Recent Advances in Supported Metal Catalysts for Syngas Production from Methane
Bibliographic record
Abstract
Over the past few years, great attention is paid to syngas production processes from different resources especially from abundant sources, such as methane. This review of the literature is intended for syngas production from methane through the dry reforming (DRM) and the steam reforming of methane (SRM). The catalyst development for DRM and SRM represents the key factor to realize a commercial application through the utilization of more efficient catalytic systems. Due to the enormous amount of published literature in this field, the current work is mainly dedicated to the most recent achievements in the metal-oxide catalyst development for DRM and SRM in the past five years. Ni-based supported catalysts are considered the most widely used catalysts for DRM and SRM, which are commercially available; hence, this review has focused on the recent advancements achieved in Ni catalysts with special focus on the various attempts to address the catalyst deactivation challenge in both DRM and SRM applications. Furthermore, other catalytic systems, including Co-based catalysts, noble metals (Pt, Rh, Ru, and Ir), and bimetallic systems have been included in this literature review to understand the observed improvements in the catalytic activities and coke suppression property of these catalysts.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".