Non-thermal plasma-assisted dry reforming of methane: catalyst design, in situ characterization and hybrid system development
Bibliographic record
Abstract
The synergistic integration of non-thermal plasma and catalysis in dry reforming of methane (DRM) has unveiled new avenues for transforming greenhouse gases (CH 4 and CO 2 ) into valuable products while concurrently reducing environmental impact. Despite its operation at low temperatures and atmospheric pressure, high energy consumption and low selectivity hinder industrial-scale adoption. Addressing these challenges requires the development of catalysts with enhanced selectivity, improved interactions with electric fields, and prolonged stability. This review examines how the physical and chemical properties of catalysts profoundly impact DRM. Physical properties influence plasma discharge, altering the electric field and electron energy, while chemical properties play a crucial role in surface reactions, especially in forming specific products like liquid oxygenates. This review also highlights advanced in situ characterization techniques that reveal related reaction mechanisms and explores emerging hybrid systems that combine plasma with thermal catalysis, photocatalysis, or electrocatalysis, offering promising solutions for practical plasma-assisted DRM implementation. It emphasizes the need for unified catalyst performance prediction criteria and advanced in-situ characterization to unravel the reaction mechanisms. By covering both traditional and novel hybrid plasma-catalyst systems, this review aims to serve as a comprehensive guide for researchers and industry professionals to advance their expertise in this transformative field.
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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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".