Insights into Mechanisms of Reverse Water Gas Shift Activity Enhancement over Reverse Microemulsion‐Synthesized CuCeO<sub>2</sub>
Bibliographic record
Abstract
Abstract Copper‐doped ceria (CuCeO2) catalysts with 0–26.5 Cu/(Cu+Ce) at% were synthesized via the reverse microemulsion method. X‐ray diffraction analysis of freshly synthesized and spent (post‐reaction) catalysts showed no separate phase of copper or copper oxide, indicating that Cu was incorporated into the CeO2 lattice, replacing Ce. Temperature programmed desorption experiments showed that the activation energy of CO2 desorption increased for higher Cu loadings, indicating stronger CO2 adsorption. This phenomenon was attributed to enhanced formation of oxygen vacancies due to Cu doping. X‐ray photoelectron spectroscopy further confirmed the enhanced generation of oxygen vacancies due to Cu incorporation. Catalytic performance evaluation with the H2/CO2 feed in the 300–600 °C range showed that all catalysts were 100 % selective to CO generation, with higher Cu loadings resulting in CO2 conversion close to equilibrium values at 500–600 °C. The activation energy of the reaction, determined through reaction tests, exhibited inverse relationship with the activation energy of CO2 desorption. The relationship between these two energy barriers is explored, providing valuable insights into the mechanism of RWGS activity enhancement.
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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".