Nanostructure and Reactivity of Copper–Cerium Bimetallic Oxide Catalysts under Operando Conditions: Insights from First-Principles Simulations
Bibliographic record
Abstract
Ceria-based bimetallic oxides are promising catalysts for various thermo- and electrocatalytic transformations in renewable energy and chemical sectors. In particular, experimental studies have demonstrated that Cu doping in CeO 2 significantly enhances its catalytic performance in the reverse water–gas shift (RWGS) reaction. However, the structure–activity relationship and the reaction mechanism remain under debate. In this work, we construct Cu-doped CeO 2 model nanostructures with varying dopant concentrations, guided by published experimental characterization data, and investigate their structural and catalytic behavior by using first-principles calculations. Our results show that increased Cu loading promotes the formation of surface oxygen vacancy clusters, which shifts the Ce oxidation state toward Ce 3+, enhancing the reducibility of the catalyst. Density functional theory (DFT) calculations reveal that CO 2 prefers to adsorb in the carbonate configuration near Cu-induced vacancy sites, where it is more readily activated to form *COOH intermediates. These pathways are more favorable than the formate route and are consistent with the high CO selectivity observed experimentally. These insights are further supported by finite-temperature, unbiased ab initio molecular dynamics (AIMD) simulations, which show spontaneous oxygen release from Cu-adjacent lattice sites and the formation of *COOH species under operando conditions. This study provides mechanistic insights into the catalytic behavior of Cu-doped CeO 2 under operando conditions and offers a predictive framework for understanding surface structure–activity relationships in bimetallic oxide catalysts for CO 2 utilization.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".