Graphene synthesis via thermal plasma: a comprehensive review of key process parameters and outcomes
Bibliographic record
Abstract
Thermal plasma provides several advantages for graphene synthesis, including high conversion efficiency and rapid, continuous production. Furthermore, it enables precise control over process parameters, resulting in the production of high-quality graphene with low defects, leading to its usage in diverse applications. Despite these advancements, the primary challenge remains the low production rate of graphene, which limits the scalability and commercial viability of the synthesis process. The gas-phase graphene synthesis process is significantly influenced by factors such as pressure, power, hydrocarbon feedstock type and flow rates, plasma gas composition, catalyst presence, quenching rate, and oxygen/hydrogen content. A wide range of carbon nanostructures (including graphene (Gr), graphite, carbon black (CB), carbon nanotubes (CNTs), and carbon nanohorns (CNHs)) can be produced by modulating these factors. This review systematically investigates the impact of the aforementioned parameters on graphene synthesis and the underlying reaction mechanisms. It demonstrates the critical role of carbon concentration, temperature, and residence time in influencing plasma chemistry, and consequently the graphene quality, with major implications for advancing graphene-based technologies. This review article contributes to the existing literature by providing a comprehensive overview of the state of the art in thermal plasma-based graphene synthesis.
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.005 |
| Scholarly communication | 0.000 | 0.001 |
| 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".