Optimizing the <scp> CH <sub>4</sub> </scp> / <scp> CO <sub>2</sub> </scp> dry reforming catalysts: Insights from Ni/ <scp>SBA</scp> ‐16, Ni/ <scp>KIT</scp> ‐6, and Ni/ <scp>MCM</scp> ‐41 supported on silica with different mesopore symmetries
Bibliographic record
Abstract
Abstract Catalyst deactivation due to carbon deposition and sintering poses a significant challenge to the efficient dry reforming of CH 4 and CO 2 into syngas, particularly at high Ni loadings. To tackle this difficulty, ordered mesoporous silicas with varied pore symmetries were employed as supports in this study to synthesize the Ni‐based catalysts using hydrothermal treatment. The 10% Ni/MCM‐41 catalyst exhibited the highest conversion rates for CO 2 (81%) and CH 4 (77%), with favourable H 2 /CO (0.92) ratios indicating efficient syngas production Higher Ni concentrations increase active sites and catalytic performance, they also increase carbon deposition, reducing catalyst durability. At lower Ni loadings (4% Ni), catalysts with a 3D pore structure showed improved activity and stability, effectively reducing carbon deposition. This study demonstrates that optimized mesopore symmetries and Ni loadings can enhance catalyst efficiency and durability for syngas production.
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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".