Ultra-Fine Pt Entities on High-Index CeO <sub>2</sub> (112) Facet Significantly Boosting Low Temperature Water–Gas Shift Reaction
Bibliographic record
Abstract
In this study, two tactics were employed to develop a very efficient and durable Pt-based catalyst for low-temperature water gas shift (WGS) reaction. First, through facet engineering of CeO 2, the dominantly exposed (112)/(110) facets of the oxygen-deficient CeO 2 hexagonal prism (CeO 2 –HP) were established. Second, the Pt single atoms (SAs) and Pt fine clusters (FCs) were anchored to CeO 2 –HP(112)/(110) with the Pt δ+ –O v –Ce 3+ and Pt 2+ –O 2– –Ce 4+ interfacial sites, verified by AC-HAADF-STEM and XAFS measurements. The as-obtained 0.10Pt/CeO 2 –HP achieved a CO conversion rate of 12.63 mmol CO g Pt –1 s –1 and a TOF as high as 2.46 s –1 at 250 °C, 2.6-fold that of benchmark Pt/Fe-0.01 SAC at 300 °C (0.93 s –1 ). The exposed CeO 2 {112} facets constitute abundant oxygen vacancies and an enhanced Pt dispersion. In-situ FTIR study demonstrated the synergetic effect between the Pt δ+ –O v –Ce 3+ and Pt 2+ –O 2– –Ce 4+ interfacial sites which modulates the competitive adsorption of CO and H 2 O. The DFT simulations revealed that the high density of oxygen vacancy over CeO 2 –HP(112) boosts H 2 O dissociation, causing a substantial enhancement in catalytic performance and a variation in the reaction route. This work provides in-depth insights into how the well-controllably assembled interfacial structure functions electronically and is structurally efficient for the target reaction.
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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".