Graphene Produced in RF-ICP Thermal Plasma Reactor: A Ni-Catalyst Support for CO <sub>2</sub> Methanation
Bibliographic record
Abstract
This study investigates a methane-to-graphene-to-methane cycle as a sustainable approach for graphene synthesis and greenhouse gas mitigation. Few-layer graphene (FLG) sheets were synthesized via methane pyrolysis in an RF-ICP thermal plasma reactor and characterized using TEM, SEM, Raman spectroscopy, TGA, and BET surface area analysis. The synthesized graphene exhibited a high specific surface area and stability, enabling uniform nickel nanoparticle dispersion through wetness impregnation. The resulting Ni/graphene catalyst demonstrated strong performance in CO 2 methanation. Without prereduction, 58% CO 2 conversion and 74% CH 4 selectivity were achieved under atmospheric pressure at a gas hourly space velocity of 12,000 mL g –1 h –1 . In situ H 2 reduction enhanced performance, yielding 72.5% CO 2 conversion and 90% CH 4 selectivity. These findings highlight graphene’s potential as an efficient catalyst support and underscore the benefits of pressure optimization for improving CO 2 conversion and methane yield.
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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".