Plasma-enhanced microwave-driven methane pyrolysis for hydrogen and carbon production
Bibliographic record
Abstract
Microwave-driven methane pyrolysis is a promising pathway for low-GHG hydrogen production. In this process, carbon particles absorb microwave radiation, heat the gas phase, and promote the decomposition of methane. Previous studies hypothesize that localized microplasmas, formed by arcing between conductive particles, may enhance pyrolysis by creating non-thermal excitation of methane molecules. However, the role of microplasmas has not been systematically isolated or quantified. This study investigates the impact of non-thermal plasma discharges on methane conversion and hydrogen yield using a microwave-driven fluidized-bed reactor. Graphitized carbon particles and tungsten electrodes were used to generate intense controlled plasma discharges while maintaining constant microwave power and bulk temperature. Results show that microplasmas induced by graphite alone do not significantly affect methane conversion. In contrast, the addition of unpowered electrodes results in a marked increase in methane conversion (up to 20%) and hydrogen yield. Carbon products formed in the plasma region were characterized by SEM, Raman, and XPS, revealing nanostructured, disordered carbon distinct from thermal film deposits. These findings suggest that only intense, electrode-driven discharges substantially enhance pyrolysis and carbon black production, informing reactor design strategies for efficient hydrogen generation. • Non-thermal plasma boosts thermal CH 4 pyrolysis without increasing power or temperature. • Microplasmas alone show negligible effect on CH 4 conversion or H 2 yield. • Electrode discharges increase conversion by an absolute 20% at constant microwave input. • Plasma yields nanoscale, disordered carbon distinct from thermal film deposits. • Reactor zones can be decoupled to optimize hydrogen and carbon product quality.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".