Transition‐Metal‐Doped CeO<sub>2</sub> for the Reverse Water‐Gas Shift Reaction: An Experimental and Theoretical Study on CO<sub>2</sub> Adsorption and Surface Vacancy Effects
Bibliographic record
Abstract
Abstract Transition metal‐doped ceria (M−CeO 2 ) catalysts (M=Fe, Co, Ni and Cu) with multiple loadings were experimentally investigated for reverse water gas shift (RWGS) reaction. Density functional theory (DFT) calculations were performed to benchmark the properties that impact catalytic activity of CO 2 reduction. Temperature‐programmed desorption (TPD) was conducted to study the CO 2 binding strength on doped CeO 2 surfaces; the trend of the energy along increasing metal loading agrees with the DFT calculations. Notably, CO 2 dissociative adsorption energy and oxygen vacancy (OV) formation energy are key descriptors obtained from both DFT and experiments, which can be used to evaluate catalytic performance. Results show the effectiveness of transition metal doping in enhancing CO 2 adsorption and reducibility of the surfaces, with Fe showing particularly promising results, i. e., CO 2 conversion higher than 56 % at 600 °C and 100 % selectivity to CO. Cu exhibits 100 % selectivity to CO but low CO 2 conversion, while Co and Ni showed notable ability of methanation, particularly at high loadings. This study finds that an effective CeO 2 based RWGS catalyst corresponds to OV sites that have low OV formation energies for surface reduction, and moderate CO 2 adsorption energies for strong interaction with the surface to promote C−O bond scission.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".