Characterization of carbon products from microwave-driven methane pyrolysis
Bibliographic record
Abstract
Hydrogen production via methane pyrolysis is projected to have a key role in achieving net zero GHG emission by 2050. Carbon makes up to 75% by mass of the products of methane pyrolysis, indicating the importance of a thorough characterization to find its potential applications. A new approach is methane pyrolysis utilizing microwaves, where the carbon product serves as receptors for the microwave energy. In this method, methane decomposes and forms solid carbon. In this study, we evaluated the morphological and structural features of the produced carbon using SEM, XRD, XPS, Raman spectroscopy, and thermogravimetric analysis. The analysis indicates that the pyrolytic carbon produced through this process is highly pure and exhibits a semi-graphitic structure with localized defects. Additionally, the surface morphology of the carbon material was observed to possess a rough cauliflower-like texture. A thorough characterization of carbon revealed several potential applications, including the utilization of granular carbon in electric arc furnace for steelmaking, the feedstock for graphene production, and serving as an anode material in Na-ion batteries with further optimization of the production process. Safe sequestration of the carbon due to limited demand is feasible as the carbon is pure and oxygen-free, indicating its potential to remain unreacted over a long period after sequestration.
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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 | 0.001 | 0.001 |
| 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) | 0.002 | 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".