How do Core–Shell Structure Features Impact on the Activity/Stability of the Co‐based Catalyst in Dry Reforming of Methane?
Bibliographic record
Abstract
Abstract Dry reforming of methane has been systematically investigated over a series of x‐Co@SiO2‐y catalysts where x is the Co particle size ranging from 11.1 to 121.3 nm while y denotes the silica shell thickness ranging from 6.0 to 21.9 nm. Various techniques including TEM, XRD, H2‐TPR/‐TPD, XPS, BET, O2‐TPO, TG, and H2‐TPSR‐MS were employed to characterize physicochemical properties of catalysts. H2‐TPR and XPS results indicate that the core–shell interaction is dependent on the core size: the smaller the Co particle size is; the stronger the core–shell interaction. The investigations employing H2‐TRSR‐MS and XPS on the spent catalysts demonstrated that a fraction of metallic Co was re‐oxidized on a large‐core catalyst such as 121.3‐Co@SiO2‐72.2 during the reaction, and such oxidation leads to lower catalytic activity and stability. O2‐TPO results indicated that the catalyst with smaller core size caused significant coking. TG analysis together with TEM investigation on the used samples suggested that carbon deposition is notably core‐size‐dependent and responsible for deactivation of the small‐core catalyst. Among various core–shell structured catalysts, 27.8‐Co@SiO2‐14.3 showed superior activity and durability, owing to the well‐balanced property between coking and anti‐oxidation of Co cores.
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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".