation state. With increasing coverage, the binding energy of a Ni adatom on the (110) surface was found to decrease from -0.45 eV at 0.083 ML coverage to -0.32 eV at 0.25 ML coverage. Oxidation of the Ni adatom to Ni(+) reduces one Ce(4+) ion on the ceria surface to Ce(3+) which preferred to be located next to the Ni(+) ion in the nearest neighbor location. The Ce(3+) ions on the (100) surface also prefer to stay in the vicinity of the adsorbed Ni atom, while they prefer to be located away from the Ni adatom on the (111) surface. No reduction of Ce(4+) ions was observed upon substitution of Ce atoms by Ni atoms. Two Ni substituents preferred to be distributed on adjacent metal ion sites on the (110) surface. Ni adsorbate and substituent on the (110) surface were both found to induce significant structural distortions. In comparison to the pure ceria (110) and (100) surfaces, we show that a Ni adsorbate increases the energy required to create an oxygen vacancy while a Ni dopant reduces it. While multiple dopants on the (110) surface do reduce the vacancy formation energy, the degree of reduction is smaller compared to a single dopant indicating the presence of an optimum level of doping to obtain enhanced catalytic activity.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".