In Situ Graphene Synthesis Study in Inductively Coupled Radiofrequency Thermal Plasma Reactor using Methane Precursor
Bibliographic record
Abstract
Abstract While numerous studies are available on methane pyrolysis chemical kinetics and the effect of plasma parameters on graphene synthesis, a comprehensive understanding of the formation mechanism remains elusive without in situ analysis. This study aims to utilize a sampling probe for the first time to collect graphene locally on transmission electron microscopy grids and perform a localized sampling and analysis of the gas composition (during graphene synthesis) using mass spectrometry. This technique provides a 3D tracking of methane pyrolysis in radiofrequency inductively coupled thermal plasma reactor for graphene production. Response surface methodology based on central composite design is employed to obtain a 3D visualization of the synthesis process. Quadratic and cubic models are developed, followed by comprehensive analysis of variance. A comparison of the gas-phase chemistry resulting from the in situ measurements with thermodynamic equilibrium calculations reveals that the process is controlled by thermochemical kinetics. H2, C2H2, C2H4, C3H6, and C6H6, as well as residual CH4, are the main hydrocarbons found in the graphene nucleation zone. The primary pathway for methane pyrolysis and graphene formation in RF plasma is through H2 and C2 hydrocarbons, while graphene nucleation and growth reactions are terminated 350 mm from the plasma torch nozzle exit. Morphology, quality, mean particle size, and the number of layers of the produced graphene samples, locally collected at different locations by 3D axisymmetric probe scanning, were investigated using TEM, high-resolution TEM imaging, and Raman analysis. The gathered information is highly valuable for plasma reactor design.
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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".