Micron size graphene sheets synthesis by methane pyrolysis in an RF-ICP thermal plasma reactor
Bibliographic record
Abstract
Abstract Bottom–up synthesis of free-standing graphene using thermal plasma technology often results in flakes with smaller lateral dimensions (hundreds of nanometers) compared to top–down and substrate-based approaches (reaching centimeters in size) Dato (2019 J. Mater. Res. 34 214–30). This limitation in size restricts the applicability of graphene in various applications. This study investigates a method to overcome this limitation by studying the reactor’s quenching effect on the plasma plume exiting an radiofrequency inductively coupled thermal plasma thermal plasma torch. Local gas phase chemistry and graphene morphology were investigated during methane (CH 4 ) pyrolysis in argon plasma. Natural quenching suppression led to a production of few-layer (2–5 layers), near-micrometer-sized un-supported graphene sheets (∼2.8 µ m perimeter) with less crumpling and a projected area of (2–5) × 10 5 nm 2 . Raman, transmission electron microscopy, thermogravimetric analysis, and x-ray photoelectron spectroscopy (XPS) analysis confirmed the high quality of the synthesized graphene. Sp 2 carbon composition in the sample was calculated using the D parameter obtained from the differentiated C KLL Auger peak in the XPS spectrum. A correlation between the gas phase chemistry and the graphene morphology demonstrated the significant effect of plasma reactor natural quenching and recirculation on the graphene synthesis and offers a potential for controlling the structure of unsupported graphene. The current study provides valuable insights that can pave the way for the development of reactors with a definite control over the morphology of synthesized graphene.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".